diff --git a/.github/workflows/deploy-pages.yml b/.github/workflows/deploy-pages.yml index 5a9bb43..8147b40 100644 --- a/.github/workflows/deploy-pages.yml +++ b/.github/workflows/deploy-pages.yml @@ -6,7 +6,7 @@ on: workflow_dispatch: permissions: - contents: read + contents: write pages: write id-token: write @@ -19,6 +19,42 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: '20' + cache: 'npm' + cache-dependency-path: external/ai-research-ebook/package-lock.json + + - name: Install dependencies for research ebook + working-directory: external/ai-research-ebook + run: npm ci + + - name: Build research ebook with custom base path + working-directory: external/ai-research-ebook + env: + SITE_URL: https://adongwanai.github.io + BASE_PATH: /AgentGuide/research + GITHUB_USERNAME: adongwanai + GITHUB_REPO: AgentGuide + GITHUB_REPOSITORY_URL: https://github.com/adongwanai/AgentGuide + run: npm run build + + - name: Copy to research directory + run: | + rm -rf research + mkdir -p research + cp -r external/ai-research-ebook/dist/* research/ + + - name: Commit research directory if changed + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git add research/ + git diff --quiet && git diff --staged --quiet || git commit -m "Auto-update research ebook" + git push || echo "No changes to push" + - uses: actions/upload-pages-artifact@v3 with: path: . @@ -31,4 +67,4 @@ jobs: needs: build steps: - id: deployment - uses: actions/deploy-pages@v4 \ No newline at end of file + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore index 4e3ffbc..22c0dc9 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,8 @@ LICENSE CHANGELOG.md READY_TO_LAUNCH.md +# ai-research-ebook 构建产物和依赖 +external/ai-research-ebook/dist/ +external/ai-research-ebook/node_modules/ +external/ai-research-ebook/.astro/ + diff --git a/external/ai-research-ebook/.vscode/extensions.json b/external/ai-research-ebook/.vscode/extensions.json new file mode 100644 index 0000000..22a1505 --- /dev/null +++ b/external/ai-research-ebook/.vscode/extensions.json @@ -0,0 +1,4 @@ +{ + "recommendations": ["astro-build.astro-vscode"], + "unwantedRecommendations": [] +} diff --git a/external/ai-research-ebook/.vscode/launch.json b/external/ai-research-ebook/.vscode/launch.json new file mode 100644 index 0000000..d642209 --- /dev/null +++ b/external/ai-research-ebook/.vscode/launch.json @@ -0,0 +1,11 @@ +{ + "version": "0.2.0", + "configurations": [ + { + "command": "./node_modules/.bin/astro dev", + "name": "Development server", + "request": "launch", + "type": "node-terminal" + } + ] +} diff --git a/external/ai-research-ebook/README.md b/external/ai-research-ebook/README.md new file mode 100644 index 0000000..d5071be --- /dev/null +++ b/external/ai-research-ebook/README.md @@ -0,0 +1,92 @@ +# Vibe Research - AI 科研指南 + +> 从 Idea 到论文发表的完整 AI 科研工作流指南 + +[![Built with Starlight](https://astro.badg.es/v2/built-with-starlight/tiny.svg)](https://starlight.astro.build) +[![Deploy to GitHub Pages](https://github.com/yiweinanzi/ai-research-ebook/workflows/Deploy%20to%20GitHub%20Pages/badge.svg)](https://github.com/yiweinanzi/ai-research-ebook/actions) + +## 简介 + +本书是一份面向 AI 研究者的实用指南，使用 **Astro + Starlight** 构建，涵盖了从 idea 生成到论文发表的完整工作流程。 + +## 在线阅读 + +📖 **默认地址：`https://yiweinanzi.github.io/ai-research-ebook`** + +你也可以通过环境变量覆盖站点地址（无需改代码）： + +```bash +GITHUB_USERNAME=<你的用户名> npm run build +``` + +## 本地开发 + +```bash +# 安装依赖 +npm install + +# 启动开发服务器 +npm run dev + +# 构建生产版本 +npm run build + +# 预览构建结果 +npm run preview +``` + +## 部署前配置 + +`astro.config.mjs` 已支持环境变量配置： + +- `GITHUB_USERNAME`：GitHub 用户名（默认 `yiweinanzi`） +- `GITHUB_REPO`：仓库名（默认 `ai-research-ebook`） +- `SITE_URL`：站点域名（默认 `https://.github.io`） +- `BASE_PATH`：子路径（默认 `/`） +- `GITHUB_REPOSITORY_URL`：仓库完整 URL + +示例： + +```bash +GITHUB_USERNAME=<你的用户名> GITHUB_REPO=ai-research-ebook npm run build +``` + +## 内容大纲 + +- **1. Idea 生成**: 系统化的文献调研方法，使用多模型交叉验证 +- **2. 代码实现**: Claude Code、GPT-5.2、多Agent框架的最佳实践 +- **3. 论文图表**: 自动化绘图工具推荐，专业图表设计规范 +- **4. 论文写作**: 论证链构建方法，多模型协作写作 +- **5. 审稿与 Rebuttal**: 结构化审稿流水线，Rebuttal 写作策略 +- **6. 工具生态**: Elicit、Zotero-MCP 等最新工具，构建完整科研工具链 + +## 技术栈 + +- [Astro](https://astro.build) - 现代静态站点生成器 +- [Starlight](https://starlight.astro.build) - Astro 的文档主题 +- [GitHub Pages](https://pages.github.com) - 免费静态托管 +- [GitHub Actions](https://github.com/features/actions) - 自动部署 + +## 项目结构 + +``` +. +├── public/ +│ └── logo.svg +├── src/ +│ ├── content/ +│ │ └── docs/ # Markdown 内容文件 +│ ├── styles/ +│ │ └── custom.css # 自定义样式 +│ └── content.config.ts +├── astro.config.mjs +└── package.json +``` + +## 许可证 + +MIT License + +--- + +最后更新: 2026年2月9日 diff --git a/external/ai-research-ebook/astro.config.mjs b/external/ai-research-ebook/astro.config.mjs new file mode 100644 index 0000000..74bf0ec --- /dev/null +++ b/external/ai-research-ebook/astro.config.mjs @@ -0,0 +1,106 @@ +// @ts-check +import { defineConfig } from 'astro/config'; +import starlight from '@astrojs/starlight'; + +const repoName = process.env.GITHUB_REPO ?? 'ai-research-ebook'; +const githubUsername = process.env.GITHUB_USERNAME ?? 'yiweinanzi'; +const siteUrl = process.env.SITE_URL ?? `https://${githubUsername}.github.io`; +const basePath = process.env.BASE_PATH ?? `/${repoName}`; +const normalizedBasePath = basePath === '/' ? '' : basePath; +const mermaidScriptVersion = process.env.MERMAID_SCRIPT_VERSION ?? '20260209e'; +const repositoryUrl = + process.env.GITHUB_REPOSITORY_URL ?? `https://github.com/${githubUsername}/${repoName}`; + +// https://astro.build/config +export default defineConfig({ + site: siteUrl, + base: basePath, + integrations: [ + starlight({ + title: 'Vibe Research - AI 科研指南', + description: 'AI研究电子书 - 从idea到论文的完整指南', + head: [ + { + tag: 'script', + content: + "(() => { try { const key = 'starlight-theme'; localStorage.setItem(key, 'light'); document.documentElement.dataset.theme = 'light'; } catch { document.documentElement.dataset.theme = 'light'; } })();", + }, + { + tag: 'script', + attrs: { + type: 'module', + src: `${normalizedBasePath}/scripts/mermaid-init.js?v=${mermaidScriptVersion}`, + }, + }, + ], + logo: { + src: './src/assets/logo.svg', + alt: 'Vibe Research', + }, + social: [{ icon: 'github', label: 'GitHub', href: repositoryUrl }], + sidebar: [ + { + label: '首页', + items: [{ label: '关于本书', slug: 'index' }], + }, + { + label: '1. Idea 生成', + items: [ + { label: '1.1 调研方法', slug: 'idea/research' }, + { label: '1.2 调研项目', slug: 'idea/projects' }, + ], + }, + { + label: '2. 代码实现', + items: [ + { label: '2.1 Claude Code', slug: 'code/claude' }, + { label: '2.2 GPT-5.2 系列', slug: 'code/gpt' }, + { label: '2.3 多Agent框架', slug: 'code/agents' }, + ], + }, + { + label: '3. 论文图表', + items: [ + { label: '3.1 自动化绘图工具', slug: 'figures/tools' }, + { label: '3.2 专业绘图对比', slug: 'figures/comparison' }, + { label: '3.3 图表设计规范', slug: 'figures/design' }, + ], + }, + { + label: '4. 论文写作', + items: [ + { label: '4.1 写作方法', slug: 'writing/methods' }, + { label: '4.2 Prism 编辑器', slug: 'writing/prism' }, + { label: '4.3 多模型协作', slug: 'writing/collaboration' }, + ], + }, + { + label: '5. 审稿与Rebuttal', + items: [ + { label: '5.1 结构化审稿', slug: 'review/structured' }, + { label: '5.2 会议审稿模板', slug: 'review/templates' }, + { label: '5.3 Rebuttal策略', slug: 'writing/rebuttal' }, + ], + }, + { + label: '6. 工具生态', + items: [ + { label: '6.1 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-0,0 +1 @@ + \ No newline at end of file diff --git a/external/ai-research-ebook/public/logo.svg b/external/ai-research-ebook/public/logo.svg new file mode 100644 index 0000000..802ca61 --- /dev/null +++ b/external/ai-research-ebook/public/logo.svg @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/external/ai-research-ebook/public/scripts/mermaid-init.js b/external/ai-research-ebook/public/scripts/mermaid-init.js new file mode 100644 index 0000000..c6cbf3c --- /dev/null +++ b/external/ai-research-ebook/public/scripts/mermaid-init.js @@ -0,0 +1,248 @@ +// Mermaid initialization for Starlight + Expressive Code +(function () { + 'use strict'; + + const SCRIPT_VERSION = '20260209e'; + const SOURCE_SELECTOR = '.sl-markdown-content pre[data-language="mermaid"]'; + const RENDER_CLASS = 'mermaid-diagram'; + const HIDDEN_CLASS = 'mermaid-source-hidden'; + const THEME_OBSERVER_FLAG = '__vrMermaidThemeObserver'; + let mermaidModule = null; + let isRendering = false; + let needsRerender = false; + + function isDarkTheme() { + return document.documentElement.dataset.theme === 'dark'; + } + + function getSourceRoot(block) { + return block.closest('.expressive-code') || block; + } + + function normalizeMermaidCode(rawText) { + return String(rawText || '') + .replace(/\u007f/g, '\n') + .replace(/\u001f/g, '\n') + .replace(/[\u2028\u2029]/g, '\n') + .replace(/\u00a0/g, ' ') + .replace(/\r\n?/g, '\n') + .replace(/[\u200B-\u200D\uFEFF]/g, '') + .replace(/[\x00-\x08\x0B\x0C\x0E-\x1E]/g, '') + .split('\n') + .map((line) => line.replace(/\s+$/g, '')) + .join('\n') + .trim(); + } + + function fixLegacySubgraphSyntax(code) { + let autoSubgraphId = 0; + const lines = code.split('\n').map((line) => { + const legacyParen = line.match(/^(\s*)subgraph\s+([A-Za-z_][\w-]*)\s+\((.+)\)\s*$/); + if (legacyParen) { + const [, indent, id, title] = legacyParen; + return `${indent}subgraph ${id}["${String(title).replace(/"/g, '\\"')}"]`; + } + + const bracketOnly = line.match(/^(\s*)subgraph\s+\[(.+)\]\s*$/); + if (bracketOnly) { + autoSubgraphId += 1; + const [, indent, rawTitle] = bracketOnly; + const trimmed = String(rawTitle).trim(); + const title = /^".*"$/.test(trimmed) ? trimmed : `"${trimmed.replace(/"/g, '\\"')}"`; + return `${indent}subgraph SG_AUTO_${autoSubgraphId}[${title}]`; + } + + const quoteOnly = line.match(/^(\s*)subgraph\s+"(.+)"\s*$/); + if (quoteOnly) { + autoSubgraphId += 1; + const [, indent, title] = quoteOnly; + return `${indent}subgraph SG_AUTO_${autoSubgraphId}["${String(title).replace(/"/g, '\\"')}"]`; + } + + return line; + }); + + return lines + .join('\n') + .replace(/subgraph\s+([A-Za-z_][\w-]*)\s+\(([^\n\)]*)\)/g, (_, id, title) => { + return `subgraph ${id}["${String(title).replace(/"/g, '\\"')}"]`; + }); + } + + function finalizeMermaidCode(rawText) { + const normalized = normalizeMermaidCode(rawText); + return fixLegacySubgraphSyntax(normalized); + } + + function getCodeFromEcLines(block) { + const lineNodes = block.querySelectorAll('.ec-line .code'); + if (!lineNodes.length) return ''; + return Array.from(lineNodes) + .map((node) => node.textContent || '') + .join('\n'); + } + + function getCodeFromCopyButton(block) { + const button = block.closest('.expressive-code')?.querySelector('.copy button[data-code]'); + return button?.dataset.code || ''; + } + + function extractMermaidSource(block) { + const fromEcLines = getCodeFromEcLines(block); + if (fromEcLines.trim()) return finalizeMermaidCode(fromEcLines); + + const fromCopy = getCodeFromCopyButton(block); + if (fromCopy.trim()) return finalizeMermaidCode(fromCopy); + + const codeNode = block.querySelector('code'); + const fallback = codeNode ? codeNode.textContent || codeNode.innerText || '' : ''; + return finalizeMermaidCode(fallback); + } + + function cleanupRenderedDiagrams() { + document.querySelectorAll(`.${RENDER_CLASS}[data-vr-mermaid="true"]`).forEach((node) => node.remove()); + document.querySelectorAll(SOURCE_SELECTOR).forEach((block) => { + const root = getSourceRoot(block); + root.classList.remove(HIDDEN_CLASS); + root.style.display = ''; + block.removeAttribute('data-vr-mermaid-rendered'); + }); + } + + function buildErrorFallback(message, source) { + const wrapper = document.createElement('div'); + wrapper.className = RENDER_CLASS; + wrapper.dataset.vrMermaid = 'true'; + wrapper.style.borderColor = 'rgba(239, 68, 68, 0.45)'; + + const title = document.createElement('div'); + title.textContent = `[mermaid] render failed: ${message}`; + title.style.cssText = 'color:#b91c1c;font-weight:600;margin-bottom:8px;'; + wrapper.appendChild(title); + + const pre = document.createElement('pre'); + pre.textContent = source; + pre.style.cssText = 'white-space:pre-wrap;word-break:break-word;max-height:320px;overflow:auto;margin:0;'; + wrapper.appendChild(pre); + + return wrapper; + } + + async function ensureMermaid() { + if (mermaidModule) return mermaidModule; + const mod = await import('https://cdn.jsdelivr.net/npm/mermaid@10.9.1/dist/mermaid.esm.min.mjs'); + mermaidModule = mod.default || mod; + return mermaidModule; + } + + async function doRender() { + console.log('[mermaid] init script', SCRIPT_VERSION); + const mermaid = await ensureMermaid(); + mermaid.initialize({ + startOnLoad: false, + securityLevel: 'loose', + theme: isDarkTheme() ? 'dark' : 'base', + }); + + cleanupRenderedDiagrams(); + const blocks = Array.from(document.querySelectorAll(SOURCE_SELECTOR)); + if (!blocks.length) return; + + console.log('[mermaid] Found', blocks.length, 'diagrams'); + + let ok = 0; + let failed = 0; + for (let index = 0; index < blocks.length; index += 1) { + const block = blocks[index]; + const sourceRoot = getSourceRoot(block); + const source = extractMermaidSource(block); + if (!source) continue; + + const wrapper = document.createElement('div'); + wrapper.className = RENDER_CLASS; + wrapper.dataset.vrMermaid = 'true'; + + const host = document.createElement('div'); + host.className = 'mermaid'; + host.textContent = source; + wrapper.appendChild(host); + sourceRoot.insertAdjacentElement('afterend', wrapper); + + try { + await mermaid.run({ nodes: [host] }); + sourceRoot.classList.add(HIDDEN_CLASS); + sourceRoot.style.display = 'none'; + block.dataset.vrMermaidRendered = 'true'; + ok += 1; + } catch (error) { + wrapper.remove(); + sourceRoot.classList.remove(HIDDEN_CLASS); + sourceRoot.style.display = ''; + const fallback = buildErrorFallback(error?.message || String(error), source); + sourceRoot.insertAdjacentElement('afterend', fallback); + console.warn(`[mermaid] Diagram ${index + 1} render failed:`, error?.message || error); + failed += 1; + } + } + + console.log('[mermaid] render done:', { ok, failed, total: blocks.length }); + } + + async function renderAllDiagrams() { + if (isRendering) { + needsRerender = true; + return; + } + + isRendering = true; + try { + do { + needsRerender = false; + await doRender(); + } while (needsRerender); + } catch (error) { + console.error('[mermaid] Load failed:', error); + } finally { + isRendering = false; + } + } + + function observeThemeChanges() { + if (window[THEME_OBSERVER_FLAG]) return; + + let themeTimer = null; + const observer = new MutationObserver((mutations) => { + for (const mutation of mutations) { + if (mutation.type === 'attributes' && mutation.attributeName === 'data-theme') { + if (themeTimer) clearTimeout(themeTimer); + themeTimer = setTimeout(() => { + renderAllDiagrams(); + }, 80); + break; + } + } + }); + + observer.observe(document.documentElement, { + attributes: true, + attributeFilter: ['data-theme'], + }); + + window[THEME_OBSERVER_FLAG] = observer; + } + + function bootMermaid() { + renderAllDiagrams(); + observeThemeChanges(); + } + + document.addEventListener('astro:after-swap', bootMermaid); + + if (document.readyState === 'loading') { + document.addEventListener('DOMContentLoaded', bootMermaid, { once: true }); + } else { + bootMermaid(); + } + + window.renderMermaidDiagrams = renderAllDiagrams; +})(); diff --git a/external/ai-research-ebook/src/assets/houston.webp b/external/ai-research-ebook/src/assets/houston.webp new file mode 100644 index 0000000..930c164 Binary files /dev/null and b/external/ai-research-ebook/src/assets/houston.webp differ diff --git a/external/ai-research-ebook/src/assets/logo.svg b/external/ai-research-ebook/src/assets/logo.svg new file mode 100644 index 0000000..802ca61 --- /dev/null +++ b/external/ai-research-ebook/src/assets/logo.svg @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/external/ai-research-ebook/src/content.config.ts b/external/ai-research-ebook/src/content.config.ts new file mode 100644 index 0000000..d9ee8c9 --- /dev/null +++ b/external/ai-research-ebook/src/content.config.ts @@ -0,0 +1,7 @@ +import { defineCollection } from 'astro:content'; +import { docsLoader } from '@astrojs/starlight/loaders'; +import { docsSchema } from '@astrojs/starlight/schema'; + +export const collections = { + docs: defineCollection({ loader: docsLoader(), schema: docsSchema() }), +}; diff --git a/external/ai-research-ebook/src/content/docs/code/agents.mdx b/external/ai-research-ebook/src/content/docs/code/agents.mdx new file mode 100644 index 0000000..fd20acc --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/code/agents.mdx @@ -0,0 +1,609 @@ +--- +title: 多Agent框架 +description: LangChain、LlamaIndex、CrewAI、AutoGen 等多Agent框架对比 +sidebar: + order: 3 +--- + +import { Card, CardGrid } from '@astrojs/starlight/components'; + +## 1. 框架对比概览 + +当研究任务需要多个Agent协作时，选择合适的框架： + +| 框架 | 核心优势 | 研究用途 | GitHub | +|------|---------|---------|--------| +| **LangChain/LangGraph** | 复杂推理工作流，状态管理 | 多步骤文献分析、实验设计 | [langchain-ai](https://github.com/langchain-ai) | +| **LlamaIndex** | 知识库集成，RAG 优化 | 文档密集型研究、论文库查询 | [run-llama](https://github.com/run-llama) | +| **CrewAI** | 角色定义清晰，企业级 | 协作研究项目、团队模拟 | [crewAIInc](https://github.com/crewAIInc) | +| **Microsoft AutoGen** | 对话优先框架 | 讨论式研究、辩论式分析 | [microsoft/autogen](https://github.com/microsoft/autogen) | + +--- + +## 2. 选择决策树 + +```mermaid +flowchart TD + A(任务类型) --> B{需要复杂工作流?} + A --> C{需要知识库/RAG?} + A --> D{需要角色扮演?} + A --> E{需要并行协作?} + + B -->|是| F[LangGraph] + B -->|否| G[简单顺序执行] + + C -->|是| H[LlamaIndex] + D -->|是| I[CrewAI] + E -->|是| J[Agent Teams] +``` + +--- + +## 3. Claude Agent Teams + +### 3.1 核心能力 + +Agent Teams 让多个 Claude Code 会话并行协作： + +| 特性 | 说明 | +|------|------| +| **Team lead** | 负责拆解任务、分派、汇总 | +| **Teammates** | 独立执行，各自有上下文窗口 | +| **Delegate mode** | 队长只调度不实现，队友执行 | + +### 3.2 两种展示方式 + +| 模式 | 说明 | +|------|------| +| **In-process** | 都在同一个终端里跑（无需额外配置） | +| **Split panes** | 每个队友一个 pane（需要 tmux 或 iTerm2） | + +### 3.3 Agent Teams 四步法 + +1. **队长会话（Lead）先进入 Plan/Delegate**：先拆任务，不急着写代码 +2. 把任务拆成：可并行、低耦合、验收明确的子任务 +3. 每个子任务指定：输入、输出、验收（DoD） +4. 队友完成后，Lead 做集成、跑全套测试、开 PR + +### 3.4 Claude Code SDK 实战 + +```python +from claude_code import ClaudeAgent, TaskResult + +# 创建并行任务 +agents = [ + ClaudeAgent(name="Architect", role="架构设计"), + ClaudeAgent(name="Implementer", role="核心实现"), + ClaudeAgent(name="Tester", role="测试验证"), +] + +# 并行执行 +results = agent_team.run_parallel(agents, task="实现特征提取模块") +``` + +--- + +## 4. LangGraph 实战：科研状态机 + +```python +from typing import TypedDict, List, Optional +from langgraph.graph import StateGraph, END + +class ResearchState(TypedDict): + """研究流水线状态""" + topic: str # 研究主题 + papers: List[dict] # 论文列表 + analysis: str # 当前分析 + gaps: List[str] # 识别到的空白 + claims: List[str] # 候选主张 + evaluation: str # 评估结果 + +def search_papers(state: ResearchState) -> ResearchState: + """搜索相关论文""" + papers = elicit_search(state["topic"]) + return {"papers": papers} + +def analyze_papers(state: ResearchState) -> ResearchState: + """分析论文，提取关键信息""" + analysis = claude_analyze(state["papers"]) + return {"analysis": analysis} + +def extract_gaps(state: ResearchState) -> ResearchState: + """从分析中提取研究空白""" + gaps = identify_gaps(state["analysis"]) + return {"gaps": gaps} + +def generate_claims(state: ResearchState) -> ResearchState: + """基于空白生成可验证主张""" + claims = [] + for gap in state["gaps"]: + claim = formulate_claim(gap) + claims.append(claim) + return {"claims": claims} + +def evaluate_claims(state: ResearchState) -> ResearchState: + """评估主张质量""" + evaluation = gpt5_evaluate(state["claims"]) + return {"evaluation": evaluation} + +# 构建工作流 +workflow = StateGraph(ResearchState) +workflow.add_node("search", search_papers) +workflow.add_node("analyze", analyze_papers) +workflow.add_node("extract_gaps", extract_gaps) +workflow.add_node("generate_claims", generate_claims) +workflow.add_node("evaluate", evaluate_claims) + +# 定义边 +workflow.add_edge("search", "analyze") +workflow.add_edge("analyze", "extract_gaps") +workflow.add_edge("extract_gaps", "generate_claims") +workflow.add_edge("generate_claims", "evaluate") +workflow.add_edge("evaluate", END) + +app = workflow.compile() +``` + +--- + +## 5. CrewAI 研究团队配置 + +```python +from crewai import Agent, Task, Crew, Process + +# 1. 定义专业角色 +literature_reviewer = Agent( + role="文献综述专家", + goal="全面梳理指定领域的研究现状", + backstory="你是一位经验丰富的研究员，拥有10年AI领域研究经验。", + tools=[elicit_tool, semantic_scholar_tool], + verbose=True +) + +gap_analyst = Agent( + role="研究空白分析师", + goal="从现有研究中识别可探索的空白", + backstory="你擅长发现被忽视的研究方向。", + tools=[claude_tool], + verbose=True +) + +claim_validator = Agent( + role="主张验证专家", + goal="评估研究主张的可验证性和创新性", + backstory="你严格审查每一个科学主张。", + tools=[gpt_tool], + verbose=True +) + +# 2. 定义任务 +task_review = Task( + description="调研 {topic} 领域近3年的顶会论文，产出：1）研究地图；2）主流方法对比表；3）潜在空白清单", + expected_output="结构化的研究综述文档", + agent=literature_reviewer +) + +task_gap = Task( + description="基于文献综述，识别 {topic} 领域最值得探索的5个研究方向", + expected_output="研究空白分析报告", + agent=gap_analyst +) + +task_claim = Task( + description="将每个研究空白转化为可验证的科研主张", + expected_output="主张清单及详细说明", + agent=claim_validator +) + +# 3. 组建团队 +research_crew = Crew( + agents=[literature_reviewer, gap_analyst, claim_validator], + tasks=[task_review, task_gap, task_claim], + process=Process.sequential, + verbose=True, + memory=True +) + +result = research_crew.kickoff(inputs={"topic": "多模态学习"}) +``` + +--- + +## 6. AutoGen 对话式研究 + +```python +import autogen + +# 定义对话Agent +critic = autogen.AssistantAgent( + name="Critic", + system_message="""你是审稿人Agent，负责批判性分析。 + 质疑假设、找出漏洞、提出改进建议。 + 每次发言必须提出至少一个建设性批评。""" +) + +author = autogen.AssistantAgent( + name="Author", + system_message="""你是作者Agent，负责辩护和解释你的研究。 + 回应批评，补充论据，完善论点。""" +) + +# 启动对话 +critic.initiate_chat( + author, + message="请简要介绍你的研究主张，然后我会提出质疑。" +) +``` + +--- + +## 7. Ralph 循环：简单而强大的自治模式 + +### 7.1 核心原理 + +Ralph 循环通过简单的 bash 循环重复执行操作，直到任务成功： + +```bash +#!/usr/bin/env bash +set -euo pipefail + +MAX_ITERS="${MAX_ITERS:-25}" + +for i in $(seq 1 "$MAX_ITERS"); do + echo "=== Iteration $i/$MAX_ITERS ===" + cat PROMPT.md | claude -p "按PROMPT.md完成任务。必须跑 tests；失败要修到通过。" + ./scripts/test-fast.sh && ./scripts/lint.sh +done + +echo "Reached MAX_ITERS=$MAX_ITERS without passing. Stop." +exit 1 +``` + +### 7.2 关键技巧 + +| 技巧 | 说明 | +|------|------| +| **明确完成标准** | 定义可验证的完成条件（测试通过、无错误） | +| **小迭代原则** | 每轮迭代只处理少量任务 | +| **反馈循环** | 集成自动化检查确保每轮结果有效 | +| **安全限制** | 设置迭代次数上限（20-50次） | + +### 7.3 适用场景 + +| 适合 | 不适合 | +|------|--------| +| 可量化、可验证的任务 | 探索性任务 | +| 重构、写测试、文档化 | 主观设计 | +| 补测试、修复 Bug | 安全敏感代码 | + +--- + +## 8. 实战案例：16 个 Claude 写 C 编译器 + +### 8.1 实验概览 + +- **目标**：让 16 个 Claude 并行开发一个能编译 Linux 内核的 C 编译器 +- **成果**：两周内，近 2000 次会话，$2 万成本，10 万行代码 +- **支持架构**：x86、ARM、RISC-V + +### 8.2 成功的关键 + +1. **极高质量的测试体系**：引入成熟测试集，CI 强制要求新提交不破坏功能 +2. **站在 AI 视角设计系统**： + - 简化测试输出，结构化日志便于 AI 检索 + - 提供 `--fast` 模式只运行 1%-10% 测试子集 +3. **确保任务可拆分**：让每个智能体修复独立测试用例 +4. **利用并行实现角色分工**：性能优化、代码重构、文档维护 + +--- + +## 9. Everything Claude Code 项目 + +### 9.1 项目概览 + +| 项目 | 说明 | +|------|------| +| **创建者** | Anthropic 黑客松冠军 Affaan Mustafa | +| **定位** | 完整配置集合，将 Claude 升级为"虚拟开发团队" | +| **GitHub** | [affaan-m/everything-claude-code](https://github.com/affaan-m/everything-claude-code) | + +### 9.2 核心组件 + +| 组件 | 功能 | +|------|------| +| **Agents** | 规划、代码审查、安全检测、TDD 指导 | +| **Skills** | 工作流和领域经验封装 | +| **Commands** | 快速执行预定义流程 | +| **Rules** | 开发规范、测试覆盖率要求 | +| **Hooks** | 提交前检查、console.log 检测 | +| **MCP** | 外部工具连接 | + +### 9.3 实战效果 + +| 指标 | 提升 | +|------|------| +| 功能实现速度 | 65% | +| 代码问题减少 | 75% | +| 测试覆盖率 | 82% | +| 会话切换减少 | 70% | + +--- + +## 10. 多终端并行配置 + +### 10.1 tmux 工作流 + +```bash +# 安装 tmux (macOS) +brew install tmux + +# 配置文件 ~/.tmux.conf +set -g mouse on +bind-key C-d set -g mouse on + +# 一键启动多 AI 模型 +./start-ai-agents.sh +``` + +### 10.2 多模型同步控制 + +| 快捷键 | 功能 | +|--------|------| +| `Ctrl+b D` | 切换同步模式 | +| `Ctrl+b X` | 退出多窗口模式 | + +### 10.3 适用场景 + +- **同时对比**：Claude、Codex、Gemini、OpenCode +- **并行实现**：多个 worktree 同时推进 +- **多视角评审**：一个实现、一个审查、一个测试 + +--- + +## 11. 使用建议 + + + + + +不要过度设计，Claude Code 单实例足够处理大多数代码任务。 + + + + + +需要复杂分支时使用 LangGraph 或 Agent Teams。 + + + + + +论文库查询优先考虑 LlamaIndex。 + + + + + +需要角色扮演时使用 CrewAI。 + + + + + +--- + +## 12. Execution-Grounded Agent 实战要点 + +为了让多 Agent 真正用于科研而不只是"写建议"，建议采用 execution-grounded 架构： + +1. **Idea Agent**：提出可执行改动（不是抽象口号） +2. **Patch Agent**：输出可应用的代码 diff +3. **Executor Agent**：在标准环境中自动训练/评测（含 GPU 资源调度） +4. **Feedback Agent**：回收 Wandb/日志结果，驱动下一轮搜索 + +### 12.1 两类标准化环境 + +| 环境 | 典型配置 | +|------|---------| +| **Post-training** | GRPO + MATH | +| **Pre-training** | nanoGPT + FineWeb | + +这种环境化设计的价值： +- 可以批量并行比较 idea，不依赖主观判断 +- 可以把"创新性"转化为"可执行且可复验"的工程流程 +- 可以把失败样本积累为后续搜索的先验约束 + +--- + +## 13. AI Scientist 系列脉络（补充） + +为避免把自动化科研理解成“单一框架”，这里补一条系列演进视角（来自 `AI research.md`）： + +| 路线 | 核心特点 | 对工程落地的启发 | +|------|---------|------------------| +| **AI Scientist (早期)** | 自动生成 idea 与初步实验 | 适合快速探索，但执行闭环较弱 | +| **Execution-Grounded 路线** | 强调 patch/run/feedback 闭环 | 更适合做真实迭代与可复现实验 | +| **Narrative 路线（Idea2Story）** | 从想法到完整论文叙事 | 适合写作联动与产出打磨 | + +实操建议：把三条路线合成“探索 → 执行 → 叙事”的分层流程，而不是把某一个框架当万能解。 + +--- + +## 14. Book 实战增补（逐篇并入） + +> 本节并入 3 篇与多 Agent 协作强相关的文章，统一为"何时用团队协作、如何控成本、如何防跑偏"的实操框架。 + +### 14.1 1) Agent Swarm（蜂群）实战：从单助手到数字团队 + +#### 14.1.1 核心机制（可落地） + +1. **共享任务列表**：统一看板，便于并行认领和状态同步。 +2. **消息邮箱系统**：队员间可直接通信，减少 Lead 中转瓶颈。 +3. **独立上下文**：每个 Agent 独立窗口，避免大任务互相污染。 + +#### 14.1.2 六种典型玩法（按成熟度） + +- 新手：提示词里显式要求“尽可能拆分并行 + 尽可能调用 skill”。 +- 进阶：主模型统筹，便宜模型执行（文档/测试等）做成本分层。 +- 复用：优先搜索社区 skill，找不到再降级到本地或通用流程。 +- 降返工：先计划、后评审、再执行，卡住就回到计划阶段。 +- 大项目：Lead-Member 架构，分角色并行交付。 +- 深水区：Hooks/动态路由/Subagent Fork 等做质量与成本精调。 + +#### 14.1.3 详细步骤（从 0 开始跑 Swarm） + +1. 先写任务卡：目标、范围、交付物、验收标准（四项缺一不可）。 +2. 指定 Lead 只做拆解与验收，不直接写核心实现代码。 +3. 把任务拆成并行子任务（每个子任务都要有独立输入输出）。 +4. 要求每个成员用统一汇报模板：完成项 / 风险 / 下一步。 +5. Lead 在每个里程碑做一次冲突检查（文件冲突、接口冲突、依赖冲突）。 +6. 最后统一跑集成验证，再回到主分支合并。 + +#### 14.1.4 文章细节补充（Swarm 六玩法模板） + +##### 玩法1：新手并行提示模板 + +```text +调用 Agent Swarm 执行任务：<任务描述> +要求： +1) 尽可能拆分并行子任务 +2) 优先使用已安装 skills +3) 每个子任务输出“结果+风险+下一步” +``` + +##### 玩法2：跨模型协作（成本分层） + +- 复杂架构/核心逻辑：强模型； +- 文档/测试/样板代码：低成本模型； +- 最终合并与验收：回到主模型统一把关。 + +##### 玩法3：社区 Skill 复用 + +1. 先搜索可用 skill； +2. 匹配则直接调用； +3. 无匹配再降级到通用流程。 + +##### 玩法4：计划+评审循环 + +1. 先产 Todo 与依赖； +2. 评审 agent 指出遗漏与风险； +3. 评审通过后才并行执行。 + +##### 玩法5：Lead-Member 组织结构 + +- Lead：任务拆解、里程碑验收； +- Members：按角色并行实现； +- 里程碑结束统一集成。 + +##### 玩法6：高级功能组合 + +- Hooks 做质量门禁； +- 动态模型路由控成本； +- Subagent Fork 隔离上下文污染。 + +#### 14.1.5 实战摘录 + +> Swarm 不是"更会聊天"，而是"可协作的生产组织"。核心收益来自任务拆解质量，而不是 agent 数量。 + +### 14.2 2) Agent Teams 团战模式：并行收益与协调成本 + +#### 14.2.1 适用/不适用边界 + +| 任务类型 | 建议 | +|---|---| +| 研究审查、多假设调试、跨层联动 | 优先 Agent Teams | +| 顺序任务、同文件高冲突、强依赖链 | 优先单会话或 Subagent | + +#### 14.2.2 启用与操作要点 + +```json +{ + "env": { + "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" + } +} +``` + +- 启用后先定义：Lead 职责、队员分工、里程碑验收。 +- 明确要求：`Wait for teammates before proceeding`，避免 Lead 抢做。 +- 文件级分工防冲突：每个队员拥有独立文件集。 +- 控 token：团队规模与任务收益匹配，避免“并行过度”。 + +#### 14.2.3 详细步骤（Agent Teams 落地） + +1. 在 `settings.json` 打开 `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1`。 +2. 重启会话，确认团队模式已可用（可看到 team/teammates 指令反馈）。 +3. 创建团队时先给角色定义：Lead、Frontend、Backend、Test、Docs。 +4. 给每个角色分配**不重叠文件域**，防止写冲突。 +5. 执行中每 20~30 分钟做一次状态同步：完成/阻塞/待协作。 +6. 收尾时执行“等待队员完成”策略，再统一集成测试。 + +#### 14.2.4 文章细节补充（Teams 全流程） + +1. 升级到支持 Teams 的版本（必要时先执行更新命令）。 +2. 在 `settings.json` 注入实验开关并重启会话。 +3. 选择显示模式（同进程或分屏），确认可观察到每个成员状态。 +4. 指定成员角色与模型，给每个成员写明 DoD（Definition of Done）。 +5. 启用委托模式，Lead 不直接写实现。 +6. 执行阶段每轮只推进一个里程碑，防止团队漂移。 +7. 全部成员完成后再统一回归测试与文档收口。 + +#### 14.2.5 限制说明（实操前必读） + +- 每会话仅一个团队； +- 不支持嵌套团队； +- 进程内成员恢复能力有限； +- 分屏依赖终端能力（tmux/iTerm2 等）。 + +### 14.3 3) 进阶实战（Skills/Subagents/MCP 组合） + +#### 14.3.1 组合原则 + +- **Skill**：固化团队规则与复用流程（先描述触发条件，后写具体动作）。 +- **Subagent**：把高噪声探索外包，主上下文只保留摘要结论。 +- **MCP**：把外部系统接入同一会话，减少标签页切换与复制粘贴。 + +#### 14.3.2 Subagent 设计建议 + +- 明确 `description` 与输出格式，避免“回传整段上下文”。 +- 只给必要工具权限（只读审查与可写实现分离）。 +- 推荐链路：Explore → Implementer → Tester，主代理只做整合与决策。 + +#### 14.3.3 详细步骤（Subagents + MCP 组合） + +1. 先定义 Explore 输出格式：只返回“文件路径 + 关键结论 + 风险点”。 +2. Implementer 仅接收结构化输入，不接收原始长对话。 +3. Tester 固定执行同一套测试清单，结果以表格返回。 +4. 主代理整合三者输出后，再调用 MCP 写回 issue/文档系统。 +5. 若发现信息漂移，回滚到 Explore 结果重建链路，不在末端硬修。 + +#### 14.3.4 文章细节补充（命令级落地） + +```bash +# 查看可用 subagents +/agents + +# 查看 MCP 连接 +/mcp + +# 示例：添加 HTTP MCP（按实际服务替换） +claude mcp add --transport http notion https://mcp.notion.com/mcp +``` + +1. 先建只读 `security-reviewer`，限制到 Read/Grep/Glob。 +2. 再建可写 implementer，明确只改指定目录。 +3. 所有 subagent 返回统一格式：`summary / changed_files / risks`。 +4. MCP 只接入本任务必要服务，避免权限过大。 + +#### 14.3.5 MCP 使用建议 + +- 优先接入：GitHub、Slack、DB、文档系统。 +- 第三方连接器先做安全评估（权限、日志、凭证范围）。 +- 通过 `/mcp` 定期盘点已加载连接，避免“隐式工具漂移”。 + +#### 14.3.6 常见坑与处理 + +- **坑1：并行太多导致 token 爆涨** → 限制活跃队员数量，优先高价值并行。 +- **坑2：成员写同一文件互相覆盖** → 启动前做文件分区。 +- **坑3：Lead 抢做实现导致协作失效** → 明确 Lead 只做调度与验收。 +- **坑4：总结过长污染主上下文** → 强制摘要长度与格式。 diff --git a/external/ai-research-ebook/src/content/docs/code/claude.mdx b/external/ai-research-ebook/src/content/docs/code/claude.mdx new file mode 100644 index 0000000..3b1d546 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/code/claude.mdx @@ -0,0 +1,684 @@ +--- +title: Claude Code +description: Claude Code 4.5/4.6 最佳实践，用于项目构建阶段 +sidebar: + order: 1 +--- + +## 1. Claude Opus 4.5/4.6 (2025年11月发布) + +### 1.1 核心能力 + +| 能力 | 数据 | 说明 | +|------|------|------| +| **SWE-bench Verified** | 80.9% | 领先业界的代码生成基准 | +| **上下文窗口** | 200K tokens | 64K token 输出限制 | +| **成本降低** | 67% | $5/$25 per million tokens | +| **tau2-bench-lite** | 88.9% | Agent 工具使用评估 | + +### 1.2 适用场景 + +Claude Code 适合项目的**构建阶段**，可以开两三个（甚至更多）并行推进。 + +目标：让执行文档里的任何**被真实跑通**，直到代码结构、实验入口、评测脚本都稳定。 + +--- + +## 2. 安装与快速上手 + +### 2.1 安装方式 + +```bash +# macOS / Linux / WSL +curl -fsSL https://claude.ai/install.sh | bash + +# Windows PowerShell +irm https://claude.ai/install.ps1 | iex + +# Windows CMD +curl -fsSL https://claude.ai/install.cmd -o install.cmd && install.cmd && del install.cmd +``` + +### 2.2 启动方式 + +```bash +cd your-project +claude +``` + +首次会提示登录。([Claude Code](https://code.claude.com/docs/en/overview)) + +--- + +## 3. 交互模式与快捷键 + +### 3.1 核心快捷键 + +| 快捷键 | 功能 | +|--------|------| +| `?` | 显示当前环境可用快捷键 | +| `Esc Esc` | 回退/总结（rewind / summarize） | +| `Shift+Tab` | 切换模式（普通 ↔ Plan ↔ Auto-accept ↔ Delegate） | +| `Ctrl+B` | 把运行中的任务后台化 | +| `Ctrl+T` | 显示/隐藏任务列表 | +| `Option/Alt + P` | 切换模型 | +| `Shift+Enter` | 多行输入 | + +### 3.2 内置斜杠命令 + +| 命令 | 功能 | +|------|------| +| `/clear` | 清空对话 | +| `/context` | 看上下文占用 | +| `/export [filename]` | 导出对话 | +| `/resume [session]` | 恢复历史会话 | +| `/rename ` | 重命名会话 | +| `/plan` | 进入 plan mode | +| `/rewind` | 回退代码/对话或总结 | +| `/doctor` | 安装健康检查 | +| `/debug [desc]` | 读 debug log 排查 | +| `/config` | 打开设置界面 | +| `/status` | 状态页 | +| `/permissions` | 权限规则 | +| `/model` | 选模型 | +| `/mcp` | 管 MCP 连接 | +| `/tasks` | 背景任务管理 | + +--- + +## 4. 权限模式与安全边界 + +### 4.1 四种模式 + +| 模式 | 能力 | 适用场景 | +|------|------|---------| +| **default** | 先问再做 | 日常稳妥开发 | +| **acceptEdits** | 自动允许文件编辑 | 你在旁边盯着、改动明确 | +| **plan** | 只读分析+出计划 | 需求澄清、方案评审、审计 | +| **delegate** | 只做 team 管理 | Agent Teams 并行协作时 | + +按 `Shift+Tab` 循环切换。 + +### 4.2 权限配置建议 + +在 `.claude/settings.json` 中配置： + +```json +{ + "permissions": { + "allow": ["npm run test *", "Bash(git status)"], + "deny": [".env", "secrets/**", "Bash(curl *)"] + } +} +``` + +--- + +## 5. 上下文管理 + +### 5.1 核心原则 + +**上下文再大也会被填满，可控上下文 = 可控质量。** + +### 5.2 工具对比 + +| 命令 | 功能 | 何时用 | +|------|------|--------| +| `/context` | 看上下文占用 | 判断是否该清理了 | +| `/clear` | 清掉对话但保留项目状态 | 任务切换、对话混乱 | +| `/export` | 导出关键对话 | 交接、复盘、团队沉淀 | +| `Esc Esc` / `/rewind` | 回退到之前状态 | 跑偏时回到正确轨道 | + +### 5.3 /catchup Skill 模板 + +```markdown +--- +name: catchup +description: Rebuild context after /clear by reading repo state +--- + +1) 运行并总结： +- git status +- git diff --stat +- git diff +- git log -n 20 --oneline + +2) 优先阅读： +- CLAUDE.md +- README / docs/ +- TODO.md / PLAN.md（如存在） + +3) 输出三段： +A. 当前仓库状态 +B. 当前目标与待办（按优先级） +C. 风险点与建议的下一步命令 +``` + +--- + +## 6. 项目记忆：CLAUDE.md + +### 6.1 必须包含的内容 + +> **CLAUDE.md 是智能体的"宪法"，应保持简洁、聚焦高频使用场景和关键约束。** + +| 类别 | 内容示例 | +|------|---------| +| **项目启动** | `npm run dev` / `python main.py` | +| **测试运行** | `pytest tests/` / `npm test` | +| **代码风格** | `ruff check .` / `prettier --write` | +| **代码约束** | 禁止读取 secrets、单测必写 | +| **常见坑** | 数据路径用 pathlib、随机种子固定 | + +### 6.2 模板示例 + +```markdown +# 项目约定 + +## 启动命令 +- 开发: `npm run dev` +- 测试: `npm test` +- 构建: `npm run build` + +## 代码风格 +- Python: ruff format + ruff check +- 命名: snake_case 函数, PascalCase 类 + +## 常见坑 +- 数据路径必须用 pathlib +- 随机种子固定为 42 +- batch_size 先从 32 开始 + +## Debug 流程 +1. 复现 bug +2. 最小化用例 +3. 定位问题 +4. 修复 +5. 加测试 +``` + +--- + +## 7. Plan Mode 优先闭环 + +### 7.1 何时必须先 Plan + +- 影响架构、鉴权、支付、数据结构、跨模块重构 +- 需要写/改测试策略 +- 希望"一次性正确实现" + +### 7.2 标准三段式提示 + +```text +你先进入 /plan。 +1) 用要点列出：目标、非目标、约束（性能/兼容/安全）、验收标准。 +2) 给出分步实现计划（每步产出什么、改哪些文件）。 +3) 列出风险与回滚方案。 +等我确认后再开始编码实现。 +``` + +--- + +## 8. 权限与沙箱 + +### 8.1 沙箱模式 + +运行 `/sandbox` 打开菜单选择沙箱模式。 + +**WSL2 依赖安装：** +```bash +# Ubuntu/Debian +sudo apt-get install bubblewrap socat + +# Fedora +sudo dnf install bubblewrap socat +``` + +### 8.2 企业护栏策略 + +- **轻量提醒**：发现 `console.log` / debug flag → 提醒但不阻断 +- **强拦截**：提交时如果 tests 未过 → 阻止并要求修复 + +--- + +## 9. Skills：可复用工作流 + +### 9.1 Skills 是什么 + +Skills 把最佳实践固化成可调用、可共享、可组合的流程，解决"重复指导困境"。 + +### 9.2 存放位置 + +| 位置 | 说明 | +|------|------| +| 项目内 `.claude/skills/` | 团队共享 | +| `~/.claude/skills/` | 个人全局 | +| 插件内 `/skills/` | 插件分发 | + +### 9.3 推荐技能模板 + +#### 9.3.1 `/plan` - 强制计划 + +```markdown +--- +name: plan +description: Create an implementation plan with acceptance criteria +--- + +输出必须包含： +1) Goals / Non-goals / Constraints +2) Acceptance criteria（给出可运行命令） +3) Step-by-step plan +4) Risks + rollback +除非用户明确批准，否则不要写代码。 +``` + +#### 9.3.2 `/code-review` - 代码审查 + +```markdown +--- +name: code-review +description: Review current changes with a production checklist +--- + +执行： +- git diff +- git diff --stat + +按以下结构输出： +1) Correctness（边界/错误处理） +2) Security（输入校验/鉴权） +3) Tests（覆盖、缺失用例） +4) Performance（热点、N+1） +5) Maintainability（复杂度、命名） +最后给出"必须改/建议改/可不改"三类结论。 +``` + +#### 9.3.3 `/tdd` - 测试驱动 + +```markdown +--- +name: tdd +description: Apply a TDD workflow +--- + +1) 先写失败测试（说明为什么失败） +2) 再最小实现让测试通过 +3) 再重构（保持测试通过） +每一步都给出运行的测试命令与结果摘要。 +``` + +#### 9.3.4 `/pr` - PR 准备 + +```markdown +--- +name: pr +description: Prepare a PR: summarize changes, run checks +--- + +执行并总结： +- git status +- git diff --stat +- lint/test/build 命令 + +输出： +1) PR Title（50字内） +2) Summary（要点） +3) Testing（跑了什么） +4) Risk/rollback +``` + +--- + +## 10. Hooks：事件驱动自动化 + +### 10.1 Hooks 能做什么 + +Hooks 在关键节点自动执行或拦截操作： + +- 在 Claude 调用工具前/后 +- 在提交 prompt 时 +- 在会话开始/结束 +- 在 compact/子任务结束 + +### 10.2 推荐落地类型 + +| Hook 类型 | 功能 | 优先级 | +|----------|------|--------| +| **格式化** | 改完文件后自动跑 formatter | 高 | +| **测试** | 关键模块改动后自动跑测试 | 高 | +| **危险拦截** | 检测到 `curl | bash` 阻止 | 中 | +| **PR 质量** | 提交前检查 TODO/敏感信息 | 中 | +| **SessionStart** | 启动时加载项目环境 | 中 | + +### 10.3 Hooks vs Skills 对比 + +- **Skills**：告诉它"按步骤做事"（流程化） +- **Hooks**：保证"在关键节点一定发生"（自动化 + 强制） + +--- + +## 11. MCP：外部工具接入 + +### 11.1 推荐场景 + +| 场景 | 推荐程度 | +|------|---------| +| 访问外部系统数据（Notion/Jira/GitHub/DB） | ✅ 强推荐 | +| 有状态工具（Playwright） | ⚠️ 谨慎 | +| 所有内部 API 包进 MCP | ❌ 避免 | + +### 11.2 添加 MCP Server + +```bash +# HTTP 方式（推荐） +claude mcp add --transport http notion https://mcp.notion.com/mcp + +# SSE 方式（已废弃） +claude mcp add --transport sse asana https://mcp.asana.com/sse + +# 查看列表 +claude mcp list +``` + +### 11.3 核心 MCP 工具 + +| 工具 | 功能 | 适用场景 | +|------|------|---------| +| **Zotero-MCP** | 直接访问本地 Zotero 文献库 | 文献管理、引用分析 | +| **Perplexity MCP** | Deep Research 模式 | 技术调研、综述写作 | +| **ClickHouse MCP** | 数据库集成 | 大数据分析 | +| **GitHub MCP** | 仓库操作 | 版本控制自动化 | + +--- + +## 12. 最佳实践：团队协作模式 + +### 12.1 角色分工 + +| 角色 | 职责 | Claude 实例数 | +|------|------|--------------| +| Architect | 总体结构与接口设计 | 1-2 | +| Implementer | 核心模块实现 | 2-3 | +| Tester | 测试用例与评测 | 1-2 | +| Reviewer | Code review 与边界检查 | 1-2 | + +### 12.2 7天落地路线 + +| 天数 | 任务 | +|------|------| +| Day 1 | 编写简洁的 `CLAUDE.md` | +| Day 2 | 添加底线 `rules`（安全、测试） | +| Day 3 | 落地核心命令 `/plan` | +| Day 4 | 落地命令 `/code-review` | +| Day 5 | 添加提醒型 Hooks | +| Day 6 | 添加一致性 Hooks | +| Day 7 | 引入 Subagents | + +--- + +## 13. 下一步行动 + +1. 在项目里跑一次 `/init`，生成并精简 `CLAUDE.md` +2. 新建 `.claude/skills/`，把常用 Skills 复制进去 +3. 开始复杂任务前：先 `/plan`，确认后再实现 +4. 跑偏就 `Esc Esc` → rewind +5. 想减少弹窗就 `/sandbox` +6. 需要并行就用 Agent Teams + +--- + +## 14. Vibe 实战并集补充（融合版） + +> 本节把 `vibe/Vibe.md`、`vibe/Vibe Working.md`、`vibe/VIbe2.md` 的重合观点去重后并入当前章节，只保留与工程落地直接相关的增量。 + +### 14.1 高频共识（去重后） + +1. **Plan 先行**：复杂任务默认 `/plan`，先给验收标准再改代码。 +2. **上下文治理**：高频使用 `/context` + `/clear` + `/rewind`，避免会话污染。 +3. **流程资产化**：把重复动作沉淀为 skills / hooks / agents，而不是靠临场提示词。 +4. **结果导向**：以“可运行 + 可测试 + 可评审（PR）”作为完成标准。 + +### 14.2 团队落地的最小闭环 + +- **Day 1-2**：整理 `CLAUDE.md` + `settings.json`（定义底线约束） +- **Day 3-4**：落地 `/plan`、`/code-review` 两个核心 skill +- **Day 5-7**：接入提醒型 hooks，再逐步升级到一致性/阻断型 hooks + +这条路线的目标是先稳定质量，再追求并行规模。 + +--- + +## 15. Book 实战增补（逐篇并入） + +> 本节将 `book/` 中 5 篇与 Claude Code 直接相关的文章，按"教程正文 + 实战摘录"方式并入本章，重点保留可执行方法与避坑经验。 + +### 15.1 1) 470万阅读实战心得：先思考，再动手 + +#### 15.1.1 教程化整合 + +- 复杂任务默认先进入 Plan Mode（双击 `Shift + Tab`），先定架构与验收标准再写代码。 +- `CLAUDE.md` 要短、具体、可执行：优先写项目特有约束，而非常识性说明。 +- 指令要同时包含"做什么 + 为什么"：让模型在边界场景做出更稳判断。 +- 上下文不要用满才清理：20%~40% 已可能出现质量退化，建议任务级会话拆分。 +- 使用外部记忆文件（如 `plan.md`、`SCRATCHPAD.md`）承接跨会话进度。 + +#### 15.1.2 可执行清单 + +1. 每次复杂需求先写 5~10 行目标与非目标。 +2. 运行一次 `/plan` 或 Plan Mode 输出分步方案。 +3. 将重复纠错项沉淀到 `CLAUDE.md`（按 `#` 快捷更新）。 +4. 对话跑偏时使用 `/compact` → `/clear` → 粘贴关键上下文重启。 + +#### 15.1.3 详细步骤（避免"云里雾里"） + +1. 新建 `plan.md`，固定写 4 行：目标 / 非目标 / 输入 / 验收。 +2. 进入 Plan Mode 后要求输出：文件列表 + 变更顺序 + 测试命令。 +3. 若计划中有"可选增强"，先全部标记为不做，只保留 MVP。 +4. 执行中每完成一个子任务，在 `plan.md` 写"已完成 + 证据链接"。 +5. 出现循环修复两次以上，立即停下执行 `/clear`，重开会话重述任务。 + +#### 15.1.4 实战摘录 + +> 如果同一问题解释三次仍跑偏，优先换会话或换问题建模方式，而不是继续加长提示词。 + +#### 15.1.5 文章细节补充（保留原文有效动作） + +- **CLAUDE.md 四原则**：短、具体、解释原因、持续更新。 +- **上下文治理动作**：限定单任务会话、外部记忆文件、`/compact` 摘要、`/clear` 重启。 +- **Prompt 质量三要素**：具体目标、明确禁做项、业务原因说明。 +- **卡住时四步修复**：清会话 → 拆小任务 → 给最小示例 → 换建模角度（如状态机）。 + +#### 15.1.6 可复制提示词模板 + +```text +请先进入规划模式，不要立即编码。 +目标：<目标> +约束：<技术栈/目录/边界> +禁止事项：<不允许新增抽象/不允许改动模块> +验收标准：<测试命令 + 通过条件> +输出：按步骤计划（每步含输入、输出、风险） +``` + +### 15.2 2) 资深工程师完全指南：把 Claude 变成系统组件 + +#### 15.2.1 教程化整合 + +- 把 Claude Code 从"问答工具"升级为"工程系统部件"：支持脚本化、日志化、可审计。 +- 模型协作建议：架构与关键决策用强模型，日常实现用高性价比模型。 +- 推荐在常见任务上沉淀 Slash Commands，减少重复 prompt 编写成本。 +- 借助 Hooks/MCP 做前后置检查，把质量控制前移到流程节点。 + +#### 15.2.2 可执行清单 + +- 对高风险模块启用强制检查：安全、权限、数据一致性。 +- 复用无头执行：`claude -p "任务描述"` 集成到脚本或 CI。 +- 每周回顾一次“失败任务日志”，更新 `CLAUDE.md` 与命令模板。 + +#### 15.2.3 详细步骤（系统化落地） + +1. 把高频任务抽成命令：`/commands/review.md`、`/commands/debug.md`。 +2. 在 CI 中增加 `claude -p` 的只读审查任务（不直接写仓库）。 +3. 每次失败记录三件事：触发条件、错误表现、修复动作。 +4. 每周挑 3 条重复问题，更新 `CLAUDE.md` 的"禁止/必须"规则。 + +#### 15.2.4 文章细节补充（系统化飞轮） + +- **飞轮机制**：错误发生 → 查看日志 → 改 CLAUDE.md/命令模板 → 下次更稳。 +- **模型分工建议**：复杂架构和疑难排查用强模型，常规实现用高性价比模型。 +- **会话切换信号**：当问题定义已变，优先新会话，不在旧上下文强行续写。 + +#### 15.2.5 推荐命令清单 + +```bash +# 无头运行（脚本/CI） +claude -p "审查当前改动的安全风险，给出按严重级别排序结果" + +# 继续历史会话 +claude --continue + +# 选择历史会话 +claude --resume +``` + +### 15.3 3) 31个效率技巧：从快捷键到自动化分层使用 + +#### 15.3.1 教程化整合 + +将 31 个技巧收敛为 5 层能力： + +| 层级 | 代表能力 | 关键命令/动作 | +|---|---|---| +| 启动层 | 建立项目记忆 | `/init`、Memory Updates | +| 交互层 | 快速上下文与命令执行 | `@文件`、`! bash`、`Esc Esc` | +| 会话层 | 恢复、命名、导出 | `claude --continue`、`/export` | +| 安全层 | 权限控制与审计 | `/sandbox`、Hooks | +| 自动化层 | 流水线复用 | `claude -p`、Commands、Skills、Subagents | + +#### 15.3.2 可执行清单 + +- 把高频动作固化成 `/commands`； +- 复杂任务统一走 Plan + Review； +- 批处理任务优先转为 `-p` 无头流程。 + +#### 15.3.3 详细步骤（31 技巧落地顺序） + +1. 第一周只上手 6 个：`/init`、`@`、`!`、`Esc Esc`、`/context`、`/sandbox`。 +2. 第二周新增 5 个：`/export`、`/stats`、`/usage`、`/statusline`、`/commands`。 +3. 第三周再启用自动化能力：Hooks、Subagents、Headless。 +4. 每新增一个能力，记录"触发条件 + 不适用场景"，避免误用。 + +#### 15.3.4 文章细节补充（关键技巧分组） + +| 目标 | 技巧组合 | 典型收益 | +|---|---|---| +| 快速拿上下文 | `@文件/目录` + `!命令` | 减少切终端/复制粘贴 | +| 快速回滚 | `Esc Esc` + 会话恢复 | 降低试错成本 | +| 长任务续航 | 命名会话 + `/export` | 可追踪、可交接 | +| 自动质检 | Hooks + `/sandbox` | 提前阻断风险变更 | +| 流水线复用 | Commands + Headless | 把经验固化为资产 | + +#### 15.3.5 新手执行顺序（详细） + +1. 先学「上下文与回滚」（`@`、`!`、`Esc Esc`）。 +2. 再学「计划与安全」（Plan Mode、`/sandbox`、Hooks）。 +3. 最后学「规模化」（Subagents、Headless、插件体系）。 + +### 15.4 4) Code Simplifier：把"能跑"整理成"可维护" + +#### 15.4.1 教程化整合 + +- `code-simplifier` 定位是"语义不变的结构优化"：改写法，不改行为。 +- 与 `ESLint/Prettier` 互补：后者做格式，前者做可读性与结构简化。 +- 特别适合 AI 快速生成后的清理阶段：先生成，再简化，再测试。 + +#### 15.4.2 可执行清单 + +```bash +claude plugin install code-simplifier +# 或会话内 +/plugin marketplace update claude-plugins-official +/plugin install code-simplifier +``` + +- 在 `CLAUDE.md` 明确导入、命名、类型、函数风格，供 simplifier 遵循。 +- 建议在 PR 前固定跑一次，结合 `git diff` 做人工复核。 + +#### 15.4.3 详细步骤（PR 前整理标准流程） + +1. 功能实现完成后先跑测试，确保"当前代码可用"。 +2. 执行 `code-simplifier` 做结构整理。 +3. 立刻跑同一套测试，确认整理后行为不变。 +4. 打开 `git diff`，重点审查：边界判断、异常路径、公共接口。 +5. 通过后再进入 Code Review，避免把"脏代码"交给审查环节。 + +#### 15.4.4 文章细节补充（工具对比与定位） + +| 工具 | 擅长 | 不擅长 | +|---|---|---| +| ESLint/Prettier | 语法与格式统一 | 语义级简化 | +| Code Simplifier | 可读性、结构化重写 | 规则化格式细节 | +| 人工 Review | 业务语义与架构判断 | 高重复清理劳动 | + +#### 15.4.5 使用场景建议 + +- 长时间编码后清理； +- 大重构后整理； +- AI 生成代码后做第一轮“去噪”； +- 提交 PR 前统一风格并减少理解负担。 + +### 15.5 5) 非技术用户落地指南：安装、配置与工作流心法 + +#### 15.5.1 教程化整合 + +- 非技术用户最容易卡在 API 配置，而不是功能使用。 +- 推荐"先可用，再优化"路径：先连上一个稳定模型，再逐步引入 Skills/MCP。 +- 将复杂配置 GUI 化（如 API 切换器），可显著降低误配置概率。 +- 工具使用的核心不是"会命令"，而是"能给清晰输入并持续迭代"。 + +#### 15.5.2 可执行清单 + +1. 安装 Claude Code（Mac/Windows）后先跑一个最小任务验证闭环。 +2. 选定一个主模型通道（国产直连或第三方中转）并完成健康检查。 +3. 使用四步法：定义问题 → 方案探索 → 计划确认 → 执行迭代。 +4. 每完成一个阶段，把关键结论写入文档，防止上下文丢失。 + +#### 15.5.3 详细步骤（从 0 到可用） + +```bash +# macOS / Linux +curl -fsSL https://claude.ai/install.sh | bash + +# Windows PowerShell +irm https://claude.ai/install.ps1 | iex +``` + +1. 安装后执行 `claude`，完成登录与首轮环境检查。 +2. 先做一个最小任务：例如"读取当前目录并总结项目结构"。 +3. 配置 API 通道后做健康检查：固定 3 个测试 prompt（速度/格式/稳定）。 +4. 用四步工作流跑一个小任务（如批量重命名）验证完整闭环。 +5. 将成功流程写入 `CLAUDE.md`，后续直接复用。 + +#### 15.5.4 文章细节补充（安装与配置路线） + +##### 15.5.4.1 A. 安装路线 + +- macOS/Linux：脚本安装； +- Windows：PowerShell 安装； +- 若脚本受限，再退回 `npm` 安装方案。 + +##### 15.5.4.2 B. API 路线 + +1. **国产模型直连**：稳定、低成本、上手快； +2. **第三方中转**：接近原模型体验，需关注服务稳定性； +3. **多源备份**：至少准备 2 个可切换通道，防止单点中断。 + +##### 15.5.4.3 C. 常见卡点排查 + +- 安装成功但命令不可用：重开终端并检查 PATH。 +- 模型无响应：先跑最小 prompt 验证网络与 key。 +- 会话表现忽好忽坏：检查上下文是否过载，必要时重启会话。 + +#### 15.5.5 实战摘录 + +> 普通用户的门槛不是编程语法，而是把模糊需求转成可执行目标与验收条件。 diff --git a/external/ai-research-ebook/src/content/docs/code/gpt.mdx b/external/ai-research-ebook/src/content/docs/code/gpt.mdx new file mode 100644 index 0000000..c0791b7 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/code/gpt.mdx @@ -0,0 +1,388 @@ +--- +title: GPT-5.2 系列 +description: GPT-5.2 系列（Instant/Thinking/Pro）在科研中的使用 +sidebar: + order: 2 +--- + +import { Card, CardGrid } from '@astrojs/starlight/components'; + +## 1. GPT-5.2 系列概览 + +GPT-5.2 系列（2025年12月发布）适合做**细节 review**和**深度分析**。 + +### 1.1 模型选择矩阵 + +| 模型 | 特点 | 适用场景 | 定价 | +|------|------|---------|------| +| **GPT-5.2 Instant** | 快速响应，延迟低 | 日常代码辅助、快速问答 | 低 | +| **GPT-5.2 Thinking** | 深度推理，复杂分析 | 复杂问题分析、算法设计 | 中 | +| **GPT-5.2 Pro** | 最强专业版 | 关键代码审查、论文评审 | 高 | + +--- + +## 2. 核心能力 + +### 2.1 基准测试 + +| 能力 | 数据 | 说明 | +|------|------|------| +| **ARC-AGI-1** | 90%+ | 首次突破 90% 通用推理基准 | +| **知识截止** | 2025年8月 | 支持最新研究成果 | +| **长上下文** | 1M+ tokens | 支持大型文档分析 | + +--- + +## 3. GPT-5.3-Codex 核心升级 + +### 3.1 核心特性 + +| 特性 | 数据 | 说明 | +|------|------|------| +| **自我参与开发** | - | 早期版本加速自身开发 | +| **速度提升** | 25%+ | 比前代快 25% | +| **Token 减半** | 50% | 相同任务所需 token 减半 | +| **训练速度** | 4x | 训练速度提升 4 倍 | + +### 3.2 性能优势 + +| 基准 | 得分 | 说明 | +|------|------|------| +| **Terminal-Bench 2.0** | 77.3% | 智能体编码领先 | +| **OSWorld** | 64.7% | 桌面自动化领先 | +| **网络安全** | High 级别 | OpenAI 首个获评 | + +--- + +## 4. Claude Opus 4.6 vs GPT-5.3-Codex + +### 4.1 核心对比 + +| 维度 | Claude Opus 4.6 | GPT-5.3-Codex | +|------|-----------------|---------------| +| **上下文窗口** | 100万 tokens (Beta) | 400K tokens | +| **复杂知识工作** | 更强 | 稍弱 | +| **自主编码任务** | 稍弱 | 更强 | +| **发展方向** | 广度（多智能体、Office） | 深度（效率、交互性） | + +### 4.2 适用场景选择 + + + + + +- 需要处理大型代码库 +- 多智能体协作任务 +- 需要 Office 集成 +- 复杂知识工作 + + + + + +- 追求极致的编码效率 +- 需要快速迭代 +- 交互式引导需求 +- 自主编码和自动化任务 + + + + + +--- + +## 5. 科研使用场景 + +
+ + + +验证代码接口设计是否一致，避免隐藏的 API 变更。 + + + + + + +检查实验设置是否公平，baseline 对比是否合理。 + + + + + + +发现数据泄漏、指标计算错误等隐蔽问题。 + + + + + + +代码审查，发现潜在边界条件和异常情况。 + + + + + + +确保评测脚本正确实现，无实现偏差。 + + + + + + +独立复现核心实验，验证结果可复现性。 + + + + +
+ +--- + +## 6. 与 Claude 的配合策略 + +```mermaid +flowchart TD + subgraph 研究流程 + A(Idea 生成) --> B(项目设计) + B --> C(代码实现) + C --> D(实验验证) + D --> E(论文写作) + end + + subgraph 工具分工 + F(Claude Opus 4.6) --> A + G(Claude Code 2.0) --> C + H(GPT-5.2 Pro) --> D + I(GPT-5.2 Instant) --> F + end +``` + +| 任务 | 推荐工具 | 理由 | +|------|---------|------| +| 项目架构设计 | Claude Opus 4.6 | 长上下文，200K tokens | +| 代码实现 | Claude Code 2.0 | SWE-bench 80.9% | +| 细节审查 | GPT-5.2 Pro | ARC-AGI 90%+，深度推理 | +| 快速问答 | GPT-5.2 Instant | 响应速度快 | +| 漏洞检测 | GPT-5.2 Thinking | 深度推理能力 | +| 文献调研 | Claude + Elicit | 自动化综述能力 | + +--- + +## 7. 项目文档生成流程 + +### 7.1 步骤 1: 生成项目基准文档 + +将 idea 交给 GPT Pro/Thinking，让其生成可用的项目文档： + +```markdown +# 项目基准文档模板 + +## 0. 一页速览（30秒读懂） +[核心问题 + 核心贡献] + +## 1. 研究问题与动机（Problem Statement） +- 解决什么问题？ +- 为什么这个问题重要？ + +## 2. 相关工作地图（Related Work Map） +- 领域分类 +- 主要方法 +- 你的定位 + +## 3. 核心研究假设与可验证主张（Claims） +- Claim 1: [必须对应实验] +- Claim 2: [必须对应实验] + +## 4. 形式化（Problem Formulation） +[数学形式化] + +## 5. 系统设计（System Architecture） +[架构图 + 模块说明] + +## 6. 学习目标（Learning Objective） +[损失函数 + 优化目标] + +## 7. 实验与评测（Experiments） +- 主实验 +- 消融实验 +- 压力测试 + +## 8. 预期贡献（Expected Outcome） +- 论文贡献点 +- 潜在影响 + +## 9. 风险与备选（Risks & Alternatives） +- 最大风险 +- Plan B +``` + +### 7.2 步骤 2: 人工 Review + +- 主要看"claim-实验对齐" +- baseline 是否足够强 +- 是否有致命假设 + +**关键问题**： +- 哪些段落最容易被审稿人抓住？ +- 哪些实验设计不够严谨？ +- 哪些 claim 缺证据？ + +### 7.3 步骤 3: 生成执行文档 + +再次交给 GPT Pro/Thinking，生成专门给 Claude Code 用的**执行文档**（更偏工程步骤与任务拆分）。 + +### 7.4 步骤 4: 迭代到稳定 + +用这种方式迭代到文档稳定为止。 + +--- + +## 8. Scitex：自动化科研平台 + +### 8.1 核心功能 + +| 功能 | 说明 | +|------|------| +| **合成数据生成** | 根据假设自动生成符合统计规律的合成数据 | +| **统计分析** | 自动完成 ANOVA、效应量计算 | +| **可视化** | 生成出版级图表（箱线图、散点图） | +| **论文撰写** | LaTeX 模板自动填充 | +| **同行评审** | AI 模拟审稿人，自动生成 Rebuttal | + +### 8.2 工作流程 + +```mermaid +flowchart LR + A(研究假设) --> B(合成数据) + B --> C(统计分析) + C --> D(可视化) + D --> E(论文撰写) + E --> F(AI 评审) + F --> G(自动修订) +``` + +### 8.3 适用场景 + +- 快速原型验证（合成数据验证假设） +- 人机协作科研 +- 教育与实践培训 + +--- + +## 最佳实践 + +
+ + + +**GPT-5.2 核心优势**：深度推理能力，特别适合发现隐藏假设和逻辑漏洞。 + + + + +
+ +### 9.1 使用建议 + +1. **不要用它写代码**：Claude Code 在代码任务上更强 +2. **用它做审查**：让它找 bug、提质疑 +3. **用它做分析**：让它分析论文、发现逻辑问题 +4. **多用它的 Thinking 模式**：深度推理输出更可靠 + +--- + +## 10. Book 实战增补（逐篇并入） + +### 10.1 Kimi-K2.5 免费通道：作为 GPT/Claude 之外的成本缓冲层 + +这部分整合自 `book/土豪英伟达_Kimi_K2_5_免费用_API_支持OpenCode.md`，核心价值不是“替代一切”，而是给工作流增加一条**低成本可切换通道**。 + +#### 10.1.1 实操配置要点 + +- 平台：NVIDIA Build（注册后获取 API Key）。 +- Base URL：`https://integrate.api.nvidia.com/v1` +- Model ID：`moonshotai/kimi-k2.5` + +#### 10.1.2 推荐使用策略 + +| 任务类型 | 建议模型层 | +|---|---| +| 快速草稿、批量改写、低风险总结 | Kimi-K2.5（成本优先） | +| 高风险技术决策、关键代码审查 | GPT/Claude 强模型（质量优先） | +| 大批量流水任务 | Kimi 先跑，再用强模型抽检 | + +#### 10.1.3 接入清单 + +1. 在平台获取 `API Key` 并保存。 +2. 在客户端填入 `base-url + model-id + key`。 +3. 用 3 个固定测试 prompt 做烟雾测试（速度、稳定性、格式）。 +4. 把它加入模型路由规则，作为“降本 fallback”。 + +#### 10.1.4 详细步骤（可直接执行） + +1. 进入 NVIDIA Build 平台创建 key，并记录创建时间与配额说明。 +2. 在工具侧新增供应商： + - `base_url`: `https://integrate.api.nvidia.com/v1` + - `model`: `moonshotai/kimi-k2.5` + - `api_key`: `<你的key>` +3. 运行三条基准提示词： + - 摘要任务（看压缩质量） + - 结构化输出（看 JSON 稳定性） + - 长文本改写（看一致性） +4. 记录延迟、失败率、输出格式稳定性，写入模型路由文档。 +5. 配置“降级策略”：主模型失败时自动回退到 Kimi。 + +#### 最小验证脚本（示意） + +```bash +curl -sS https://integrate.api.nvidia.com/v1/chat/completions \ + -H "Authorization: Bearer $NVIDIA_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "moonshotai/kimi-k2.5", + "messages": [{"role":"user","content":"用三点总结RAG评测重点"}], + "temperature": 0.2 + }' +``` + +#### 10.1.5 文章细节补充（按原文落地） + +##### 10.1.5.1 A. 账号与密钥准备 + +1. 访问 NVIDIA Build 开发者平台并完成注册。 +2. 在控制台创建 API Key，创建后立即复制保存。 +3. 记录模型标识与基座 URL，后续统一写入配置中心。 + +##### 10.1.5.2 B. 在多工具中的统一接入 + +- **OpenCode/Coding CLI**：新增一个可切换模型配置项； +- **笔记或写作工具（如 Copilot 类）**：复用同一套 key/base/model； +- **脚本任务**：通过环境变量统一注入，减少散落配置。 + +建议新增一个 `models.md`（或等价配置文件）记录： + +- 供应商名称 +- base URL +- model ID +- 适用任务 +- 失败回退路径 + +##### 10.1.5.3 C. 三段式验证（避免上线即翻车） + +1. **连通性验证**：确认接口可通、响应码正常； +2. **能力验证**：固定三组任务评估质量（摘要、结构化、长改写）； +3. **稳定性验证**：连续跑 20~50 次，观察超时与格式漂移。 + +##### 10.1.5.4 D. 故障回退策略（建议默认开启） + +- 主模型失败（超时/限流）→ 自动切 Kimi； +- Kimi 输出不满足格式约束 → 再交给主模型做最终规范化； +- 两者都失败 → 返回可重试错误而不是静默空结果。 + +#### 10.1.6 风险提示 + +- 免费额度/策略可能变化，生产任务需保留备选供应商。 +- 同一任务跨模型切换时，需明确输出格式与验收标准，避免结果漂移。 diff --git a/external/ai-research-ebook/src/content/docs/figures/comparison.mdx b/external/ai-research-ebook/src/content/docs/figures/comparison.mdx new file mode 100644 index 0000000..6c96326 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/figures/comparison.mdx @@ -0,0 +1,142 @@ +--- +title: 专业绘图对比 +description: 主流专业绘图工具对比分析 +sidebar: + order: 2 +--- + +## 1. 论文图表类型清单 + +从几千篇论文里提取"表达强"的图，按类型建一个参考库： + +### 1.1 方法结构图 (Architecture) + +- 整体系统架构 +- 模块关系 +- 数据流向 + +### 1.2 流程图 (Pipeline) + +- 训练流程 +- 推理流程 +- 数据处理流程 + +### 1.3 训练/推理示意图 + +- 算法步骤可视化 +- 时序关系 + +### 1.4 主结果表 + 消融图 + +- 对比实验表格 +- 消融研究图表 + +### 1.5 失败案例可视化 + +- 边界情况 +- 错误分析 + +--- + +## 2. 工具详细对比 + +### 2.1 Mermaid.js + +**优点**: +- 纯文本语法，易于版本控制 +- 支持多种图表类型 +- GitHub/GitLab 原生支持 + +**缺点**: +- 定制能力有限 +- 复杂图表难以实现 + +**代码示例**: + +```mermaid +graph TD + A(输入数据) --> B(特征提取) + B --> C{决策} + C -->|条件1| D(路径1) + C -->|条件2| E(路径2) + D --> F(输出) + E --> F + + +``` + +--- + +### 2.2 PlantUML + +**优点**: +- UML 标准化 +- 支持时序图、用例图等 +- 文本驱动，易于维护 + +**缺点**: +- 学习曲线较陡 +- 主要针对 UML 场景 + +--- + +### 2.3 D3.js + +**优点**: +- 高度可定制 +- 支持复杂交互 +- 强大的数据绑定能力 + +**缺点**: +- 学习成本高 +- 开发时间长 + +**适用场景**: 创新性的数据可视化，需要展示论文的独特洞见 + +--- + +### 2.4 draw.io + +**优点**: +- 完全免费 +- 支持 Git 集成 +- 丰富的图形库 + +**缺点**: +- 大型项目性能下降 +- 协作功能不如 Figma + +--- + +### 2.5 Figma + +**优点**: +- 专业级设计工具 +- 实时协作 +- 强大的组件系统 + +**缺点**: +- 需要学习成本 +- 免费版有功能限制 + +**适用场景**: 高质量插图，特别是需要多人协作时 + +--- + +## 3. 推荐工作流 + +### 3.1 阶段1: 快速原型 + +使用 **draw.io** 或 **Mermaid.js** 快速搭建图表框架 + +### 3.2 阶段2: 精细化 + +对于关键图表，使用 **Figma** 进行精细化设计 + +### 3.3 阶段3: 代码生成 + +对于数据可视化，使用 **D3.js** 或 **Python (matplotlib/plotly)** + +### 3.4 阶段4: 整合 + +使用 **PPT** 或 **Figma** 进行最终整合 diff --git a/external/ai-research-ebook/src/content/docs/figures/design.mdx b/external/ai-research-ebook/src/content/docs/figures/design.mdx new file mode 100644 index 0000000..546421f --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/figures/design.mdx @@ -0,0 +1,160 @@ +--- +title: 图表设计规范 +description: 学术论文图表的视觉语言和设计规范 +sidebar: + order: 3 +--- + +## 1. 图表设计规范 + +选定视觉语言后要定规范（否则全篇风格会散） + +--- + +## 2. 配色方案 + +### 2.1 原则 + +**颜色尽量少**（论文更稳），强调用粗细/虚实区分 + +### 2.2 推荐配色 + +| 用途 | 颜色 | HEX | +|------|------|-----| +| 主色 | 蓝色 | #3B82F6 | +| 强调色 | 橙色 | #F97316 | +| 辅助色 | 绿色 | #10B981 | +| 中性色 | 灰色 | #6B7280 | + +### 2.3 顶会配色参考 + +| 会议 | 主色调 | 特点 | +|------|--------|------| +| NeurIPS | 蓝色系 | 专业、冷静 | +| ICML | 紫色系 | 学术、严谨 | +| ICLR | 橙色系 | 活力、创新 | +| CVPR | 蓝绿色系 | 视觉、科技 | + +--- + +## 3. 字体选择 + +### 3.1 英文字体 + +| 字体 | 适用场景 | 特点 | +|------|---------|------| +| Arial | 通用 | 安全、清晰 | +| Helvetica | 标题 | 专业、现代 | +| Times New Roman | 正文 | 传统、学术 | + +### 3.2 中文字体 + +| 字体 | 适用场景 | 特点 | +|------|---------|------| +| 宋体 | 正文 | 传统、正式 | +| 黑体 | 标题 | 现代、清晰 | + +### 3.3 统一原则 + +- 全篇使用**同一字体族** +- 图表字体与正文保持一致 +- 避免超过3种字体 + +--- + +## 4. 线条规范 + +### 4.1 线宽规范 + +| 用途 | 线宽 | 样式 | +|------|------|------| +| 主要边界 | 2px | 实线 | +| 次要边界 | 1px | 实线 | +| 辅助线 | 0.5px | 虚线 | +| 数据流 | 1.5px | 箭头线 | + +### 4.2 箭头样式 + +- 实心箭头：主要数据流 +- 空心箭头：辅助关系 +- 无箭头：包含关系 + +--- + +## 5. 标注规范 + +### 5.1 图例 + +- 放置位置：右上角或底部 +- 字体：比正文小1-2号 +- 边框：可选，保持简洁 + +### 5.2 说明文字 + +- 字体大小：不小于8pt（确保印刷可读） +- 颜色：与主色调一致 +- 对齐：左对齐，便于阅读 + +--- + +## 6. 尺寸规范 + +### 6.1 期刊/会议要求 + +| 类型 | 单栏 | 双栏 | +|------|------|------| +| 宽度 | 3.5英寸 | 7英寸 | +| 分辨率 | 300 DPI | 300 DPI | +| 格式 | PDF/EPS | PDF/EPS | + +### 6.2 保持可读性 + +- 文字不小于8pt +- 线条不小于0.5pt +- 标注与元素留足够间距 + +--- + +## 7. 常见错误 + +### 7.1 ❌ 错误示范 + +- 颜色过多（超过5种） +- 字体不一致 +- 线条粗细不统一 +- 箭头样式混乱 +- 标注过小难以阅读 + +### 7.2 ✅ 正确示范 + +- 使用2-3种主色 +- 统一字体和字号 +- 线条规范统一 +- 箭头清晰一致 +- 标注清晰易读 + +--- + +## 8. GPT 辅助流程 + +1. GPT 根据论文 + 参考图生成： + - "图的元素清单"（模块/箭头/标注） + - "布局草案"（从左到右还是从上到下） + - "提示词"（给绘图工具出初版草图） + +2. 最后用 PPT/Figma 统一绘制与整合 + +### 8.1 提示词模板 + +``` +请为以下论文内容设计一张架构图，遵循学术规范： + +[论文内容] + +要求： +1. 元素清单：列出所有模块、箭头、标注 +2. 布局：从左到右的数据流 +3. 配色：使用2-3种颜色，符合学术规范 +4. 线条：主要边界2px实线，辅助线1px虚线 +5. 标注：清晰易读，字体不小于10pt +``` diff --git a/external/ai-research-ebook/src/content/docs/figures/tools.mdx b/external/ai-research-ebook/src/content/docs/figures/tools.mdx new file mode 100644 index 0000000..4255633 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/figures/tools.mdx @@ -0,0 +1,426 @@ +--- +title: 自动化绘图工具 +description: PaperBanana 等自动化学术插图工具介绍 +sidebar: + order: 1 +--- + + + +## 1. PaperBanana (arXiv:2601.23265, 2026) + +### 1.1 核心价值 + +**PaperBanana** 是首个专门为 AI 科学家设计的自动化学术插图生成系统。 + +**论文**: [arXiv:2601.23265](https://arxiv.org/abs/2601.23265) + +### 1.2 核心功能 + +| 功能 | 说明 | +|------|------| +| 自动插图需求识别 | 从论文文本自动识别需要可视化的部分 | +| 顶会风格生成 | 生成符合 NeurIPS/ICML/ICLR 审美的初版 | +| 智能图表推荐 | 根据内容推荐最适合的图表类型 | + +--- + +## 2. 工具生态全景 + +```mermaid +flowchart TD + subgraph ["原型阶段"] + A[Mermaid.js] --> B(快速原型) + C[draw.io] --> B + end + + subgraph ["专业阶段"] + B --> D[Figma] + D --> E(专业插图) + end + + subgraph ["自动化阶段"] + F[PaperBanana] --> G(自动生成) + G --> E + end + + subgraph ["整合阶段"] + E --> H(PPT 整合) + H --> I(最终发布) + end + + + +``` + +--- + +## 3. 工具对比 + +| 工具 | 类型 | 优势 | 适用场景 | 学习曲线 | +|------|------|------|---------|---------| +| **Mermaid.js** | 文本驱动 | 代码驱动、版本友好 | 流程图、时序图 | 低 | +| **PlantUML** | UML | 标准化 | 系统架构图 | 中 | +| **D3.js** | 自定义 | 高度可定制 | 创新可视化 | 高 | +| **draw.io** | 在线绘图 | 免费、协作 | 快速原型 | 低 | +| **Figma** | 协作设计 | 专业级 | 高质量插图 | 中 | +| **PPT** | 整合工具 | 方便整合 | 最终发布 | 低 | + +--- + +## 4. 工具选择决策树 + +```mermaid +flowchart TD + A(图表类型) --> B{是流程图/时序图?} + A --> C{需要高度定制?} + A --> D{团队协作?} + A --> E{快速原型?} + + B -->|是| F[Mermaid.js] + C -->|是| G[D3.js] + D -->|是| H[Figma] + E -->|是| I[draw.io] + F --> J(初版) + G --> K(精细化) + H --> L(协作) + I --> M(快速迭代) + + M --> N(PPT 整合) + + + +``` + +--- + +## 5. GPT 辅助绘图流程 + +### 5.1 阶段 1: 需求分析 + +``` +你是一位专业的学术插图设计师。请分析以下论文内容： + +["粘贴论文的方法部分"] + +输出： +1. 需要可视化的核心概念 +2. 概念之间的关系 +3. 适合的图表类型建议 +``` + +### 5.2 阶段 2: 元素清单 + +``` +基于以下论文内容, 生成架构图的元素清单： + +["论文内容"] + +请输出： +1. 所有模块及其功能描述 +2. 模块间的数据流向 +3. 需要标注的关键信息 +4. 建议的颜色方案 +``` + +### 5.3 阶段 3: 布局建议 + +``` +请为以下内容设计布局方案： + +["元素清单"] + +考虑： +- 从左到右还是从上到下？ +- 中心辐射还是层次结构？ +- 留白和平衡 +``` + +### 5.4 阶段 4: Mermaid 代码生成 + +```mermaid +flowchart LR + subgraph ["输入处理"] + A(原始数据) --> B(预处理) + end + + subgraph ["核心模型"] + B --> C(特征提取) + C --> D(注意力模块) + D --> E(输出生成) + end + + subgraph ["损失函数"] + F(主损失) --> G(多任务Loss) + H(正则项) --> G + end + + E --> I(最终输出) + + + +``` + +--- + +## 6. 配色方案示例 + +### 6.1 AI 顶会风格 + +| 颜色 | 用途 | HEX | +|------|------|-----| +| 背景 | 图表背景 | #FFFFFF | +| 主模块 | 核心方法 | #10A37F | +| 辅助模块 | 对比方法 | #007AFF | +| 数据流 | 箭头连接 | #6B7280 | +| 高亮 | 关键部分 | #AB68FF | + +### 6.2 暗色主题 + +| 颜色 | 用途 | HEX | +|------|------|-----| +| 背景 | 图表背景 | #212121 | +| 主模块 | 核心方法 | #10A37F | +| 辅助模块 | 对比方法 | #007AFF | +| 数据流 | 箭头连接 | #9CA3AF | +| 高亮 | 关键部分 | #AB68FF | + +--- + +## 7. 最佳实践 + +
+ + +整个论文使用统一的配色和字体方案 + + + +避免过多颜色, 每个颜色有明确含义 + + + +确保在黑白打印时也能清晰区分 + + + +每个图都有清晰的图例和说明 + + +
+ +--- + +## 8. PaperBanana 论文要点补充（2601.23265） + +为响应“把论文内容并入现有章节”的要求, 这里补充 PaperBanana 原文中的关键信息（不上传 PDF 文件）： + +### 8.1 研究动机 + +论文明确指出：自动科研系统已能生成方法与实验, 但**发表级插图**仍是人工瓶颈。PaperBanana 的目标是把“论文图”从手工绘制转为可迭代自动流程。 + +### 8.2 方法框架 + +PaperBanana 采用 agentic pipeline, 将插图生成拆为可控阶段： + +1. 检索参考图与风格约束 +2. 规划图的语义内容与视觉结构 +3. 图像渲染与矢量生成 +4. 自我批评与迭代修正 + +### 8.3 评测设置 + +论文引入 **PaperBananaBench（292 个测试样例）**, 覆盖方法图等常见学术插图场景, 用于评估自动生成质量与可用性。 + +### 8.4 工程启发（对本书工作流） + +- 图表要与方法叙事同源：由 method text 驱动, 而不是事后美化。 +- 优先保证“信息正确 + 可复现”, 再追求视觉高级感。 +- 生成后必须保留可编辑中间产物（SVG/分层素材）, 便于审稿期快速改图。 + +### 8.5 AutoFigure-edit 项目补充（根目录项目） + +来自 `AutoFigure-Edit-main.zip` 的 README 还提供了一条很实用的“方法图工程化”路线： + +- 从 method text 先生成草图 `figure.png` +- 用 SAM3 做区域检测与框选（产出 `samed.png` + `boxlib.json`） +- 对图元做去背景（RMBG-2.0）得到可复用 icon +- 生成占位模板 `template.svg` 并可迭代优化 +- 最终组装成可编辑的 `final.svg` + +这条链路和 PaperBanana 的共同点是：都强调中间产物可追溯, 便于审稿期快速改图。 + +--- + +## 9. Book 实战增补（逐篇并入） + +> 本节并入 3 篇与"图表/视觉产物自动化"直接相关的文章，重点补齐从论文 Figure 到 PPT/视频转场的可执行流程。 + +### 9.1 1) PaperBanana：论文级 Figure 生成与润色 + +来源：`book/谷歌做了个论文专用版nano_banana_顶会级Figure直出.md` + +#### 9.1.1 关键增量 + +- Figure 目标从“好看”升级为“语义正确 + 学术审美一致”。 +- 支持两类核心产物：方法结构图（Methodology）与统计图（Statistical Plots）。 +- 支持从草图/初版图自动润色：重排层级、统一配色、强化箭头逻辑。 + +#### 9.1.2 多 Agent 生产线（可复用） + +1. 检索参考图与领域范式； +2. 规划结构化图描述（模块、关系、层级）； +3. 按审美约束生成初稿； +4. 评论代理迭代纠错（语义一致性检查）； +5. 输出论文可用版与可编辑资产。 + +#### 9.1.3 实操结论 + +- 统计图场景下，优先“AI 生成绘图代码再出图”，通常比直接生图更可控。 +- 生成完成后必须保留中间素材，避免审稿期改图返工。 + +#### 9.1.4 详细步骤（PaperBanana 风格流程） + +1. 准备输入：方法段落、关键模块列表、模块关系表（A→B）。 +2. 先产出“结构草稿”而不是终稿，确认语义无误后再美化。 +3. 进入风格增强阶段：统一配色、字体、间距、箭头方向。 +4. 对照正文逐项核验：术语一致、关系一致、无缺漏模块。 +5. 导出两份：投稿图 + 可编辑源文件（SVG/分层）。 + +#### 9.1.5 文章细节补充（质量核验表） + +- 语义一致：图中术语与正文完全一致； +- 逻辑一致：箭头方向与流程关系无冲突； +- 审美一致：配色、字号、间距统一； +- 投稿可用：分辨率与版式满足会议模板要求。 + +### 9.2 2) PPT Skills：从静态页面到转场视频 + +来源：`book/学会了PPTSkills_豆包Kimi都感觉不香了_但90_的人卡在第一步_Gemini_NanoBananaPro_API国内用不了_这篇帮你全解决（8个坑_国内可用）.md` + +#### 9.2.1 可执行流程 + +1. 输入主题与约束（页数、风格、分辨率）； +2. 批量生成每页视觉稿； +3. 两两页面分析视觉差异； +4. 自动生成转场提示词； +5. 调用视频模型生成过渡片段； +6. 用 `ffmpeg` 合成完整演示视频。 + +#### 9.2.2 工程化建议 + +- 优先“二次开发已有 skill”，避免从零造轮子。 +- 配置分层：官方 API 与第三方 API 兼容两套路径。 +- 对“连通性、鉴权、路径、依赖版本”做启动前自检，减少运行中断。 + +#### 9.2.3 详细步骤（PPT Skills 国内可用改造） + +1. 复制原项目到新目录，避免直接污染上游版本。 +2. 创建虚拟环境并安装依赖： + +```bash +python3 -m venv venv +source venv/bin/activate +pip install google-genai pillow python-dotenv -i https://pypi.org/simple +``` + +3. 复制配置模板并填写： + +```bash +cp .env.example .env +``` + +4. 先做 API 连接测试（返回 200 再继续主流程）。 +5. 官方 SDK 不稳定时切 HTTP 直调，便于定位原始请求/响应。 +6. 生成完成后再执行转场视频流水线，避免前置失败放大损失。 + +#### 9.2.4 文章细节补充（可复制命令顺序） + +```bash +# 初始化 +cd +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt + +# 生成静态页 +python generate_ppt.py --plan plans/demo.md --style styles/vector-illustration.md --resolution 2K + +# 生成转场视频（按项目脚本） +python generate_transition.py --input outputs//images --output outputs//videos +ffmpeg -f concat -safe 0 -i outputs//videos/list.txt -c copy outputs//full_ppt_video.mp4 +``` + +#### 9.2.5 常见问题排查（细化） + +1. **API 连通失败**：先用独立脚本验证请求，再跑主流程。 +2. **模型返回空结果**：检查模型名是否与供应商兼容。 +3. **转场合成失败**：检查 ffmpeg 是否安装、输入列表是否存在。 +4. **分辨率不一致**：统一生成参数，避免混用 2K/4K 导致拼接失败。 + +#### 9.2.6 高频坑位清单 + +- Base URL 误配（多拼路径导致 404）； +- 运行环境不一致（系统 Python 与虚拟环境混用）； +- 官方 SDK 报错不透明（必要时改 HTTP 直调便于排错）。 + +### 9.3 3) 图文一体化 Skill：文章生成 + 配图落地 + +来源：`book/Claude_Code_Skill_自动生成文章内容与配图.md` + +#### 9.3.1 双 Skill 架构 + +- `article-generator`：负责文章结构、SEO 信息与图片占位符。 +- `image-generator`：负责读取占位符上下文、生成 prompt、调用图像流水线。 + +#### 9.3.2 8 步流水线（建议模板） + +1. 生成文章与占位图； +2. 提取占位符上下文； +3. 生成每图 prompt； +4. 批量生图； +5. 转 `webp` 压缩； +6. 上传对象存储（如 R2）； +7. 回填 CDN 链接； +8. 输出可发布版本。 + +#### 9.3.3 关键命令示例 + +```bash +npm run generate-images +npm run compress-images +npm run upload-images +npm run replace-images +``` + +#### 9.3.4 详细步骤（图文流水线） + +1. 用 `article-generator` 产出文章与占位图。 +2. 运行 `image-generator` 读取占位并生成 prompt。 +3. 依次执行四个命令：生图 → 压缩 → 上传 → 链接替换。 +4. 每一步检查输出目录是否存在且非空。 +5. 替换完成后抽查 3 处图片链接，确认 CDN 可访问。 + +#### 9.3.5 文章细节补充（目录规范建议） + +```text +articles/.md +prompts//*.md +images//*.png +images//*.webp +``` + +建议每一步都写入一个 `status.json`，用于中断后恢复： + +- `step` +- `ok` +- `updated_at` +- `error` + +#### 9.3.6 章节衔接说明 + +这套方案与本章已有的 `PaperBanana + AutoFigure-edit` 形成互补： + +- 论文 Figure 偏“科研表达规范化”； +- 图文流水线偏“内容生产自动化”； +- 两者都强调中间产物与可追溯性。 diff --git a/external/ai-research-ebook/src/content/docs/idea/projects.mdx b/external/ai-research-ebook/src/content/docs/idea/projects.mdx new file mode 100644 index 0000000..2bd6a4e --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/idea/projects.mdx @@ -0,0 +1,192 @@ +--- +title: 调研项目 +description: 重要调研项目分析, 包括 Sci-Reasoning、AI Research、Idea2Story 等 +sidebar: + order: 2 +--- + +import { Card, CardGrid } from '@astrojs/starlight/components'; + +## 1. Sci-Reasoning 数据集 + +### 1.1 核心价值 + +**Sci-Reasoning** 通过 LLM+Human Review的方法, 对 2023–2025 年 NeurIPS、ICML、ICLR 的 **3819 篇 Oral/Spotlight 论文**进行"学术谱系"建模。 + +**资源链接**: +- 代码: [Sci-Reasoning GitHub](https://github.com/AmberLJC/Sci-Reasoning) +- 数据集: [HuggingFace Dataset](https://huggingface.co/datasets/AmberLJC/Sci-Reasoning) + +```mermaid +flowchart TD + A[3819 Papers] --> B[LLM Annotation] + B --> C[Human Review] + C --> D[15 Patterns] + C --> E[Innovation Recipes] + D --> F(科研创新规律) + E --> F + + + +``` + +--- + +## 2. 15 种科研思维模式详解 + +
+ + +**诊断+重构**：识别当前方法的局限性, 将限制转化为设计约束。 + + + +**跨域融合**：从相邻领域引入解决方案, 激活创新可能。 + + + +**表示转化**：替换问题的基本表示单元, 开辟新视角。 + + +
+ +### 2.1 完整模式列表 + +| 编号 | 模式 | 比例 | 核心价值 | +|------|------|------|---------| +| P01 | Gap-Driven Reframing | 24.2% | 转化限制为设计约束 | +| P02 | Cross-Domain Synthesis | 18.0% | 跨领域融合 | +| P03 | Representation Shift | 10.5% | 表示转化 | +| P04 | Modular Pipeline Composition | 4.2% | 模块化组合 | +| P05 | Data & Evaluation Engineering | 5.4% | 数据与评测工程 | +| P06 | Principled Probabilistic Modeling | 5.4% | 概率建模 | +| P07 | Formal-Experimental Tightening | 6.7% | 理论实验紧密结合 | +| P08 | Approximation Engineering | 4.9% | 可扩展性近似 | +| P09 | Inference-Time Control | 2.4% | 推理时控制 | +| P10 | Structural Inductive Bias | 5.1% | 结构归纳偏置 | +| P11 | Multiscale Modeling | 1.4% | 多尺度建模 | +| P12 | Mechanistic Decomposition | 1.9% | 机制分解 | +| P13 | Adversary Modeling | 1.5% | 对手建模 | +| P14 | Numerics & Systems Co-design | 1.4% | 数值与系统协同 | +| P15 | Data-Centric Optimization | 2.1% | 以数据为中心 | + +--- + +## 3. Innovation Recipes（Innovation Recipes） + +### 3.1 高价值组合 + +```mermaid +flowchart LR + A[Gap-Driven] -->|诊断+重构| B[Representation Shift] + C[Cross-Domain] -->|导入+适配| D[Representation Shift] + E[Gap-Driven] -->|诊断+借用| F[Cross-Domain] + + B --> G("318次") + D --> H("233次") + F --> I("204次") + + + +``` + +| 组合模式 | 出现次数 | 策略 | +|---------|---------|------| +| Gap-Driven + Representation Shift | 318 | "诊断+重构" | +| Cross-Domain + Representation Shift | 233 | "导入+适配" | +| Gap-Driven + Cross-Domain | 204 | "诊断+借用" | + +--- + +## 4. 会议偏好分析 + +| 会议 | 偏好模式 | 写作建议 | +|------|---------|---------| +| **ICML** | 形式化方法 (8.3%), 概率建模 (7.5%) | 增加理论分析和证明 | +| **ICLR** | 表示创新 (11.8%), 基准测试 (8.5%) | 侧重表示分析和可视化 | +| **NeurIPS** | 平衡跨学科覆盖 | 强调方法的生物学合理性 | + +--- + +## 5. LLM 评估基准 (Hit@10) + +这个评估衡量：给定前人工作, LLM 能否预测出研究方向的准确度。 + +| 模型 | Hit@10 得分 | +|------|-------------| +| **Gemini 2.5 Pro** | 49.35% | +| Claude Opus 4 | 42.86% | +| GPT-5.2 | 38.89% | +| Claude Sonnet 4 | 29.87% | + +--- + +## 6. AI Scientist 系列 + +### 6.1 完整对比 + +| 项目 | 时间 | 核心特点 | 代码 | +|------|------|---------|------| +| **The AI Scientist** | 2024.08 | 完整研究自动化 | [SakanaAI](https://github.com/SakanaAI/AI-Scientist) | +| **The AI Scientist-v2** | 2025 | 无代码模板自动化 | - | +| **ChemCrow** | 2024 | 化学研究 Agent (728+ 引用) | - | +| **Virtual-Scientists** | ACL 2025 | 端到端科研协作 | [InternScience](https://github.com/InternScience/Virtual-Scientists) | +| **Kosmos** | 2025 | 自主数据驱动发现 | - | +| **AI-Researcher** | 2025 | 完全自主研究系统 | - | + +--- + +## 7. 实践建议 + +### 7.1 如何借鉴 Sci-Reasoning + +1. **构建调研库时做标签化** + - 每篇论文标注其"创新类型" + - 便于后续 pattern mining + +2. **关注Innovation Recipes** + - 高频组合往往意味着"成熟赛道" + - 低频组合可能有更大的创新空间 + +3. **结合会议偏好** + - ICML 侧重理论 + - ICLR 侧重表示 + - NeurIPS 平衡跨学科 + +
+ +**核心启示**：创新不是玄学, 是可以被系统化分析和复制的模式。 + +
+ +--- + +## 8. 根目录论文内容整合（不上传 PDF 本体） + +### 8.1 论文一：Towards Execution-Grounded Automated AI Research (2601.14525) + +根据原文摘要与项目环境说明, 这项工作提出了一个 **execution-grounded** 的自动科研框架, 核心不是只生成“看起来合理”的 idea, 而是把 idea 真实落地执行并用反馈反哺下一轮搜索。 + +**关键点：** +- 构建自动执行器：自动打补丁、提交代码、分配 GPU、回收训练日志。 +- 设定两个可执行研究环境： + - post-training：GRPO + MATH（Qwen2.5-Math-1.5B） + - pre-training：nanoGPT + FineWeb +- 研究目标从“语言 plausibility”转为“执行后有效性”。 +- 强调 execution feedback 对后续 idea 迭代的价值。 + +### 8.2 论文二：Idea2Story (2601.20833) + +该工作把"research idea → 可写论文叙事"的过程做成了结构化流水线, 核心贡献是 **pre-computation-driven** 思路：尽量把文献理解和结构化表征前置, 降低在线大上下文推理成本与幻觉风险。 + +**关键点：** +- 用知识图谱组织 Idea / Pattern / Domain / Paper 节点。 +- 通过模式选择、故事生成、反思修正, 产出完整 scientific narrative skeleton。 +- 引入 anchored multi-agent review, 使打分更可审计、更可复现。 +- 输出可直接进入写作阶段的结构化结果（title/abstract/problem/method/contrib/experiments）。 + +### 8.3 并入现有工作流的建议 + +1. 在 `1.1 调研` 阶段优先落地“可执行环境”, 避免只停留在概念验证。 +2. 在 `1.2 项目` 阶段增加“叙事骨架检查”, 确保 idea 到论文表达不断层。 +3. 让“执行反馈”与“叙事反馈”双闭环并行：一个校验有效性, 一个校验可写性。 diff --git a/external/ai-research-ebook/src/content/docs/idea/research.mdx b/external/ai-research-ebook/src/content/docs/idea/research.mdx new file mode 100644 index 0000000..9f4ce9d --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/idea/research.mdx @@ -0,0 +1,160 @@ +--- +title: 调研方法 +description: 系统化的文献调研方法，从"看论文"到"产出空白"的完整流程 +sidebar: + order: 1 +--- + +## 1. 调研目标 + +调研的目标不是"看了很多论文"，而是要产出**能直接导向可写论文的结果**： + +1. 该方向到底在解决什么问题 +2. 大家默认的设定/评测是什么 +3. 现在的瓶颈在哪里 +4. 哪些空白值得写 +5. 你的 idea 能否形成可验证的 claim + +## 2. 工具选择与分工 + +### 2.1 主力调研工具 + +| 工具 | 适用场景 | +|------|---------| +| **ChatGPT 5.2 Pro** | 体系化综述、把一堆材料整理成"研究地图"、提炼问题定义与评价体系 | +| **Manus / GPT 5.2 Thinking** | 抓漏洞（这篇论文到底新在哪里？有没有偷换设定？） | +| **Gemini 3 Pro Deep Research** | 补全资料、扩展相关工作、找被忽略的假设 | + +### 2.2 推荐分工 + +- **Pro**: 搭主框架（问题→方法→实验→结论）+ 产出"标准化笔记" +- **Thinking**: 抓漏洞（创新点、评价公平性、潜在问题） +- **Deep Research**: 补引用与证据（相关工作漏了谁？还有哪些强 baseline？） + +## 3. 调研的产物清单 + +建议把调研结果固定成 6 份输出： + +1. **Related Work Map（研究地图）** + - 按"问题子类/数据模态/方法范式/训练范式/评测范式"分层 + +2. **Benchmark & Metric Sheet（评测与指标表）** + - 主流数据集/指标/协议/常见坑 + +3. **Assumption Matrix（隐含假设矩阵）** + - 每篇论文默认假设是什么？（资源、标注、算力、可访问信息等） + +4. **Failure Taxonomy（失败模式分类）** + - 方法为什么失败？失败发生在什么条件下？ + +5. **Gap List（研究空白清单）** + - 空白不是"没人做"，而是"没人把它做成可验证主张 + 可复现实验" + +6. **Claim Candidate Pool（候选主张池）** + - 每个 claim 必须能写成一句话，并且对应至少一个主实验 + 一个消融 + +## 4. 调研流程 + +### 4.1 定义边界 (Scope) + +- 你要解决的"核心问题"一句话是什么？ +- 哪些工作算"同类问题"，哪些只是"相关但不同问题"？ + +### 4.2 建语料库 (Corpus) + +- 每篇至少记录：任务定义、输入输出、数据集、指标、训练资源、关键 novelty、关键 baseline + +### 4.3 标准化阅读 + +不要自由笔记，要"字段化"： + +- **Problem**: 解决什么，设定是什么？ +- **Key Idea**: 一句话新意是什么？ +- **Mechanism**: 为什么有效（作者的因果叙事）？ +- **Evidence**: 哪些实验真正在支撑 claim？ +- **Baselines**: 对比是否公平？ +- **Weakness**: 最大漏洞是什么？ +- **Reproduce**: 复现难点在哪里？ + +### 4.4 对齐与冲突检测 + +多模型调研结果可能冲突，冲突往往就是"机会点"： + +- 该方向究竟默认设定是什么？ +- 某些工作是不是在换设定？ +- 指标是不是被 hack 了？ +- 某些强 baseline 是否被刻意忽略？ + +### 4.5 产出 gap → 变成可写 idea + +每个 gap 最终要落到**可写论文的形式**： + +- Claim（主张） +- Setup（设定） +- Method（方法） +- Experiment（实验） +- Expected Outcome（预期） +- Risk（最大风险与备选方案） + +## 5. 文献检索工具 (2025最新) + +| 工具 | 核心功能 | 适用场景 | 链接 | +|------|---------|---------|------| +| **Elicit** | 1.25亿+论文库，自动化系统综述 | 系统性文献调研 | elicit.com | +| **Consensus AI** | AI驱动搜索，快速找证据 | 快速验证假设 | consensus.app | +| **Semantic Scholar** | AI提取论文含义，深度覆盖 | 探索研究脉络 | semanticscholar.org | +| **Scite** | 智能引用追踪(支持/反驳/提及) | 评估论文影响力 | scite.ai | +| **Connected Papers** | 可视化论文关系图 | 发现相关论文 | connectedpapers.com | +| **ResearchRabbit** | 论文推荐类似 Spotify | 持续追踪新工作 | researchrabbit.ai | + +### 5.1 工作流建议 + +1. **初步探索**: Semantic Scholar + Connected Papers +2. **深度调研**: Elicit (自动化筛选和数据提取) +3. **证据验证**: Consensus AI + Scite +4. **持续追踪**: ResearchRabbit (新论文推送) + +## 6. 多模型协作调研 + +使用多个 LLM 并行可以交叉验证，避免单一模型的偏见。 + +### 6.1 推荐配置 + +| 模型 | 角色 | 优势 | +|------|------|------| +| **Claude Opus 4.6** | 主力分析师 | 深度推理，长上下文 (200K) | +| **GPT-5.2 Thinking** | 漏洞检测者 | 捕捉被忽略的假设 | +| **Gemini 2.5 Pro** | 资料补充员 | 强大的检索和整合能力 | + +### 6.2 协作模式 + +1. **分工阅读**: 不同模型负责不同子领域 +2. **交叉验证**: 同一篇论文由多个模型独立分析 +3. **冲突解决**: 冲突点往往是研究空白 +4. **综合汇总**: 由最强模型整合所有发现 + +--- + +## 7. 个人高通量选题法（补充） + +> 这部分来自 `AI research.md` 的个人实践段落，已融合为可执行版本，用于把"读很多论文"变成"筛出能发的 claim"。 + +### 7.1 三层过滤流程 + +1. **候选池层（广覆盖）** + - 先建立 2000+ 论文级别的候选池，按任务、数据模态、评测协议分桶。 +2. **可验证层（可落地）** + - 对每个候选方向补齐：主实验、消融、压力测试、失败预案。 +3. **可投稿层（可叙事）** + - 只保留能形成完整论证链的方向：问题价值 → 方法机制 → 实验证据 → 反质疑预案。 + +### 7.2 快速打分模板（避免主观拍脑袋） + +| 维度 | 问题 | 评分标准（1-5） | +|------|------|----------------| +| **Novelty** | 是否只是已有方法换壳？ | 5 = 明确新设定或新机制 | +| **Executability** | 两周内能否跑通主实验？ | 5 = 依赖清晰、实现成本可控 | +| **Evaluability** | 是否有公认基准和强 baseline？ | 5 = 可直接复现实验协议 | +| **Narrative Fit** | 能否写成完整论文故事？ | 5 = 贡献/证据/风险闭环完整 | + +建议只推进总分最高的 Top 1-3 个方向，降低“多线开坑”风险。 diff --git a/external/ai-research-ebook/src/content/docs/index.mdx b/external/ai-research-ebook/src/content/docs/index.mdx new file mode 100644 index 0000000..21e2d05 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/index.mdx @@ -0,0 +1,138 @@ +--- +title: Vibe Research +description: AI研究电子书 - 从idea到论文发表的完整指南 +template: splash +hero: + title: Vibe Research + tagline: 从 Idea 到顶会投稿的完整 AI 科研操作系统 + actions: + - text: 开始阅读 + link: idea/research + icon: right-arrow + - text: 工具总览 + link: tools/overview + icon: star + variant: minimal + - text: 完整工作流 + link: tools/workflow + variant: secondary +--- + +## 研究工作流概览 + +```mermaid +flowchart LR + subgraph Phase1["Phase 1: Idea"] + A(文献调研) --> B(创新模式分析) + B --> C(Gap 识别) + C --> D(Claim 产出) + end + + subgraph Phase2["Phase2:代码"] + D --> E(项目设计) + E --> F(Claude Code 构建) + F --> G(实验验证) + end + + subgraph Phase3["Phase3:写作"] + G --> H(结构化写作) + H --> I(图表制作) + I --> J(审稿迭代) + end + + Phase1 --> Phase2 + Phase2 --> Phase3 + + classDef phase fill:#f8fafc,stroke:#10a37f,stroke-width:1.6px,color:#111827 + class Phase1,Phase2,Phase3 phase + + +``` + +## 快速价值 + +
    +
  • 🚀 用 14 周路线，把科研任务拆成可执行流水线。
    • +
  • 🧠 用多模型交叉审查，减少方向性失误和实验返工。
    • +
  • 🧪 用结构化实验与审稿闭环，提升投稿成功率。
    • +
  • 📊 用流程图与模板化文档，让团队协作更稳定。
    • +
+ +## 核心功能 + +### Idea 生成 +系统化的文献调研方法，使用多模型交叉验证，从2000+论文中发现可写的研究空白。 + +**核心方法：** +- Related Work Map 构建 +- Assumption Matrix 分析 +- Gap List 产出 +- Claim Candidate Pool + +### 代码实现 +Claude Code、GPT-5.2系列、多Agent框架的最佳实践，高效构建实验系统。 + +**核心能力：** +- 并行协作架构 +- CLAUDE.md 团队记忆 +- Sub-agents 交叉验证 +- 自动化验证闭环 + +### 论文图表 +自动化绘图工具推荐，专业图表设计规范，让论文插图更具说服力。 + +**工具生态：** +- PaperBanana 自动生成 +- Mermaid 流程图 +- D3.js 自定义可视化 +- Figma 专业设计 + +### 论文写作 +论证链构建方法，多模型协作写作，Prism等新一代写作工具使用指南。 + +**写作方法：** +- Claim → Evidence 论证链 +- 多模型协作 tmux 配置 +- 会议特定写作模板 + +### 审稿迭代 +结构化审稿流水线，会议特定审稿模板，Rebuttal写作策略。 + +**审稿流水线：** +- 10+ 轮自动审稿 +- TODO 驱动修改 +- 分类回应策略 + +### 工具生态 +Elicit、Zotero-MCP、Sci-Reasoning等最新工具，构建完整科研工具链。 + +--- + +## 14 周完整时间线 + +| 周次 | 阶段 | 核心任务 | 输出 | +|------|------|---------|------| +| Week 1-2 | 调研 | Elicit 扫描 + Sci-Reasoning 分析 | Gap 清单 | +| Week 3 | 设计 | Claude Code 生成文档 | 项目设计 | +| Week 4-7 | 实现 | Sub-agents 并行开发 | 代码 + 实验 | +| Week 8-9 | 写作 | Prism + 图表生成 | 初稿 | +| Week 10 | 审稿 | 10+ 轮自动审稿 | 修改版 | +| Week 11 | 投稿 | 格式检查 | 最终版 | +| Week 12+ | Rebuttal | 如有需要 | 录用 | + +--- + +## 适用场景 + +- **研究生/博士生**: 系统学习 AI 研究方法，提升论文产出质量 +- **工业界研究员**: 快速掌握最新工具，提高研发效率 +- **独立研究者**: 建立完整的科研工作流，从零开始发表顶会论文 + +## 从这里开始 + +- 新手建议先读：[`idea/research`](idea/research) → [`idea/projects`](idea/projects) +- 需要快速落地：[`tools/workflow`](tools/workflow) + [`review/structured`](review/structured) +- 关注工程效率：[`code/claude`](code/claude) + [`code/agents`](code/agents) + +--- +

本书持续更新中 · 视觉系统升级：2026-02-09 · 默认主题：浅色

diff --git a/external/ai-research-ebook/src/content/docs/library/root-assets.mdx b/external/ai-research-ebook/src/content/docs/library/root-assets.mdx new file mode 100644 index 0000000..0e64d0a --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/library/root-assets.mdx @@ -0,0 +1,591 @@ +--- +title: 科研圣经 +description: 零基础入门科研全指南 - 从顶会对比到Idea生成，从论文阅读到速成写作 +sidebar: + order: 1 +--- + +# 科研圣经 + +本章节汇集零基础入门科研的核心知识，涵盖顶会顶刊对比、Idea生成方法、论文阅读流程与速成写作技巧。 + +--- + +## 一、顶会顶刊投稿对比 + +### 1. 审稿人意见分类 + +- Reject（拒稿） +- Major Revision（大修） +- Minor Revision（小修） +- Accept（接收） + +**会议**：一般只有 Reject（拒稿）和 Accept（接收）两种 + +**期刊**：四种都有 + +> 意见及应对方法见：[知乎文章](https://blog.csdn.net/qq_39856931/article/details/106751954) + +### 2. 审稿人提意见的意图 + +| 类型 | 意图 | +|------|------| +| **会议** | 提意见为了拒绝文章 | +| **期刊** | 审稿人提意见是为了改正文章，意见精确（跟哪些baseline进行对比、补充哪些评价指标、新增哪些数据集实验） | + +### 3. 审稿人细致及专业程度 + +| 类型 | 特点 | +|------|------| +| **会议** | 很少有细节性的意见，质量参差不齐，不懂这个领域也可能是你的审稿人 | +| **期刊** | 认真细致，专业 | + +### 4. 期刊和会议的优缺点 + +#### 会议 + +**时间较短**（顶会可能2-3月左右） + +##### CVPR-2026 时间线 + +| 重要时间节点 | 阶段 | +|-------------|------| +| 2025年10月 | OpenReview投稿网站开放**作者注册** [OpenReview](https://openreview.net/) | +| 2025年10月 | OpenReview投稿网站开放**论文提交** | +| 2025年11月 | **摘要**提交截止（晚上11:59 UTC-12） | +| 2025年11月 | **完整论文**提交截止（晚上11:59 UTC-12） | +| 2025年12月 | **补充材料**提交截止（晚上11:59 UTC-12） | +| 2026年1月 | **第一阶段拒稿**通知 | +| 2026年2月 | 作者**反馈**窗口期 | +| 2026年3月 | **最终录用或拒稿**通知（主要技术轨道） | +| 2026年3月 | **最终版**（Camera-ready）文件提交（主要技术轨道） | +| 2026年6月 | CVPR-2026正式举办 | + +##### AAAI-2026 时间线 + +| 重要时间节点 | 阶段 | +|-------------|------| +| 2025年6月16日 | OpenReview投稿网站开放**作者注册** | +| 2025年6月25日 | OpenReview投稿网站开放**论文提交** | +| 2025年7月25日 | **摘要**提交截止 | +| 2025年8月1日 | **完整论文**提交截止 | +| 2025年8月4日 | **代码/附录材料**截止 | +| 2026年9月8日 | **第一轮结果出炉**（首轮拒稿无rebuttal） | +| 2025年10月2-8日 | rebuttal阶段 | +| 2025年11月3日 | **最终录用通知** | +| 2025年11月13日 | **最终版**（Camera-ready）文件提交 | +| 2026年1月20-27日 | AAAI-2026正式举办 | + +#### 期刊 + +**周期较长**（顶刊可能8月到1年左右不等） + +LetPub 中科院一区 - 人工智能领域期刊查询结果 + +### 5. 顶刊顶会目录 + +**（1）2025年中科院新分区 1区 共53本期刊** + +**（2）CCF会议分类** + +[中国计算机学会推荐国际学术会议和期刊目录（2022更名版，2025年使用）.pdf](https://kyc.lsu.edu.cn/_upload/article/files/df/ed/9257ff9c401dabebfe7428c8a9ce/cc033863-6b2b-4616-8bab-ae183ed7a04b.pdf) + +### 6. 计算机可投期刊信息查询及会议截止时间 + +#### （1）期刊 + +**LetPub** 是一个提供最新SCI期刊影响因子查询及投稿分析系统的网站，可按照期刊名、研究方向、影响因子、收录情况等条件筛选和排序 + +[网站地址](https://www.letpub.com.cn/index.php?page=./journalapp) + +#### （2）会议 + +**CCF会议分类及截稿倒计时网站** + +- 网站1：[ccfddl.cn](https://ccfddl.cn/) - 右上角可扫码关注小程序 +- 网站2：[ccfddl.com](https://ccfddl.com/) + +--- + +## 二、零基础入门科研如何想Idea + +### 1. 一篇论文诞生的整体流程图 + +``` +寻找领域内当前效果最好的论文（最好开源） + ↓ +看论文里展示的SOTA结果比较了哪些baseline + ↓ +顺着 baseline 与引用一路往前读 + ↓ +在理论层面进行分析，找改进点 +（哪些工作上不work，能否扩展） + ↓ +复现代码 + ↓ +在代码层面进行分析，找缺陷 +（运行速度慢、内存占用大等） + ↓ +改出自己的Idea +``` + +**举例**：寻找领域内当前效果最好的那篇论文，最好**开源**→看论文里面展示的**SOTA结果比较了哪些baseline**，顺着 baseline 与引用一路往前读→在**理论层面**进行分析，找改进点，例如在哪些工作上是不work的，能否在这个领域进行一些扩展→复现代码→在**代码层面**进行分析，找缺陷，例如虽然某方法性能有提升，但是运行速度很慢、内存占用大，能否进行加速和优化→改出自己的点子。 + +### 2. 寻找论文的渠道 + +#### （1）看最新出顶会中新中的论文 + +**官网** +- NAACL2025接收文章列表：https://2025.naacl.org/program/accepted_papers/ +- CVPR2025接收文章列表：https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers + +**GitHub科研总结** +- CVPR-2025-带代码的阅读论文：https://github.com/Paper2Chinese/CVPR-2025-reading-papers-with-code + +#### （2）大机构数据库 + +- **arxiv平台**新出论文/预印本idea：https://arxiv.org/ +- **谷歌学术**：https://scholar.google.com.hk/ +- **dblp**：https://dblp.org/ +- **Aminer**：https://www.aminer.cn/ +- **huggingface**新出论文：https://huggingface.co/blog/zh?tag=research + +#### （3）Paper with Code上面SOTA结果 + +网址：https://paperswithcode.com/ + +### 3. 建议部分 + +#### （1）阶段划分 + +1. **资格达成期** + - **目标**：满足学位授予的硬性成果指标（篇数、等级） + - **策略**：锁定 1–2 个垂直子领域，开展深度研究，尽量避免进入高曝光热门赛道，降低idea被抢占风险 + +2. **影响拓展期** + - **目标**：在完成资格后，利用已积累的模型、工具链与领域认知，向相邻学科或跨模态任务延伸，追求更高学术影响力 + +#### （2）日常科研习惯 + +1. **输入端** + - **文献精读**：每周 2 篇，输出结构化摘要（问题-方法-缺陷-可改进点） + - **审稿意见逆向阅读**：收集公开评审意见，**提前预判可能被审稿人质疑点** + + **例如**：在 VLPose 这篇论文中，作者引入新模块的同时不可避免地引入了额外参数。为回应可能关于"性能提升是否仅源于参数增长"的质疑，作者在发表前通过对比实验设计，**在参数量可控的前提下**展示了其方法在性能上的显著优势，体现出结构设计本身的有效性。 + + [论文链接](https://arxiv.org/pdf/2402.14456) + + **Table3(b)表**反映的是**在不同模型尺寸下，各微调方法需要训练的参数量**，单位为 MB。 + + ##### 1. Visual Prompt Tuning（视觉提示微调） + + - **核心思想**：不改动模型本体，仅在图像输入的"前面"加上可学习的"提示 embedding" + - 就像在 Transformer 里加了一个"引导标签"，帮助模型适应新任务 + - **只训练这部分新加的 prompt**，其余所有模型参数都 **被冻结（frozen）** + - 这叫做**参数高效的微调方法**，很轻量，不会破坏原模型 + + ##### 2. Last Layer Tuning（最后一层微调） + + - **传统做法**：固定前面的模型结构，只微调最后一层（通常是预测层） + - 相比全参数微调，它已经是"轻量"了，但仍然要训练模型的一部分原始参数 + + ##### 两种训练方式的不同点 + + | 项目 | Visual Prompt Tuning | Last Layer Tuning | + |------|---------------------|-------------------| + | 是否改动原模型 | 否 不改，只加东西 | 是 改动最后一层 | + | 可训练参数来源 | 全是新加的 prompts | 原模型中的部分参数 | + | 参数量 | 通常更少 | 较多 | + | 泛化能力保留 | 更强（原模型没动） | 中等（最后一层被改） | + | 结构可逆性 | 可随时删除 prompts 恢复原模型 | 改过原模型不可逆 | + + **结论**是 VLPose 虽然只用了一点点额外的 prompt 参数（比 Visual Prompt 多，但远少于 Last Layer），但性能最好，结构还可逆，泛化也好。以高效的结构设计**消解了对"参数堆砌"的潜在质疑**。 + +2. **输出端** + - **即时记录**：手机备忘录等 + - **定期研讨**：组会、会议、在线论坛等 + +### 4. 科研实操 + +#### 4.1 入门大模型怎么选方向 + +**目标**：先搞清楚"能做多大的实验" + +按**资源**情况划分： + +| 手里的卡 | 推荐任务 | 为什么要这样做 | 真实例子 | +|---------|---------|---------------|---------| +| **资源多** ≥100 张 A100 | 基于开源模型做大规模预训练、指令微调、强化学习 | 卡多=数据吞吐大，能把基线刷高 | LLaMA-65B 原版就是 2048 张 A100 训的 | +| **资源中等** 8 张 V100 | 小领域微调 | 卡不多不少，可以训"窄场景"专用模型 | **纯语言领域**：MoTCoder论文没有在humaneval或者mbpp这样的数据集上做验证，而是使用的Code Contest/apps：在专门做竞赛难度的编程数据集实验，数据量小但难度高。**多模态领域**：Lyra论文训练了一个视觉、语音、语言的多模态大模型，之前的多模态大模型在长音频和长视频上的效果不好，Lyra对应长视频音频的大海捞针任务做了提升 | +| **资源少** 4 张 3090 | 免训练（training-free） | 训练一次要一周，迭代慢；免训可直接推理，每轮迭代速度都很快，无需大量资源 | **纯语言领域**：Quick LLaMa：只做"长文本推理加速"，不改权重。**视觉领域**：VisionZIP：对图像的token做压缩 | + +**MoTCoder论文链接**：https://arxiv.org/pdf/2312.15960 + +**Lyra论文链接**：https://arxiv.org/pdf/2412.09501 + +**QuickLLaMA论文链接**：https://arxiv.org/abs/2406.07528 + +**VisionZIP论文链接**：https://arxiv.org/pdf/2412.04467 + +#### 4.2 如何判断一篇论文是否值得精读 + +**值得精读 = 效果好 + 可复现** + +##### （1）效果好 + +**情况1**：在某个Benchmark上面是SOTA + +> **Benchmark**（基准测试）是指一套标准化的测试任务、数据集和评估指标，用于公平、可重复地比较不同方法或系统的性能。 + +**情况2**：不是SOTA，但是现有SOTA是在它的基础上进行改进 + +可能出现这样的情况，假设某论文A提出一个基础模型，后续论文B在A的基础上加了一个"花哨的模块"成为SOTA，但这个模块在数据量小或任务简单时反而拖后腿。所以可能基于论文A的baseline去改进效果会更好。 + +**情况3**：某工作是现有SOTA的基础或它启发了现有SOTA + +如果不知道某论文是否和现有SOTA有关，可从以下4个方面来判断该篇论文的质量： + +**方面1**：大机构 > 小机构 + +###### 常见的大机构类型 + +1. **顶尖高校及实验室** + - 例如：MIT、Stanford、Tsinghua University等 + - 这些学校下的研究实验室（如MIT CSAIL、Stanford AI Lab）在学术圈具有很强影响力 + +2. **知名科研院所** + - 例如：中国科学院、Max Planck Institute、Allen Institute for AI (AI2) 等 + +3. **科技巨头的研究部门** + - 例如：Google Research（Gemini）、DeepMind、Microsoft Research（DeBERTa）、Meta AI（PyTorch、LLaMA）、OpenAI（GPT、CLIP）、NVIDIA Research（Diffusion Models） + - 这些公司投入巨资，且产出大量SOTA研究成果 + +4. **开源社区与合作研究组织** + - Hugging Face、OpenMMLab [香港中文大学多媒体实验室网址](https://openmmlab.com/codebase) + +**方面2**：简单 > 复杂 + +复杂论文可能只是某个模块在起作用，作者为了使这篇论文工作更丰富创新，可能会融合很多模块，导致论文难以理解。 + +**方面3**：清晰 > 繁琐 + +优先论文有公式推导、伪代码和图示。 + +**方面4**：开源可复现 + +（2）可复现 + +优先有公开源码的论文，训练数据和训练参数齐全，近期公开/维护 + +#### Benchmark 是什么？ + +**基本定义** + +- 在学术研究中，Benchmark 通常是： + - 一个公开可用的 **标准数据集**（比如 COCO、ImageNet、HumanArt） + - 配有 **明确的评价指标**（如 AP、Accuracy、F1-score） + - 用于对不同模型/方法进行 **公平比较** + +##### 举例 + +| 领域 | Benchmark 示例 | 作用 | +|------|---------------|------| +| 图像分类 | ImageNet | 比谁分类准 | +| 人体姿态估计 | MS COCO / HumanArt | 比谁关键点预测准 | +| 自然语言处理 | GLUE / SuperGLUE | 比谁语言理解好 | +| 大模型能力 | MMLU / GSM8K | 比谁多任务泛化能力强 | + +一个完整的 Benchmark 往往包括： + +| 组件 | 说明 | +|------|------| +| 数据集 | 统一的数据输入（比如图片、文本） | +| 标注 | 统一的"正确答案"或ground truth | +| 评价指标 | 用来打分，比如AP、Recall等 | +| 测试协议 | 比如是否允许使用额外数据、是否使用ground truth框等 | + +#### 4.3 看论文的流程 + +##### （1）标题：大致知道是做什么方面的工作 + +译为：QuickLLaMa：用于大型语言模型的查询感知推理加速 + +通过QuickLLaMa：知道大致在做一件加速的事情 + +##### （2）摘要&示意图：大致知道这篇论文在做什么事情 + +用户询问了关于哈利波特中很细节的问题，普通的Llama3无法给出回答，但是QuickLlama可以在30s内阅读完一本100k token的哈利波特并给出准确回答。 + +如果对这篇文章不感兴趣的话，可以去看下一篇文章了。 + +##### （3）深入看方法/架构示意图：知道这篇论文大致是在用什么框架/方式来实现这件事情 + +###### 图示关键词解释 + +| 名称 | 含义 | 用途 | 举例 | +|------|------|------|------| +| Global Tokens (G) | 模型运行时始终存在的全局信息，如系统提示、任务说明等 | 作为整个推理过程中每一步都可访问的公共信息；增强模型对任务和角色的理解 | 系统提示："你是一个图书问答助手" | +| Query Tokens (Q) | 用户输入的问题或查询句 | 用来指导模型重点关注哪些记忆块（Memory Blocks） | "哈利第一年结束时金妮指向了谁？" | +| Context Tokens (C) | 模型历史接收过的长文本内容，被拆分成很多 memory blocks 存入"记忆"中 | 构成"记忆仓库"；供后续从中查找与 Query 或当前生成位置相关的上下文信息使用 | 整本《哈利·波特》的文本内容被切分成块 | +| Memory Blocks (B) | 上述 Context Tokens 划分后的一块块"笔记段落" | 用来支持查询感知（query-aware）的查找：哪些内容是现在需要调出来使用的 | Block 1 是"火车站送别"，Block 2 是"魔法课" 等 | +| Query-related Blocks (R) | 与 Query 或 Current Token 高度相关的 memory blocks | 是从 Memory Blocks 中"筛选"出的重要内容，用于当前推理窗口 | 当前提问与"火车站"段落相关，系统只加载这几段 | +| Local Tokens (L) | 当前生成位置前的短距离上下文 token（相对于 Current Token） | 让模型知道"现在在哪"，并为相关性打分提供"当前位置的上下文" | "昨天张三来了，他…" 是生成"是"之前的 L | +| Current Token (H) | 当前要生成/预测的 token 的隐藏向量表示（注意力机制中的 query 向量） | 用于决定当前哪些 memory block 有用（用于 s(B, H) 打分） | 当前正要生成当前准备生成/预测的那个 token"他是___"的"是"这个词 | +| Current Key-Value Cache (M) | 当前这一步模型使用的 key/value 信息组合，包含 G, Q, R, L 四部分 | 作为 Attention 层的输入；也是每一步模型做决策时真正"看到"的全部上下文 | 当前模型输入 = 全局说明 + 问题 + 相关段落 + 上下文 | + +**疑惑**：先有的RAG和QuickLLaMa谁出现时间较早？它们有什么区别？ + +——>使用AI工具，用kimi k2查询得到以下对比表格： + +| 维度 | RAG(2020年) | QuickLLaMA（2024年） | +|------|-------------|---------------------| +| 核心机制 | 在生成前，**先检索外部文档**，再让 LLM 结合检索结果生成答案 | **在生成过程中**，动态从长上下文中**查找与查询相关的片段** | +| 数据来源 | 通常是**外部知识库**（如 Wikipedia、向量数据库） | 是**当前输入的长文本本身**（如一本书、一篇长文） | +| 是否需要训练 | 需要训练检索器和生成器 | **无需训练**，可直接插入现有 LLM | +| 使用场景 | 回答**知识密集型问题**（如"2024 年奥运会举办城市"） | 回答**长文本内部问题**（如"哈利·波特中谁最后拿到魔杖？"） | +| 检索粒度 | 以**文档或段落**为单位 | 以**token 或记忆块**为单位，更细粒度 | + +##### （4）看实验表格结果：看这篇论文的方法比较的baseline有哪些，明确这论文的**优势和提升**是什么 + +图一第一列展示了，不同 benchmark 中的任务（多项选择问答、阅读理解题、对话摘要...） + +第一行：方法对比；第二行：上下文窗口大小（Context Window） + +该方法可以在长文本输入的Benchmark（基准测试）上取得很好的效果。 + +| 方法名称 | 是否需要重新训练 | 是否 Query-aware（查问题重点） | 推理速度 | 显存占用 | 记忆能力 | 综合表现 | +|---------|----------------|------------------------------|---------|---------|---------|---------| +| LLaMA3-8B-1048K | 需要 | 否 | 慢 | 极高 | 很强（靠训练） | 精度高但代价大 | +| StreamingLLM (Stream) | 不需要 | 否 | 快 | 中等 | 差（会忘） | 快但记不住长文本 | +| InfLLM | 不需要 | 否 | 快 | 低 | 一定记忆力 | 比 Stream 更强，但不够聪明 | +| QLLM (QuickLLaMA) | 不需要 | 是 | 最快 | 最低 | 最强（精准查找） | 表现最全面优秀 | + +图二展示了该方法在显存占用和时间上面和baseline的对比，Llama3都显存占用会随着token数量增加呈现指数增长，很快就会OOM超显存，但本文方法最多只使用了20b左右的显存，且时间增长是线性的。故有此结论，该方法相比于之前的baseline在性能、显存占用、速度都有提升。 + +如果对这篇文章不感兴趣的话，认为某个步骤写的不好，可以去看下一篇文章了 + +##### （5）进一步看表格内部的baseline：找baseline对应论文，用上面的流程再读一遍 + +比如这里比较了英伟达的Stream LLM，去找这篇baseline对应的论文，用上面的流程再读一遍，把表格里面所有的baseline都搞懂，就差不多理解当前这篇论文了 + +##### （6）正文/代码：想有哪些地方可以改进，基于当前文章产生一个idea + +#### 4.4 代码跑通流程 + +（1）去 Papers with Code /github等搜索任务关键词。 + +（2）选有官方代码+有训练脚本+最近一年有更新的论文代码（有readme文件、环境版本号具体、参数详细、训练验证等使用教程清晰）。 + +（3）复现 = 配环境 → 下载数据 → 跑脚本 → 得到和作者差不多的分数。 + +**详细流程**：配置好代码环境和数据 → 理解代码运行逻辑（从主函数开始，哪些部分是数据处理，哪些是核心算法） → 基于哪些部分进行改进 → 对核心部分插入断点 + +##### 阶段1 代码理解 + +例如：MoTCoder这篇论文中的模型，通过拆分子模块的方式来对代码问题进行求解，那么你可以输入给他一个例子，观察它是如何对输入问题进行子模块的拆分，从而理解这个模型的运行逻辑。 + +如果代码库提供可视化，可以基于可视化来帮助理解。 + +##### 阶段2 在代码理解之上改进 + +基于现有代码进行改进，比如观察到论文中的attention热力图对某些局部信息没有体现，你就可以新增一个模块来重点关注这些局部细节信息。经过你的新增模块之后，再次绘制热力图，发现之前没有被标注的细节信息被highlight，那么可以证明你新增的模块是有效的。 + +**例子**：论文TagCLIP新增了一个Trusty Learner模块，作者在论文中配了对应的可视化示意图，来展示该模块的作用，用以验证该模块的有效性。 + +**论文研究问题**：现有的基于对比语言-图像预训练（CLIP）的零样本语义分割方法在处理未见类别时存在显著的误分类问题，尤其是容易将未见类别与语义相似的已知类别混淆。 + +**对于阶段2例子的详细解释** + +1. **研究问题：模型会把"没见过的东西"当成"见过的"** + +研究的核心问题是：在图像分割中，模型需要给每个像素打上正确的类别标签。但**很多模型只见过训练集里的"已知类别"**（比如飞机、汽车、牛），一旦遇到"没见过的类别"（比如盆栽、电视、沙发），它**常常会误认为这些新东西也是已知类别**。比如，它可能会把"盆栽"误认为是"瓶子"，因为两者颜色或形状有点像。 + +2. **解决方法：让模型学会"这是不是我认识的东西"** + +TagCLIP 的方法是增加了一个 **"Trusty Token"**，它的工作就像一个"可信度检测器"——**先判断这个像素是不是已知类别**。如果它觉得"不像是我认识的东西"，就会**抑制**模型给它贴上已知类别的标签。 + +3. **为什么要放 Fig.4？** + +**Fig.4 就是用来证明这个"可信度检测器"真的好用。** + +- 图里左两列显示，检测器对"已知类别"的反应很明显（像飞机、汽车这些它认识的东西被高亮） +- 右一列显示，它对"未知类别"几乎不亮，说明它能**有效忽略没见过的东西** + +简单说，**这张图是"证据"**，让读者一眼就能看出： + +> "看！我们的新方法确实能分清什么是它见过的，什么是它没见过的。" + +#### 4.5 怎么读代码 + +**目标**：搞清楚"哪一段代码是我未来要改的地方" + +**流程**： + +##### 情况1：普通github代码 + +整体框架粗读，重点部分代码亲自跑一遍（插断点），一行行去看其中的变量是如何变化的、变量的形状是什么。 + +##### 情况2：代码库 + +比如在**大模型训练**中常见的**代码库LLaMA-Factory** + +或者在视觉检测中常见的**MMDetection**，但这个代码库的封装很厉害，找变量入口和变化方式都很麻烦。因此这种类型的库不必层层debug，无需完全理解它的代码运作流程，**会用即可**。在使用说明里面看，如何添加一个新的模型、数据集等。 + +**（1）粗读**： + +打开github仓库，先看 `README.md`，找到入口脚本（通常叫 `main.py` 或 `train.py`）。 + +**（2）细读**： + +在核心函数里打断点。 + +1. **IDE**：用 VS Code 打开代码，`F9` 打断点，`F5` 启动调试 +2. **断点**：让程序跑到这行就暂停，方便查看变量值 +3. **变量形状**：`print(x.shape)`，例如 `[32, 128, 768]` 表示 batch=32、序列长 128、特征维度 768 + +**具体例子**：在QuickLLaMA这篇文章中，它的核心框架是attention部分的实现，在代码中嵌套了方程来代替原本模型attention中的forward函数。除了论文中实现的QLLM之外，代码库还包含了这个方向的一系列论文，比如StreamLLM、LM-Infinite、InfLLM这些代码都是用同样的框架实现的，因此可以很容易比较他们之间的不同。 + +[StreamLLM论文](https://arxiv.org/pdf/2309.17453) + +[LM-Infinite论文](https://arxiv.org/pdf/2308.16137) + +#### 4.6 改进现有SOTA + +##### 从论文方法角度进行分析 + +效果好的论文，分成三类： + +**（1）效果好，方法创新——顶级论文** + +能否把这个方法用在新领域，比如Transformer出了之后——>做vision Transformer、Mamba出了之后——>做vision Mamba、Sam出了之后——>做3D Sam。 + +- **优点**：如果是第一个把这个方法用在新领域的人，会受到很多关注 +- **缺点**：竞争大，各大机构都在同台竞技 + +**（2）效果好，方法简单** + +- **缺点**：方法简单但改进有难度 + +**法1**：可以把一项工作尝试用在新领域 + +MOOD的这篇CVPR论文就是把掩码图像建模用在了异常检测方面。掩码图像建模方法在很多领域都证明了其有效性，但未在异常检测领域进行应用，可以实验证明该方法在异常检测的各个方向上是有效的。 + +**法2**：把这篇论文和其他论文的方法相结合（不只是ABC模块组合，也可以是A模块+B方法+C评价指标） + +比如MOODv2就是把**MOOD结合**上**新的预训练策略BEiTv2**再**结合新的异常检测指标ViM** + +**（3）效果好，方法复杂** + +因为方法复杂，尝试把方法进行拆解，把某些**模块替换**成你觉得更好的模块或把某些**模块简化** + +##### 两种通用的方法 + +**法1**：找到现有方法在某些场景不work的缺陷，去掉现有方法成立的一些必要条件 + +现有方法缺陷：比如QuickLLaMa这篇，核心框架是：利用问题在长文本中进行关键信息的查找，有一种情况是，如果用户先输入的是长文本但是却不输入问题，在这种情况下可以改进这一点来形成新的文章。 + +**核心思路就是：去掉现有方法成立的一些必要条件。** + +在新的场景下，这些现有方法就不work了，你就可以针对这种新场景来完成一篇论文。 + +**法2**：关注新出的Benchmark + +比如HumanArt数据集是第一个在艺术领域上的姿态检测数据集，之前的人体姿态估计方法在这个数据集上的效果不好，于是出现了VLPose这个方法，在艺术检测这个数据集上和通用数据集上都做出了改进，形成一篇新论文。 + +**法3**：添加新的Benchmark、消融实验、Task + +当idea已经证明可行，但性能不是SOTA时： + +- **前提建议**：选题阶段即对标SOTA + +- **已验证有效但非SOTA性能：改进与发表策略** + +**（1）尝试和现有SOTA相结合** + +**（2）无法结合SOTA时考虑：是否必须对比？** + +可能**无需比较**的情况： + +- 情况1：是arxiv的预印本，还没有中稿 +- 情况2：是同期工作 + +**必须比较**的情况（想尽办法去证明你的工作是有价值的）： + +**方法1**：添加新的 Benchmark（覆盖更多任务维度，提升普适性说服力） + +- 某单一任务性能不如SOTA，但在其他10个任务上表现领先 +- 在多跳推理上优于SOTA，虽然单跳问题上稍逊 + +**方法2**：加新的消融实验（精细化对比同一设置下的表现） + +**原则**： + +- 比较对象限定为同等规模模型/方法 +- 若你贡献在于训练策略，仅需对比其他训练策略 +- 开源只和开源比，小贡献但有价值 + +**方法3**：添加新的task（做别人做不到或未做之事，强调独特性） + +- 多模态模型实现视觉+语音+语言，领先仅支持视觉+语言的方法 +- 能处理2小时视频而非其他方法仅支持2秒视频 +- 引入新语言/场景/规模等独特任务设定 + +**总结** + +若以上方法均尝试后性能仍无竞争力，该idea或许尚不成熟。 + +**科研初期优先级建议**： + +- **效果优于创新**：方法普通但效果好，也值得发表 +- **工程贡献也是贡献**：通过数据工程、训练技巧取得SOTA，也会获得关注 + +> 例如：无需新颖方法，仅凭高质量数据 + 大参数量 + 微调策略也能发表高水平成果（如许多大模型训练类论文）。 + +#### 4.8 怎么把论文的故事讲好 + +**目标**：让审稿人快速相信"你的工作合理且有效" + +##### 解释为什么A+B是有效的 + +**（1）理论上** + +验证A+B > A，用公式推导进行证明（最有说服力，但很多情况下无法用理论求解） + +**（2）现象上** + +观察到了A中的一些现象，B这一模块可以弥补这一缺陷，为什么能够弥补，可以搭配一些可视化，与**4.4 代码跑通流程阶段2 在代码理解之上改进**这部分的原理是相同的 + +**（3）工程上** + +把系统中的每个模块都相对应的**比较同期模块**，验证你的系统所采用的模块是最有效的。 + +**例如** MOOD这篇论文比较了不同的预训练策略、不同的模型架构、不同的OOD检测指标，证明在这个系统中所使用的每一个模块都是最优的。 + +**什么是OOD检测？** + +在现实世界中，很多AI系统面临这样的问题：它们在训练时只见过一部分"已知的数据"（称为**In-Distribution（ID）数据**），但测试或应用时可能会遇到"陌生的数据"（即**Out-of-Distribution（OOD）数据**）。OOD检测的任务就是要让AI系统学会识别出这些陌生的、没见过的数据，并避免错误地对它们做出判断。 + +**（4）总结** + +把论文的**形成过程**讲清楚，包括整个**系统的脉络**、以及**每个模块的作用**、每个模块都要配合对应的**消融实验**来验证模块的有效性 + +--- + +## 三、怎么3天速成一篇论文 + +### 工具 + +- **GPT-4o**（英文） + **DeepSeek**（中文） +- **LaTeX格式论文在线编译网站**：[Overleaf](https://www.overleaf.com/login) + +[Overleaf知乎简介](https://zhuanlan.zhihu.com/p/694594439) + +### 应用举例 + +**（1）给图片写标题**：向GPT输入：图片＋提示词（例如：给图片写英文latex格式的caption） + +**（2）写相关工作**：相关工作的每篇论文摘要粘贴下来，输入给GPT可生成一个相关工作初稿 + +**（3）介绍数据集**：将数据集相关介绍输入给GPT生成data部分的段落 + +**（4）组会汇报**：做PPT可以使用DeepSeek：把一周工作内容输入给DeepSeek做PPT，将生成的表格直接粘贴到PPT中，提高工作效率 diff --git a/external/ai-research-ebook/src/content/docs/review/structured.mdx b/external/ai-research-ebook/src/content/docs/review/structured.mdx new file mode 100644 index 0000000..34e4b6d --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/review/structured.mdx @@ -0,0 +1,241 @@ +--- +title: 结构化审稿 +description: 从让它挑刺到迭代闭环的审稿流水线 +sidebar: + order: 1 +--- + + + +## 1. 审稿流水线概览 + +```mermaid +flowchart TD + A(初稿完成) --> B(多模型并行审稿) + B --> C(交叉验证意见) + C --> D(按优先级排序) + D --> E(批量修复) + E --> F(验证修复) + F --> G{通过?} + G -->|否| B + G -->|是| H(定稿) + + classDef key fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827 + class A,H key + + +``` + +--- + +## 2. 审稿输出要结构化 + +审稿必须回答以下关键问题： + +| 问题 | 重要性 | +|------|-------| +| 哪条 claim 缺证据？ | 🔴 高 | +| 哪个实验最关键但没做？ | 🔴 高 | +| 哪个 baseline 必须补？ | 🔴 高 | +| 哪段叙事最像空话？ | 🟡 中 | +| 哪个地方最容易被说不新？ | 🔴 高 | + +--- + +## 3. 审稿输出模板 + +```markdown +# 审稿意见 + +## 1. 缺证据的 Claim + +| Claim | 问题 | 需要补充 | +|-------|------|---------| +| XXX机制是关键创新 | 没有消融实验 | 去掉XXX后的性能对比 | +| 显著优于SOTA | 评价指标不公平 | 统一指标后重新对比 | + +## 2. 缺失的实验 + +| 实验类型 | 现状 | 需要补充 | +|---------|------|---------| +| 压力测试 | 无 | 低资源/噪声/分布外测试 | +| 消融实验 | 不完整 | 每个模块的独立贡献 | + +## 3. 缺失的 Baseline + +| 方法 | 年份 | 问题 | 需要 | +|------|------|------|------| +| YYY方法 | 2024 | 最新强baseline | 补充对比 | + +## 4. 空话段落 + +| 位置 | 问题 | 建议 | +|------|------|------| +| 3.2节第二段 | 显著提升无数据 | 补充具体数字或删除 | + +## 5. 创新性质疑预案 + +| 可能的质疑 | 应对策略 | +|-----------|---------| +| 只是简单组合 | 强调发现的XXX insight | +| 不公平对比 | 补充公平对比实验 | +``` + +--- + +## 4. TODO 转化模板 + +每条意见对应一个改动 + 验收标准： + +```markdown +# TODO List + +## 紧急（提交前必须完成） + +- [ ] 补消融实验 + - 内容: 去掉模块 X + - 验收: 指标下降 ≥ 5% + +- [ ] 补公平对比 + - 内容: 训练步数对齐 + 算力对齐 + - 验收: 与 YYY 方法公平对比 + +- [ ] 补压力测试 + - 内容: 低资源/噪声/分布外测试 + - 验收: 每个场景至少 1 组实验 + +## 重要（提升质量） + +- [ ] 重写3.2节 + - 内容: 添加具体数据或删除空洞表述 + - 验收: 每段有具体数字支撑 + +- [ ] 增加与ZZZ的对比讨论 + - 内容: 强调我们的 XXX insight + - 验收: 明确说明区别和优势 + +## 可选（锦上添花） + +- [ ] 补充人类评估实验 +- [ ] 增加可视化分析 +``` + +--- + +## 5. 多模型审稿配置 + +### 5.1 模型分工 + +| 模型 | 侧重点 | Prompt 重点 | +|------|-------|-----------| +| Claude Opus 4.6 | 整体逻辑 | 叙事连贯性、创新性 | +| GPT-5.2 Pro | 技术细节 | 方法正确性、实验设计 | +| Gemini Pro | 可复现性 | 代码完整性、数据可用性 | + +### 5.2 审稿 Prompt + +```python + +你是一位 ["会议"] 的资深审稿人。请严格审查以下论文： + +["粘贴论文内容"] + +请从以下角度审查： + +1. **创新性 (Novelty)** + - 主要贡献是什么？ + - 与之前工作有何不同？ + +2. **技术正确性 (Technical Soundness)** + - 方法是否正确？ + - 理论是否成立？ + +3. **实验完整性 (Experiments)** + - Baseline 是否足够？ + - 消融实验是否完整？ + +4. **写作质量 (Writing)** + - 逻辑是否清晰？ + - 表述是否准确？ + +请用以下格式输出： + +--- +## Overall Score: (1-5) + +## Strengths: +- ... + +## Weaknesses: +1. ["严重"] ... +2. ["中等"] ... +3. ["轻微"] ... + +## Detailed Comments: +... +--- + +``` + +--- + +## 6. 迭代循环 + +### 6.1 10+ 轮迭代流程 + +```mermaid +flowchart TD + A(第1轮审稿) --> B(整理意见) + B --> C(按优先级排序) + C --> D(选择 Top 3-5 问题) + D --> E(批量修复) + E --> F(验证修复) + F --> G{满意?} + G -->|否| A + G -->|是| H(进入下一轮) + + +``` + +### 6.2 迭代原则 + +1. **不要贪多**：每轮只修复 3-5 条 +2. **优先级排序**：先解决最致命的 +3. **验证修复**：修复后重新审稿确认 +4. **记录进度**：保留修改历史 + +### 6.3 审稿闭环增强（补充） + +来自 `AI research.md` 与 `vibe` 并集经验的补充做法： + +- 每轮审稿输出后先做 **Issue 聚类**（证据不足 / 实验缺口 / 叙事问题 / 格式问题） +- 每类只挑 Top 1-2 个高影响问题进入当轮修复，避免“全面开花但都不深” +- 对每个修复项明确“验证证据”字段（新增实验、修改段落、补引用） +- 保留一份 **审稿→修复映射表**，方便 Rebuttal 时直接引用 + +--- + +## 7. 常见问题与应对 + +| 问题 | 应对策略 | +|------|---------| +| 审稿意见冲突 | 以技术细节为准 | +| 无法复现的实验 | 补充复现步骤 | +| 创新性被质疑 | 增加对比实验 | +| 实验设计被质疑 | 解释设计理由 | +| 写作被吐槽 | 精修语言表达 | + +--- + +## 8. 审稿检查清单 + +### 8.1 提交前检查 + +- [ ] 所有 claim 都有实验支撑 +- [ ] 强 baseline 都已对比 +- [ ] 消融实验完整 +- [ ] 压力测试覆盖边界情况 +- [ ] 引用格式正确 +- [ ] 语言表达清晰 +- [ ] 图表清晰可读 +- [ ] 补充材料完整 diff --git a/external/ai-research-ebook/src/content/docs/review/templates.mdx b/external/ai-research-ebook/src/content/docs/review/templates.mdx new file mode 100644 index 0000000..fcd5012 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/review/templates.mdx @@ -0,0 +1,134 @@ +--- +title: 会议审稿模板 +description: 不同会议的特定审稿标准和模板 +sidebar: + order: 2 +--- + +## 1. 会议特定审稿模板 + +不同会议有不同的审稿标准，以下是定制化模板要点： + +--- + +## 2. 审稿标准对比 + +| 会议 | 审稿重点 | 常见质疑 | +|------|---------|---------| +| **NeurIPS** | 理论创新 + 实验验证 | "神经科学合理性？""SOTA 对比？" | +| **ICML** | 数学严谨 + 理论分析 | "证明是否完整？""设定是否合理？" | +| **ICLR** | 表示理解 + 可视化 | "真正学到了什么？""可视化？" | +| **CVPR** | 实验完整性 | "消融是否充分？""数据集是否公平？" | +| **IROS** | 系统实现 + 真实场景 | "硬件实现？""真实机器人验证？" | +| **ACL/EMNLP** | 语言学洞察 + 评估 | "人类评估？""定性分析？" | + +--- + +## 3. NeurIPS 审稿模板 + +### 3.1 评分标准 + +- **Originality**: 问题/方法/设定的创新性 +- **Significance**: 对领域的影响 +- **Quality**: 技术正确性和实验完整性 +- **Clarity**: 写作和展示质量 + +### 3.2 常见质疑 + +1. "神经科学合理性在哪里？" + - 回应：引用相关神经科学文献 + +2. "与现有方法X的区别是什么？" + - 回应：创建对比表格 + +3. "为什么只在这些数据集上测试？" + - 回应：解释数据集选择的合理性 + +--- + +## 4. ICML 审稿模板 + +### 4.1 评分标准 + +- **Relevance**: 是否符合会议主题 +- **Technical Correctness**: 数学是否正确 +- **Novelty**: 方法或理论的创新性 +- **Clarity**: 表述是否清晰 + +### 4.2 常见质疑 + +1. "理论分析不够完整" + - 回应：补充定理和证明 + +2. "设定是否合理？" + - 回应：论证假设的合理性 + +3. "理论结果与实验的关系不清晰" + - 回应：明确解释理论如何指导实验设计 + +--- + +## 5. ICLR 审稿模板 + +### 5.1 评分标准 + +- **Technical Quality**: 方法的正确性 +- **Novelty**: 创新性 +- **Reproducibility**: 可复现性 +- **Presentation**: 展示质量 +- **Insightfulness**: 洞察深度 + +### 5.2 常见质疑 + +1. "真正学到了什么表示？" + - 回应：添加表示可视化分析 + +2. "可视化不够" + - 回应：添加更多图表 + +3. "为什么这个表示有效？" + - 回应：添加理论或实验分析 + +--- + +## 6. CVPR 审稿模板 + +### 6.1 评分标准 + +- **Originality**: 创新性 +- **Quality**: 技术质量 +- **Clarity**: 清晰度 +- **Significance**: 重要性 + +### 6.2 常见质疑 + +1. "消融是否充分？" + - 回应：添加每个模块的消融 + +2. "数据集是否公平？" + - 回应：确保训练/测试分离 + +3. "SOTA 对比是否完整？" + - 回应：补充最新方法的对比 + +--- + +## 7. IROS 审稿模板 + +### 7.1 评分标准 + +- **Originality**: 创新性 +- **Technical Quality**: 技术质量 +- **Presentation**: 展示质量 +- **Hardware/Real-world**: 硬件实现或真实场景验证 + +### 7.2 常见质疑 + +1. "硬件实现了吗？" + - 回应：描述硬件设置 + +2. "真实机器人验证了吗？" + - 回应：添加真实实验结果 + +3. "仿真到实际的差距？" + - 回应：讨论 sim-to-real 问题 diff --git a/external/ai-research-ebook/src/content/docs/tools/overview.mdx b/external/ai-research-ebook/src/content/docs/tools/overview.mdx new file mode 100644 index 0000000..f9145f5 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/tools/overview.mdx @@ -0,0 +1,205 @@ +--- +title: 工具总览 +description: AI 研究全流程工具选择矩阵 +sidebar: + order: 1 +--- + + + +## 1. 工具选择矩阵 + +```mermaid +flowchart TD + subgraph ["调研"] + A[Elicit] --> B(文献检索) + C[Sci-Reasoning] --> D(创新分析) + end + + subgraph ["实现"] + E[Claude Code] --> F(代码生成) + G[LangGraph] --> H(工作流编排) + end + + subgraph ["写作"] + I[Prism] --> J(论文写作) + K[PaperBanana] --> L(图表生成) + end + + subgraph ["审稿"] + M[Claude + GPT-5.2] --> N(多模型审稿) + end + + + +``` + +| 任务阶段 | 推荐工具 | 备选方案 | 选择理由 | +|---------|---------|---------|---------| +| **文献检索** | Elicit | Consensus, Semantic Scholar | 1.25亿+论文, 自动化综述 | +| **文献管理** | Zotero-MCP | Mendeley, Papers | Claude 集成, 语义搜索 | +| **代码生成** | Claude Code | GPT-5.2, Codex | 200K上下文, 80.9% SWE-bench | +| **Agent编排** | LangChain/LangGraph | CrewAI, AutoGen | 灵活工作流, 强大社区 | +| **论文写作** | Prism | Overleaf | GPT-5.2 集成, LaTeX 原生 | +| **审稿模拟** | Claude + GPT-5.2 | Gemini Pro | 多模型交叉验证 | +| **画图** | PaperBanana + Mermaid | D3.js, Figma | 自动化 + 版本控制 | + +--- + +## 2. 工具能力对比 + +### 2.1 LLM 能力对比 + +| 模型 | 上下文 | SWE-bench | ARC-AGI | 适用场景 | +|------|--------|-----------|---------|---------| +| **Claude Opus 4.6** | 200K | 80.9% | - | 长文档、代码 | +| **GPT-5.2 Pro** | - | - | 90%+ | 复杂推理 | +| **GPT-5.2 Thinking** | - | - | - | 深度分析 | +| **Gemini 2.5 Pro** | 1M+ | - | 49.35% | 超长文档 | + +### 2.2 文献检索工具对比 + +| 工具 | 论文数 | 特色 | 价格 | +|------|--------|------|------| +| **Elicit** | 1.25亿+ | 自动化综述 | 免费+付费 | +| **Semantic Scholar** | 2亿+ | AI驱动 | 免费 | +| **Consensus** | - | 证据搜索 | 免费+付费 | +| **Scite** | - | 引用质量 | 付费 | + +--- + +## 3. 快速参考 + +### 3.1 常用命令速查 + +```bash +# Claude Code +npm run dev # 启动开发服务器 +astro build # 构建项目 +astro preview # 预览构建结果 + +# MCP +mcp list # 列出已安装的 MCP 服务器 +mcp install zotero # 安装 Zotero-MCP + +# 文献检索 +elicit search "topic" # Elicit 搜索 +``` + +### 3.2 关键链接汇总 + +| 资源 | 链接 | +|------|------| +| **Sci-Reasoning** | https://github.com/AmberLJC/Sci-Reasoning | +| **The AI Scientist** | https://github.com/SakanaAI/AI-Scientist | +| **Elicit** | https://elicit.com | +| **Prism** | https://openai.com/prism/ | +| **claude-scientific-skills** | https://github.com/K-Dense-AI/claude-scientific-skills | +| **LangChain** | https://github.com/langchain-ai | +| **LlamaIndex** | https://github.com/run-llama | + +--- + +## 4. 根目录项目/论文内容并入说明 + +以下内容来自你根目录的项目与论文文件, 已并入现有章节（未上传 PDF 文件本体）： + +| 来源 | 并入内容 | 已落地章节 | +|---|---|---| +| 2601.14525 + Automated-AI-Researcher | execution-grounded 自动科研执行闭环（idea→patch→run→feedback） | `idea/projects`, `code/agents` | +| 2601.20833 + Idea2Paper | pre-computation + KG + anchored review 的叙事生成路线 | `idea/projects` | +| 2601.23265 + PaperBanana | 自动学术插图框架、基准与工程策略 | `figures/tools` | +| AutoFigure-edit README | 方法图到可编辑 SVG 的工程流水线实践 | `figures/tools` | + +### 4.1 额外说明 + +- `AI research.md` 的增量内容已融合到现有章节正文（idea / code / figures / writing / review / tools），未新增独立来源页。 +- `vibe` 三文档已先做并集去重，再融合到现有 `2. 代码实现` 及相关工作流章节。 +- 严格来源映射表见 [`library/root-assets`](../library/root-assets) 的“严格映射清单（来源 → 现有章节）”。 + +--- + +## 5. Book 实战增补（逐篇并入） + +### 5.1 1) VSCode Agent Skills：从启用到复用 + +来源：`book/在_VScode_中玩转_Agent_Skills（科研工作者必备skill推荐_一定要收藏）.md` + +#### 5.1.1 详细步骤（VSCode 路线） + +1. 打开设置并启用 `chat.useAgentSkills`。 +2. 在项目内创建目录：`.github/skills//`。 +3. 创建 `SKILL.md`，写清触发条件、输入输出、执行步骤。 +4. 按需添加资源文件（示例、脚本、模板）。 +5. 在会话中通过任务描述触发，验证 skill 是否被自动加载。 + +#### 5.1.2 复用策略 + +- 优先复用社区技能库，再做本地改造； +- 改造时优先替换路径、命名、验证命令； +- 先用小样本测试，再扩展到完整任务。 + +#### 5.1.3 文章细节补充（VSCode 实操顺序） + +1. 先创建一个最小 skill（仅 1 个步骤），验证可触发。 +2. 再添加 `workflow-example` 与脚本资源，验证可读取。 +3. 最后引入社区 skill 并本地改造，避免一次导入过大难排错。 + +#### 5.1.4 推荐社区资源（与原文一致） + +- `awesome-copilot`（社区技能集合） +- `anthropics/skills`（官方参考） +- `K-Dense-AI/claude-scientific-skills`（科研场景） + +### 5.2 2) 三 CLI 联动：Claude + Codex + Gemini + +来源：`book/老金开源_Claude4_5_Codex5_1_Gemini3_3大顶级CLI终极联动整合技巧_省钱_高质量_快_.md` + +#### 5.2.1 详细步骤（最小可跑版） + +1. 安装三个 CLI：Claude Code、Codex CLI、Gemini CLI。 +2. 配置 API 与认证（优先图形化管理，避免手工环境变量错误）。 +3. 先从 `Command` 模式启动：分析 → 生成 → 审查。 +4. 任务稳定后升级到 `Skill` 模式（保存中间结果与重试机制）。 +5. 长周期协作用 `MCP` 打通上下文，再按需引入 Subagent 与 Hooks。 + +#### 5.2.2 五种编排方式对照 + +| 方式 | 适用 | 风险 | +|---|---|---| +| Command | 快速、一次性任务 | 复用差、可观测性弱 | +| Skill | 多步骤稳定流程 | 初期配置成本较高 | +| MCP | 长会话与系统互联 | 服务器与权限管理复杂 | +| Subagent | 并行提速 | token 成本上升 | +| Hooks | 自动质检 | 误触发与调试复杂 | + +#### 5.2.3 常见坑提醒 + +- CLI 订阅/授权失效； +- 代理/网络导致认证失败； +- sandbox 权限不足导致写入失败； +- 多模型切换后输出格式漂移。 + +#### 5.2.4 文章细节补充（三CLI联动最小命令） + +```bash +# 1) 安装 +npm i -g @anthropic-ai/claude-code +npm i -g @openai/codex +npm i -g @google/gemini-cli + +# 2) 鉴权（示意，按各自官方流程） +gemini auth login +codex auth login + +# 3) 在 Claude 内先跑 Command 模式 +/ai-team "实现一个最小登录接口并附测试" +``` + +#### 5.2.5 五编排方式的选型建议（更细） + +- 30 分钟内一次性任务：`Command` +- 反复执行的标准流程：`Skill` +- 需要跨系统持久上下文：`MCP` +- 模块可并行且预算允许：`Subagent` +- 固定时机自动质检：`Hooks` diff --git a/external/ai-research-ebook/src/content/docs/tools/workflow.mdx b/external/ai-research-ebook/src/content/docs/tools/workflow.mdx new file mode 100644 index 0000000..ff11b10 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/tools/workflow.mdx @@ -0,0 +1,528 @@ +--- +title: 完整工作流 +description: 从 idea 到论文发表的完整时间线和步骤 +sidebar: + order: 2 +--- + +import { Card, CardGrid } from '@astrojs/starlight/components'; + +## 1. 完整研究时间线 + +```mermaid +gantt + title 14周顶会论文发表时间线 + dateFormat YYYY-MM-DD + section 调研 + Elicit扫描 :a1, 2026-02-10, 5d + Sci-Reasoning :a2, after a1, 5d + Gap清单产出 :a3, after a2, 4d + section 设计 + 项目文档 :b1, after a3, 5d + 实验计划 :b2, after b1, 3d + section 实现 + 核心代码 :c1, after b2, 14d + 主实验 :c2, after c1, 7d + 消融实验 :c3, after c2, 5d + section 写作 + 初稿 :d1, after c3, 10d + 图表 :d2, after d1, 5d + section 审稿 + 自动审稿 :e1, after d2, 5d + 迭代修改 :e2, after e1, 5d + section 投稿 + 最终版 :f1, after e2, 3d + 提交 :f2, 2026-05-20, 1d + + +``` + +--- + +## 2. Week 1-2: 调研阶段 + +### 2.1 目标 +产出结构化的研究地图和空白清单 + +### 2.2 详细工作流 + +```mermaid +flowchart LR + A(Elicit 扫描) --> B[Semantic Scholar] + B --> C[Sci-Reasoning] + C --> D(整理研究地图) + D --> E(产出 Gap 清单) + + classDef key fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827 + class A,C,E key + + +``` + +### 2.3 具体任务 + +1. **使用 Elicit** 扫描领域关键词（1-2天） +2. **使用 Semantic Scholar** 探索研究脉络（1-2天） +3. **使用 Sci-Reasoning** 分析创新模式（2-3天） +4. **整理研究地图**（2-3天）： + - 问题子类分类 + - 方法范式分类 + - 评测基准整理 + +### 2.4 产出 + +| 产出物 | 说明 | +|-------|------| +| Research Gap 清单 | 10-20个候选方向 | +| Benchmark & Metric Sheet | 主流数据集和指标 | +| Assumption Matrix | 隐含假设分析 | + +--- + +## 3. Week 3: 项目设计 + +### 3.1 目标 +确定研究问题和实验计划 + +### 3.2 详细工作流 + +```mermaid +flowchart TD + A(Claude Code 生成文档) --> B(GPT-5.2 审查) + B --> C(人工Review) + C --> D(确定实验计划) + + classDef review fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827 + classDef audit fill:#eff6ff,stroke:#2563eb,stroke-width:1.5px,color:#1e3a8a + class A review + class B,C audit + + +``` + +### 3.3 具体任务 + +1. **生成项目文档**（使用 Claude Code, 2-3天） +2. **审查文档**（使用 GPT-5.2 Pro, 1天）： + - claim-实验是否对齐？ + - baseline 是否足够强？ + - 是否有致命假设？ +3. **确定实验计划**（1天）： + - 主实验设计 + - 消融实验清单 + - 压力测试场景 + +### 3.4 产出 + +| 产出物 | 说明 | +|-------|------| +| 项目文档 | 20-30页详细文档 | +| 实验计划 | 主实验+消融+压力测试 | +| 里程碑时间表 | 每周任务分解 | + +--- + +## 4. Week 4-7: 代码实现 + 实验 + +### 4.1 目标 +完成实现和主要实验 + +### 4.2 团队配置 + +```mermaid +flowchart TD + subgraph ["开发团队"] + A[Architect x1] --> E(核心模块) + B[Implementer x2] --> E + C[Tester x1] --> F(测试评测) + end + + subgraph ["质量保障"] + F --> G(自动汇总) + end + + classDef role fill:#ecfeff,stroke:#0ea5e9,stroke-width:1.5px,color:#0f172a + class A,B,C role + + +``` + +### 4.3 具体任务 + +1. **并行开发**（Claude Code, 2-3周） +2. **实验执行**（1周）： + - 主实验：与 baseline 对比 + - 消融实验：每个模块贡献 + - 压力测试：边界条件 +3. **结果汇总**（2-3天） + +### 4.4 产出 + +| 产出物 | 说明 | +|-------|------| +| 可复现代码库 | 完整的实验代码 | +| 实验结果 | 主表+消融+分析 | +| 复现指南 | 便于他人复现 | + +--- + +## 5. Week 8-9: 论文写作 + +### 5.1 目标 +完成论文初稿 + +### 5.2 写作流程 + +```mermaid +flowchart LR + A(Claude Opus 大纲) --> B(多实例并行写作) + B --> C(PaperBanana 图表) + C --> D(多模型协作完善) + + classDef writing fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827 + class A,B,C writing + + +``` + +### 5.3 具体任务 + +1. **生成大纲**（Claude Opus 4.6, 1天） +2. **并行写作**（多实例, 5-7天） +3. **图表制作**（2-3天） + +### 5.4 产出 + +| 产出物 | 说明 | +|-------|------| +| 论文初稿 | 8-10页正文 | +| 所有图表 | 方法图、结果图 | +| 补充材料 | 额外实验、细节 | + +--- + +## 6. Week 10: 审稿迭代 + +### 6.1 目标 +通过多轮审稿迭代提升论文质量 + +### 6.2 审稿配置 + +| 轮次 | 模型 | 侧重点 | +|------|------|-------| +| 第1轮 | Claude Opus 4.6 | 整体逻辑 | +| 第2轮 | GPT-5.2 Pro | 技术细节 | +| 第3轮 | Gemini Pro | 可复现性 | + +### 6.3 具体任务 + +1. **多模型并行审稿**（2天） +2. **交叉验证意见**（1天） +3. **批量修改**（2天） + +### 6.4 产出 + +| 产出物 | 说明 | +|-------|------| +| 稳定投稿版本 | 准备提交 | +| 审稿意见记录 | 历史迭代 | +| 修改说明 | 响应记录 | + +--- + +## 7. Week 11: 投稿 + +### 检查清单 + +- [ ] 格式符合会议要求 +- [ ] 页数在限制内 +- [ ] 补充材料完整 +- [ ] 作者信息正确 +- [ ] 代码/数据链接有效 + +--- + +## 8. Week 12+: Rebuttal + +### Rebuttal 策略 + +| 意见类型 | 应对策略 | +|---------|---------| +| 可修复的 | 承诺修改, 补充说明 | +| 误解的 | 礼貌澄清, 补充证据 | +| 不同意但合理 | 讨论, 引用支持文献 | +| 不同意的 | 坚定反驳, 有理有据 | + +--- + +## 9. 时间线总览 + +``` +Week 1-2: ██████████████ 调研 +Week 3: ██████ 项目设计 +Week 4-7: █████████████████████████ 代码+实验 +Week 8-9: ██████████████ 写作 +Week 10: ██████████████ 审稿迭代 +Week 11: ████ 投稿 +Week 12+: ██████████████ Rebuttal（如需要） +``` + +### 执行闭环强化（补充） + +在 Week 4-10 阶段建议显式加入 execution-grounded 子循环： + +```mermaid +flowchart LR + A(Idea) --> B(Patch) + B --> C(Run) + C --> D(Feedback) + D --> A +``` + +- **Idea**：提出可验证改动（必须绑定指标） +- **Patch**：最小可执行改动（便于回滚） +- **Run**：在固定环境里跑通训练/评测 +- **Feedback**：记录收益/失败模式并更新下一轮计划 + +这样做可以把“灵感驱动”转换为“证据驱动”，减少无效迭代。 + +--- + +## 10. 每日任务模板 + +### 工作日（周一至周五） + +| 时间 | 任务 | 时长 | +|------|------|------| +| 09:00-10:00 | 计划与回顾 | 1h | +| 10:00-12:00 | 核心工作 | 2h | +| 14:00-17:00 | 深度工作 | 3h | +| 19:00-20:00 | 总结整理 | 1h | + +### 周末 + +| 时间 | 任务 | 时长 | +|------|------|------| +| 10:00-12:00 | 论文写作 | 2h | +| 14:00-17:00 | 审稿迭代 | 3h | +| 19:00-20:00 | 周总结 | 1h | + +--- + +
+ +**核心原则**：工具是辅助, 核心是你的研究洞察力。让 AI 帮你处理重复性工作, 把时间留给真正的思考。 + +
+ +--- + +## 11. Book 实战增补（逐篇并入） + +> 本节整合 6 篇工作流与 Skill 工程化文章，形成“规范定义 → 任务拆解 → 执行闭环 → 验证交付”的统一流程。 + +### 1) Skill 开发完全指南：从可用到可复用 + +来源：`book/Claude_Code_Skill_开发完全指南_从入门到精通.md` + +#### 结构化实践 + +- Skill 本质是标准作业流程（SOP），核心目标是稳定复现而非一次性发挥。 +- 目录按复杂度递进：最小可用 `SKILL.md` → 标准工作流（`workflow/` + `scripts/` + `config/`）。 +- 执行者分工：可确定任务交脚本，不确定任务交 Agent，协调任务交主会话。 + +#### 关键约束 + +- 能脚本化就脚本化，减少 token 消耗； +- Subagent 并行控制节奏（分轮执行 + `/compact`）； +- 每一步都有验证点与可恢复状态（`progress.json`）。 + +#### 详细步骤（Skill 从 0 到 1） + +1. 先做最小版：只建 `SKILL.md`，跑通单步骤任务。 +2. 任务稳定后再拆 `workflow/stepNN-*.md`。 +3. 把可确定操作下沉到 `scripts/`，并统一 JSON 返回。 +4. 为每步定义检查点（文件存在、格式正确、字段完整、业务通过）。 +5. 增加断点恢复文件，确保中断后可继续执行。 + +#### 文章细节补充（执行者决策树） + +| 任务类型 | 推荐执行者 | 原因 | +|---|---|---| +| API 调用、文件转换、批处理 | 脚本 | 快、稳、低 token | +| 需要判断/生成/归纳 | Subagent | 语义处理更强 | +| 轻协调与流程推进 | 主 Agent | 全局可控 | + +建议：默认先脚本，只有脚本无法覆盖时再交 Agent。 + +### 2) Skill 最佳实践：文档驱动与需求模板 + +来源：`book/Claude_Code_Skill_最佳实践与创建规范.md` + +#### 三件必做 + +1. 全局约束写入 `CLAUDE.md`； +2. 输出样例放入参考资产（结果参照）； +3. 需求清单模板化（输入、输出、约束、验收）。 + +#### 工程建议 + +- Skill 粒度控制在“单一职责 + 可组合”； +- 强制 Plan → Execute → Review 循环； +- `assets/` 作为素材库直取直用，减少上下文消耗。 + +#### 详细步骤（需求模板实装） + +1. 新建 `requirements.md`，固定十项：目标、输入、输出、约束、验收等。 +2. 缺失信息先问清，不允许“边做边猜”。 +3. 先用模板生成首版 Skill，再做两轮真实任务回测。 +4. 将回测问题反写到模板，持续迭代。 + +#### 文章细节补充（十项模板最小集合） + +1. Skill 名称与触发条件； +2. 场景与边界； +3. 必填输入与可选输入； +4. 输出类型与文件结构； +5. 主流程与分支流程； +6. 资源依赖（脚本/参考/资产）； +7. 技术与业务约束； +8. 自动/手动验证方式； +9. 参考实现与链接； +10. 优先级与扩展计划。 + +### 3) Skill Creator MVP：先约束行为，再让 AI 写代码 + +来源：`book/我是如何用_Skill_Creator_把一个真实项目拆成一整套_Agent_Skills_的（MVP_实战）.md` + +#### MVP 六件套（按项目按需启用） + +- `product-design.skill` +- `frontend-dev.skill` +- `backend-dev.skill` +- `api-contract.skill` +- `testing.skill` +- `deployment.skill` + +核心思想：Skill 要服从项目现实，不存在的模块不生成对应技能。 + +#### 详细步骤（MVP 六件套落地） + +1. 扫描仓库判断是否存在前后端/测试/部署。 +2. 仅为存在模块创建 Skill，其他跳过。 +3. `api-contract` 先行，前后端执行都以接口契约为准。 +4. `testing` 明确“什么算完成”，避免功能完成但不可验收。 + +#### 文章细节补充（各 Skill 的禁止项） + +- `product-design`：不写实现代码； +- `frontend-dev`：不擅自引新库/改工程配置； +- `backend-dev`：不改架构与数据库主结构； +- `api-contract`：只定义契约，不写实现； +- `testing`：不跳过失败用例； +- `deployment`：只记录当前真实发布方式。 + +### 4) Superpowers + Planning-with-Files：流程与记忆分层 + +来源：`book/大佬的Claude_Code是神器你的像玩具_Superpowers___Planning_with_Files的Skills组合拳让_Coding_Agent效率翻倍.md` + +#### 组合策略 + +- Superpowers：负责“流程纪律”（brainstorm → plan → execute → verify）。 +- Planning-with-Files：负责“外部记忆”（`task_plan.md`、`progress.md`、`knowledge.md`）。 + +#### 推荐闭环 + +1. 先澄清目标、受众、约束； +2. 写入结构化计划与验收； +3. 执行时持续记录进度； +4. 结束前跑验证并补齐证据。 + +#### 详细步骤（Superpowers + Planning Files） + +1. 触发 brainstorm 收集关键约束（目标、对象、时间、边界）。 +2. 产出计划并写入 `task_plan.md`。 +3. 每完成子任务更新 `progress.md`（含失败记录）。 +4. 关键决策沉淀到 `knowledge.md`，便于新会话接续。 +5. 交付前执行 verification，保存验证证据。 + +#### 文章细节补充（命令顺序） + +```text +/superpowers:brainstorm +/superpowers:write-plan +/superpowers:execute-plan +# 收尾执行 verification-before-completion +``` + +建议每次执行后同步三文件： + +- `task_plan.md`（目标与步骤） +- `progress.md`（过程与阻塞） +- `knowledge.md`（决策与约束） + +### 5) 规范驱动开发：拒绝“直接开写” + +来源：`book/拒绝屎山代码_让你的_Claude_code_具备类似高级架构师的全局规划与规范执行能力_附实操_.md` + +#### 章节增补要点 + +- AI 本质执行规格，不是自动理解业务语境。 +- 需前置限定：技术栈、目录结构、实现边界、阶段目标。 +- 将“计划文档、执行记录、验证命令”纳入硬门槛，避免伪完成。 + +#### 详细步骤（规范先行） + +1. 在 `CLAUDE.md` 固定四类限制：技术栈、目录、边界、验收。 +2. 任务开始先生成 PLAN，不允许直接进入编码。 +3. 每完成一个任务强制跑测试与审查。 +4. 未通过验证禁止标记完成。 + +#### 文章细节补充（强约束检查项） + +- 是否存在“先写后想”的越序行为； +- 是否缺少测试与验证命令； +- 是否改动了禁止目录或越界模块； +- 是否提供可复验结果（日志/测试报告）。 + +### 6) feature-dev + ralph-loop：规划引擎与长循环执行 + +来源：`book/用_Claude_Code_搭建_从需求到交付_的自动化工作流.md` + +#### 双引擎分工 + +- `feature-dev`：跑 7 阶段（Discovery → Review → Summary）。 +- `ralph-loop`：针对单任务持续迭代，直到达到 completion promise。 + +#### 实战模板 + +```text +feature-dev 负责：方向、架构、验收标准 +ralph-loop 负责：持续执行、测试打磨、问题收敛 +``` + +#### 适用场景 + +- 大特性拆解：先 `feature-dev`，再 `ralph-loop` 跑实现细节。 +- 测试补齐：让 `ralph-loop` 专注覆盖率与边界用例收敛。 + +#### 详细步骤（双引擎协作） + +1. 先用 `feature-dev` 产出设计与实施计划。 +2. 把计划转成 `ralph-loop` 的阶段任务与完成短语。 +3. 设置 `--max-iterations`，避免无限循环。 +4. 循环结束后回到 `feature-dev` 做最终 Review + Summary。 + +#### 文章细节补充（组合使用模板） + +1. `feature-dev` 产出：`DESIGN.md` + `IMPLEMENTATION_PLAN.md` + `QUALITY_CRITERIA.md`。 +2. `ralph-loop` 读取上述文档并分阶段执行。 +3. 每轮循环必须输出：完成内容、失败原因、下一轮计划。 +4. 达到 `completion-promise` 才允许结束循环。 + +### 本章统一检查清单（新增） + +- 是否先完成需求澄清与计划文档？ +- 是否为关键步骤定义了验证标准？ +- 是否记录了执行进度与中断恢复点？ +- 是否把复用能力沉淀为 Skill/命令，而非临时对话？ diff --git a/external/ai-research-ebook/src/content/docs/writing/collaboration.mdx b/external/ai-research-ebook/src/content/docs/writing/collaboration.mdx new file mode 100644 index 0000000..7b42201 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/writing/collaboration.mdx @@ -0,0 +1,217 @@ +--- +title: 多模型协作写作 +description: 使用多个 AI 模型协作进行论文写作 +sidebar: + order: 3 +--- + +import { Card, CardGrid } from '@astrojs/starlight/components'; + +## 1. 多模型协作概览 + +```mermaid +flowchart TD + subgraph ["写作流程"] + A(大纲生成) --> B(分段写作) + B --> C(一致性检查) + C --> D(整合优化) + end + + subgraph ["模型分工"] + E[Claude Opus 4.6] --> A + F[Claude Code] --> B + G[GPT-5.2 Pro] --> C + H[Gemini Pro] --> C + end + + + +``` + +--- + +## 2. AI-research-SKILLs + +"20-ml-paper-writing"模块制定了严格规则：**绝不凭记忆生成引用**, 强制 5 步： + +```mermaid +flowchart LR + A(检索) --> B(双源验证) + B --> C(获取官方 BibTeX) + C --> D(核对摘要) + D --> E(写入) + + + +``` + +覆盖 AI 科研全流程的 **77 项技能**（20 个技术板块）。 + +--- + +## 3. tmux 工作流配置 + +让 Claude / GPT-5.2 / Gemini Pro 分工协作： + +| tmux 实例 | 负责内容 | 模型选择 | 协作方式 | +|---------|---------|---------|---------| +| Instance 1 | Related Work | Claude Code | 文献梳理与对比 | +| Instance 2 | Method 写作 | Claude Code | 方法描述与形式化 | +| Instance 3 | 实验描述 | GPT-5.2 Pro | 实验设计和结果 | +| Instance 4 | 一致性校对 | Gemini Pro | 术语、符号统一 | + +--- + +## 4. 协作写作流程 + +### 4.1 阶段 1: 大纲生成 + +使用 Claude Opus 4.6 生成长大纲（200K 上下文） + +```markdown +# 论文大纲模板 + +## 1. Introduction (1.5页) +- 研究问题 +- 核心挑战 +- 你的贡献（3-4点） + +## 2. Related Work (2页) +- 领域分类 +- 主要方法 +- 你的定位 + +## 3. Method (4页) +- 问题形式化 +- 核心方法 +- 技术细节 + +## 4. Experiments (3页) +- 实验设置 +- 主结果 +- 消融实验 + +## 5. Conclusion (0.5页) +- 总结 +- 局限 +- 未来方向 +``` + +### 4.2 阶段 2: 分段写作 + +各实例并行处理不同章节： + +```bash +# tmux 窗口布局 +# ================= +# 窗口 1: Related Work +# 窗口 2: Method +# 窗口 3: Experiments +# 窗口 4: Introduction/Conclusion +# 窗口 5: 整合校对 +``` + +### 4.3 阶段 3: 一致性检查 + +使用专门实例校对一致性： + +| 检查项 | 工具 | 说明 | +|-------|------|------| +| 术语一致性 | Claude Code | 统一术语使用 | +| 符号一致性 | GPT-5.2 Pro | 数学符号检查 | +| 引用格式 | Gemini Pro | BibTeX 格式 | + +### 4.4 阶段 4: 整合优化 + +由最强模型整合所有内容, 确保叙事流畅。 + +--- + +## 5. 术语表管理 + +### 5.1 术语表示例 + +```markdown +# 术语表 (GLOSSARY.md) + +| 中文 | 英文 | 缩写 | 定义 | +|------|------|------|------| +| 注意力机制 | Attention Mechanism | Attn | ... | +| 多头注意力 | Multi-Head Attention | MHA | ... | +| 自注意力 | Self-Attention | SA | ... | +``` + +### 5.2 共享术语表工作流 + +```mermaid +flowchart TD + A(创建术语表) --> B(推送到共享仓库) + B --> C(各实例拉取最新) + C --> D(写作时引用) + D --> E(发现新术语) + E --> B + + + +``` + +### 5.3 使用建议 + +
+ + +单一文件管理所有术语, 避免分散 + + + +使用 Git 追踪变更, 可回滚 + + + +各实例定期更新术语表 + + + +建立术语决策流程 + + +
+ +--- + +## 6. 会议特定建议 + +| 会议 | 偏好 | 写作建议 | +|------|------|---------| +| **NeurIPS** | 神经科学结合 | 强调方法的生物学合理性 | +| **ICML** | 理论基础 | 增加理论分析和证明 | +| **ICLR** | 表示学习 | 侧重表示分析和可视化 | +| **CVPR** | 实验完整性 | 充分的消融和对比实验 | +| **ACL/EMNLP** | 语言学洞察 | 增加定性分析和案例分析 | + +--- + +## 7. 常见写作误区 + +
+ +❌ **"像论文但没信息"** - 缺乏实质贡献 + +❌ **过度声明** - claim 超出实验支持 + +❌ **忽略关键 baseline** - 对比不充分 + +❌ **叙事不清** - 逻辑跳跃 + +
+ +--- + +## 8. 协作检查清单 + +- [ ] 术语表已创建并同步 +- [ ] 各章节使用统一格式 +- [ ] 引用已验证 +- [ ] 符号系统一致 +- [ ] 叙事逻辑连贯 +- [ ] 实验claim对齐 diff --git a/external/ai-research-ebook/src/content/docs/writing/methods.mdx b/external/ai-research-ebook/src/content/docs/writing/methods.mdx new file mode 100644 index 0000000..7f88378 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/writing/methods.mdx @@ -0,0 +1,154 @@ +--- +title: 写作方法 +description: 论文写作的核心方法和论证链构建 +sidebar: + order: 1 +--- + + + +## 1. 写作核心原则 + +写作最核心的是：**让审稿人相信你的 claim**。 + +
+ +**核心流程**：论证链 → Outline → 文字生成 + +
+ +--- + +## 2. 构建论证链（不写正文） + +先把论文的"论证链"写出来： + +```mermaid +flowchart TD + A[Claim 1] --> B(需要哪些证据?) + A --> C(哪些是必要的?) + A --> D(哪些是锦上添花?) + + B --> E(主实验) + B --> F(消融实验) + B --> G(压力测试) + + +``` + +### 2.1 论证链示例 + +``` +Claim 1: 我们的方法在低资源场景下优于baseline +├── 主实验: 在3个数据集上对比, 指标提升X% +├── 消融: 验证每个模块的贡献 +└── 压力测试: 资源减少10%/50%/90%时的表现 + +Claim 2: 方法的关键创新是XXX机制 +├── 消融: 去掉XXX后性能下降Y% +├── 可视化: 展示XXX激活模式 +└── 理论分析: XXX为什么有效 +``` + +--- + +## 3. 写 Outline（只写要点） + +每小节回答一个问题： + +| 问题 | 含义 | +|------|------| +| 这节要说服审稿人什么？ | 核心主张 | +| 审稿人可能质疑什么？ | 潜在反驳 | +| 我用什么证据挡住？ | 实验支撑 | + +### 3.1 Outline 模板 + +```markdown +# 1. Introduction + +## 要回答的问题 +- 这个问题为什么重要？ +- 现有方法有什么问题？ +- 我们的核心贡献是什么？ + +## 可能的质疑 +- "这个问题真的重要吗？" + → 回答：用具体应用场景和数据说明 +- "你们只是简单的组合吧？" + → 回答：强调我们发现的XXX insight + +## 关键证据 +- 实验1: 在3个标准数据集上提升X% +- 实验2: 验证了XXX机制的必要性 +``` + +--- + +## 4. 生成具体文字 + +最后再让模型生成具体文字： + +**好处**： +- 不会空谈 +- 每个观点都有支撑 +- 逻辑清晰 + +--- + +## 5. MCP 工具与 Skills + +### 5.1 MCP工具（文献搜索与访问） + +| 工具 | 功能 | 适用场景 | +|------|------|---------| +| **Zotero-MCP** | 直接访问本地文献库 | 文献管理、引用分析 | +| **Perplexity MCP** | Deep Research 模式 | 技术调研、综述写作 | +| **ClickHouse MCP** | 数据库集成 | 大数据分析 | +| **Zen MCP** | 研究工作流 | 流程自动化 | +| **Paper Search MCP** | 多数据库搜索 | 快速获取新论文 | + +### 5.2 Skills工具（科研分析与写作） + +| 工具 | 技能数量 | 特色 | +|------|---------|------| +| **claude-scientific-skills** | 140+ | 即用型技能, 多领域覆盖 | +| **claude-scientific-writer** | - | 专注科研写作 | +| **Content-Research-Writer** | - | 长文协作写作 | +| **20-ml-paper-writing** | 77项 | 完整 ML 论文写作流程 | + +### 5.3 GitHub 资源 + +- [claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills) + +--- + +## 6. 写作检查清单 + +
+ + +每个 claim 都有对应的实验支撑 + + + +对比了所有强 baseline + + + +叙事流畅, 无逻辑跳跃 + + + +全文使用统一的术语 + + + +引用格式正确, 来源可靠 + + + +图表有清晰的图例和说明 + + +
diff --git a/external/ai-research-ebook/src/content/docs/writing/prism.mdx b/external/ai-research-ebook/src/content/docs/writing/prism.mdx new file mode 100644 index 0000000..abc5245 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/writing/prism.mdx @@ -0,0 +1,210 @@ +--- +title: Prism 编辑器 +description: OpenAI Prism LaTeX 原生科研写作工作区 +sidebar: + order: 2 +--- + + + +## 1. Prism 概述 + +**Prism** 是 OpenAI 推出的 **LaTeX 原生科研写作工作区**, 发布于 2026年1月27日。 + +
+ +**核心优势**：GPT-5.2 原生集成, 无需插件即可获得最强 AI 写作辅助。 + +
+ +--- + +## 2. 核心特性 + +```mermaid +flowchart TD + subgraph ["核心能力"] + A(LaTeX 原生) --> B(无缝迁移) + C(GPT-5.2 集成) --> D(智能辅助) + E(实时协作) --> F(多人编辑) + end + + subgraph ["写作支持"] + G(中英文支持) --> H(双语写作) + I(公式图表) --> J(可视化) + K(引用管理) --> L(智能引用) + end + + + +``` + +| 特性 | 说明 | +|------|------| +| **LaTeX 原生** | 无缝迁移现有 Overleaf 项目 | +| **GPT-5.2 集成** | 最强 AI 写作辅助 | +| **实时协作** | 多人同时编辑 | +| **中英文支持** | 双语写作无障碍 | +| **公式图表** | 内置可视化工具 | +| **引用管理** | 智能文献引用 | + +--- + +## 3. Prism vs Overleaf + +| 维度 | Prism | Overleaf | +|------|--------|----------| +| **AI 集成** | GPT-5.2 原生 | 需外部插件 | +| **实时协作** | ✓ | ✓ | +| **中文支持** | 原生 | 需配置 | +| **定价** | 免费（个人） | 有限制 | +| **模板库** | 基础模板 | 丰富模板 | +| **编译速度** | 快 | 快 | + +--- + +## 4. 写作工作流 + +### 4.1 阶段 1: 项目设置 + +```bash +# 创建新项目 +1. 访问 openai.com/prism/ +2. 点击 "New Project" +3. 选择论文模板 (NeurIPS/ICML/ICLR) +4. 配置基础设置 +``` + +### 4.2 阶段 2: AI 辅助写作 + +```latex +% 使用 AI 辅助生成内容 +% 在 Prism 中, 选中文本并点击 "Ask AI" + +\section{Method} +我们的方法基于 Transformer 架构, +引入了一种新颖的 XXX 机制。 + +% AI 建议扩展： +% 基于 Transformer 架构, 我们提出了一种 +% 自适应注意力机制 (Adaptive Attention), +% 能够根据输入动态调整注意力分布。 +``` + +### 4.3 阶段 3: 协作编辑 + +| 功能 | 操作 | +|------|------| +| 实时同步 | 自动保存到云端 | +| 评论讨论 | Cmd+Click 添加评论 | +| 版本历史 | 查看历史版本 | +| 任务分配 | @mention 协作者 | + +--- + +## 5. 中英文写作支持 + +### 5.1 混合写作 + +```latex +% 中文摘要 + 英文正文 + +\begin{abstract} +本文提出了一种新颖的 XXX 方法, +在多个基准数据集上取得了显著的性能提升。 +\end{abstract} + +\section{Introduction} +Deep learning has achieved remarkable success... +% 可随时切换语言 +``` + +### 5.2 翻译辅助 + +``` +Prism 内置翻译功能： +1. 选中中文段落 +2. 点击 "Translate to English" +3. AI 生成学术英文版本 +4. 可选择保留/修改 +``` + +--- + +## 6. 引用管理 + +### 6.1 智能引用 + +```latex +% Prism 会自动推荐相关引用 + +我们的方法与 XXX[1] 有显著不同。 + +% AI 建议： +% 相比之前的工作[2,3], 我们的方法... +``` + +### 6.2 引用格式 + +| 会议 | 格式 | +|------|------| +| NeurIPS | \cite{} | +| ICML | \citep{} | +| ICLR | \cite{} | + +--- + +## 7. 使用场景 + +
+ + +AI 辅助生成大纲、扩展段落、优化表达 + + + +快速定位、修改、验证修改内容 + + + +多人实时编辑, 版本管理 + + + +中英文互译, 保持学术表达 + + + +自动格式化, 符合会议要求 + + + +自动验证引用完整性 + + +
+ +--- + +## 8. 快速开始 + +1. **访问官网**: [openai.com/prism](https://openai.com/prism/) +2. **创建账户**: 使用 OpenAI 账户登录 +3. **新建项目**: 选择论文模板 +4. **开始写作**: 使用 GPT-5.2 辅助 + +--- + +## 9. 最佳实践 + +
+ +**建议**：先用 AI 生成初稿, 再人工精修, 确保内容的准确性和创新性。 + +
+ +1. **大纲先行**：让 AI 生成结构化大纲 +2. **分段写作**：逐章节完善, 避免大段重写 +3. **及时保存**：利用版本历史回溯 +4. **引用验证**：提交前检查引用完整性 +5. **格式检查**：确认符合会议模板 diff --git a/external/ai-research-ebook/src/content/docs/writing/rebuttal.mdx b/external/ai-research-ebook/src/content/docs/writing/rebuttal.mdx new file mode 100644 index 0000000..463bd07 --- /dev/null +++ b/external/ai-research-ebook/src/content/docs/writing/rebuttal.mdx @@ -0,0 +1,186 @@ +--- +title: Rebuttal 策略 +description: 如何有效撰写 Rebuttal 回应审稿意见 +sidebar: + order: 3 +--- + +## 1. Rebuttal 策略 + +--- + +## 2. 分类回应 + +### 2.1 可修复的 + +**策略**: 明确承诺修改，给出具体方案 + +**模板**: + +``` +感谢审稿人的建议。我们将按以下方式修改： + +1. 补充消融实验（详见附件图X） +2. 增加与方法Y的对比 +3. 重写第3节以更清晰地表达... + +我们已将这些修改在附件中标注。 +``` + +### 2.2 误解的 + +**策略**: 礼貌澄清，补充说明 + +**模板**: + +``` +感谢审稿人的关注。我们想澄清以下几点： + +1. 关于XXX：我们的方法确实做了... +2. 关于YYY：这可能是一个误解，实际上是... + +我们在文中已添加更清晰的说明。 +``` + +### 2.3 不同意但合理的 + +**策略**: 讨论并引用支持文献 + +**模板**: + +``` +审稿人提出了一个很好的观点。我们同意...， + +但我们选择这种方法的原因是... + +相关文献[1,2]也支持这种做法。 + +我们会在文中增加相关讨论。 +``` + +### 2.4 不同意的 + +**策略**: 坚定但有理有据地反驳 + +**模板**: + +``` +我们尊重审稿人的观点，但不同意... + +原因是...（实验证据/理论分析/文献支持） + +我们的方法在这一方面确实有优势... +``` + +--- + +## 3. 常见意见应对模板 + +### 3.1 缺少消融实验 + +``` +感谢审稿人的建议。我们已在补充材料中添加了消融实验[表X]， +显示去掉模块A后性能下降Y%，证实了其必要性。 +``` + +### 3.2 对比不公平 + +``` +我们已确保所有方法使用相同的训练设置（学习率、batch size等）， +详见补充材料[实验设置]。 + +我们注意到方法Y的原始设置与我们的有所不同， +为了公平对比，我们也用他们的设置重新运行了实验。 +``` + +### 3.3 创新性不足 + +``` +与[ABC方法]不同，我们关注的是X场景下的Y问题， +这在之前工作中未被探索。 + +我们的贡献在于： +1. 发现了Z现象 +2. 提出了W方法 +3. 在N个数据集上验证了有效性 + +相关工作[附表]详细列出了区别。 +``` + +### 3.4 缺少某篇文献 + +``` +感谢审稿人指出这篇重要文献。我们已将其加入相关工作并讨论： + +- 该方法与我们的相似之处：... +- 关键区别：... +- 我们的方法如何扩展了它：... +``` + +### 3.5 结果不够显著 + +``` +我们认为结果的显著性在于： + +1. 在X场景下（之前未被探索），我们的方法优于baseline +2. 消融显示每个模块都有贡献 +3. 在Y个数据集上一致有效 + +虽然绝对提升不大，但在...场景下很有价值。 +``` + +--- + +## 4. Rebuttal 成功要素 + +1. **感谢审稿人**（即使意见尖锐） + +2. **具体修改措施**（不是泛泛而谈） + +3. **实验数据支持**（新增结果） + +4. **保持专业和礼貌** + +--- + +## 5. Rebuttal 结构模板 + +```markdown +# Rebuttal + +## 概述 + +感谢所有审稿人的宝贵意见。我们已认真考虑每条意见， +并准备了相应的修改和补充实验。 + +## 对审稿人A的回应 + +### 意见1: ... +感谢审稿人的建议。[具体回应] + +### 意见2: ... +[回应...] + +## 对审稿人B的回应 + +### 意见1: ... +[回应...] + +## 对审稿人C的回应 + +### 意见1: ... +[回应...] + +## 主要修改总结 + +1. 补充了X个消融实验 +2. 增加了与方法Y的对比 +3. 重写了第Z节以更清晰 + +修改后的全文已附后/已在附件中标注。 + +## 附件 + +- 补充实验结果 +- 修改后的全文（标注版） +``` diff --git a/external/ai-research-ebook/src/styles/custom.css b/external/ai-research-ebook/src/styles/custom.css new file mode 100644 index 0000000..b87b2f2 --- /dev/null +++ b/external/ai-research-ebook/src/styles/custom.css @@ -0,0 +1,794 @@ +:root, +[data-theme='light'] { + --chat-bg-primary: #f7f7f8; + --chat-bg-secondary: #ffffff; + --chat-bg-tertiary: #f0f1f3; + --chat-sidebar-bg: #f9f9fb; + --chat-border: #e3e5e9; + --chat-border-strong: #d1d5db; + --chat-text-primary: #171717; + --chat-text-secondary: #374151; + --chat-text-tertiary: #6b7280; + --chat-link: #2563eb; + --chat-link-hover: #1d4ed8; + --chat-accent: #10a37f; + --chat-accent-hover: #0d8e6f; + --chat-selection: #bfdbfe; + --chat-shadow-sm: 0 1px 2px rgba(15, 23, 42, 0.06); + --chat-shadow-md: 0 10px 24px rgba(15, 23, 42, 0.08); + --chat-radius-sm: 0.375rem; + --chat-radius-md: 0.625rem; + --chat-radius-lg: 0.875rem; + --chat-radius-xl: 1.125rem; + --chat-transition-fast: 0.15s ease; + --chat-transition-normal: 0.25s ease; + --sl-color-accent: var(--chat-accent); + --sl-color-accent-high: var(--chat-accent-hover); + --sl-color-bg: var(--chat-bg-primary); + --sl-color-bg-nav: var(--chat-sidebar-bg); + --sl-color-bg-sidebar: var(--chat-sidebar-bg); + --sl-color-hairline: var(--chat-border); + --sl-color-hairline-light: var(--chat-border); + --sl-color-text: var(--chat-text-primary); + --sl-color-text-accent: var(--chat-accent); + --sl-color-text-muted: var(--chat-text-tertiary); +} + +[data-theme='dark'] { + --chat-bg-primary: #212121; + --chat-bg-secondary: #2f2f2f; + --chat-bg-tertiary: #424242; + --chat-sidebar-bg: #171717; + --chat-border: hsla(0, 0%, 100%, 0.1); 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+} + +.sidebar-content [aria-current='page'], +.sidebar-content [aria-current='page']:hover { + background: color-mix(in srgb, var(--chat-accent) 16%, var(--chat-bg-secondary)); + color: var(--chat-accent); + font-weight: 600; +} + +.main-pane { + background: var(--chat-bg-primary); +} + +.main-frame { + background: + radial-gradient(circle at 86% 4%, color-mix(in srgb, var(--chat-accent) 7%, transparent), transparent 32%), + radial-gradient(circle at 8% 12%, color-mix(in srgb, var(--chat-link) 7%, transparent), transparent 28%), + var(--chat-bg-primary); +} + +.content-panel { + background: transparent !important; + border: 0 !important; + box-shadow: none !important; + padding-block: 0.25rem !important; +} + +.sl-container { + max-width: min(76rem, 96vw); +} + +html:not([data-has-hero]) .content-panel .sl-container { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-xl); + box-shadow: var(--chat-shadow-sm); + padding-inline: clamp(1rem, 2vw, 1.35rem); + padding-block: 0.35rem; + margin-bottom: 0.75rem; +} + +html:not([data-has-hero]) .content-panel:first-of-type .sl-container { + padding-top: 0.6rem; + padding-bottom: 0.3rem; +} + +html:not([data-has-hero]) .content-panel:first-of-type h1 { + margin-bottom: 0.2rem; +} + +.sidebar .group-label, +.sidebar .title { + color: var(--chat-text-tertiary); + font-weight: 600; + letter-spacing: 0.01em; +} + +.open-menu, +.close-menu, +.mobile-preferences { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-md); + color: var(--chat-text-secondary); +} + +.mobile-preferences { + padding: 0.4rem 0.6rem; + gap: 0.5rem; +} + +.right-sidebar-container { + background: color-mix(in srgb, var(--chat-bg-secondary) 76%, transparent); + border-inline-start: 1px solid var(--chat-border); +} + +.right-sidebar-panel .sl-container { + padding-top: 0.5rem; +} + +@keyframes hero-fade-in { + 0% { + opacity: 0; + transform: translateY(14px) scale(0.995); + } + 100% { + opacity: 1; + transform: translateY(0) scale(1); + } +} + +@keyframes hero-float-left { + 0%, + 100% { + transform: translate3d(0, 0, 0); + } + 50% { + transform: translate3d(8px, -10px, 0); + } +} + +@keyframes hero-float-right { + 0%, + 100% { + transform: translate3d(0, 0, 0); + } + 50% { + transform: translate3d(-10px, 9px, 0); + } +} + +@keyframes hero-content-rise { + 0% { + opacity: 0; + transform: translateY(12px); + } + 100% { + opacity: 1; + transform: translateY(0); + } +} + +.hero { + position: relative; + isolation: isolate; + overflow: hidden; + background: + linear-gradient(122deg, color-mix(in srgb, var(--chat-accent) 9%, transparent), transparent 38%), + linear-gradient(0deg, var(--chat-bg-secondary), var(--chat-bg-secondary)); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-xl); + box-shadow: var(--chat-shadow-sm); + padding: 2.75rem 3rem; + margin-bottom: 2rem; + opacity: 0; + transform-origin: center; + animation: hero-fade-in 0.7s cubic-bezier(0.2, 0.8, 0.2, 1) 0.1s forwards; +} + +.hero::before { + content: ''; + position: absolute; + inset: -5rem auto auto -5rem; + width: 13rem; + height: 13rem; + border-radius: 999px; + background: radial-gradient(circle, color-mix(in srgb, var(--chat-link) 20%, transparent), transparent 72%); + filter: blur(1px); + pointer-events: none; + z-index: -1; + animation: hero-float-left 8.8s ease-in-out infinite; +} + +.hero::after { + content: ''; + position: absolute; + inset: auto -4rem -5rem auto; + width: 14rem; + height: 14rem; + border-radius: 999px; + background: radial-gradient(circle, color-mix(in srgb, var(--chat-accent) 20%, transparent), transparent 70%); + filter: blur(1px); + pointer-events: none; + z-index: -1; + animation: hero-float-right 10.4s ease-in-out infinite; +} + +.hero .copy, +.hero .actions { + opacity: 0; + transform: translateY(10px); + animation: hero-content-rise 0.6s cubic-bezier(0.22, 0.83, 0.19, 0.99) forwards; +} + +.hero .copy { + animation-delay: 0.24s; +} + +.hero .actions { + animation-delay: 0.36s; +} + +.hero h1 { + font-size: clamp(2rem, 3.4vw, 2.8rem); + font-weight: 700; + letter-spacing: -0.02em; + color: var(--chat-text-primary); +} + +.hero .tagline { + color: var(--chat-text-secondary); + font-size: 1.08rem; + max-width: 58ch; +} + +.hero .actions .sl-link-button { + border-radius: 999px; + padding: 0.7rem 1.25rem; + font-weight: 600; + transition: transform var(--chat-transition-fast), box-shadow var(--chat-transition-fast), background-color var(--chat-transition-fast); +} + +.hero .actions .sl-link-button.primary { + background: var(--chat-accent); + border-color: var(--chat-accent); + color: #ffffff; + box-shadow: 0 8px 16px color-mix(in srgb, var(--chat-accent) 34%, transparent); +} + +.hero .actions .sl-link-button.primary:hover { + background: var(--chat-accent-hover); + border-color: var(--chat-accent-hover); + transform: translateY(-1px); +} + +.hero .actions .sl-link-button.minimal, +.hero .actions .sl-link-button.secondary { + border-color: var(--chat-border-strong); + color: var(--chat-text-primary); + background: transparent; +} + +.hero .actions .sl-link-button.minimal:hover, +.hero .actions .sl-link-button.secondary:hover { + background: var(--chat-bg-tertiary); + transform: translateY(-1px); +} + +.sl-markdown-content { + max-width: 74ch; + color: var(--chat-text-primary); +} + +.sl-markdown-content :is(h1, h2, h3, h4, h5, h6) { + color: var(--chat-text-primary); + font-weight: 650; + letter-spacing: -0.01em; + scroll-margin-top: 6rem; +} + +.sl-markdown-content h2 { + border-bottom: 1px solid var(--chat-border); + padding-bottom: 0.5rem; + margin-top: 2rem; + margin-bottom: 1rem; +} + +.sl-heading-wrapper { + gap: 0.35rem; +} + +.sl-anchor-link { + opacity: 0; + transform: translateX(-2px); + transition: opacity var(--chat-transition-fast), transform var(--chat-transition-fast); +} + +.sl-heading-wrapper:hover .sl-anchor-link, +.sl-heading-wrapper:focus-within .sl-anchor-link { + opacity: 1; + transform: translateX(0); +} + +.sl-markdown-content p, +.sl-markdown-content li, +.sl-markdown-content td { + color: var(--chat-text-secondary); +} + +.sl-markdown-content ul, +.sl-markdown-content ol { + padding-inline-start: 1.2rem; + margin-block: 0.7rem; +} + +.sl-markdown-content li + li { + margin-top: 0.2rem; +} + +.sl-markdown-content strong { + color: var(--chat-text-primary); + font-weight: 650; +} + +.sl-markdown-content a { + color: var(--chat-link); + text-decoration: none; + transition: color var(--chat-transition-fast); +} + +.sl-markdown-content a:hover { + color: var(--chat-link-hover); + text-decoration: underline; +} + +.sl-markdown-content hr { + border: none; + border-top: 1px solid var(--chat-border); + margin-block: 2rem; +} + +.sl-markdown-content blockquote { + border-inline-start: 3px solid var(--chat-accent); + background: color-mix(in srgb, var(--chat-accent) 10%, var(--chat-bg-secondary)); + padding: 0.8rem 1rem; + border-radius: 0 var(--chat-radius-md) var(--chat-radius-md) 0; + color: var(--chat-text-secondary); +} + +.sl-markdown-content details { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-md); + padding: 0.75rem 0.9rem; + margin-block: 1rem; +} + +.sl-markdown-content .tip, +.sl-markdown-content .warning { + border-radius: var(--chat-radius-md); + border: 1px solid var(--chat-border); + padding: 0.75rem 0.95rem; + margin: 1rem 0; + box-shadow: var(--chat-shadow-sm); +} + +.sl-markdown-content .tip { + background: color-mix(in srgb, var(--chat-accent) 9%, var(--chat-bg-secondary)); + border-inline-start: 3px solid var(--chat-accent); +} + +.sl-markdown-content .warning { + background: color-mix(in srgb, #f59e0b 10%, var(--chat-bg-secondary)); + border-inline-start: 3px solid #f59e0b; +} + +.sl-markdown-content .contains-task-list { + list-style: none; + padding-inline-start: 0; +} + +.sl-markdown-content .task-list-item { + display: flex; + align-items: flex-start; + gap: 0.55rem; + padding: 0.15rem 0; +} + +.sl-markdown-content .task-list-item input[type='checkbox'] { + accent-color: var(--chat-accent); + margin-top: 0.2rem; +} + +.sl-markdown-content table { + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-md); + overflow: hidden; + background: var(--chat-bg-secondary); + box-shadow: var(--chat-shadow-sm); +} + +.sl-markdown-content th { + background: var(--chat-bg-tertiary); + border-bottom: 1px solid var(--chat-border); + color: var(--chat-text-primary); + font-weight: 600; + padding: 0.8rem 1rem; +} + +.sl-markdown-content td { + border-bottom: 1px solid var(--chat-border); + padding: 0.8rem 1rem; +} + +.sl-markdown-content tbody tr:hover td { + background: color-mix(in srgb, var(--chat-bg-tertiary) 55%, transparent); +} + +.sl-markdown-content code:not(pre code) { + background: var(--chat-bg-tertiary); + border: 1px solid var(--chat-border); + padding: 0.12rem 0.4rem; + border-radius: var(--chat-radius-sm); + font-size: 0.9em; + color: var(--chat-accent); +} + +.expressive-code, +.sl-markdown-content pre { + border: 1px solid var(--chat-border) !important; + border-radius: var(--chat-radius-md) !important; + background: var(--chat-bg-secondary) !important; + box-shadow: var(--chat-shadow-sm) !important; +} + +.expressive-code .copy button { + background: var(--chat-bg-tertiary) !important; + border: 1px solid var(--chat-border) !important; + border-radius: var(--chat-radius-sm) !important; + color: var(--chat-text-secondary) !important; +} + +.expressive-code .copy button:hover { + color: var(--chat-text-primary) !important; + border-color: var(--chat-border-strong) !important; +} + +.card-grid { + gap: 0.9rem; + margin-block: 1.4rem; +} + +.card-grid > * { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-lg); + padding: 1rem; + box-shadow: var(--chat-shadow-sm); + transition: transform var(--chat-transition-fast), border-color var(--chat-transition-fast), box-shadow var(--chat-transition-fast); +} + +.card-grid > *:hover { + transform: translateY(-2px); + border-color: var(--chat-border-strong); + box-shadow: var(--chat-shadow-md); +} + +.right-sidebar-panel { + border-inline-start: 1px solid var(--chat-border); + background: transparent; +} + +starlight-toc nav h2 { + color: var(--chat-text-tertiary); + font-weight: 600; + letter-spacing: 0; +} + +starlight-toc a { + color: var(--chat-text-tertiary); + border-radius: var(--chat-radius-sm); + transition: color var(--chat-transition-fast), background-color var(--chat-transition-fast); +} + +starlight-toc a:hover { + color: var(--chat-text-primary); + background: var(--chat-bg-tertiary); +} + +starlight-toc a[aria-current='true'] { + color: var(--chat-accent); + font-weight: 600; +} + +.right-sidebar-panel starlight-toc nav { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-lg); + padding: 0.8rem; + box-shadow: var(--chat-shadow-sm); +} + +button[data-open-modal], +.search-button { + background: var(--chat-bg-secondary) !important; + border: 1px solid var(--chat-border) !important; + color: var(--chat-text-secondary) !important; + border-radius: var(--chat-radius-md) !important; + transition: all var(--chat-transition-fast); +} + +button[data-open-modal]:hover, +.search-button:hover { + background: var(--chat-bg-tertiary) !important; + border-color: var(--chat-border-strong) !important; + color: var(--chat-text-primary) !important; +} + +.pagination-links a { + background: var(--chat-bg-secondary); + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-lg); + box-shadow: var(--chat-shadow-sm); + transition: transform var(--chat-transition-fast), border-color var(--chat-transition-fast), box-shadow var(--chat-transition-fast); +} + +.pagination-links a:hover { + transform: translateY(-2px); + border-color: var(--chat-border-strong); + box-shadow: var(--chat-shadow-md); +} + +.pagination-links .link-title { + color: var(--chat-text-primary); +} + +starlight-theme-select { + --theme-button-border: var(--chat-border); + --theme-button-bg: var(--chat-bg-secondary); + --theme-button-text: var(--chat-text-secondary); + --theme-button-hover-bg: var(--chat-bg-tertiary); + --theme-button-hover-text: var(--chat-text-primary); +} + +footer { + border-top: 1px solid var(--chat-border); + padding-top: 1.75rem; + margin-top: 2.25rem; + color: var(--chat-text-tertiary); +} + +.index-highlights { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(14rem, 1fr)); + gap: 0.75rem; + margin: 1.2rem 0 1.8rem; + padding: 0; +} + +.index-highlights > li { + list-style: none; + padding: 0.85rem 1rem; + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-lg); + background: var(--chat-bg-secondary); + color: var(--chat-text-secondary); + box-shadow: var(--chat-shadow-sm); +} + +.index-footnote { + margin-top: 2.4rem; + padding-top: 1rem; + border-top: 1px solid var(--chat-border); + text-align: center; + font-size: 0.9rem; + color: var(--chat-text-tertiary); +} + +.mermaid-source-hidden { + display: none !important; +} + +.mermaid-diagram { + margin: 1rem 0 1.5rem; + padding: 1rem; + border: 1px solid var(--chat-border); + border-radius: var(--chat-radius-lg); + background: var(--chat-bg-secondary); + overflow-x: auto; + box-shadow: var(--chat-shadow-sm); +} + +.mermaid-diagram .mermaid { + min-width: max-content; + text-align: center; +} + +.mermaid-diagram svg { + max-width: none; + height: auto; + display: block; + margin-inline: auto; +} + +@media (max-width: 72rem) { + html:not([data-has-hero]) .content-panel .sl-container { + border-radius: var(--chat-radius-lg); + padding-inline: 0.85rem; + } + + .right-sidebar-container { + border-inline-start: 0; + background: transparent; + } + + .hero { + padding: 1.35rem; + border-radius: var(--chat-radius-lg); + } + + .hero h1 { + font-size: clamp(1.7rem, 7vw, 2.1rem); + } + + .hero .tagline { + font-size: 0.98rem; + } + + .hero .actions { + display: grid; + grid-template-columns: 1fr; + gap: 0.6rem; + } + + .hero .actions .sl-link-button { + width: 100%; + justify-content: center; + } + + .sl-markdown-content { + max-width: 100%; + } + + .sl-markdown-content table { + display: block; + overflow-x: auto; + white-space: nowrap; + } + + .sl-markdown-content th, + .sl-markdown-content td { + padding: 0.68rem 0.8rem; + } + + .index-highlights { + grid-template-columns: 1fr; + margin: 1rem 0 1.35rem; + } + + .mermaid-diagram { + margin: 0.85rem 0 1.2rem; + padding: 0.75rem; + } + + .pagination-links a { + padding-block: 0.72rem; + } + + .open-menu, + .close-menu { + min-height: 2.35rem; + min-width: 2.35rem; + } + + .right-sidebar-panel { + border-inline-start: 0; + } +} + +@media (max-width: 50rem) { + html { + font-size: 15px; + } + + .content-panel { + padding-block: 0.1rem !important; + } + + .sl-markdown-content h2 { + font-size: 1.26rem; + } + + .sl-markdown-content h3 { + font-size: 1.08rem; + } + + button[data-open-modal] { + min-height: 2.35rem; + } + + .sidebar-pane { + padding-bottom: 0.45rem; + } +} + +@media (prefers-reduced-motion: reduce) { + .hero, + .hero::before, + .hero::after, + .hero .copy, + .hero .actions { + animation: none !important; + opacity: 1 !important; + transform: none !important; + } + + .card-grid > *, + .pagination-links a, + .hero .actions .sl-link-button { + transition: none !important; + } +} diff --git a/external/ai-research-ebook/tsconfig.json b/external/ai-research-ebook/tsconfig.json new file mode 100644 index 0000000..8bf91d3 --- /dev/null +++ b/external/ai-research-ebook/tsconfig.json @@ -0,0 +1,5 @@ +{ + "extends": "astro/tsconfigs/strict", + "include": [".astro/types.d.ts", "**/*"], + "exclude": ["dist"] +} diff --git a/index.html b/index.html index b0b91d7..799f8f3 100644 --- a/index.html +++ b/index.html @@ -1016,6 +1016,7 @@

📚 核心内容

技术栈与框架 系统设计与实战 面试指南 + 🔬 AI科研指南 (Vibe Research)
diff --git a/research/404.html b/research/404.html new file mode 100644 index 0000000..c7a1ab6 --- /dev/null +++ b/research/404.html @@ -0,0 +1,48 @@ + 404 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

404

Page not found. Check the URL or try using the search bar.
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MutationObserver(o=>o.forEach(e=>e.addedNodes.forEach(a=>{i(a)})));m.observe(document.body,{childList:!0,subtree:!0});document.addEventListener("astro:page-load",()=>{i(document)});})();}catch(e){console.error("[EC] copy-js-module failed:",e)} \ No newline at end of file diff --git a/research/_astro/ec.v4551.css b/research/_astro/ec.v4551.css new file mode 100644 index 0000000..06a9068 --- /dev/null +++ b/research/_astro/ec.v4551.css @@ -0,0 +1 @@ +@layer starlight.components{.expressive-code{font-family:var(--ec-uiFontFml);font-size:var(--ec-uiFontSize);font-weight:var(--ec-uiFontWg);line-height:var(--ec-uiLineHt);text-size-adjust:none;-webkit-text-size-adjust:none}.expressive-code *:not(:is(svg, svg *)){all:revert;box-sizing:border-box}.expressive-code pre{display:flex;margin:0;padding:0;border:var(--ec-brdWd) solid var(--ec-brdCol);border-radius:calc(var(--ec-brdRad) + var(--ec-brdWd));background:var(--ec-codeBg)}.expressive-code pre:focus-visible{outline:3px solid var(--ec-focusBrd);outline-offset:-3px}.expressive-code pre > code{all:unset;display:block;flex:1 0 100%;padding:var(--ec-codePadBlk) 0;color:var(--ec-codeFg);font-family:var(--ec-codeFontFml);font-size:var(--ec-codeFontSize);font-weight:var(--ec-codeFontWg);line-height:var(--ec-codeLineHt)}.expressive-code pre{overflow-x:auto}.expressive-code pre.wrap .ec-line .code{white-space:pre-wrap;overflow-wrap:break-word;min-width:min(20ch, var(--ecMaxLine, 20ch))}.expressive-code pre.wrap .ec-line .code span.indent{white-space:pre}.expressive-code pre::-webkit-scrollbar,.expressive-code pre::-webkit-scrollbar-track{background-color:inherit;border-radius:calc(var(--ec-brdRad) + var(--ec-brdWd));border-top-left-radius:0;border-top-right-radius:0}.expressive-code pre::-webkit-scrollbar-thumb{background-color:var(--ec-sbThumbCol);border:4px solid transparent;background-clip:content-box;border-radius:10px}.expressive-code pre::-webkit-scrollbar-thumb:hover{background-color:var(--ec-sbThumbHoverCol)}.expressive-code .ec-line{direction:ltr;unicode-bidi:isolate;display:grid;grid-template-areas:'gutter code';grid-template-columns:auto 1fr;position:relative}.expressive-code .ec-line .gutter{grid-area:gutter;color:var(--ec-gtrFg)}.expressive-code .ec-line .gutter > *{pointer-events:none;user-select:none;-webkit-user-select:none}.expressive-code .ec-line .gutter ~ .code{--ecLineBrdCol:var(--ec-gtrBrdCol)}.expressive-code .ec-line.highlight .gutter{color:var(--ec-gtrHlFg)}.expressive-code .ec-line .code{grid-area:code;position:relative;box-sizing:content-box;padding-inline-start:calc(var(--ecIndent, 0ch) + var(--ec-codePadInl) - var(--ecGtrBrdWd));padding-inline-end:var(--ec-codePadInl);text-indent:calc(var(--ecIndent, 0ch) * -1)}.expressive-code .ec-line .code::before,.expressive-code .ec-line .code::after,.expressive-code .ec-line .code :where(*){text-indent:0}.expressive-code .ec-line .code{--ecGtrBrdWd:var(--ec-gtrBrdWd);border-inline-start:var(--ecGtrBrdWd) solid var(--ecLineBrdCol, transparent)}.expressive-code .sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0, 0, 0, 0);white-space:nowrap;border-width:0}.expressive-code .ec-line.mark{--tmLineBgCol:var(--ec-tm-markBg)}.expressive-code .ec-line.mark .code{--ecLineBrdCol:var(--ec-tm-markBrdCol)}.expressive-code .ec-line.ins{--tmLineBgCol:var(--ec-tm-insBg);--tmLabel:var(--ec-tm-insDiffIndContent)}.expressive-code .ec-line.ins .code{--ecLineBrdCol:var(--ec-tm-insBrdCol)}.expressive-code .ec-line.ins .code::before{color:var(--ec-tm-insDiffIndCol)}.expressive-code .ec-line.del{--tmLineBgCol:var(--ec-tm-delBg);--tmLabel:var(--ec-tm-delDiffIndContent)}.expressive-code .ec-line.del .code{--ecLineBrdCol:var(--ec-tm-delBrdCol)}.expressive-code .ec-line.del .code::before{color:var(--ec-tm-delDiffIndCol)}.expressive-code .ec-line.mark,.expressive-code .ec-line.ins,.expressive-code .ec-line.del{background:var(--tmLineBgCol)}.expressive-code .ec-line.mark .code,.expressive-code .ec-line.ins .code,.expressive-code .ec-line.del .code{--ecGtrBrdWd:var(--ec-tm-lineMarkerAccentWd)}.expressive-code .ec-line.mark .code::before,.expressive-code .ec-line.ins .code::before,.expressive-code .ec-line.del .code::before{display:block;position:absolute;left:0;box-sizing:border-box;content:var(--tmLabel, ' ');padding-inline-start:var(--ec-tm-lineDiffIndMargLeft);text-align:center;white-space:pre}.expressive-code .ec-line.mark.tm-label .code::before,.expressive-code .ec-line.ins.tm-label .code::before,.expressive-code .ec-line.del.tm-label .code::before{background:var(--ecLineBrdCol);padding:0 calc(var(--ec-tm-lineMarkerLabelPadInl) + var(--ec-tm-lineMarkerAccentWd)) 0 var(--ec-tm-lineMarkerLabelPadInl);color:var(--ec-tm-lineMarkerLabelCol)}.expressive-code .ec-line mark{--tmInlineBgCol:var(--ec-tm-markBg);--tmInlineBrdCol:var(--ec-tm-markBrdCol)}.expressive-code .ec-line ins{--tmInlineBgCol:var(--ec-tm-insBg);--tmInlineBrdCol:var(--ec-tm-insBrdCol)}.expressive-code .ec-line del{--tmInlineBgCol:var(--ec-tm-delBg);--tmInlineBrdCol:var(--ec-tm-delBrdCol)}.expressive-code .ec-line mark,.expressive-code .ec-line ins,.expressive-code .ec-line del{all:unset;display:inline-block;position:relative;--tmBrdL:var(--ec-tm-inlMarkerBrdWd);--tmBrdR:var(--ec-tm-inlMarkerBrdWd);--tmRadL:var(--ec-tm-inlMarkerBrdRad);--tmRadR:var(--ec-tm-inlMarkerBrdRad);margin-inline:0.025rem;padding-inline:var(--ec-tm-inlMarkerPad);border-radius:var(--tmRadL) var(--tmRadR) var(--tmRadR) var(--tmRadL);background:var(--tmInlineBgCol);background-clip:padding-box}.expressive-code .ec-line mark.open-start,.expressive-code .ec-line ins.open-start,.expressive-code .ec-line del.open-start{margin-inline-start:0;padding-inline-start:0;--tmBrdL:0px;--tmRadL:0}.expressive-code .ec-line mark.open-end,.expressive-code .ec-line ins.open-end,.expressive-code .ec-line del.open-end{margin-inline-end:0;padding-inline-end:0;--tmBrdR:0px;--tmRadR:0}.expressive-code .ec-line mark::before,.expressive-code .ec-line ins::before,.expressive-code .ec-line del::before{content:'';position:absolute;pointer-events:none;display:inline-block;inset:0;border-radius:var(--tmRadL) var(--tmRadR) var(--tmRadR) var(--tmRadL);border:var(--ec-tm-inlMarkerBrdWd) solid var(--tmInlineBrdCol);border-inline-width:var(--tmBrdL) var(--tmBrdR)}.expressive-code .frame{all:unset;position:relative;display:block;--header-border-radius:calc(var(--ec-brdRad) + var(--ec-brdWd));--tab-border-radius:calc(var(--ec-frm-edTabBrdRad) + var(--ec-brdWd));--button-spacing:0.4rem;--code-background:var(--ec-frm-edBg);border-radius:var(--header-border-radius);box-shadow:var(--ec-frm-frameBoxShdCssVal)}.expressive-code .frame .header{display:none;z-index:1;position:relative;border-radius:var(--header-border-radius) var(--header-border-radius) 0 0}.expressive-code .frame.has-title pre,.expressive-code .frame.has-title code,.expressive-code .frame.is-terminal pre,.expressive-code .frame.is-terminal code{border-top:none;border-top-left-radius:0;border-top-right-radius:0}.expressive-code .frame .title:empty:before{content:'\a0'}.expressive-code .frame.has-title:not(.is-terminal){--button-spacing:calc(1.9rem + 2 * (var(--ec-uiPadBlk) + var(--ec-frm-edActTabIndHt)))}.expressive-code .frame.has-title:not(.is-terminal) .title{position:relative;color:var(--ec-frm-edActTabFg);background:var(--ec-frm-edActTabBg);background-clip:padding-box;margin-block-start:var(--ec-frm-edTabsMargBlkStart);padding:calc(var(--ec-uiPadBlk) + var(--ec-frm-edActTabIndHt)) var(--ec-uiPadInl);border:var(--ec-brdWd) solid var(--ec-frm-edActTabBrdCol);border-radius:var(--tab-border-radius) var(--tab-border-radius) 0 0;border-bottom:none;overflow:hidden}.expressive-code .frame.has-title:not(.is-terminal) .title::after{content:'';position:absolute;pointer-events:none;inset:0;border-top:var(--ec-frm-edActTabIndHt) solid var(--ec-frm-edActTabIndTopCol);border-bottom:var(--ec-frm-edActTabIndHt) solid var(--ec-frm-edActTabIndBtmCol)}.expressive-code .frame.has-title:not(.is-terminal) .header{display:flex;background:linear-gradient(to top, var(--ec-frm-edTabBarBrdBtmCol) var(--ec-brdWd), transparent var(--ec-brdWd)),linear-gradient(var(--ec-frm-edTabBarBg), var(--ec-frm-edTabBarBg));background-repeat:no-repeat;padding-inline-start:var(--ec-frm-edTabsMargInlStart)}.expressive-code .frame.has-title:not(.is-terminal) .header::before{content:'';position:absolute;pointer-events:none;inset:0;border:var(--ec-brdWd) solid var(--ec-frm-edTabBarBrdCol);border-radius:inherit;border-bottom:none}.expressive-code .frame.is-terminal{--button-spacing:calc(1.9rem + var(--ec-brdWd) + 2 * var(--ec-uiPadBlk));--code-background:var(--ec-frm-trmBg)}.expressive-code .frame.is-terminal .header{display:flex;align-items:center;justify-content:center;padding-block:var(--ec-uiPadBlk);padding-block-end:calc(var(--ec-uiPadBlk) + var(--ec-brdWd));position:relative;font-weight:500;letter-spacing:0.025ch;color:var(--ec-frm-trmTtbFg);background:var(--ec-frm-trmTtbBg);border:var(--ec-brdWd) solid var(--ec-brdCol);border-bottom:none}.expressive-code .frame.is-terminal .header::before{content:'';position:absolute;pointer-events:none;left:var(--ec-uiPadInl);width:2.1rem;height:0.56rem;line-height:0;background-color:var(--ec-frm-trmTtbDotsFg);opacity:var(--ec-frm-trmTtbDotsOpa);-webkit-mask-image:var(--ec-frm-trmIcon);-webkit-mask-repeat:no-repeat;mask-image:var(--ec-frm-trmIcon);mask-repeat:no-repeat}.expressive-code .frame.is-terminal .header::after{content:'';position:absolute;pointer-events:none;inset:0;border-bottom:var(--ec-brdWd) solid var(--ec-frm-trmTtbBrdBtmCol)}.expressive-code .frame pre{background:var(--code-background)}.expressive-code .copy{display:flex;gap:0.25rem;flex-direction:row;position:absolute;inset-block-start:calc(var(--ec-brdWd) + var(--button-spacing));inset-inline-end:calc(var(--ec-brdWd) + var(--ec-uiPadInl) / 2)}@media (scripting: none){.expressive-code .copy{display:none}}.expressive-code .copy{direction:ltr;unicode-bidi:isolate}.expressive-code .copy button{position:relative;align-self:flex-end;margin:0;padding:0;border:none;border-radius:0.2rem;z-index:1;cursor:pointer;transition-property:opacity, background, border-color;transition-duration:0.2s;transition-timing-function:cubic-bezier(0.25, 0.46, 0.45, 0.94);width:2.5rem;height:2.5rem;background:var(--code-background);opacity:0.75}.expressive-code .copy button div{position:absolute;inset:0;border-radius:inherit;background:var(--ec-frm-inlBtnBg);opacity:var(--ec-frm-inlBtnBgIdleOpa);transition-property:inherit;transition-duration:inherit;transition-timing-function:inherit}.expressive-code .copy button::before{content:'';position:absolute;pointer-events:none;inset:0;border-radius:inherit;border:var(--ec-brdWd) solid var(--ec-frm-inlBtnBrd);opacity:var(--ec-frm-inlBtnBrdOpa)}.expressive-code .copy button::after{content:'';position:absolute;pointer-events:none;inset:0;background-color:var(--ec-frm-inlBtnFg);-webkit-mask-image:var(--ec-frm-copyIcon);-webkit-mask-repeat:no-repeat;mask-image:var(--ec-frm-copyIcon);mask-repeat:no-repeat;margin:0.475rem;line-height:0}.expressive-code .copy button:hover,.expressive-code .copy button:focus:focus-visible{opacity:1}.expressive-code .copy button:hover div,.expressive-code .copy button:focus:focus-visible div{opacity:var(--ec-frm-inlBtnBgHoverOrFocusOpa)}.expressive-code .copy button:active{opacity:1}.expressive-code .copy button:active div{opacity:var(--ec-frm-inlBtnBgActOpa)}.expressive-code .copy .feedback{--tooltip-arrow-size:0.35rem;--tooltip-bg:var(--ec-frm-tooltipSuccessBg);color:var(--ec-frm-tooltipSuccessFg);pointer-events:none;user-select:none;-webkit-user-select:none;position:relative;align-self:center;background-color:var(--tooltip-bg);z-index:99;padding:0.125rem 0.75rem;border-radius:0.2rem;margin-inline-end:var(--tooltip-arrow-size);opacity:0;transition-property:opacity, transform;transition-duration:0.2s;transition-timing-function:ease-in-out;transform:translate3d(0, 0.25rem, 0)}.expressive-code .copy .feedback::after{content:'';position:absolute;pointer-events:none;top:calc(50% - var(--tooltip-arrow-size));inset-inline-end:calc(-2 * (var(--tooltip-arrow-size) - 0.5px));border:var(--tooltip-arrow-size) solid transparent;border-inline-start-color:var(--tooltip-bg)}.expressive-code .copy .feedback.show{opacity:1;transform:translate3d(0, 0, 0)}@media (hover: hover){.expressive-code{}.expressive-code .copy button{opacity:0;width:2rem;height:2rem}.expressive-code .frame:hover .copy button:not(:hover),.expressive-code .frame:focus-within :focus-visible ~ .copy button:not(:hover),.expressive-code .frame .copy .feedback.show ~ button:not(:hover){opacity:0.75}}.expressive-code :nth-child(1 of .ec-line) .code{padding-inline-end:calc(2rem + var(--ec-codePadInl))}}@layer starlight.components{:root,:root:not([data-theme='dark']) .expressive-code[data-theme='dark']{--ec-brdRad:0px;--ec-brdWd:1px;--ec-brdCol:color-mix(in srgb, var(--sl-color-gray-5), transparent 25%);--ec-codeFontFml:var(--__sl-font-mono);--ec-codeFontSize:var(--sl-text-code);--ec-codeFontWg:400;--ec-codeLineHt:var(--sl-line-height);--ec-codePadBlk:0.75rem;--ec-codePadInl:1rem;--ec-codeBg:#23262f;--ec-codeFg:#d6deeb;--ec-codeSelBg:#1d3b53;--ec-gtrFg:#63798b;--ec-gtrBrdCol:#63798b33;--ec-gtrBrdWd:1.5px;--ec-gtrHlFg:#c5e4fd97;--ec-uiFontFml:var(--__sl-font);--ec-uiFontSize:0.9rem;--ec-uiFontWg:400;--ec-uiLineHt:1.65;--ec-uiPadBlk:0.25rem;--ec-uiPadInl:1rem;--ec-uiSelBg:#234d708c;--ec-uiSelFg:#ffffff;--ec-focusBrd:#122d42;--ec-sbThumbCol:#ffffff17;--ec-sbThumbHoverCol:#ffffff47;--ec-tm-lineMarkerAccentMarg:0rem;--ec-tm-lineMarkerAccentWd:0.15rem;--ec-tm-lineMarkerLabelPadInl:0.2rem;--ec-tm-lineMarkerLabelCol:white;--ec-tm-lineDiffIndMargLeft:0.25rem;--ec-tm-inlMarkerBrdWd:1.5px;--ec-tm-inlMarkerBrdRad:0.2rem;--ec-tm-inlMarkerPad:0.15rem;--ec-tm-insDiffIndContent:'+';--ec-tm-delDiffIndContent:'-';--ec-tm-markBg:#ffffff17;--ec-tm-markBrdCol:#ffffff40;--ec-tm-insBg:#1e571599;--ec-tm-insBrdCol:#487f3bd0;--ec-tm-insDiffIndCol:#79b169d0;--ec-tm-delBg:#862d2799;--ec-tm-delBrdCol:#b4554bd0;--ec-tm-delDiffIndCol:#ed8779d0;--ec-frm-shdCol:#011627;--ec-frm-frameBoxShdCssVal:none;--ec-frm-edActTabBg:var(--sl-color-gray-6);--ec-frm-edActTabFg:var(--sl-color-text);--ec-frm-edActTabBrdCol:transparent;--ec-frm-edActTabIndHt:1px;--ec-frm-edActTabIndTopCol:var(--sl-color-accent-high);--ec-frm-edActTabIndBtmCol:transparent;--ec-frm-edTabsMargInlStart:0;--ec-frm-edTabsMargBlkStart:0;--ec-frm-edTabBrdRad:0px;--ec-frm-edTabBarBg:var(--sl-color-black);--ec-frm-edTabBarBrdCol:color-mix(in srgb, var(--sl-color-gray-5), transparent 25%);--ec-frm-edTabBarBrdBtmCol:color-mix(in srgb, var(--sl-color-gray-5), transparent 25%);--ec-frm-edBg:var(--sl-color-gray-6);--ec-frm-trmTtbDotsFg:color-mix(in srgb, var(--sl-color-gray-5), transparent 25%);--ec-frm-trmTtbDotsOpa:0.75;--ec-frm-trmTtbBg:var(--sl-color-black);--ec-frm-trmTtbFg:var(--sl-color-text);--ec-frm-trmTtbBrdBtmCol:color-mix(in srgb, var(--sl-color-gray-5), transparent 25%);--ec-frm-trmBg:var(--sl-color-gray-6);--ec-frm-inlBtnFg:var(--sl-color-text);--ec-frm-inlBtnBg:var(--sl-color-text);--ec-frm-inlBtnBgIdleOpa:0;--ec-frm-inlBtnBgHoverOrFocusOpa:0.2;--ec-frm-inlBtnBgActOpa:0.3;--ec-frm-inlBtnBrd:var(--sl-color-text);--ec-frm-inlBtnBrdOpa:0.4;--ec-frm-tooltipSuccessBg:#158744;--ec-frm-tooltipSuccessFg:white;--ec-frm-copyIcon:url("data:image/svg+xml,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%2024%2024'%20fill%3D'none'%20stroke%3D'black'%20stroke-width%3D'1.75'%3E%3Cpath%20d%3D'M3%2019a2%202%200%200%201-1-2V2a2%202%200%200%201%201-1h13a2%202%200%200%201%202%201'%2F%3E%3Crect%20x%3D'6'%20y%3D'5'%20width%3D'16'%20height%3D'18'%20rx%3D'1.5'%20ry%3D'1.5'%2F%3E%3C%2Fsvg%3E");--ec-frm-trmIcon:url("data:image/svg+xml,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%2060%2016'%20preserveAspectRatio%3D'xMidYMid%20meet'%3E%3Ccircle%20cx%3D'8'%20cy%3D'8'%20r%3D'8'%2F%3E%3Ccircle%20cx%3D'30'%20cy%3D'8'%20r%3D'8'%2F%3E%3Ccircle%20cx%3D'52'%20cy%3D'8'%20r%3D'8'%2F%3E%3C%2Fsvg%3E")}.expressive-code .ec-line :where(span[style^='--']:not([class])),:root:not([data-theme='dark']) .expressive-code[data-theme='dark'] .ec-line :where(span[style^='--']:not([class])){color:var(--0, inherit);background-color:var(--0bg, transparent);font-style:var(--0fs, inherit);font-weight:var(--0fw, inherit);text-decoration:var(--0td, inherit)}@media (prefers-color-scheme: light){:root:not([data-theme='dark']){--ec-codeBg:#f6f7f9;--ec-codeFg:#403f53;--ec-codeSelBg:#e0e0e0;--ec-gtrFg:#788b94;--ec-gtrBrdCol:#788b9433;--ec-gtrHlFg:#403f53c4;--ec-uiSelBg:#d3e8f8;--ec-uiSelFg:#403f53;--ec-focusBrd:#93a1a1;--ec-sbThumbCol:#0000001a;--ec-sbThumbHoverCol:#0000005c;--ec-tm-markBg:#0000001a;--ec-tm-markBrdCol:#00000055;--ec-tm-insBg:#8ec77d99;--ec-tm-insDiffIndCol:#336a28d0;--ec-tm-delBg:#ff9c8e99;--ec-tm-delDiffIndCol:#9d4138d0;--ec-frm-shdCol:#d9d9d9;--ec-frm-edActTabBg:var(--sl-color-gray-7);--ec-frm-edActTabIndTopCol:var(--sl-color-accent);--ec-frm-edTabBarBg:var(--sl-color-gray-6);--ec-frm-edBg:var(--sl-color-gray-7);--ec-frm-trmTtbBg:var(--sl-color-gray-6);--ec-frm-trmBg:var(--sl-color-gray-7);--ec-frm-tooltipSuccessBg:#078662}:root:not([data-theme='dark']) .expressive-code .ec-line :where(span[style^='--']:not([class])){color:var(--1, inherit);background-color:var(--1bg, transparent);font-style:var(--1fs, inherit);font-weight:var(--1fw, inherit);text-decoration:var(--1td, inherit)}}:root[data-theme='light'] .expressive-code:not([data-theme='dark']),.expressive-code[data-theme='light']{--ec-codeBg:#f6f7f9;--ec-codeFg:#403f53;--ec-codeSelBg:#e0e0e0;--ec-gtrFg:#788b94;--ec-gtrBrdCol:#788b9433;--ec-gtrHlFg:#403f53c4;--ec-uiSelBg:#d3e8f8;--ec-uiSelFg:#403f53;--ec-focusBrd:#93a1a1;--ec-sbThumbCol:#0000001a;--ec-sbThumbHoverCol:#0000005c;--ec-tm-markBg:#0000001a;--ec-tm-markBrdCol:#00000055;--ec-tm-insBg:#8ec77d99;--ec-tm-insDiffIndCol:#336a28d0;--ec-tm-delBg:#ff9c8e99;--ec-tm-delDiffIndCol:#9d4138d0;--ec-frm-shdCol:#d9d9d9;--ec-frm-edActTabBg:var(--sl-color-gray-7);--ec-frm-edActTabIndTopCol:var(--sl-color-accent);--ec-frm-edTabBarBg:var(--sl-color-gray-6);--ec-frm-edBg:var(--sl-color-gray-7);--ec-frm-trmTtbBg:var(--sl-color-gray-6);--ec-frm-trmBg:var(--sl-color-gray-7);--ec-frm-tooltipSuccessBg:#078662}:root[data-theme='light'] .expressive-code:not([data-theme='dark']) .ec-line :where(span[style^='--']:not([class])),.expressive-code[data-theme='light'] .ec-line :where(span[style^='--']:not([class])){color:var(--1, inherit);background-color:var(--1bg, transparent);font-style:var(--1fs, inherit);font-weight:var(--1fw, inherit);text-decoration:var(--1td, inherit)}} \ No newline at end of file diff --git a/research/_astro/index.D_81y6nM.css b/research/_astro/index.D_81y6nM.css new file mode 100644 index 0000000..2deb450 --- /dev/null +++ b/research/_astro/index.D_81y6nM.css @@ -0,0 +1 @@ +:root,[data-theme=light]{--chat-bg-primary: #f7f7f8;--chat-bg-secondary: #ffffff;--chat-bg-tertiary: #f0f1f3;--chat-sidebar-bg: #f9f9fb;--chat-border: #e3e5e9;--chat-border-strong: #d1d5db;--chat-text-primary: #171717;--chat-text-secondary: #374151;--chat-text-tertiary: #6b7280;--chat-link: #2563eb;--chat-link-hover: #1d4ed8;--chat-accent: #10a37f;--chat-accent-hover: #0d8e6f;--chat-selection: #bfdbfe;--chat-shadow-sm: 0 1px 2px rgba(15, 23, 42, .06);--chat-shadow-md: 0 10px 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80%);--sl-hue-blue: 234;--sl-color-blue-low: hsl(var(--sl-hue-blue), 54%, 20%);--sl-color-blue: hsl(var(--sl-hue-blue), 100%, 60%);--sl-color-blue-high: hsl(var(--sl-hue-blue), 100%, 87%);--sl-hue-purple: 281;--sl-color-purple-low: hsl(var(--sl-hue-purple), 39%, 22%);--sl-color-purple: hsl(var(--sl-hue-purple), 82%, 63%);--sl-color-purple-high: hsl(var(--sl-hue-purple), 82%, 89%);--sl-hue-red: 339;--sl-color-red-low: hsl(var(--sl-hue-red), 39%, 22%);--sl-color-red: hsl(var(--sl-hue-red), 82%, 63%);--sl-color-red-high: hsl(var(--sl-hue-red), 82%, 87%);--sl-color-accent-low: hsl(224, 54%, 20%);--sl-color-accent: hsl(224, 100%, 60%);--sl-color-accent-high: hsl(224, 100%, 85%);--sl-color-text: var(--sl-color-gray-2);--sl-color-text-accent: var(--sl-color-accent-high);--sl-color-text-invert: var(--sl-color-accent-low);--sl-color-bg: var(--sl-color-black);--sl-color-bg-nav: var(--sl-color-gray-6);--sl-color-bg-sidebar: var(--sl-color-gray-6);--sl-color-bg-inline-code: var(--sl-color-gray-5);--sl-color-bg-accent: var(--sl-color-accent-high);--sl-color-hairline-light: var(--sl-color-gray-5);--sl-color-hairline: var(--sl-color-gray-6);--sl-color-hairline-shade: var(--sl-color-black);--sl-color-backdrop-overlay: hsla(223, 13%, 10%, .66);--sl-shadow-sm: 0px 1px 1px hsla(0, 0%, 0%, .12), 0px 2px 1px hsla(0, 0%, 0%, .24);--sl-shadow-md: 0px 8px 4px hsla(0, 0%, 0%, .08), 0px 5px 2px hsla(0, 0%, 0%, .08), 0px 3px 2px hsla(0, 0%, 0%, .12), 0px 1px 1px hsla(0, 0%, 0%, .15);--sl-shadow-lg: 0px 25px 7px hsla(0, 0%, 0%, .03), 0px 16px 6px hsla(0, 0%, 0%, .1), 0px 9px 5px hsla(223, 13%, 10%, .33), 0px 4px 4px hsla(0, 0%, 0%, .75), 0px 4px 2px hsla(0, 0%, 0%, .25);--sl-text-2xs: .75rem;--sl-text-xs: .8125rem;--sl-text-sm: .875rem;--sl-text-base: 1rem;--sl-text-lg: 1.125rem;--sl-text-xl: 1.25rem;--sl-text-2xl: 1.5rem;--sl-text-3xl: 1.8125rem;--sl-text-4xl: 2.1875rem;--sl-text-5xl: 2.625rem;--sl-text-6xl: 4rem;--sl-text-body: var(--sl-text-base);--sl-text-body-sm: var(--sl-text-xs);--sl-text-code: var(--sl-text-sm);--sl-text-code-sm: var(--sl-text-xs);--sl-text-h1: var(--sl-text-4xl);--sl-text-h2: var(--sl-text-3xl);--sl-text-h3: var(--sl-text-2xl);--sl-text-h4: var(--sl-text-xl);--sl-text-h5: var(--sl-text-lg);--sl-line-height: 1.75;--sl-line-height-headings: 1.2;--sl-font-system: ui-sans-serif, system-ui, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--sl-font-system-mono: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--__sl-font: var(--sl-font, var(--sl-font-system)), var(--sl-font-system);--__sl-font-mono: var(--sl-font-mono, var(--sl-font-system-mono)), var(--sl-font-system-mono);--sl-nav-height: 3.5rem;--sl-nav-pad-x: 1rem;--sl-nav-pad-y: .75rem;--sl-mobile-toc-height: 3rem;--sl-sidebar-width: 18.75rem;--sl-sidebar-pad-x: 1rem;--sl-content-width: 45rem;--sl-content-pad-x: 1rem;--sl-content-gap-y: 1rem;--sl-main-pad: 0 0 3vh 0;--sl-menu-button-size: 2rem;--sl-nav-gap: var(--sl-content-pad-x);--sl-outline-offset-inside: -.1875rem;--sl-z-index-toc: 4;--sl-z-index-menu: 5;--sl-z-index-navbar: 10;--sl-z-index-skiplink: 20}:root[data-theme=light],[data-theme=light] ::backdrop{--sl-color-white: hsl(224, 10%, 10%);--sl-color-gray-1: hsl(224, 14%, 16%);--sl-color-gray-2: hsl(224, 10%, 23%);--sl-color-gray-3: hsl(224, 7%, 36%);--sl-color-gray-4: hsl(224, 6%, 56%);--sl-color-gray-5: hsl(224, 6%, 77%);--sl-color-gray-6: hsl(224, 20%, 94%);--sl-color-gray-7: hsl(224, 19%, 97%);--sl-color-black: hsl(0, 0%, 100%);--sl-color-orange-high: hsl(var(--sl-hue-orange), 80%, 25%);--sl-color-orange: hsl(var(--sl-hue-orange), 90%, 60%);--sl-color-orange-low: hsl(var(--sl-hue-orange), 90%, 88%);--sl-color-green-high: hsl(var(--sl-hue-green), 80%, 22%);--sl-color-green: hsl(var(--sl-hue-green), 90%, 46%);--sl-color-green-low: hsl(var(--sl-hue-green), 85%, 90%);--sl-color-blue-high: hsl(var(--sl-hue-blue), 80%, 30%);--sl-color-blue: hsl(var(--sl-hue-blue), 90%, 60%);--sl-color-blue-low: hsl(var(--sl-hue-blue), 88%, 90%);--sl-color-purple-high: hsl(var(--sl-hue-purple), 90%, 30%);--sl-color-purple: hsl(var(--sl-hue-purple), 90%, 60%);--sl-color-purple-low: hsl(var(--sl-hue-purple), 80%, 90%);--sl-color-red-high: hsl(var(--sl-hue-red), 80%, 30%);--sl-color-red: hsl(var(--sl-hue-red), 90%, 60%);--sl-color-red-low: hsl(var(--sl-hue-red), 80%, 90%);--sl-color-accent-high: hsl(234, 80%, 30%);--sl-color-accent: hsl(234, 90%, 60%);--sl-color-accent-low: hsl(234, 88%, 90%);--sl-color-text-accent: var(--sl-color-accent);--sl-color-text-invert: var(--sl-color-black);--sl-color-bg-nav: var(--sl-color-gray-7);--sl-color-bg-sidebar: var(--sl-color-bg);--sl-color-bg-inline-code: var(--sl-color-gray-6);--sl-color-bg-accent: var(--sl-color-accent);--sl-color-hairline-light: var(--sl-color-gray-6);--sl-color-hairline-shade: var(--sl-color-gray-6);--sl-color-backdrop-overlay: hsla(225, 9%, 36%, .66);--sl-shadow-sm: 0px 1px 1px hsla(0, 0%, 0%, .06), 0px 2px 1px hsla(0, 0%, 0%, .06);--sl-shadow-md: 0px 8px 4px hsla(0, 0%, 0%, .03), 0px 5px 2px hsla(0, 0%, 0%, .03), 0px 3px 2px hsla(0, 0%, 0%, .06), 0px 1px 1px hsla(0, 0%, 0%, .06);--sl-shadow-lg: 0px 25px 7px rgba(0, 0, 0, .01), 0px 16px 6px hsla(0, 0%, 0%, .03), 0px 9px 5px hsla(223, 13%, 10%, .08), 0px 4px 4px hsla(0, 0%, 0%, .16), 0px 4px 2px hsla(0, 0%, 0%, .04)}@media(min-width:50em){:root{--sl-nav-height: 4rem;--sl-nav-pad-x: 1.5rem;--sl-text-h1: var(--sl-text-5xl);--sl-text-h2: var(--sl-text-4xl);--sl-text-h3: var(--sl-text-3xl);--sl-text-h4: var(--sl-text-2xl)}}@media(min-width:72rem){:root{--sl-content-pad-x: 1.5rem;--sl-mobile-toc-height: 0rem}}}@layer starlight.reset{*,*:before,*:after{box-sizing:border-box}*{margin:0}html{color-scheme:dark;accent-color:var(--sl-color-accent);-webkit-text-size-adjust:100%;text-size-adjust:100%}html[data-theme=light]{color-scheme:light}body{font-family:var(--__sl-font);line-height:var(--sl-line-height);-webkit-font-smoothing:antialiased;color:var(--sl-color-text);background-color:var(--sl-color-bg)}input,button,textarea,select{font:inherit}p,h1,h2,h3,h4,h5,h6,code{overflow-wrap:break-word}code{font-family:var(--__sl-font-mono)}}@layer starlight.components{.starlight-aside{padding:1rem;border-inline-start:.25rem solid var(--sl-color-asides-border);color:var(--sl-color-white)}.starlight-aside--note{--sl-color-asides-text-accent: var(--sl-color-blue-high);--sl-color-asides-border: var(--sl-color-blue);background-color:var(--sl-color-blue-low)}.starlight-aside--tip{--sl-color-asides-text-accent: var(--sl-color-purple-high);--sl-color-asides-border: var(--sl-color-purple);background-color:var(--sl-color-purple-low)}.starlight-aside--caution{--sl-color-asides-text-accent: var(--sl-color-orange-high);--sl-color-asides-border: var(--sl-color-orange);background-color:var(--sl-color-orange-low)}.starlight-aside--danger{--sl-color-asides-text-accent: var(--sl-color-red-high);--sl-color-asides-border: var(--sl-color-red);background-color:var(--sl-color-red-low)}.starlight-aside__title{display:flex;gap:.5rem;align-items:center;font-size:var(--sl-text-h5);font-weight:600;line-height:var(--sl-line-height-headings);color:var(--sl-color-asides-text-accent)}.starlight-aside__icon{font-size:1.333em;width:1em;height:1em}.starlight-aside__title+.starlight-aside__content{margin-top:.5rem}.starlight-aside__content a{color:var(--sl-color-asides-text-accent)}}@layer starlight.utils{.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);white-space:nowrap;border-width:0}.sl-hidden{display:none}.sl-flex{display:flex}.sl-block{display:block}@media(min-width:50rem){.md\:sl-hidden{display:none}.md\:sl-flex{display:flex}.md\:sl-block{display:block}}@media(min-width:72rem){.lg\:sl-hidden{display:none}.lg\:sl-flex{display:flex}.lg\:sl-block{display:block}}[data-theme=light] .light\:sl-hidden,[data-theme=dark] .dark\:sl-hidden{display:none}[dir=rtl] .rtl\:flip:not(:where([dir=rtl] [dir=ltr] *)){transform:scaleX(-1)}}@layer starlight.content{.sl-markdown-content .sl-heading-wrapper{--sl-anchor-icon-size: .8275em;--sl-anchor-icon-gap: .25em;--sl-anchor-icon-space: calc(var(--sl-anchor-icon-size) + var(--sl-anchor-icon-gap));line-height:var(--sl-line-height-headings)}.sl-markdown-content :not(h1,h2,h3,h4,h5,h6,.sl-heading-wrapper)+:is(.sl-heading-wrapper){margin-top:1.5em}.sl-markdown-content .sl-heading-wrapper.level-h1{font-size:var(--sl-text-h1)}.sl-markdown-content .sl-heading-wrapper.level-h2{font-size:var(--sl-text-h2)}.sl-markdown-content .sl-heading-wrapper.level-h3{font-size:var(--sl-text-h3)}.sl-markdown-content .sl-heading-wrapper.level-h4{font-size:var(--sl-text-h4)}.sl-markdown-content .sl-heading-wrapper.level-h5{font-size:var(--sl-text-h5)}.sl-markdown-content .sl-heading-wrapper.level-h6{font-size:var(--sl-text-h6)}.sl-markdown-content .sl-heading-wrapper>:first-child{display:inline;padding-inline-end:var(--sl-anchor-icon-space)}.sl-markdown-content .sl-anchor-link{position:relative;margin-inline-start:calc(-1 * var(--sl-anchor-icon-size));-webkit-user-select:none;user-select:none;display:inline-flex}.sl-markdown-content .sl-anchor-link:after{content:"";position:absolute;inset:-.25rem -.5rem}.sl-markdown-content .sl-anchor-icon>svg{display:inline;width:var(--sl-anchor-icon-size);vertical-align:top;transform:translateY(calc((var(--sl-line-height-headings) * 1em - var(--sl-anchor-icon-size)) / 2))}@media(hover:hover){.sl-markdown-content .sl-anchor-link{opacity:0}.sl-markdown-content .sl-anchor-link:focus,.sl-markdown-content .sl-heading-wrapper:hover .sl-anchor-link{opacity:1}}}@layer starlight.core{.sl-banner:where(.astro-laz2plt2){--__sl-banner-text: var(--sl-color-banner-text, var(--sl-color-text-invert));padding:var(--sl-nav-pad-y) var(--sl-nav-pad-x);background-color:var(--sl-color-banner-bg, var(--sl-color-bg-accent));color:var(--__sl-banner-text);line-height:var(--sl-line-height-headings);text-align:center;text-wrap:balance;box-shadow:var(--sl-shadow-sm)}.sl-banner:where(.astro-laz2plt2) a{color:var(--__sl-banner-text)}}@layer starlight.core{.content-panel:where(.astro-7nkwcw3z){padding:1.5rem var(--sl-content-pad-x)}.content-panel:where(.astro-7nkwcw3z)+.content-panel:where(.astro-7nkwcw3z){border-top:1px solid var(--sl-color-hairline)}.sl-container:where(.astro-7nkwcw3z){max-width:var(--sl-content-width)}.sl-container:where(.astro-7nkwcw3z)>*+*{margin-top:1.5rem}@media(min-width:72rem){.sl-container:where(.astro-7nkwcw3z){margin-inline:var(--sl-content-margin-inline, auto)}}}@layer starlight.core{p:where(.astro-opzsrvew){border:1px solid var(--sl-color-orange);padding:.75em 1em;background-color:var(--sl-color-orange-low);color:var(--sl-color-orange-high);width:max-content;max-width:100%;align-items:center;gap:.75em;font-size:var(--sl-text-body-sm);line-height:var(--sl-line-height-headings)}}@layer starlight.core{a:where(.astro-eez2twj6){gap:.5rem;align-items:center;text-decoration:none;color:var(--sl-color-gray-3)}a:where(.astro-eez2twj6):hover{color:var(--sl-color-white)}}@layer starlight.core{.pagination-links:where(.astro-u2l5gyhi){display:grid;grid-template-columns:repeat(auto-fit,minmax(min(18rem,100%),1fr));gap:1rem}a:where(.astro-u2l5gyhi){display:flex;align-items:center;justify-content:flex-start;gap:.5rem;width:100%;flex-basis:calc(50% - .5rem);flex-grow:1;border:1px solid var(--sl-color-gray-5);border-radius:.5rem;padding:1rem;text-decoration:none;color:var(--sl-color-gray-2);box-shadow:var(--sl-shadow-md);overflow-wrap:anywhere}:where(.astro-u2l5gyhi)[rel=next]{justify-content:end;text-align:end;flex-direction:row-reverse}a:where(.astro-u2l5gyhi):hover{border-color:var(--sl-color-gray-2)}.link-title:where(.astro-u2l5gyhi){color:var(--sl-color-white);font-size:var(--sl-text-2xl);line-height:var(--sl-line-height-headings)}svg:where(.astro-u2l5gyhi){flex-shrink:0}}@layer starlight.core{footer:where(.astro-3yyafb3n){flex-direction:column;gap:1.5rem}.meta:where(.astro-3yyafb3n){gap:.75rem 3rem;justify-content:space-between;flex-wrap:wrap;margin-top:3rem;font-size:var(--sl-text-sm);color:var(--sl-color-gray-3)}.meta:where(.astro-3yyafb3n)>p:only-child{margin-inline-start:auto}.kudos:where(.astro-3yyafb3n){align-items:center;gap:.5em;margin:1.5rem auto;font-size:var(--sl-text-xs);text-decoration:none;color:var(--sl-color-gray-3)}.kudos:where(.astro-3yyafb3n):hover{color:var(--sl-color-white)}}@layer starlight.components{.kudos:where(.astro-3yyafb3n) svg{color:var(--sl-color-orange)}}@layer starlight.core{label:where(.astro-4yphtoen){--sl-label-icon-size: .875rem;--sl-caret-size: 1.25rem;--sl-inline-padding: .5rem;position:relative;display:flex;align-items:center;gap:.25rem;color:var(--sl-color-gray-1)}label:where(.astro-4yphtoen):hover{color:var(--sl-color-gray-2)}.icon:where(.astro-4yphtoen){position:absolute;top:50%;transform:translateY(-50%);pointer-events:none}select:where(.astro-4yphtoen){border:0;padding-block:.625rem;padding-inline:calc(var(--sl-label-icon-size) + var(--sl-inline-padding) + .25rem) calc(var(--sl-caret-size) + var(--sl-inline-padding) + .25rem);margin-inline:calc(var(--sl-inline-padding) * -1);width:calc(var(--sl-select-width) + var(--sl-inline-padding) * 2);background-color:transparent;text-overflow:ellipsis;color:inherit;cursor:pointer;appearance:none}option:where(.astro-4yphtoen){background-color:var(--sl-color-bg-nav);color:var(--sl-color-gray-1)}@media(min-width:50rem){select:where(.astro-4yphtoen){font-size:var(--sl-text-sm)}}}@layer starlight.components{.label-icon:where(.astro-4yphtoen){font-size:var(--sl-label-icon-size);inset-inline-start:0}.caret:where(.astro-4yphtoen){font-size:var(--sl-caret-size);inset-inline-end:0}}@layer starlight.core{.pagefind-ui__result.svelte-j9e30.svelte-j9e30{list-style-type:none;display:flex;align-items:flex-start;gap:min(calc(40px * var(--pagefind-ui-scale)),3%);padding:calc(30px * var(--pagefind-ui-scale)) 0 calc(40px * var(--pagefind-ui-scale));border-top:solid var(--pagefind-ui-border-width) var(--pagefind-ui-border)}.pagefind-ui__result.svelte-j9e30.svelte-j9e30:last-of-type{border-bottom:solid var(--pagefind-ui-border-width) var(--pagefind-ui-border)}.pagefind-ui__result-thumb.svelte-j9e30.svelte-j9e30{width:min(30%,calc((30% - (100px * var(--pagefind-ui-scale))) * 100000));max-width:calc(120px * var(--pagefind-ui-scale));margin-top:calc(10px * var(--pagefind-ui-scale));aspect-ratio:var(--pagefind-ui-image-box-ratio);position:relative}.pagefind-ui__result-image.svelte-j9e30.svelte-j9e30{display:block;position:absolute;left:50%;transform:translate(-50%);font-size:0;width:auto;height:auto;max-width:100%;max-height:100%;border-radius:var(--pagefind-ui-image-border-radius)}.pagefind-ui__result-inner.svelte-j9e30.svelte-j9e30{flex:1;display:flex;flex-direction:column;align-items:flex-start;margin-top:calc(10px * var(--pagefind-ui-scale))}.pagefind-ui__result-title.svelte-j9e30.svelte-j9e30{display:inline-block;font-weight:700;font-size:calc(21px * var(--pagefind-ui-scale));margin-top:0;margin-bottom:0}.pagefind-ui__result-title.svelte-j9e30 .pagefind-ui__result-link.svelte-j9e30{color:var(--pagefind-ui-text);text-decoration:none}.pagefind-ui__result-title.svelte-j9e30 .pagefind-ui__result-link.svelte-j9e30:hover{text-decoration:underline}.pagefind-ui__result-excerpt.svelte-j9e30.svelte-j9e30{display:inline-block;font-weight:400;font-size:calc(16px * var(--pagefind-ui-scale));margin-top:calc(4px * var(--pagefind-ui-scale));margin-bottom:0;min-width:calc(250px * var(--pagefind-ui-scale))}.pagefind-ui__loading.svelte-j9e30.svelte-j9e30{color:var(--pagefind-ui-text);background-color:var(--pagefind-ui-text);border-radius:var(--pagefind-ui-border-radius);opacity:.1;pointer-events:none}.pagefind-ui__result-tags.svelte-j9e30.svelte-j9e30{list-style-type:none;padding:0;display:flex;gap:calc(20px * var(--pagefind-ui-scale));flex-wrap:wrap;margin-top:calc(20px * var(--pagefind-ui-scale))}.pagefind-ui__result-tag.svelte-j9e30.svelte-j9e30{padding:calc(4px * var(--pagefind-ui-scale)) calc(8px * var(--pagefind-ui-scale));font-size:calc(14px * var(--pagefind-ui-scale));border-radius:var(--pagefind-ui-border-radius);background-color:var(--pagefind-ui-tag)}.pagefind-ui__result.svelte-4xnkmf.svelte-4xnkmf{list-style-type:none;display:flex;align-items:flex-start;gap:min(calc(40px * var(--pagefind-ui-scale)),3%);padding:calc(30px * var(--pagefind-ui-scale)) 0 calc(40px * var(--pagefind-ui-scale));border-top:solid var(--pagefind-ui-border-width) var(--pagefind-ui-border)}.pagefind-ui__result.svelte-4xnkmf.svelte-4xnkmf:last-of-type{border-bottom:solid var(--pagefind-ui-border-width) var(--pagefind-ui-border)}.pagefind-ui__result-nested.svelte-4xnkmf.svelte-4xnkmf{display:flex;flex-direction:column;padding-left:calc(20px * var(--pagefind-ui-scale))}.pagefind-ui__result-nested.svelte-4xnkmf.svelte-4xnkmf:first-of-type{padding-top:calc(10px * var(--pagefind-ui-scale))}.pagefind-ui__result-nested.svelte-4xnkmf .pagefind-ui__result-link.svelte-4xnkmf{font-size:.9em;position:relative}.pagefind-ui__result-nested.svelte-4xnkmf .pagefind-ui__result-link.svelte-4xnkmf:before{content:"⤷ ";position:absolute;top:0;right:calc(100% + .1em)}.pagefind-ui__result-thumb.svelte-4xnkmf.svelte-4xnkmf{width:min(30%,calc((30% - (100px * var(--pagefind-ui-scale))) * 100000));max-width:calc(120px * var(--pagefind-ui-scale));margin-top:calc(10px * var(--pagefind-ui-scale));aspect-ratio:var(--pagefind-ui-image-box-ratio);position:relative}.pagefind-ui__result-image.svelte-4xnkmf.svelte-4xnkmf{display:block;position:absolute;left:50%;transform:translate(-50%);font-size:0;width:auto;height:auto;max-width:100%;max-height:100%;border-radius:var(--pagefind-ui-image-border-radius)}.pagefind-ui__result-inner.svelte-4xnkmf.svelte-4xnkmf{flex:1;display:flex;flex-direction:column;align-items:flex-start;margin-top:calc(10px * var(--pagefind-ui-scale))}.pagefind-ui__result-title.svelte-4xnkmf.svelte-4xnkmf{display:inline-block;font-weight:700;font-size:calc(21px * var(--pagefind-ui-scale));margin-top:0;margin-bottom:0}.pagefind-ui__result-title.svelte-4xnkmf .pagefind-ui__result-link.svelte-4xnkmf{color:var(--pagefind-ui-text);text-decoration:none}.pagefind-ui__result-title.svelte-4xnkmf .pagefind-ui__result-link.svelte-4xnkmf:hover{text-decoration:underline}.pagefind-ui__result-excerpt.svelte-4xnkmf.svelte-4xnkmf{display:inline-block;font-weight:400;font-size:calc(16px * var(--pagefind-ui-scale));margin-top:calc(4px * var(--pagefind-ui-scale));margin-bottom:0;min-width:calc(250px * var(--pagefind-ui-scale))}.pagefind-ui__loading.svelte-4xnkmf.svelte-4xnkmf{color:var(--pagefind-ui-text);background-color:var(--pagefind-ui-text);border-radius:var(--pagefind-ui-border-radius);opacity:.1;pointer-events:none}.pagefind-ui__result-tags.svelte-4xnkmf.svelte-4xnkmf{list-style-type:none;padding:0;display:flex;gap:calc(20px * var(--pagefind-ui-scale));flex-wrap:wrap;margin-top:calc(20px * var(--pagefind-ui-scale))}.pagefind-ui__result-tag.svelte-4xnkmf.svelte-4xnkmf{padding:calc(4px * var(--pagefind-ui-scale)) calc(8px * var(--pagefind-ui-scale));font-size:calc(14px * 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.pagefind-ui__result-nested:before{content:"";position:absolute;inset-block:0;inset-inline-start:var(--sl-search-tree-diagram-inline-start);width:var(--sl-search-tree-diagram-size);background:var(--sl-color-gray-4);-webkit-mask:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' stroke='currentColor' stroke-linecap='round' viewBox='0 0 16 1000' preserveAspectRatio='xMinYMin slice'%3E%3Cpath d='M8 0v1000m6-988H8'/%3E%3C/svg%3E") 0% 0% / 100% no-repeat;mask:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' stroke='currentColor' stroke-linecap='round' viewBox='0 0 16 1000' preserveAspectRatio='xMinYMin slice'%3E%3Cpath d='M8 0v1000m6-988H8'/%3E%3C/svg%3E") 0% 0% / 100% no-repeat}#starlight__search .pagefind-ui__result-nested:last-of-type:before{-webkit-mask-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' stroke='currentColor' stroke-linecap='round' stroke-linejoin='round' viewBox='0 0 16 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starlight.core{site-search:where(.astro-v37mnknz){display:contents}button:where(.astro-v37mnknz)[data-open-modal]{display:flex;align-items:center;gap:.5rem;border:0;background-color:transparent;color:var(--sl-color-gray-1);cursor:pointer;height:2.5rem;font-size:var(--sl-text-xl)}@media(min-width:50rem){button:where(.astro-v37mnknz)[data-open-modal]{border:1px solid var(--sl-color-gray-5);border-radius:.5rem;padding-inline-start:.75rem;padding-inline-end:.5rem;background-color:var(--sl-color-black);color:var(--sl-color-gray-2);font-size:var(--sl-text-sm);width:100%;max-width:22rem}button:where(.astro-v37mnknz)[data-open-modal]:hover{border-color:var(--sl-color-gray-2);color:var(--sl-color-white)}button:where(.astro-v37mnknz)[data-open-modal]>:where(.astro-v37mnknz):last-child{margin-inline-start:auto}}button:where(.astro-v37mnknz)>kbd:where(.astro-v37mnknz){border-radius:.25rem;font-size:var(--sl-text-2xs);gap:.25em;padding-inline:.375rem;background-color:var(--sl-color-gray-6)}kbd:where(.astro-v37mnknz){font-family:var(--__sl-font)}dialog:where(.astro-v37mnknz){margin:0;background-color:var(--sl-color-gray-6);border:1px solid var(--sl-color-gray-5);width:100%;max-width:100%;height:100%;max-height:100%;box-shadow:var(--sl-shadow-lg)}dialog:where(.astro-v37mnknz)[open]{display:flex}dialog:where(.astro-v37mnknz)::backdrop{background-color:var(--sl-color-backdrop-overlay);-webkit-backdrop-filter:blur(.25rem);backdrop-filter:blur(.25rem)}.dialog-frame:where(.astro-v37mnknz){position:relative;overflow:auto;flex-direction:column;flex-grow:1;gap:1rem;padding:1rem}button:where(.astro-v37mnknz)[data-close-modal]{position:absolute;z-index:1;align-items:center;align-self:flex-end;height:calc(64px * var(--pagefind-ui-scale));padding:.25rem;border:0;background:transparent;cursor:pointer;color:var(--sl-color-text-accent)}#starlight__search:where(.astro-v37mnknz){--pagefind-ui-primary: var(--sl-color-text);--pagefind-ui-text: var(--sl-color-gray-2);--pagefind-ui-font: var(--__sl-font);--pagefind-ui-background: var(--sl-color-black);--pagefind-ui-border: var(--sl-color-gray-5);--pagefind-ui-border-width: 1px;--pagefind-ui-tag: var(--sl-color-gray-5);--sl-search-cancel-space: 5rem}:root[data-theme=light] #starlight__search:where(.astro-v37mnknz){--pagefind-ui-tag: var(--sl-color-gray-6)}@media(min-width:50rem){#starlight__search:where(.astro-v37mnknz){--sl-search-cancel-space: 0px}dialog:where(.astro-v37mnknz){margin:4rem auto auto;border-radius:.5rem;width:90%;max-width:40rem;height:max-content;min-height:15rem;max-height:calc(100% - 8rem)}.dialog-frame:where(.astro-v37mnknz){padding:1.5rem}}}@layer starlight.core{.site-title:where(.astro-m46x6ez3){align-items:center;gap:var(--sl-nav-gap);font-size:var(--sl-text-h4);font-weight:600;color:var(--sl-color-text-accent);text-decoration:none;white-space:nowrap;min-width:0}span:where(.astro-m46x6ez3){overflow:hidden}img:where(.astro-m46x6ez3){height:calc(var(--sl-nav-height) - 2 * var(--sl-nav-pad-y));width:auto;max-width:100%;object-fit:contain;object-position:0 50%}}@layer starlight.core{a:where(.astro-wy4te6ga){color:var(--sl-color-text-accent);padding:.5em;margin:-.5em}a:where(.astro-wy4te6ga):hover{opacity:.66}}@layer starlight.core{.header:where(.astro-kmkmnagf){display:flex;gap:var(--sl-nav-gap);justify-content:space-between;align-items:center;height:100%}.title-wrapper:where(.astro-kmkmnagf){overflow:clip;padding:.25rem;margin:-.25rem;min-width:0}.right-group:where(.astro-kmkmnagf),.social-icons:where(.astro-kmkmnagf){gap:1rem;align-items:center}.social-icons:where(.astro-kmkmnagf):after{content:"";height:2rem;border-inline-end:1px solid var(--sl-color-gray-5)}@media(min-width:50rem){:root[data-has-sidebar]{--__sidebar-pad: calc(2 * var(--sl-nav-pad-x))}:root:not([data-has-toc]){--__toc-width: 0rem}.header:where(.astro-kmkmnagf){--__sidebar-width: max(0rem, var(--sl-content-inline-start, 0rem) - var(--sl-nav-pad-x));--__main-column-fr: calc( ( 100% + var(--__sidebar-pad, 0rem) - var(--__toc-width, var(--sl-sidebar-width)) - (2 * var(--__toc-width, var(--sl-nav-pad-x))) - var(--sl-content-inline-start, 0rem) - var(--sl-content-width) ) / 2 );display:grid;grid-template-columns:minmax(calc(var(--__sidebar-width) + max(0rem,var(--__main-column-fr) - var(--sl-nav-gap))),auto) 1fr auto;align-content:center}}}@layer starlight.core{.hero:where(.astro-jbfsktt5){display:grid;align-items:center;gap:1rem;padding-bottom:1rem}.hero:where(.astro-jbfsktt5)>img:where(.astro-jbfsktt5),.hero:where(.astro-jbfsktt5)>.hero-html:where(.astro-jbfsktt5){object-fit:contain;width:min(70%,20rem);height:auto;margin-inline:auto}.stack:where(.astro-jbfsktt5){flex-direction:column;gap:clamp(1.5rem,calc(1.5rem + 1vw),2rem);text-align:center}.copy:where(.astro-jbfsktt5){flex-direction:column;gap:1rem;align-items:center}.copy:where(.astro-jbfsktt5)>:where(.astro-jbfsktt5){max-width:50ch}h1:where(.astro-jbfsktt5){font-size:clamp(var(--sl-text-3xl),calc(.25rem + 5vw),var(--sl-text-6xl));line-height:var(--sl-line-height-headings);font-weight:600;color:var(--sl-color-white)}.tagline:where(.astro-jbfsktt5){font-size:clamp(var(--sl-text-base),calc(.0625rem + 2vw),var(--sl-text-xl));color:var(--sl-color-gray-2)}.actions:where(.astro-jbfsktt5){gap:1rem 2rem;flex-wrap:wrap;justify-content:center}@media(min-width:50rem){.hero:where(.astro-jbfsktt5){grid-template-columns:7fr 4fr;gap:3%;padding-block:clamp(2.5rem,calc(1rem + 10vmin),10rem)}.hero:where(.astro-jbfsktt5)>img:where(.astro-jbfsktt5),.hero:where(.astro-jbfsktt5)>.hero-html:where(.astro-jbfsktt5){order:2;width:min(100%,25rem)}.stack:where(.astro-jbfsktt5){text-align:start}.copy:where(.astro-jbfsktt5){align-items:flex-start}.actions:where(.astro-jbfsktt5){justify-content:flex-start}}}@layer starlight.content{.sl-markdown-content :not(a,strong,em,del,span,input,code,br)+:not(a,strong,em,del,span,input,code,br,:where(.not-content *)){margin-top:var(--sl-content-gap-y)}.sl-markdown-content :not(a,strong,em,del,span,input,code,br)+:is(astro-island,astro-slot)>:not(a,strong,em,del,span,input,code,br,:where(.not-content *)):first-child{margin-top:var(--sl-content-gap-y)}.sl-markdown-content :not(h1,h2,h3,h4,h5,h6)+:is(h1,h2,h3,h4,h5,h6):not(:where(.not-content *)){margin-top:1.5em}.sl-markdown-content li+li:not(:where(.not-content *)),.sl-markdown-content dt+dt:not(:where(.not-content *)),.sl-markdown-content dt+dd:not(:where(.not-content *)),.sl-markdown-content dd+dd:not(:where(.not-content *)){margin-top:.25rem}.sl-markdown-content li:not(:where(.not-content *)){overflow-wrap:anywhere}.sl-markdown-content li>:is(:last-child:not(li,ul,ol,a,strong,em,del,span,input,code,br,script,:where(.not-content *)),:not(script):has(~script:last-child):not(:has(~:not(script)))){margin-bottom:1.25rem}.sl-markdown-content dt:not(:where(.not-content *)){font-weight:700}.sl-markdown-content dd:not(:where(.not-content *)){padding-inline-start:1rem}.sl-markdown-content :is(h1,h2,h3,h4,h5,h6):not(:where(.not-content *)){color:var(--sl-color-white);line-height:var(--sl-line-height-headings);font-weight:600}.sl-markdown-content :is(img,picture,video,canvas,svg,iframe):not(:where(.not-content *)){display:block;max-width:100%;height:auto}.sl-markdown-content h1:not(:where(.not-content *)){font-size:var(--sl-text-h1)}.sl-markdown-content h2:not(:where(.not-content *)){font-size:var(--sl-text-h2)}.sl-markdown-content h3:not(:where(.not-content *)){font-size:var(--sl-text-h3)}.sl-markdown-content h4:not(:where(.not-content *)){font-size:var(--sl-text-h4)}.sl-markdown-content h5:not(:where(.not-content *)){font-size:var(--sl-text-h5)}.sl-markdown-content h6:not(:where(.not-content *)){font-size:var(--sl-text-h6)}.sl-markdown-content a:not(:where(.not-content *)){color:var(--sl-color-text-accent)}.sl-markdown-content a:hover:not(:where(.not-content *)){color:var(--sl-color-white)}.sl-markdown-content code:not(:where(.not-content *)){background-color:var(--sl-color-bg-inline-code);margin-block:-.125rem;padding:.125rem .375rem;font-size:var(--sl-text-code-sm)}.sl-markdown-content :is(h1,h2,h3,h4,h5,h6) code{font-size:inherit}.sl-markdown-content pre:not(:where(.not-content *)){border:1px solid var(--sl-color-gray-5);padding:.75rem 1rem;font-size:var(--sl-text-code);tab-size:2}.sl-markdown-content pre code:not(:where(.not-content *)){all:unset;font-family:var(--__sl-font-mono)}.sl-markdown-content blockquote:not(:where(.not-content *)){border-inline-start:1px solid var(--sl-color-gray-5);padding-inline-start:1rem}.sl-markdown-content table:not(:where(.not-content *)){display:block;overflow:auto;border-spacing:0}.sl-markdown-content :is(th,td):not(:where(.not-content *)){border-bottom:1px solid var(--sl-color-gray-5);padding:.5rem 1rem;vertical-align:baseline}.sl-markdown-content :is(th:first-child,td:first-child):not(:where(.not-content *)){padding-inline-start:0}.sl-markdown-content :is(th:last-child,td:last-child):not(:where(.not-content *)){padding-inline-end:0}.sl-markdown-content th:not(:where(.not-content *)){color:var(--sl-color-white);font-weight:600}.sl-markdown-content th:not([align]):not(:where(.not-content *)){text-align:start}.sl-markdown-content .starlight-aside :is(th,td,hr,blockquote):not(:where(.not-content *)){border-color:var(--sl-color-gray-4)}@supports (border-color: color-mix(in srgb,var(--sl-color-asides-text-accent) 30%,transparent)){.sl-markdown-content .starlight-aside :is(th,td,hr,blockquote):not(:where(.not-content *)){border-color:color-mix(in srgb,var(--sl-color-asides-text-accent) 30%,transparent)}}@supports (border-color: color-mix(in srgb,var(--sl-color-asides-text-accent) 12%,transparent)){.sl-markdown-content .starlight-aside code:not(:where(.not-content *)){background-color:color-mix(in srgb,var(--sl-color-asides-text-accent) 12%,transparent)}}.sl-markdown-content hr:not(:where(.not-content *)){border:0;border-bottom:1px solid var(--sl-color-hairline)}.sl-markdown-content details:not(:where(.not-content *)){--sl-details-border-color: var(--sl-color-gray-5);--sl-details-border-color--hover: var(--sl-color-text-accent);border-inline-start:2px solid var(--sl-details-border-color);padding-inline-start:1rem}.sl-markdown-content details:not([open]):hover:not(:where(.not-content *)),.sl-markdown-content details:has(>summary:hover):not(:where(.not-content *)){border-color:var(--sl-details-border-color--hover)}.sl-markdown-content summary:not(:where(.not-content *)){color:var(--sl-color-white);cursor:pointer;display:block;font-weight:600;margin-inline-start:-.5rem;padding-inline-start:.5rem}.sl-markdown-content details[open]>summary:not(:where(.not-content *)){margin-bottom:1rem}.sl-markdown-content summary:not(:where(.not-content *))::marker,.sl-markdown-content summary:not(:where(.not-content *))::-webkit-details-marker{display:none}.sl-markdown-content summary:not(:where(.not-content *)):before{--sl-details-marker-size: 1.25rem;background-color:currentColor;content:"";display:inline-block;height:var(--sl-details-marker-size);width:var(--sl-details-marker-size);margin-inline:calc((var(--sl-details-marker-size) / 4) * -1) .25rem;vertical-align:middle;-webkit-mask-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Cpath d='M14.8 11.3 10.6 7a1 1 0 1 0-1.4 1.5l3.5 3.5-3.5 3.5a1 1 0 0 0 0 1.4 1 1 0 0 0 .7.3 1 1 0 0 0 .7-.3l4.2-4.2a1 1 0 0 0 0-1.4Z'/%3E%3C/svg%3E%0A");mask-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Cpath d='M14.8 11.3 10.6 7a1 1 0 1 0-1.4 1.5l3.5 3.5-3.5 3.5a1 1 0 0 0 0 1.4 1 1 0 0 0 .7.3 1 1 0 0 0 .7-.3l4.2-4.2a1 1 0 0 0 0-1.4Z'/%3E%3C/svg%3E%0A");-webkit-mask-repeat:no-repeat;mask-repeat:no-repeat}@media(prefers-reduced-motion:no-preference){.sl-markdown-content summary:not(:where(.not-content *)):before{transition:transform .2s ease-in-out}}.sl-markdown-content details[open]>summary:not(:where(.not-content *)):before{transform:rotate(90deg)}[dir=rtl] .sl-markdown-content summary:not(:where(.not-content *)):before,.sl-markdown-content [dir=rtl] summary:not(:where(.not-content *)):before{transform:rotate(180deg)}.sl-markdown-content summary:not(:where(.not-content *)) p:only-child{display:inline}.sl-markdown-content .starlight-aside details:not(:where(.not-content *)){--sl-details-border-color: var(--sl-color-asides-border);--sl-details-border-color--hover: var(--sl-color-asides-text-accent)}}@layer starlight.core{[data-mobile-menu-expanded]{overflow:hidden}@media(min-width:50rem){[data-mobile-menu-expanded]{overflow:auto}}}@layer starlight.core{button:where(.astro-jif73yzw){position:fixed;top:calc((var(--sl-nav-height) - var(--sl-menu-button-size)) / 2);inset-inline-end:var(--sl-nav-pad-x);z-index:var(--sl-z-index-navbar);border:0;border-radius:50%;width:var(--sl-menu-button-size);height:var(--sl-menu-button-size);padding:.5rem;background-color:var(--sl-color-white);color:var(--sl-color-black);box-shadow:var(--sl-shadow-md);cursor:pointer}:where(.astro-jif73yzw)[aria-expanded=true] button:where(.astro-jif73yzw){background-color:var(--sl-color-gray-2);box-shadow:none}:where(.astro-jif73yzw)[aria-expanded=true] button:where(.astro-jif73yzw) .open-menu:where(.astro-jif73yzw){display:none}:where(.astro-jif73yzw):not([aria-expanded=true]) button:where(.astro-jif73yzw) .close-menu:where(.astro-jif73yzw){display:none}[data-theme=light] button:where(.astro-jif73yzw){background-color:var(--sl-color-black);color:var(--sl-color-white)}[data-theme=light] :where(.astro-jif73yzw)[aria-expanded=true] button:where(.astro-jif73yzw){background-color:var(--sl-color-gray-5)}}@layer starlight.core{.page:where(.astro-vrdttmbt){flex-direction:column;min-height:100vh}.header:where(.astro-vrdttmbt){z-index:var(--sl-z-index-navbar);position:fixed;inset-inline-start:0;inset-block-start:0;width:100%;height:var(--sl-nav-height);border-bottom:1px solid var(--sl-color-hairline-shade);padding:var(--sl-nav-pad-y) var(--sl-nav-pad-x);padding-inline-end:var(--sl-nav-pad-x);background-color:var(--sl-color-bg-nav)}[data-has-sidebar] .header:where(.astro-vrdttmbt){padding-inline-end:calc(var(--sl-nav-gap) + var(--sl-nav-pad-x) + var(--sl-menu-button-size))}.sidebar-pane:where(.astro-vrdttmbt){visibility:var(--sl-sidebar-visibility, hidden);position:fixed;z-index:var(--sl-z-index-menu);inset-block:var(--sl-nav-height) 0;inset-inline-start:0;width:100%;background-color:var(--sl-color-black);overflow-y:auto}[aria-expanded=true]~.sidebar-pane:where(.astro-vrdttmbt){--sl-sidebar-visibility: visible}.sidebar-content:where(.astro-vrdttmbt){height:100%;min-height:max-content;padding:1rem var(--sl-sidebar-pad-x) 0;flex-direction:column;gap:1rem}@media(min-width:50rem){.sidebar-content:where(.astro-vrdttmbt):after{content:"";padding-bottom:1px}}.main-frame:where(.astro-vrdttmbt){padding-top:calc(var(--sl-nav-height) + var(--sl-mobile-toc-height));padding-inline-start:var(--sl-content-inline-start)}@media(min-width:50rem){[data-has-sidebar] .header:where(.astro-vrdttmbt){padding-inline-end:var(--sl-nav-pad-x)}.sidebar-pane:where(.astro-vrdttmbt){--sl-sidebar-visibility: visible;width:var(--sl-sidebar-width);background-color:var(--sl-color-bg-sidebar);border-inline-end:1px solid var(--sl-color-hairline-shade)}}}@layer starlight.core{ul:where(.astro-g2bywc46){padding:0;list-style:none}a:where(.astro-g2bywc46){--pad-inline: .5rem;display:block;border-radius:.25rem;padding-block:.25rem;padding-inline:calc(1rem * var(--depth) + var(--pad-inline)) var(--pad-inline);line-height:1.25}a:where(.astro-g2bywc46)[aria-current=true]{color:var(--sl-color-text-accent)}.isMobile:where(.astro-g2bywc46) a:where(.astro-g2bywc46){--pad-inline: 1rem;display:flex;justify-content:space-between;gap:var(--pad-inline);border-top:1px solid var(--sl-color-gray-6);border-radius:0;padding-block:.5rem;color:var(--sl-color-text);font-size:var(--sl-text-sm);text-decoration:none;outline-offset:var(--sl-outline-offset-inside)}.isMobile:where(.astro-g2bywc46):first-child>li:where(.astro-g2bywc46):first-child>a:where(.astro-g2bywc46){border-top:0}.isMobile:where(.astro-g2bywc46) a:where(.astro-g2bywc46)[aria-current=true],.isMobile:where(.astro-g2bywc46) a:where(.astro-g2bywc46)[aria-current=true]:hover,.isMobile:where(.astro-g2bywc46) a:where(.astro-g2bywc46)[aria-current=true]:focus{color:var(--sl-color-white);background-color:unset}.isMobile:where(.astro-g2bywc46) a:where(.astro-g2bywc46)[aria-current=true]:after{content:"";width:1rem;background-color:var(--sl-color-text-accent);-webkit-mask-image:url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0naHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmcnIHZpZXdCb3g9JzAgMCAxNCAxNCc+PHBhdGggZD0nTTEwLjkxNCA0LjIwNmEuNTgzLjU4MyAwIDAgMC0uODI4IDBMNS43NCA4LjU1NyAzLjkxNCA2LjcyNmEuNTk2LjU5NiAwIDAgMC0uODI4Ljg1N2wyLjI0IDIuMjRhLjU4My41ODMgMCAwIDAgLjgyOCAwbDQuNzYtNC43NmEuNTgzLjU4MyAwIDAgMCAwLS44NTdaJy8+PC9zdmc+Cg==);mask-image:url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0naHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmcnIHZpZXdCb3g9JzAgMCAxNCAxNCc+PHBhdGggZD0nTTEwLjkxNCA0LjIwNmEuNTgzLjU4MyAwIDAgMC0uODI4IDBMNS43NCA4LjU1NyAzLjkxNCA2LjcyNmEuNTk2LjU5NiAwIDAgMC0uODI4Ljg1N2wyLjI0IDIuMjRhLjU4My41ODMgMCAwIDAgLjgyOCAwbDQuNzYtNC43NmEuNTgzLjU4MyAwIDAgMCAwLS44NTdaJy8+PC9zdmc+Cg==);-webkit-mask-repeat:no-repeat;mask-repeat:no-repeat;flex-shrink:0}}@layer starlight.core{nav:where(.astro-doynk5tl){position:fixed;z-index:var(--sl-z-index-toc);top:calc(var(--sl-nav-height) - 1px);inset-inline:0;border-top:1px solid var(--sl-color-gray-5);background-color:var(--sl-color-bg-nav)}@media(min-width:50rem){nav:where(.astro-doynk5tl){inset-inline-start:var(--sl-content-inline-start, 0)}}summary:where(.astro-doynk5tl){gap:.5rem;align-items:center;height:var(--sl-mobile-toc-height);border-bottom:1px solid var(--sl-color-hairline-shade);padding:.5rem 1rem;font-size:var(--sl-text-xs);outline-offset:var(--sl-outline-offset-inside)}summary:where(.astro-doynk5tl)::marker,summary:where(.astro-doynk5tl)::-webkit-details-marker{display:none}.toggle:where(.astro-doynk5tl){flex-shrink:0;gap:1rem;align-items:center;justify-content:space-between;border:1px solid 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Try one of the following searches:",searching:"Searching for [SEARCH_TERM]..."},Ba={thanks_to:tt,comments:rt,direction:st,strings:at},lt={};F(lt,{comments:()=>it,default:()=>va,direction:()=>ot,strings:()=>_t,thanks_to:()=>nt});var nt="Pablo Villaverde ",it="",ot="ltr",_t={placeholder:"Buscar",clear_search:"Limpiar",load_more:"Ver más resultados",search_label:"Buscar en este sitio",filters_label:"Filtros",zero_results:"No se encontraron resultados para [SEARCH_TERM]",many_results:"[COUNT] resultados encontrados para [SEARCH_TERM]",one_result:"[COUNT] resultado encontrado para [SEARCH_TERM]",alt_search:"No se encontraron resultados para [SEARCH_TERM]. Mostrando en su lugar resultados para [DIFFERENT_TERM]",search_suggestion:"No se encontraron resultados para [SEARCH_TERM]. Prueba una de las siguientes búsquedas:",searching:"Buscando [SEARCH_TERM]..."},va={thanks_to:nt,comments:it,direction:ot,strings:_t},ct={};F(ct,{comments:()=>ft,default:()=>Ta,direction:()=>ht,strings:()=>dt,thanks_to:()=>Et});var Et="Mikel Larreategi ",ft="",ht="ltr",dt={placeholder:"Bilatu",clear_search:"Garbitu",load_more:"Kargatu emaitza gehiagi",search_label:"Bilatu",filters_label:"Iragazkiak",zero_results:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat",many_results:"[COUNT] emaitza [SEARCH_TERM] bilaketarentzat",one_result:"Emaitza bat [COUNT] [SEARCH_TERM] bilaketarentzat",alt_search:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat. [DIFFERENT_TERM] bilaketaren emaitzak erakusten",search_suggestion:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat. Saiatu hauetako beste bateikin:",searching:"[SEARCH_TERM] bilatzen..."},Ta={thanks_to:Et,comments:ft,direction:ht,strings:dt},Ct={};F(Ct,{comments:()=>gt,default:()=>pa,direction:()=>At,strings:()=>Rt,thanks_to:()=>mt});var mt="Ali Khaleqi Yekta ",gt="",At="rtl",Rt={placeholder:"جستجو",clear_search:"پاکسازی",load_more:"بارگذاری نتایج بیشتر",search_label:"جستجو در سایت",filters_label:"فیلترها",zero_results:"نتیجه‌ای برای [SEARCH_TERM] یافت نشد",many_results:"[COUNT] نتیجه برای [SEARCH_TERM] یافت شد",one_result:"[COUNT] نتیجه برای [SEARCH_TERM] یافت شد",alt_search:"نتیجه‌ای برای [SEARCH_TERM] یافت نشد. در عوض نتایج برای [DIFFERENT_TERM] نمایش داده می‌شود",search_suggestion:"نتیجه‌ای برای [SEARCH_TERM] یافت نشد. یکی از جستجوهای زیر را امتحان کنید:",searching:"در حال جستجوی [SEARCH_TERM]..."},pa={thanks_to:mt,comments:gt,direction:At,strings:Rt},Bt={};F(Bt,{comments:()=>Tt,default:()=>ka,direction:()=>pt,strings:()=>kt,thanks_to:()=>vt});var vt="Valtteri Laitinen ",Tt="",pt="ltr",kt={placeholder:"Haku",clear_search:"Tyhjennä",load_more:"Lataa lisää tuloksia",search_label:"Hae tältä sivustolta",filters_label:"Suodattimet",zero_results:"Ei tuloksia haulle [SEARCH_TERM]",many_results:"[COUNT] tulosta haulle [SEARCH_TERM]",one_result:"[COUNT] tulos haulle [SEARCH_TERM]",alt_search:"Ei tuloksia haulle [SEARCH_TERM]. Näytetään tulokset sen sijaan haulle [DIFFERENT_TERM]",search_suggestion:"Ei tuloksia haulle [SEARCH_TERM]. Kokeile jotain seuraavista:",searching:"Haetaan [SEARCH_TERM]..."},ka={thanks_to:vt,comments:Tt,direction:pt,strings:kt},Ft={};F(Ft,{comments:()=>bt,default:()=>Fa,direction:()=>Mt,strings:()=>St,thanks_to:()=>Dt});var Dt="Nicolas Friedli ",bt="",Mt="ltr",St={placeholder:"Rechercher",clear_search:"Nettoyer",load_more:"Charger plus de résultats",search_label:"Recherche sur ce site",filters_label:"Filtres",zero_results:"Pas de résultat pour [SEARCH_TERM]",many_results:"[COUNT] résultats pour [SEARCH_TERM]",one_result:"[COUNT] résultat pour [SEARCH_TERM]",alt_search:"Pas de résultat pour [SEARCH_TERM]. Montre les résultats pour [DIFFERENT_TERM] à la place",search_suggestion:"Pas de résultat pour [SEARCH_TERM]. Essayer une des recherches suivantes:",searching:"Recherche [SEARCH_TERM]..."},Fa={thanks_to:Dt,comments:bt,direction:Mt,strings:St},Ht={};F(Ht,{comments:()=>yt,default:()=>Da,direction:()=>wt,strings:()=>zt,thanks_to:()=>Nt});var Nt="Pablo Villaverde ",yt="",wt="ltr",zt={placeholder:"Buscar",clear_search:"Limpar",load_more:"Ver máis resultados",search_label:"Buscar neste sitio",filters_label:"Filtros",zero_results:"Non se atoparon resultados para [SEARCH_TERM]",many_results:"[COUNT] resultados atopados para [SEARCH_TERM]",one_result:"[COUNT] resultado atopado para [SEARCH_TERM]",alt_search:"Non se atoparon resultados para [SEARCH_TERM]. Amosando no seu lugar resultados para [DIFFERENT_TERM]",search_suggestion:"Non se atoparon resultados para [SEARCH_TERM]. Probe unha das seguintes pesquisas:",searching:"Buscando [SEARCH_TERM]..."},Da={thanks_to:Nt,comments:yt,direction:wt,strings:zt},jt={};F(jt,{comments:()=>Ut,default:()=>ba,direction:()=>It,strings:()=>Pt,thanks_to:()=>Ot});var Ot="Nir Tamir ",Ut="",It="rtl",Pt={placeholder:"חיפוש",clear_search:"ניקוי",load_more:"עוד תוצאות",search_label:"חיפוש באתר זה",filters_label:"מסננים",zero_results:"לא נמצאו תוצאות עבור [SEARCH_TERM]",many_results:"נמצאו [COUNT] תוצאות עבור [SEARCH_TERM]",one_result:"נמצאה תוצאה אחת עבור [SEARCH_TERM]",alt_search:"לא נמצאו תוצאות עבור [SEARCH_TERM]. מוצגות תוצאות עבור [DIFFERENT_TERM]",search_suggestion:"לא נמצאו תוצאות עבור [SEARCH_TERM]. נסו אחד מהחיפושים הבאים:",searching:"מחפש את [SEARCH_TERM]..."},ba={thanks_to:Ot,comments:Ut,direction:It,strings:Pt},Lt={};F(Lt,{comments:()=>qt,default:()=>Ma,direction:()=>Vt,strings:()=>Kt,thanks_to:()=>xt});var xt="Amit Yadav ",qt="",Vt="ltr",Kt={placeholder:"खोजें",clear_search:"साफ करें",load_more:"और अधिक परिणाम लोड करें",search_label:"इस साइट में खोजें",filters_label:"फ़िल्टर",zero_results:"कोई परिणाम [SEARCH_TERM] के लिए नहीं मिला",many_results:"[COUNT] परिणाम [SEARCH_TERM] के लिए मिले",one_result:"[COUNT] परिणाम [SEARCH_TERM] के लिए मिला",alt_search:"[SEARCH_TERM] के लिए कोई परिणाम नहीं मिला। इसके बजाय [DIFFERENT_TERM] के लिए परिणाम दिखा रहा है",search_suggestion:"[SEARCH_TERM] के लिए कोई परिणाम नहीं मिला। निम्नलिखित खोजों में से कोई एक आज़माएं:",searching:"[SEARCH_TERM] की खोज की जा रही है..."},Ma={thanks_to:xt,comments:qt,direction:Vt,strings:Kt},Gt={};F(Gt,{comments:()=>Wt,default:()=>Sa,direction:()=>Zt,strings:()=>Yt,thanks_to:()=>Jt});var Jt="Diomed ",Wt="",Zt="ltr",Yt={placeholder:"Traži",clear_search:"Očisti",load_more:"Učitaj više rezultata",search_label:"Pretraži ovu stranicu",filters_label:"Filteri",zero_results:"Nema rezultata za [SEARCH_TERM]",many_results:"[COUNT] rezultata za [SEARCH_TERM]",one_result:"[COUNT] rezultat za [SEARCH_TERM]",alt_search:"Nema rezultata za [SEARCH_TERM]. Prikazujem rezultate za [DIFFERENT_TERM]",search_suggestion:"Nema rezultata za [SEARCH_TERM]. Pokušaj s jednom od ovih pretraga:",searching:"Pretražujem [SEARCH_TERM]..."},Sa={thanks_to:Jt,comments:Wt,direction:Zt,strings:Yt},Xt={};F(Xt,{comments:()=>$t,default:()=>Ha,direction:()=>e0,strings:()=>u0,thanks_to:()=>Qt});var Qt="Adam Laki ",$t="",e0="ltr",u0={placeholder:"Keresés",clear_search:"Törlés",load_more:"További találatok betöltése",search_label:"Keresés az oldalon",filters_label:"Szűrés",zero_results:"Nincs találat a(z) [SEARCH_TERM] kifejezésre",many_results:"[COUNT] db találat a(z) [SEARCH_TERM] kifejezésre",one_result:"[COUNT] db találat a(z) [SEARCH_TERM] kifejezésre",alt_search:"Nincs találat a(z) [SEARCH_TERM] kifejezésre. Találatok mutatása inkább a(z) [DIFFERENT_TERM] kifejezésre",search_suggestion:"Nincs találat a(z) [SEARCH_TERM] kifejezésre. Próbáld meg a következő keresések egyikét:",searching:"Keresés a(z) [SEARCH_TERM] kifejezésre..."},Ha={thanks_to:Qt,comments:$t,direction:e0,strings:u0},t0={};F(t0,{comments:()=>s0,default:()=>Na,direction:()=>a0,strings:()=>l0,thanks_to:()=>r0});var r0="Nixentric",s0="",a0="ltr",l0={placeholder:"Cari",clear_search:"Bersihkan",load_more:"Muat lebih banyak hasil",search_label:"Telusuri situs ini",filters_label:"Filter",zero_results:"[SEARCH_TERM] tidak ditemukan",many_results:"Ditemukan [COUNT] hasil untuk [SEARCH_TERM]",one_result:"Ditemukan [COUNT] hasil untuk [SEARCH_TERM]",alt_search:"[SEARCH_TERM] tidak ditemukan. Menampilkan hasil [DIFFERENT_TERM] sebagai gantinya",search_suggestion:"[SEARCH_TERM] tidak ditemukan. Coba salah satu pencarian berikut ini:",searching:"Mencari [SEARCH_TERM]..."},Na={thanks_to:r0,comments:s0,direction:a0,strings:l0},n0={};F(n0,{comments:()=>o0,default:()=>ya,direction:()=>_0,strings:()=>c0,thanks_to:()=>i0});var i0="Cosette Bruhns Alonso, Andrew Janco ",o0="",_0="ltr",c0={placeholder:"Cerca",clear_search:"Cancella la cronologia",load_more:"Mostra più risultati",search_label:"Cerca nel sito",filters_label:"Filtri di ricerca",zero_results:"Nessun risultato per [SEARCH_TERM]",many_results:"[COUNT] risultati per [SEARCH_TERM]",one_result:"[COUNT] risultato per [SEARCH_TERM]",alt_search:"Nessun risultato per [SEARCH_TERM]. Mostrando risultati per [DIFFERENT_TERM] come alternativa.",search_suggestion:"Nessun risultato per [SEARCH_TERM]. Prova una delle seguenti ricerche:",searching:"Cercando [SEARCH_TERM]..."},ya={thanks_to:i0,comments:o0,direction:_0,strings:c0},E0={};F(E0,{comments:()=>h0,default:()=>wa,direction:()=>d0,strings:()=>C0,thanks_to:()=>f0});var f0="Tate",h0="",d0="ltr",C0={placeholder:"検索",clear_search:"クリア",load_more:"次を読み込む",search_label:"このサイトを検索",filters_label:"フィルタ",zero_results:"[SEARCH_TERM]の検索に一致する情報はありませんでした",many_results:"[SEARCH_TERM]の[COUNT]件の検索結果",one_result:"[SEARCH_TERM]の[COUNT]件の検索結果",alt_search:"[SEARCH_TERM]の検索に一致する情報はありませんでした。[DIFFERENT_TERM]の検索結果を表示しています",search_suggestion:"[SEARCH_TERM]の検索に一致する情報はありませんでした。次のいずれかの検索を試してください",searching:"[SEARCH_TERM]を検索しています"},wa={thanks_to:f0,comments:h0,direction:d0,strings:C0},m0={};F(m0,{comments:()=>A0,default:()=>za,direction:()=>R0,strings:()=>B0,thanks_to:()=>g0});var g0="Seokho Son ",A0="",R0="ltr",B0={placeholder:"검색어",clear_search:"비우기",load_more:"검색 결과 더 보기",search_label:"사이트 검색",filters_label:"필터",zero_results:"[SEARCH_TERM]에 대한 결과 없음",many_results:"[SEARCH_TERM]에 대한 결과 [COUNT]건",one_result:"[SEARCH_TERM]에 대한 결과 [COUNT]건",alt_search:"[SEARCH_TERM]에 대한 결과 없음. [DIFFERENT_TERM]에 대한 결과",search_suggestion:"[SEARCH_TERM]에 대한 결과 없음. 추천 검색어: ",searching:"[SEARCH_TERM] 검색 중..."},za={thanks_to:g0,comments:A0,direction:R0,strings:B0},v0={};F(v0,{comments:()=>p0,default:()=>ja,direction:()=>k0,strings:()=>F0,thanks_to:()=>T0});var T0="",p0="",k0="ltr",F0={placeholder:"Rapu",clear_search:"Whakakore",load_more:"Whakauta ētahi otinga kē",search_label:"Rapu",filters_label:"Tātari",zero_results:"Otinga kore ki [SEARCH_TERM]",many_results:"[COUNT] otinga ki [SEARCH_TERM]",one_result:"[COUNT] otinga ki [SEARCH_TERM]",alt_search:"Otinga kore ki [SEARCH_TERM]. Otinga kē ki [DIFFERENT_TERM]",search_suggestion:"Otinga kore ki [SEARCH_TERM]. whakamātau ki ngā mea atu:",searching:"Rapu ki [SEARCH_TERM]..."},ja={thanks_to:T0,comments:p0,direction:k0,strings:F0},D0={};F(D0,{comments:()=>M0,default:()=>Oa,direction:()=>S0,strings:()=>H0,thanks_to:()=>b0});var b0="Harry Min Khant ",M0="",S0="ltr",H0={placeholder:"ရှာရန်",clear_search:"ရှာဖွေမှုကို ရှင်းလင်းပါ။",load_more:"နောက်ထပ်ရလဒ်များကို တင်ပါ။",search_label:"ဤဆိုက်တွင်ရှာဖွေပါ။",filters_label:"စစ်ထုတ်မှုများ",zero_results:"[SEARCH_TERM] အတွက် ရလဒ်များ မရှိပါ",many_results:"[SEARCH_TERM] အတွက် ရလဒ် [COUNT] ခု",one_result:"[SEARCH_TERM] အတွက် ရလဒ် [COUNT]",alt_search:"[SEARCH_TERM] အတွက် ရလဒ်မရှိပါ။ ၎င်းအစား [DIFFERENT_TERM] အတွက် ရလဒ်များကို ပြသသည်။",search_suggestion:"[SEARCH_TERM] အတွက် ရလဒ်မရှိပါ။ အောက်ပါရှာဖွေမှုများထဲမှ တစ်ခုကို စမ်းကြည့်ပါ:",searching:"[SEARCH_TERM] ကို ရှာဖွေနေသည်..."},Oa={thanks_to:b0,comments:M0,direction:S0,strings:H0},N0={};F(N0,{comments:()=>w0,default:()=>Ua,direction:()=>z0,strings:()=>j0,thanks_to:()=>y0});var y0="Eirik Mikkelsen",w0="",z0="ltr",j0={placeholder:"Søk",clear_search:"Fjern",load_more:"Last flere resultater",search_label:"Søk på denne siden",filters_label:"Filtre",zero_results:"Ingen resultater for [SEARCH_TERM]",many_results:"[COUNT] resultater for [SEARCH_TERM]",one_result:"[COUNT] resultat for [SEARCH_TERM]",alt_search:"Ingen resultater for [SEARCH_TERM]. Viser resultater for [DIFFERENT_TERM] i stedet",search_suggestion:"Ingen resultater for [SEARCH_TERM]. Prøv en av disse søkeordene i stedet:",searching:"Søker etter [SEARCH_TERM]"},Ua={thanks_to:y0,comments:w0,direction:z0,strings:j0},O0={};F(O0,{comments:()=>I0,default:()=>Ia,direction:()=>P0,strings:()=>L0,thanks_to:()=>U0});var U0="Paul van Brouwershaven",I0="",P0="ltr",L0={placeholder:"Zoeken",clear_search:"Reset",load_more:"Meer resultaten laden",search_label:"Doorzoek deze site",filters_label:"Filters",zero_results:"Geen resultaten voor [SEARCH_TERM]",many_results:"[COUNT] resultaten voor [SEARCH_TERM]",one_result:"[COUNT] resultaat voor [SEARCH_TERM]",alt_search:"Geen resultaten voor [SEARCH_TERM]. In plaats daarvan worden resultaten voor [DIFFERENT_TERM] weergegeven",search_suggestion:"Geen resultaten voor [SEARCH_TERM]. Probeer een van de volgende zoekopdrachten:",searching:"Zoeken naar [SEARCH_TERM]..."},Ia={thanks_to:U0,comments:I0,direction:P0,strings:L0},x0={};F(x0,{comments:()=>V0,default:()=>Pa,direction:()=>K0,strings:()=>G0,thanks_to:()=>q0});var q0="Eirik Mikkelsen",V0="",K0="ltr",G0={placeholder:"Søk",clear_search:"Fjern",load_more:"Last fleire resultat",search_label:"Søk på denne sida",filters_label:"Filter",zero_results:"Ingen resultat for [SEARCH_TERM]",many_results:"[COUNT] resultat for [SEARCH_TERM]",one_result:"[COUNT] resultat for [SEARCH_TERM]",alt_search:"Ingen resultat for [SEARCH_TERM]. Viser resultat for [DIFFERENT_TERM] i staden",search_suggestion:"Ingen resultat for [SEARCH_TERM]. Prøv eitt av desse søkeorda i staden:",searching:"Søker etter [SEARCH_TERM]"},Pa={thanks_to:q0,comments:V0,direction:K0,strings:G0},J0={};F(J0,{comments:()=>Z0,default:()=>La,direction:()=>Y0,strings:()=>X0,thanks_to:()=>W0});var W0="Christopher Wingate",Z0="",Y0="ltr",X0={placeholder:"Søk",clear_search:"Fjern",load_more:"Last flere resultater",search_label:"Søk på denne siden",filters_label:"Filtre",zero_results:"Ingen resultater for [SEARCH_TERM]",many_results:"[COUNT] resultater for [SEARCH_TERM]",one_result:"[COUNT] resultat for [SEARCH_TERM]",alt_search:"Ingen resultater for [SEARCH_TERM]. Viser resultater for [DIFFERENT_TERM] i stedet",search_suggestion:"Ingen resultater for [SEARCH_TERM]. Prøv en av disse søkeordene i stedet:",searching:"Søker etter [SEARCH_TERM]"},La={thanks_to:W0,comments:Z0,direction:Y0,strings:X0},Q0={};F(Q0,{comments:()=>er,default:()=>xa,direction:()=>ur,strings:()=>tr,thanks_to:()=>$0});var $0="",er="",ur="ltr",tr={placeholder:"Szukaj",clear_search:"Wyczyść",load_more:"Załaduj więcej",search_label:"Przeszukaj tę stronę",filters_label:"Filtry",zero_results:"Brak wyników dla [SEARCH_TERM]",many_results:"[COUNT] wyników dla [SEARCH_TERM]",one_result:"[COUNT] wynik dla [SEARCH_TERM]",alt_search:"Brak wyników dla [SEARCH_TERM]. Wyświetlam wyniki dla [DIFFERENT_TERM]",search_suggestion:"Brak wyników dla [SEARCH_TERM]. Pokrewne wyniki wyszukiwania:",searching:"Szukam [SEARCH_TERM]..."},xa={thanks_to:$0,comments:er,direction:ur,strings:tr},rr={};F(rr,{comments:()=>ar,default:()=>qa,direction:()=>lr,strings:()=>nr,thanks_to:()=>sr});var sr="Jonatah",ar="",lr="ltr",nr={placeholder:"Pesquisar",clear_search:"Limpar",load_more:"Ver mais resultados",search_label:"Pesquisar",filters_label:"Filtros",zero_results:"Nenhum resultado encontrado para [SEARCH_TERM]",many_results:"[COUNT] resultados encontrados para [SEARCH_TERM]",one_result:"[COUNT] resultado encontrado para [SEARCH_TERM]",alt_search:"Nenhum resultado encontrado para [SEARCH_TERM]. Exibindo resultados para [DIFFERENT_TERM]",search_suggestion:"Nenhum resultado encontrado para [SEARCH_TERM]. Tente uma das seguintes pesquisas:",searching:"Pesquisando por [SEARCH_TERM]..."},qa={thanks_to:sr,comments:ar,direction:lr,strings:nr},ir={};F(ir,{comments:()=>_r,default:()=>Va,direction:()=>cr,strings:()=>Er,thanks_to:()=>or});var or="Bogdan Mateescu ",_r="",cr="ltr",Er={placeholder:"Căutare",clear_search:"Ştergeţi",load_more:"Încărcați mai multe rezultate",search_label:"Căutați în acest site",filters_label:"Filtre",zero_results:"Niciun rezultat pentru [SEARCH_TERM]",many_results:"[COUNT] rezultate pentru [SEARCH_TERM]",one_result:"[COUNT] rezultat pentru [SEARCH_TERM]",alt_search:"Niciun rezultat pentru [SEARCH_TERM]. Se afișează în schimb rezultatele pentru [DIFFERENT_TERM]",search_suggestion:"Niciun rezultat pentru [SEARCH_TERM]. Încercați una dintre următoarele căutări:",searching:"Se caută după: [SEARCH_TERM]..."},Va={thanks_to:or,comments:_r,direction:cr,strings:Er},fr={};F(fr,{comments:()=>dr,default:()=>Ka,direction:()=>Cr,strings:()=>mr,thanks_to:()=>hr});var hr="Aleksandr Gordeev",dr="",Cr="ltr",mr={placeholder:"Поиск",clear_search:"Очистить поле",load_more:"Загрузить еще",search_label:"Поиск по сайту",filters_label:"Фильтры",zero_results:"Ничего не найдено по запросу: [SEARCH_TERM]",many_results:"[COUNT] результатов по запросу: [SEARCH_TERM]",one_result:"[COUNT] результат по запросу: [SEARCH_TERM]",alt_search:"Ничего не найдено по запросу: [SEARCH_TERM]. Показаны результаты по запросу: [DIFFERENT_TERM]",search_suggestion:"Ничего не найдено по запросу: [SEARCH_TERM]. Попробуйте один из следующих вариантов",searching:"Поиск по запросу: [SEARCH_TERM]"},Ka={thanks_to:hr,comments:dr,direction:Cr,strings:mr},gr={};F(gr,{comments:()=>Rr,default:()=>Ga,direction:()=>Br,strings:()=>vr,thanks_to:()=>Ar});var Ar="Andrija Sagicc",Rr="",Br="ltr",vr={placeholder:"Претрага",clear_search:"Брисање",load_more:"Приказ више резултата",search_label:"Претрага сајта",filters_label:"Филтери",zero_results:"Нема резултата за [SEARCH_TERM]",many_results:"[COUNT] резултата за [SEARCH_TERM]",one_result:"[COUNT] резултата за [SEARCH_TERM]",alt_search:"Нема резултата за [SEARCH_TERM]. Приказ додатник резултата за [DIFFERENT_TERM]",search_suggestion:"Нема резултата за [SEARCH_TERM]. 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Vibe Research Vibe Research - AI 科研指南

多Agent框架

1. 框架对比概览

Section titled “1. 框架对比概览”
+

当研究任务需要多个Agent协作时，选择合适的框架：

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框架核心优势研究用途GitHub
LangChain/LangGraph复杂推理工作流，状态管理多步骤文献分析、实验设计langchain-ai
LlamaIndex知识库集成，RAG 优化文档密集型研究、论文库查询run-llama
CrewAI角色定义清晰，企业级协作研究项目、团队模拟crewAIInc
Microsoft AutoGen对话优先框架讨论式研究、辩论式分析microsoft/autogen
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2. 选择决策树

Section titled “2. 选择决策树”
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flowchart TD
    A(任务类型) --> B{需要复杂工作流?}
    A --> C{需要知识库/RAG?}
    A --> D{需要角色扮演?}
    A --> E{需要并行协作?}

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    B -->|是| F[LangGraph]
    B -->|否| G[简单顺序执行]

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    C -->|是| H[LlamaIndex]
    D -->|是| I[CrewAI]
    E -->|是| J[Agent Teams]
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3. Claude Agent Teams

Section titled “3. Claude Agent Teams”
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3.1 核心能力

Section titled “3.1 核心能力”
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Agent Teams 让多个 Claude Code 会话并行协作：

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特性说明
Team lead负责拆解任务、分派、汇总
Teammates独立执行，各自有上下文窗口
Delegate mode队长只调度不实现，队友执行
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3.2 两种展示方式

Section titled “3.2 两种展示方式”
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模式说明
In-process都在同一个终端里跑（无需额外配置）
Split panes每个队友一个 pane（需要 tmux 或 iTerm2）
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3.3 Agent Teams 四步法

Section titled “3.3 Agent Teams 四步法”
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  1. 队长会话（Lead）先进入 Plan/Delegate：先拆任务，不急着写代码
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  2. 把任务拆成：可并行、低耦合、验收明确的子任务
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  3. 每个子任务指定：输入、输出、验收（DoD）
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  4. 队友完成后，Lead 做集成、跑全套测试、开 PR
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3.4 Claude Code SDK 实战

Section titled “3.4 Claude Code SDK 实战”
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from claude_code import ClaudeAgent, TaskResult

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# 创建并行任务
agents = [
    ClaudeAgent(name="Architect", role="架构设计"),
    ClaudeAgent(name="Implementer", role="核心实现"),
    ClaudeAgent(name="Tester", role="测试验证"),
]

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# 并行执行
results = agent_team.run_parallel(agents, task="实现特征提取模块")
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4. LangGraph 实战：科研状态机

Section titled “4. LangGraph 实战：科研状态机”
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from typing import TypedDict, List, Optional
from langgraph.graph import StateGraph, END

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class ResearchState(TypedDict):
    """研究流水线状态"""
    topic: str                          # 研究主题
    papers: List[dict]                  # 论文列表
    analysis: str                       # 当前分析
    gaps: List[str]                     # 识别到的空白
    claims: List[str]                   # 候选主张
    evaluation: str                     # 评估结果

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def search_papers(state: ResearchState) -> ResearchState:
    """搜索相关论文"""
    papers = elicit_search(state["topic"])
    return {"papers": papers}

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def analyze_papers(state: ResearchState) -> ResearchState:
    """分析论文，提取关键信息"""
    analysis = claude_analyze(state["papers"])
    return {"analysis": analysis}

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def extract_gaps(state: ResearchState) -> ResearchState:
    """从分析中提取研究空白"""
    gaps = identify_gaps(state["analysis"])
    return {"gaps": gaps}

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def generate_claims(state: ResearchState) -> ResearchState:
    """基于空白生成可验证主张"""
    claims = []
    for gap in state["gaps"]:
        claim = formulate_claim(gap)
        claims.append(claim)
    return {"claims": claims}

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def evaluate_claims(state: ResearchState) -> ResearchState:
    """评估主张质量"""
    evaluation = gpt5_evaluate(state["claims"])
    return {"evaluation": evaluation}

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# 构建工作流
workflow = StateGraph(ResearchState)
workflow.add_node("search", search_papers)
workflow.add_node("analyze", analyze_papers)
workflow.add_node("extract_gaps", extract_gaps)
workflow.add_node("generate_claims", generate_claims)
workflow.add_node("evaluate", evaluate_claims)

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# 定义边
workflow.add_edge("search", "analyze")
workflow.add_edge("analyze", "extract_gaps")
workflow.add_edge("extract_gaps", "generate_claims")
workflow.add_edge("generate_claims", "evaluate")
workflow.add_edge("evaluate", END)

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app = workflow.compile()
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5. CrewAI 研究团队配置

Section titled “5. CrewAI 研究团队配置”
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from crewai import Agent, Task, Crew, Process

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# 1. 定义专业角色
literature_reviewer = Agent(
    role="文献综述专家",
    goal="全面梳理指定领域的研究现状",
    backstory="你是一位经验丰富的研究员，拥有10年AI领域研究经验。",
    tools=[elicit_tool, semantic_scholar_tool],
    verbose=True
)

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gap_analyst = Agent(
    role="研究空白分析师",
    goal="从现有研究中识别可探索的空白",
    backstory="你擅长发现被忽视的研究方向。",
    tools=[claude_tool],
    verbose=True
)

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claim_validator = Agent(
    role="主张验证专家",
    goal="评估研究主张的可验证性和创新性",
    backstory="你严格审查每一个科学主张。",
    tools=[gpt_tool],
    verbose=True
)

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# 2. 定义任务
task_review = Task(
    description="调研 {topic} 领域近3年的顶会论文，产出：1）研究地图；2）主流方法对比表；3）潜在空白清单",
    expected_output="结构化的研究综述文档",
    agent=literature_reviewer
)

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task_gap = Task(
    description="基于文献综述，识别 {topic} 领域最值得探索的5个研究方向",
    expected_output="研究空白分析报告",
    agent=gap_analyst
)

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task_claim = Task(
    description="将每个研究空白转化为可验证的科研主张",
    expected_output="主张清单及详细说明",
    agent=claim_validator
)

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# 3. 组建团队
research_crew = Crew(
    agents=[literature_reviewer, gap_analyst, claim_validator],
    tasks=[task_review, task_gap, task_claim],
    process=Process.sequential,
    verbose=True,
    memory=True
)

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result = research_crew.kickoff(inputs={"topic": "多模态学习"})
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6. AutoGen 对话式研究

Section titled “6. AutoGen 对话式研究”
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import autogen

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# 定义对话Agent
critic = autogen.AssistantAgent(
    name="Critic",
    system_message="""你是审稿人Agent，负责批判性分析。
    质疑假设、找出漏洞、提出改进建议。
    每次发言必须提出至少一个建设性批评。"""
)

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author = autogen.AssistantAgent(
    name="Author",
    system_message="""你是作者Agent，负责辩护和解释你的研究。
    回应批评，补充论据，完善论点。"""
)

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# 启动对话
critic.initiate_chat(
    author,
    message="请简要介绍你的研究主张，然后我会提出质疑。"
)
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7. Ralph 循环：简单而强大的自治模式

Section titled “7. Ralph 循环：简单而强大的自治模式”
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7.1 核心原理

Section titled “7.1 核心原理”
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Ralph 循环通过简单的 bash 循环重复执行操作，直到任务成功：

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#!/usr/bin/env bash
set -euo pipefail

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MAX_ITERS="${MAX_ITERS:-25}"

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for i in $(seq 1 "$MAX_ITERS"); do
  echo "=== Iteration $i/$MAX_ITERS ==="
  cat PROMPT.md | claude -p "按PROMPT.md完成任务。必须跑 tests；失败要修到通过。"
  ./scripts/test-fast.sh && ./scripts/lint.sh
done

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echo "Reached MAX_ITERS=$MAX_ITERS without passing. Stop."
exit 1
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7.2 关键技巧

Section titled “7.2 关键技巧”
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技巧说明
明确完成标准定义可验证的完成条件（测试通过、无错误）
小迭代原则每轮迭代只处理少量任务
反馈循环集成自动化检查确保每轮结果有效
安全限制设置迭代次数上限（20-50次）
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7.3 适用场景

Section titled “7.3 适用场景”
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适合不适合
可量化、可验证的任务探索性任务
重构、写测试、文档化主观设计
补测试、修复 Bug安全敏感代码
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8. 实战案例：16 个 Claude 写 C 编译器

Section titled “8. 实战案例：16 个 Claude 写 C 编译器”
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8.1 实验概览

Section titled “8.1 实验概览”
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  • 目标：让 16 个 Claude 并行开发一个能编译 Linux 内核的 C 编译器
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  • 成果：两周内，近 2000 次会话，$2 万成本，10 万行代码
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  • 支持架构：x86、ARM、RISC-V
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8.2 成功的关键

Section titled “8.2 成功的关键”
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  1. 极高质量的测试体系：引入成熟测试集，CI 强制要求新提交不破坏功能
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  2. 站在 AI 视角设计系统： +
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    • 简化测试输出，结构化日志便于 AI 检索
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    • 提供 --fast 模式只运行 1%-10% 测试子集
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  3. 确保任务可拆分：让每个智能体修复独立测试用例
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  4. 利用并行实现角色分工：性能优化、代码重构、文档维护
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9. Everything Claude Code 项目

Section titled “9. Everything Claude Code 项目”
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9.1 项目概览

Section titled “9.1 项目概览”
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项目说明
创建者Anthropic 黑客松冠军 Affaan Mustafa
定位完整配置集合，将 Claude 升级为”虚拟开发团队”
GitHubaffaan-m/everything-claude-code
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9.2 核心组件

Section titled “9.2 核心组件”
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组件功能
Agents规划、代码审查、安全检测、TDD 指导
Skills工作流和领域经验封装
Commands快速执行预定义流程
Rules开发规范、测试覆盖率要求
Hooks提交前检查、console.log 检测
MCP外部工具连接
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9.3 实战效果

Section titled “9.3 实战效果”
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指标提升
功能实现速度65%
代码问题减少75%
测试覆盖率82%
会话切换减少70%
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10. 多终端并行配置

Section titled “10. 多终端并行配置”
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10.1 tmux 工作流

Section titled “10.1 tmux 工作流”
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Terminal window
# 安装 tmux (macOS)
brew install tmux

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# 配置文件 ~/.tmux.conf
set -g mouse on
bind-key C-d set -g mouse on

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# 一键启动多 AI 模型
./start-ai-agents.sh
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10.2 多模型同步控制

Section titled “10.2 多模型同步控制”
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快捷键功能
Ctrl+b D切换同步模式
Ctrl+b X退出多窗口模式
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10.3 适用场景

Section titled “10.3 适用场景”
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  • 同时对比：Claude、Codex、Gemini、OpenCode
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  • 并行实现：多个 worktree 同时推进
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  • 多视角评审：一个实现、一个审查、一个测试
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11. 使用建议

Section titled “11. 使用建议”
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简单优先

不要过度设计，Claude Code 单实例足够处理大多数代码任务。

状态管理

需要复杂分支时使用 LangGraph 或 Agent Teams。

RAG 场景

论文库查询优先考虑 LlamaIndex。

多角色

需要角色扮演时使用 CrewAI。

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12. Execution-Grounded Agent 实战要点

Section titled “12. Execution-Grounded Agent 实战要点”
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为了让多 Agent 真正用于科研而不只是”写建议”，建议采用 execution-grounded 架构：

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  1. Idea Agent：提出可执行改动（不是抽象口号）
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  2. Patch Agent：输出可应用的代码 diff
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  3. Executor Agent：在标准环境中自动训练/评测（含 GPU 资源调度）
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  4. Feedback Agent：回收 Wandb/日志结果，驱动下一轮搜索
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12.1 两类标准化环境

Section titled “12.1 两类标准化环境”
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环境典型配置
Post-trainingGRPO + MATH
Pre-trainingnanoGPT + FineWeb
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这种环境化设计的价值：

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  • 可以批量并行比较 idea，不依赖主观判断
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  • 可以把”创新性”转化为”可执行且可复验”的工程流程
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  • 可以把失败样本积累为后续搜索的先验约束
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13. AI Scientist 系列脉络（补充）

Section titled “13. AI Scientist 系列脉络（补充）”
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为避免把自动化科研理解成“单一框架”，这里补一条系列演进视角（来自 AI research.md）：

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路线核心特点对工程落地的启发
AI Scientist (早期)自动生成 idea 与初步实验适合快速探索，但执行闭环较弱
Execution-Grounded 路线强调 patch/run/feedback 闭环更适合做真实迭代与可复现实验
Narrative 路线（Idea2Story）从想法到完整论文叙事适合写作联动与产出打磨
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实操建议：把三条路线合成“探索 → 执行 → 叙事”的分层流程，而不是把某一个框架当万能解。

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14. Book 实战增补（逐篇并入）

Section titled “14. Book 实战增补（逐篇并入）”
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+

本节并入 3 篇与多 Agent 协作强相关的文章，统一为”何时用团队协作、如何控成本、如何防跑偏”的实操框架。

+
+

14.1 1) Agent Swarm（蜂群）实战：从单助手到数字团队

Section titled “14.1 1) Agent Swarm（蜂群）实战：从单助手到数字团队”
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14.1.1 核心机制（可落地）

Section titled “14.1.1 核心机制（可落地）”
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  1. 共享任务列表：统一看板，便于并行认领和状态同步。
    2. +
  2. 消息邮箱系统：队员间可直接通信，减少 Lead 中转瓶颈。
    3. +
  3. 独立上下文：每个 Agent 独立窗口，避免大任务互相污染。
    4. +
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14.1.2 六种典型玩法（按成熟度）

Section titled “14.1.2 六种典型玩法（按成熟度）”
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  • 新手：提示词里显式要求“尽可能拆分并行 + 尽可能调用 skill”。
    • +
  • 进阶：主模型统筹，便宜模型执行（文档/测试等）做成本分层。
    • +
  • 复用：优先搜索社区 skill，找不到再降级到本地或通用流程。
    • +
  • 降返工：先计划、后评审、再执行，卡住就回到计划阶段。
    • +
  • 大项目：Lead-Member 架构，分角色并行交付。
    • +
  • 深水区：Hooks/动态路由/Subagent Fork 等做质量与成本精调。
    • +
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14.1.3 详细步骤（从 0 开始跑 Swarm）

Section titled “14.1.3 详细步骤（从 0 开始跑 Swarm）”
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  1. 先写任务卡：目标、范围、交付物、验收标准（四项缺一不可）。
    2. +
  2. 指定 Lead 只做拆解与验收，不直接写核心实现代码。
    3. +
  3. 把任务拆成并行子任务（每个子任务都要有独立输入输出）。
    4. +
  4. 要求每个成员用统一汇报模板：完成项 / 风险 / 下一步。
    5. +
  5. Lead 在每个里程碑做一次冲突检查（文件冲突、接口冲突、依赖冲突）。
    6. +
  6. 最后统一跑集成验证，再回到主分支合并。
    7. +
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14.1.4 文章细节补充（Swarm 六玩法模板）

Section titled “14.1.4 文章细节补充（Swarm 六玩法模板）”
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玩法1：新手并行提示模板
Section titled “玩法1：新手并行提示模板”
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调用 Agent Swarm 执行任务：<任务描述>
要求：
1) 尽可能拆分并行子任务
2) 优先使用已安装 skills
3) 每个子任务输出“结果+风险+下一步”
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玩法2：跨模型协作（成本分层）
Section titled “玩法2：跨模型协作（成本分层）”
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    +
  • 复杂架构/核心逻辑：强模型；
    • +
  • 文档/测试/样板代码：低成本模型；
    • +
  • 最终合并与验收：回到主模型统一把关。
    • +
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玩法3：社区 Skill 复用
Section titled “玩法3：社区 Skill 复用”
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    +
  1. 先搜索可用 skill；
    2. +
  2. 匹配则直接调用；
    3. +
  3. 无匹配再降级到通用流程。
    4. +
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玩法4：计划+评审循环
Section titled “玩法4：计划+评审循环”
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  1. 先产 Todo 与依赖；
    2. +
  2. 评审 agent 指出遗漏与风险；
    3. +
  3. 评审通过后才并行执行。
    4. +
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玩法5：Lead-Member 组织结构
Section titled “玩法5：Lead-Member 组织结构”
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    +
  • Lead：任务拆解、里程碑验收；
    • +
  • Members：按角色并行实现；
    • +
  • 里程碑结束统一集成。
    • +
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玩法6：高级功能组合
Section titled “玩法6：高级功能组合”
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    +
  • Hooks 做质量门禁；
    • +
  • 动态模型路由控成本；
    • +
  • Subagent Fork 隔离上下文污染。
    • +
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14.1.5 实战摘录

Section titled “14.1.5 实战摘录”
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+

Swarm 不是”更会聊天”，而是”可协作的生产组织”。核心收益来自任务拆解质量，而不是 agent 数量。

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14.2 2) Agent Teams 团战模式：并行收益与协调成本

Section titled “14.2 2) Agent Teams 团战模式：并行收益与协调成本”
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14.2.1 适用/不适用边界

Section titled “14.2.1 适用/不适用边界”
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任务类型建议
研究审查、多假设调试、跨层联动优先 Agent Teams
顺序任务、同文件高冲突、强依赖链优先单会话或 Subagent
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14.2.2 启用与操作要点

Section titled “14.2.2 启用与操作要点”
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{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}
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    +
  • 启用后先定义：Lead 职责、队员分工、里程碑验收。
    • +
  • 明确要求：Wait for teammates before proceeding，避免 Lead 抢做。
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  • 文件级分工防冲突：每个队员拥有独立文件集。
    • +
  • 控 token：团队规模与任务收益匹配，避免“并行过度”。
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14.2.3 详细步骤（Agent Teams 落地）

Section titled “14.2.3 详细步骤（Agent Teams 落地）”
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    +
  1. 在 settings.json 打开 CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1。
    2. +
  2. 重启会话，确认团队模式已可用（可看到 team/teammates 指令反馈）。
    3. +
  3. 创建团队时先给角色定义：Lead、Frontend、Backend、Test、Docs。
    4. +
  4. 给每个角色分配不重叠文件域，防止写冲突。
    5. +
  5. 执行中每 20~30 分钟做一次状态同步：完成/阻塞/待协作。
    6. +
  6. 收尾时执行“等待队员完成”策略，再统一集成测试。
    7. +
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14.2.4 文章细节补充（Teams 全流程）

Section titled “14.2.4 文章细节补充（Teams 全流程）”
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  1. 升级到支持 Teams 的版本（必要时先执行更新命令）。
    2. +
  2. 在 settings.json 注入实验开关并重启会话。
    3. +
  3. 选择显示模式（同进程或分屏），确认可观察到每个成员状态。
    4. +
  4. 指定成员角色与模型，给每个成员写明 DoD（Definition of Done）。
    5. +
  5. 启用委托模式，Lead 不直接写实现。
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  6. 执行阶段每轮只推进一个里程碑，防止团队漂移。
    7. +
  7. 全部成员完成后再统一回归测试与文档收口。
    8. +
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14.2.5 限制说明（实操前必读）

Section titled “14.2.5 限制说明（实操前必读）”
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    +
  • 每会话仅一个团队；
    • +
  • 不支持嵌套团队；
    • +
  • 进程内成员恢复能力有限；
    • +
  • 分屏依赖终端能力（tmux/iTerm2 等）。
    • +
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14.3 3) 进阶实战（Skills/Subagents/MCP 组合）

Section titled “14.3 3) 进阶实战（Skills/Subagents/MCP 组合）”
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14.3.1 组合原则

Section titled “14.3.1 组合原则”
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  • Skill：固化团队规则与复用流程（先描述触发条件，后写具体动作）。
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  • Subagent：把高噪声探索外包，主上下文只保留摘要结论。
    • +
  • MCP：把外部系统接入同一会话，减少标签页切换与复制粘贴。
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14.3.2 Subagent 设计建议

Section titled “14.3.2 Subagent 设计建议”
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  • 明确 description 与输出格式，避免“回传整段上下文”。
    • +
  • 只给必要工具权限（只读审查与可写实现分离）。
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  • 推荐链路：Explore → Implementer → Tester，主代理只做整合与决策。
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14.3.3 详细步骤（Subagents + MCP 组合）

Section titled “14.3.3 详细步骤（Subagents + MCP 组合）”
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  1. 先定义 Explore 输出格式：只返回“文件路径 + 关键结论 + 风险点”。
    2. +
  2. Implementer 仅接收结构化输入，不接收原始长对话。
    3. +
  3. Tester 固定执行同一套测试清单，结果以表格返回。
    4. +
  4. 主代理整合三者输出后，再调用 MCP 写回 issue/文档系统。
    5. +
  5. 若发现信息漂移，回滚到 Explore 结果重建链路，不在末端硬修。
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14.3.4 文章细节补充（命令级落地）

Section titled “14.3.4 文章细节补充（命令级落地）”
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Terminal window
# 查看可用 subagents
/agents

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# 查看 MCP 连接
/mcp

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# 示例：添加 HTTP MCP（按实际服务替换）
claude mcp add --transport http notion https://mcp.notion.com/mcp
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  1. 先建只读 security-reviewer，限制到 Read/Grep/Glob。
    2. +
  2. 再建可写 implementer，明确只改指定目录。
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  3. 所有 subagent 返回统一格式：summary / changed_files / risks。
    4. +
  4. MCP 只接入本任务必要服务，避免权限过大。
    5. +
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14.3.5 MCP 使用建议

Section titled “14.3.5 MCP 使用建议”
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  • 优先接入：GitHub、Slack、DB、文档系统。
    • +
  • 第三方连接器先做安全评估（权限、日志、凭证范围）。
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  • 通过 /mcp 定期盘点已加载连接，避免“隐式工具漂移”。
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14.3.6 常见坑与处理

Section titled “14.3.6 常见坑与处理”
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  • 坑1：并行太多导致 token 爆涨 → 限制活跃队员数量，优先高价值并行。
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  • 坑2：成员写同一文件互相覆盖 → 启动前做文件分区。
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  • 坑3：Lead 抢做实现导致协作失效 → 明确 Lead 只做调度与验收。
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  • 坑4：总结过长污染主上下文 → 强制摘要长度与格式。
    • +
\ No newline at end of file diff --git a/research/code/claude/index.html b/research/code/claude/index.html new file mode 100644 index 0000000..07d8359 --- /dev/null +++ b/research/code/claude/index.html @@ -0,0 +1,907 @@ + Claude Code | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

Claude Code

1. Claude Opus 4.5/4.6 (2025年11月发布)

Section titled “1. Claude Opus 4.5/4.6 (2025年11月发布)”
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1.1 核心能力

Section titled “1.1 核心能力”
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能力数据说明
SWE-bench Verified80.9%领先业界的代码生成基准
上下文窗口200K tokens64K token 输出限制
成本降低67%$5/$25 per million tokens
tau2-bench-lite88.9%Agent 工具使用评估
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1.2 适用场景

Section titled “1.2 适用场景”
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Claude Code 适合项目的构建阶段，可以开两三个（甚至更多）并行推进。

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目标：让执行文档里的任何被真实跑通，直到代码结构、实验入口、评测脚本都稳定。

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2. 安装与快速上手

Section titled “2. 安装与快速上手”
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2.1 安装方式

Section titled “2.1 安装方式”
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Terminal window
# macOS / Linux / WSL
curl -fsSL https://claude.ai/install.sh | bash

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# Windows PowerShell
irm https://claude.ai/install.ps1 | iex

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# Windows CMD
curl -fsSL https://claude.ai/install.cmd -o install.cmd && install.cmd && del install.cmd
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2.2 启动方式

Section titled “2.2 启动方式”
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Terminal window
cd your-project
claude
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首次会提示登录。(Claude Code)

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3. 交互模式与快捷键

Section titled “3. 交互模式与快捷键”
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3.1 核心快捷键

Section titled “3.1 核心快捷键”
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快捷键功能
?显示当前环境可用快捷键
Esc Esc回退/总结（rewind / summarize）
Shift+Tab切换模式（普通 ↔ Plan ↔ Auto-accept ↔ Delegate）
Ctrl+B把运行中的任务后台化
Ctrl+T显示/隐藏任务列表
Option/Alt + P切换模型
Shift+Enter多行输入
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3.2 内置斜杠命令

Section titled “3.2 内置斜杠命令”
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命令功能
/clear清空对话
/context看上下文占用
/export [filename]导出对话
/resume [session]恢复历史会话
/rename <name>重命名会话
/plan进入 plan mode
/rewind回退代码/对话或总结
/doctor安装健康检查
/debug [desc]读 debug log 排查
/config打开设置界面
/status状态页
/permissions权限规则
/model选模型
/mcp管 MCP 连接
/tasks背景任务管理
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4. 权限模式与安全边界

Section titled “4. 权限模式与安全边界”
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4.1 四种模式

Section titled “4.1 四种模式”
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模式能力适用场景
default先问再做日常稳妥开发
acceptEdits自动允许文件编辑你在旁边盯着、改动明确
plan只读分析+出计划需求澄清、方案评审、审计
delegate只做 team 管理Agent Teams 并行协作时
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按 Shift+Tab 循环切换。

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4.2 权限配置建议

Section titled “4.2 权限配置建议”
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在 .claude/settings.json 中配置：

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{
  "permissions": {
    "allow": ["npm run test *", "Bash(git status)"],
    "deny": [".env", "secrets/**", "Bash(curl *)"]
  }
}
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5. 上下文管理

Section titled “5. 上下文管理”
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5.1 核心原则

Section titled “5.1 核心原则”
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上下文再大也会被填满，可控上下文 = 可控质量。

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5.2 工具对比

Section titled “5.2 工具对比”
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命令功能何时用
/context看上下文占用判断是否该清理了
/clear清掉对话但保留项目状态任务切换、对话混乱
/export导出关键对话交接、复盘、团队沉淀
Esc Esc / /rewind回退到之前状态跑偏时回到正确轨道
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5.3 /catchup Skill 模板

Section titled “5.3 /catchup Skill 模板”
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---
name: catchup
description: Rebuild context after /clear by reading repo state
---

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1) 运行并总结：
- git status
- git diff --stat
- git diff
- git log -n 20 --oneline

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2) 优先阅读：
- CLAUDE.md
- README / docs/
- TODO.md / PLAN.md（如存在）

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3) 输出三段：
A. 当前仓库状态
B. 当前目标与待办（按优先级）
C. 风险点与建议的下一步命令
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6. 项目记忆：CLAUDE.md

Section titled “6. 项目记忆：CLAUDE.md”
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6.1 必须包含的内容

Section titled “6.1 必须包含的内容”
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CLAUDE.md 是智能体的”宪法”，应保持简洁、聚焦高频使用场景和关键约束。

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类别内容示例
项目启动npm run dev / python main.py
测试运行pytest tests/ / npm test
代码风格ruff check . / prettier --write
代码约束禁止读取 secrets、单测必写
常见坑数据路径用 pathlib、随机种子固定
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6.2 模板示例

Section titled “6.2 模板示例”
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# 项目约定

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## 启动命令
- 开发: `npm run dev`
- 测试: `npm test`
- 构建: `npm run build`

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## 代码风格
- Python: ruff format + ruff check
- 命名: snake_case 函数, PascalCase 类

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## 常见坑
- 数据路径必须用 pathlib
- 随机种子固定为 42
- batch_size 先从 32 开始

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## Debug 流程
1. 复现 bug
2. 最小化用例
3. 定位问题
4. 修复
5. 加测试
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7. Plan Mode 优先闭环

Section titled “7. Plan Mode 优先闭环”
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7.1 何时必须先 Plan

Section titled “7.1 何时必须先 Plan”
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  • 影响架构、鉴权、支付、数据结构、跨模块重构
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  • 需要写/改测试策略
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  • 希望”一次性正确实现”
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7.2 标准三段式提示

Section titled “7.2 标准三段式提示”
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你先进入 /plan。
1) 用要点列出：目标、非目标、约束（性能/兼容/安全）、验收标准。
2) 给出分步实现计划（每步产出什么、改哪些文件）。
3) 列出风险与回滚方案。
等我确认后再开始编码实现。
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8. 权限与沙箱

Section titled “8. 权限与沙箱”
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8.1 沙箱模式

Section titled “8.1 沙箱模式”
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运行 /sandbox 打开菜单选择沙箱模式。

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WSL2 依赖安装：

+
Terminal window
# Ubuntu/Debian
sudo apt-get install bubblewrap socat

+
# Fedora
sudo dnf install bubblewrap socat
+

8.2 企业护栏策略

Section titled “8.2 企业护栏策略”
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  • 轻量提醒：发现 console.log / debug flag → 提醒但不阻断
    • +
  • 强拦截：提交时如果 tests 未过 → 阻止并要求修复
    • +
+
+

9. Skills：可复用工作流

Section titled “9. Skills：可复用工作流”
+

9.1 Skills 是什么

Section titled “9.1 Skills 是什么”
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Skills 把最佳实践固化成可调用、可共享、可组合的流程，解决”重复指导困境”。

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9.2 存放位置

Section titled “9.2 存放位置”
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位置说明
项目内 .claude/skills/团队共享
~/.claude/skills/个人全局
插件内 <plugin>/skills/插件分发
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9.3 推荐技能模板

Section titled “9.3 推荐技能模板”
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9.3.1 /plan - 强制计划

Section titled “9.3.1 /plan - 强制计划”
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---
name: plan
description: Create an implementation plan with acceptance criteria
---

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输出必须包含：
1) Goals / Non-goals / Constraints
2) Acceptance criteria（给出可运行命令）
3) Step-by-step plan
4) Risks + rollback
除非用户明确批准，否则不要写代码。
+

9.3.2 /code-review - 代码审查

Section titled “9.3.2 /code-review - 代码审查”
+
---
name: code-review
description: Review current changes with a production checklist
---

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执行：
- git diff
- git diff --stat

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按以下结构输出：
1) Correctness（边界/错误处理）
2) Security（输入校验/鉴权）
3) Tests（覆盖、缺失用例）
4) Performance（热点、N+1）
5) Maintainability（复杂度、命名）
最后给出"必须改/建议改/可不改"三类结论。
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9.3.3 /tdd - 测试驱动

Section titled “9.3.3 /tdd - 测试驱动”
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---
name: tdd
description: Apply a TDD workflow
---

+
1) 先写失败测试（说明为什么失败）
2) 再最小实现让测试通过
3) 再重构（保持测试通过）
每一步都给出运行的测试命令与结果摘要。
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9.3.4 /pr - PR 准备

Section titled “9.3.4 /pr - PR 准备”
+
---
name: pr
description: Prepare a PR: summarize changes, run checks
---

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执行并总结：
- git status
- git diff --stat
- lint/test/build 命令

+
输出：
1) PR Title（50字内）
2) Summary（要点）
3) Testing（跑了什么）
4) Risk/rollback
+
+

10. Hooks：事件驱动自动化

Section titled “10. Hooks：事件驱动自动化”
+

10.1 Hooks 能做什么

Section titled “10.1 Hooks 能做什么”
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Hooks 在关键节点自动执行或拦截操作：

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    +
  • 在 Claude 调用工具前/后
    • +
  • 在提交 prompt 时
    • +
  • 在会话开始/结束
    • +
  • 在 compact/子任务结束
    • +
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10.2 推荐落地类型

Section titled “10.2 推荐落地类型”
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Hook 类型功能优先级
格式化改完文件后自动跑 formatter高
测试关键模块改动后自动跑测试高
危险拦截检测到 `curlbash` 阻止
PR 质量提交前检查 TODO/敏感信息中
SessionStart启动时加载项目环境中
+

10.3 Hooks vs Skills 对比

Section titled “10.3 Hooks vs Skills 对比”
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    +
  • Skills：告诉它”按步骤做事”（流程化）
    • +
  • Hooks：保证”在关键节点一定发生”（自动化 + 强制）
    • +
+
+

11. MCP：外部工具接入

Section titled “11. MCP：外部工具接入”
+

11.1 推荐场景

Section titled “11.1 推荐场景”
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场景推荐程度
访问外部系统数据（Notion/Jira/GitHub/DB）✅ 强推荐
有状态工具（Playwright）⚠️ 谨慎
所有内部 API 包进 MCP❌ 避免
+

11.2 添加 MCP Server

Section titled “11.2 添加 MCP Server”
+
Terminal window
# HTTP 方式（推荐）
claude mcp add --transport http notion https://mcp.notion.com/mcp

+
# SSE 方式（已废弃）
claude mcp add --transport sse asana https://mcp.asana.com/sse

+
# 查看列表
claude mcp list
+

11.3 核心 MCP 工具

Section titled “11.3 核心 MCP 工具”
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工具功能适用场景
Zotero-MCP直接访问本地 Zotero 文献库文献管理、引用分析
Perplexity MCPDeep Research 模式技术调研、综述写作
ClickHouse MCP数据库集成大数据分析
GitHub MCP仓库操作版本控制自动化
+
+

12. 最佳实践：团队协作模式

Section titled “12. 最佳实践：团队协作模式”
+

12.1 角色分工

Section titled “12.1 角色分工”
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角色职责Claude 实例数
Architect总体结构与接口设计1-2
Implementer核心模块实现2-3
Tester测试用例与评测1-2
ReviewerCode review 与边界检查1-2
+

12.2 7天落地路线

Section titled “12.2 7天落地路线”
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天数任务
Day 1编写简洁的 CLAUDE.md
Day 2添加底线 rules（安全、测试）
Day 3落地核心命令 /plan
Day 4落地命令 /code-review
Day 5添加提醒型 Hooks
Day 6添加一致性 Hooks
Day 7引入 Subagents
+
+

13. 下一步行动

Section titled “13. 下一步行动”
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  1. 在项目里跑一次 /init，生成并精简 CLAUDE.md
    2. +
  2. 新建 .claude/skills/，把常用 Skills 复制进去
    3. +
  3. 开始复杂任务前：先 /plan，确认后再实现
    4. +
  4. 跑偏就 Esc Esc → rewind
    5. +
  5. 想减少弹窗就 /sandbox
    6. +
  6. 需要并行就用 Agent Teams
    7. +
+
+

14. Vibe 实战并集补充（融合版）

Section titled “14. Vibe 实战并集补充（融合版）”
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+

本节把 vibe/Vibe.md、vibe/Vibe Working.md、vibe/VIbe2.md 的重合观点去重后并入当前章节，只保留与工程落地直接相关的增量。

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+

14.1 高频共识（去重后）

Section titled “14.1 高频共识（去重后）”
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  1. Plan 先行：复杂任务默认 /plan，先给验收标准再改代码。
    2. +
  2. 上下文治理：高频使用 /context + /clear + /rewind，避免会话污染。
    3. +
  3. 流程资产化：把重复动作沉淀为 skills / hooks / agents，而不是靠临场提示词。
    4. +
  4. 结果导向：以“可运行 + 可测试 + 可评审（PR）”作为完成标准。
    5. +
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14.2 团队落地的最小闭环

Section titled “14.2 团队落地的最小闭环”
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    +
  • Day 1-2：整理 CLAUDE.md + settings.json（定义底线约束）
    • +
  • Day 3-4：落地 /plan、/code-review 两个核心 skill
    • +
  • Day 5-7：接入提醒型 hooks，再逐步升级到一致性/阻断型 hooks
    • +
+

这条路线的目标是先稳定质量，再追求并行规模。

+
+

15. Book 实战增补（逐篇并入）

Section titled “15. Book 实战增补（逐篇并入）”
+
+

本节将 book/ 中 5 篇与 Claude Code 直接相关的文章，按”教程正文 + 实战摘录”方式并入本章，重点保留可执行方法与避坑经验。

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+

15.1 1) 470万阅读实战心得：先思考，再动手

Section titled “15.1 1) 470万阅读实战心得：先思考，再动手”
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15.1.1 教程化整合

Section titled “15.1.1 教程化整合”
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    +
  • 复杂任务默认先进入 Plan Mode（双击 Shift + Tab），先定架构与验收标准再写代码。
    • +
  • CLAUDE.md 要短、具体、可执行：优先写项目特有约束，而非常识性说明。
    • +
  • 指令要同时包含”做什么 + 为什么”：让模型在边界场景做出更稳判断。
    • +
  • 上下文不要用满才清理：20%~40% 已可能出现质量退化，建议任务级会话拆分。
    • +
  • 使用外部记忆文件（如 plan.md、SCRATCHPAD.md）承接跨会话进度。
    • +
+

15.1.2 可执行清单

Section titled “15.1.2 可执行清单”
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  1. 每次复杂需求先写 5~10 行目标与非目标。
    2. +
  2. 运行一次 /plan 或 Plan Mode 输出分步方案。
    3. +
  3. 将重复纠错项沉淀到 CLAUDE.md（按 # 快捷更新）。
    4. +
  4. 对话跑偏时使用 /compact → /clear → 粘贴关键上下文重启。
    5. +
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15.1.3 详细步骤（避免”云里雾里”）

Section titled “15.1.3 详细步骤（避免”云里雾里”）”
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    +
  1. 新建 plan.md，固定写 4 行：目标 / 非目标 / 输入 / 验收。
    2. +
  2. 进入 Plan Mode 后要求输出：文件列表 + 变更顺序 + 测试命令。
    3. +
  3. 若计划中有”可选增强”，先全部标记为不做，只保留 MVP。
    4. +
  4. 执行中每完成一个子任务，在 plan.md 写”已完成 + 证据链接”。
    5. +
  5. 出现循环修复两次以上，立即停下执行 /clear，重开会话重述任务。
    6. +
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15.1.4 实战摘录

Section titled “15.1.4 实战摘录”
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+

如果同一问题解释三次仍跑偏，优先换会话或换问题建模方式，而不是继续加长提示词。

+
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15.1.5 文章细节补充（保留原文有效动作）

Section titled “15.1.5 文章细节补充（保留原文有效动作）”
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    +
  • CLAUDE.md 四原则：短、具体、解释原因、持续更新。
    • +
  • 上下文治理动作：限定单任务会话、外部记忆文件、/compact 摘要、/clear 重启。
    • +
  • Prompt 质量三要素：具体目标、明确禁做项、业务原因说明。
    • +
  • 卡住时四步修复：清会话 → 拆小任务 → 给最小示例 → 换建模角度（如状态机）。
    • +
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15.1.6 可复制提示词模板

Section titled “15.1.6 可复制提示词模板”
+
请先进入规划模式，不要立即编码。
目标：<目标>
约束：<技术栈/目录/边界>
禁止事项：<不允许新增抽象/不允许改动模块>
验收标准：<测试命令 + 通过条件>
输出：按步骤计划（每步含输入、输出、风险）
+

15.2 2) 资深工程师完全指南：把 Claude 变成系统组件

Section titled “15.2 2) 资深工程师完全指南：把 Claude 变成系统组件”
+

15.2.1 教程化整合

Section titled “15.2.1 教程化整合”
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    +
  • 把 Claude Code 从”问答工具”升级为”工程系统部件”：支持脚本化、日志化、可审计。
    • +
  • 模型协作建议：架构与关键决策用强模型，日常实现用高性价比模型。
    • +
  • 推荐在常见任务上沉淀 Slash Commands，减少重复 prompt 编写成本。
    • +
  • 借助 Hooks/MCP 做前后置检查，把质量控制前移到流程节点。
    • +
+

15.2.2 可执行清单

Section titled “15.2.2 可执行清单”
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    +
  • 对高风险模块启用强制检查：安全、权限、数据一致性。
    • +
  • 复用无头执行：claude -p "任务描述" 集成到脚本或 CI。
    • +
  • 每周回顾一次“失败任务日志”，更新 CLAUDE.md 与命令模板。
    • +
+

15.2.3 详细步骤（系统化落地）

Section titled “15.2.3 详细步骤（系统化落地）”
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    +
  1. 把高频任务抽成命令：/commands/review.md、/commands/debug.md。
    2. +
  2. 在 CI 中增加 claude -p 的只读审查任务（不直接写仓库）。
    3. +
  3. 每次失败记录三件事：触发条件、错误表现、修复动作。
    4. +
  4. 每周挑 3 条重复问题，更新 CLAUDE.md 的”禁止/必须”规则。
    5. +
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15.2.4 文章细节补充（系统化飞轮）

Section titled “15.2.4 文章细节补充（系统化飞轮）”
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  • 飞轮机制：错误发生 → 查看日志 → 改 CLAUDE.md/命令模板 → 下次更稳。
    • +
  • 模型分工建议：复杂架构和疑难排查用强模型，常规实现用高性价比模型。
    • +
  • 会话切换信号：当问题定义已变，优先新会话，不在旧上下文强行续写。
    • +
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15.2.5 推荐命令清单

Section titled “15.2.5 推荐命令清单”
+
Terminal window
# 无头运行（脚本/CI）
claude -p "审查当前改动的安全风险，给出按严重级别排序结果"

+
# 继续历史会话
claude --continue

+
# 选择历史会话
claude --resume
+

15.3 3) 31个效率技巧：从快捷键到自动化分层使用

Section titled “15.3 3) 31个效率技巧：从快捷键到自动化分层使用”
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15.3.1 教程化整合

Section titled “15.3.1 教程化整合”
+

将 31 个技巧收敛为 5 层能力：

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层级代表能力关键命令/动作
启动层建立项目记忆/init、Memory Updates
交互层快速上下文与命令执行@文件、! bash、Esc Esc
会话层恢复、命名、导出claude --continue、/export
安全层权限控制与审计/sandbox、Hooks
自动化层流水线复用claude -p、Commands、Skills、Subagents
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15.3.2 可执行清单

Section titled “15.3.2 可执行清单”
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    +
  • 把高频动作固化成 /commands；
    • +
  • 复杂任务统一走 Plan + Review；
    • +
  • 批处理任务优先转为 -p 无头流程。
    • +
+

15.3.3 详细步骤（31 技巧落地顺序）

Section titled “15.3.3 详细步骤（31 技巧落地顺序）”
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    +
  1. 第一周只上手 6 个：/init、@、!、Esc Esc、/context、/sandbox。
    2. +
  2. 第二周新增 5 个：/export、/stats、/usage、/statusline、/commands。
    3. +
  3. 第三周再启用自动化能力：Hooks、Subagents、Headless。
    4. +
  4. 每新增一个能力，记录”触发条件 + 不适用场景”，避免误用。
    5. +
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15.3.4 文章细节补充（关键技巧分组）

Section titled “15.3.4 文章细节补充（关键技巧分组）”
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目标技巧组合典型收益
快速拿上下文@文件/目录 + !命令减少切终端/复制粘贴
快速回滚Esc Esc + 会话恢复降低试错成本
长任务续航命名会话 + /export可追踪、可交接
自动质检Hooks + /sandbox提前阻断风险变更
流水线复用Commands + Headless把经验固化为资产
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15.3.5 新手执行顺序（详细）

Section titled “15.3.5 新手执行顺序（详细）”
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    +
  1. 先学「上下文与回滚」（@、!、Esc Esc）。
    2. +
  2. 再学「计划与安全」（Plan Mode、/sandbox、Hooks）。
    3. +
  3. 最后学「规模化」（Subagents、Headless、插件体系）。
    4. +
+

15.4 4) Code Simplifier：把”能跑”整理成”可维护”

Section titled “15.4 4) Code Simplifier：把”能跑”整理成”可维护””
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15.4.1 教程化整合

Section titled “15.4.1 教程化整合”
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    +
  • code-simplifier 定位是”语义不变的结构优化”：改写法，不改行为。
    • +
  • 与 ESLint/Prettier 互补：后者做格式，前者做可读性与结构简化。
    • +
  • 特别适合 AI 快速生成后的清理阶段：先生成，再简化，再测试。
    • +
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15.4.2 可执行清单

Section titled “15.4.2 可执行清单”
+
Terminal window
claude plugin install code-simplifier
# 或会话内
/plugin marketplace update claude-plugins-official
/plugin install code-simplifier
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    +
  • 在 CLAUDE.md 明确导入、命名、类型、函数风格，供 simplifier 遵循。
    • +
  • 建议在 PR 前固定跑一次，结合 git diff 做人工复核。
    • +
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15.4.3 详细步骤（PR 前整理标准流程）

Section titled “15.4.3 详细步骤（PR 前整理标准流程）”
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  1. 功能实现完成后先跑测试，确保”当前代码可用”。
    2. +
  2. 执行 code-simplifier 做结构整理。
    3. +
  3. 立刻跑同一套测试，确认整理后行为不变。
    4. +
  4. 打开 git diff，重点审查：边界判断、异常路径、公共接口。
    5. +
  5. 通过后再进入 Code Review，避免把”脏代码”交给审查环节。
    6. +
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15.4.4 文章细节补充（工具对比与定位）

Section titled “15.4.4 文章细节补充（工具对比与定位）”
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工具擅长不擅长
ESLint/Prettier语法与格式统一语义级简化
Code Simplifier可读性、结构化重写规则化格式细节
人工 Review业务语义与架构判断高重复清理劳动
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15.4.5 使用场景建议

Section titled “15.4.5 使用场景建议”
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  • 长时间编码后清理；
    • +
  • 大重构后整理；
    • +
  • AI 生成代码后做第一轮“去噪”；
    • +
  • 提交 PR 前统一风格并减少理解负担。
    • +
+

15.5 5) 非技术用户落地指南：安装、配置与工作流心法

Section titled “15.5 5) 非技术用户落地指南：安装、配置与工作流心法”
+

15.5.1 教程化整合

Section titled “15.5.1 教程化整合”
+
    +
  • 非技术用户最容易卡在 API 配置，而不是功能使用。
    • +
  • 推荐”先可用，再优化”路径：先连上一个稳定模型，再逐步引入 Skills/MCP。
    • +
  • 将复杂配置 GUI 化（如 API 切换器），可显著降低误配置概率。
    • +
  • 工具使用的核心不是”会命令”，而是”能给清晰输入并持续迭代”。
    • +
+

15.5.2 可执行清单

Section titled “15.5.2 可执行清单”
+
    +
  1. 安装 Claude Code（Mac/Windows）后先跑一个最小任务验证闭环。
    2. +
  2. 选定一个主模型通道（国产直连或第三方中转）并完成健康检查。
    3. +
  3. 使用四步法：定义问题 → 方案探索 → 计划确认 → 执行迭代。
    4. +
  4. 每完成一个阶段，把关键结论写入文档，防止上下文丢失。
    5. +
+

15.5.3 详细步骤（从 0 到可用）

Section titled “15.5.3 详细步骤（从 0 到可用）”
+
Terminal window
# macOS / Linux
curl -fsSL https://claude.ai/install.sh | bash

+
# Windows PowerShell
irm https://claude.ai/install.ps1 | iex
+
    +
  1. 安装后执行 claude，完成登录与首轮环境检查。
    2. +
  2. 先做一个最小任务：例如”读取当前目录并总结项目结构”。
    3. +
  3. 配置 API 通道后做健康检查：固定 3 个测试 prompt（速度/格式/稳定）。
    4. +
  4. 用四步工作流跑一个小任务（如批量重命名）验证完整闭环。
    5. +
  5. 将成功流程写入 CLAUDE.md，后续直接复用。
    6. +
+

15.5.4 文章细节补充（安装与配置路线）

Section titled “15.5.4 文章细节补充（安装与配置路线）”
+
15.5.4.1 A. 安装路线
Section titled “15.5.4.1 A. 安装路线”
+
    +
  • macOS/Linux：脚本安装；
    • +
  • Windows：PowerShell 安装；
    • +
  • 若脚本受限，再退回 npm 安装方案。
    • +
+
15.5.4.2 B. API 路线
Section titled “15.5.4.2 B. API 路线”
+
    +
  1. 国产模型直连：稳定、低成本、上手快；
    2. +
  2. 第三方中转：接近原模型体验，需关注服务稳定性；
    3. +
  3. 多源备份：至少准备 2 个可切换通道，防止单点中断。
    4. +
+
15.5.4.3 C. 常见卡点排查
Section titled “15.5.4.3 C. 常见卡点排查”
+
    +
  • 安装成功但命令不可用：重开终端并检查 PATH。
    • +
  • 模型无响应：先跑最小 prompt 验证网络与 key。
    • +
  • 会话表现忽好忽坏：检查上下文是否过载，必要时重启会话。
    • +
+

15.5.5 实战摘录

Section titled “15.5.5 实战摘录”
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+

普通用户的门槛不是编程语法，而是把模糊需求转成可执行目标与验收条件。

+
\ No newline at end of file diff --git a/research/code/gpt/index.html b/research/code/gpt/index.html new file mode 100644 index 0000000..537de53 --- /dev/null +++ b/research/code/gpt/index.html @@ -0,0 +1,478 @@ + GPT-5.2 系列 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

GPT-5.2 系列

1. GPT-5.2 系列概览

Section titled “1. GPT-5.2 系列概览”
+

GPT-5.2 系列（2025年12月发布）适合做细节 review和深度分析。

+

1.1 模型选择矩阵

Section titled “1.1 模型选择矩阵”
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模型特点适用场景定价
GPT-5.2 Instant快速响应，延迟低日常代码辅助、快速问答低
GPT-5.2 Thinking深度推理，复杂分析复杂问题分析、算法设计中
GPT-5.2 Pro最强专业版关键代码审查、论文评审高
+
+

2. 核心能力

Section titled “2. 核心能力”
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2.1 基准测试

Section titled “2.1 基准测试”
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能力数据说明
ARC-AGI-190%+首次突破 90% 通用推理基准
知识截止2025年8月支持最新研究成果
长上下文1M+ tokens支持大型文档分析
+
+

3. GPT-5.3-Codex 核心升级

Section titled “3. GPT-5.3-Codex 核心升级”
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3.1 核心特性

Section titled “3.1 核心特性”
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特性数据说明
自我参与开发-早期版本加速自身开发
速度提升25%+比前代快 25%
Token 减半50%相同任务所需 token 减半
训练速度4x训练速度提升 4 倍
+

3.2 性能优势

Section titled “3.2 性能优势”
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基准得分说明
Terminal-Bench 2.077.3%智能体编码领先
OSWorld64.7%桌面自动化领先
网络安全High 级别OpenAI 首个获评
+
+

4. Claude Opus 4.6 vs GPT-5.3-Codex

Section titled “4. Claude Opus 4.6 vs GPT-5.3-Codex”
+

4.1 核心对比

Section titled “4.1 核心对比”
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维度Claude Opus 4.6GPT-5.3-Codex
上下文窗口100万 tokens (Beta)400K tokens
复杂知识工作更强稍弱
自主编码任务稍弱更强
发展方向广度（多智能体、Office）深度（效率、交互性）
+

4.2 适用场景选择

Section titled “4.2 适用场景选择”
+

选择 Claude

    +
  • 需要处理大型代码库
    • +
  • 多智能体协作任务
    • +
  • 需要 Office 集成
    • +
  • 复杂知识工作
    • +

选择 Codex

    +
  • 追求极致的编码效率
    • +
  • 需要快速迭代
    • +
  • 交互式引导需求
    • +
  • 自主编码和自动化任务
    • +
+
+

5. 科研使用场景

Section titled “5. 科研使用场景”
+

验证代码接口设计是否一致，避免隐藏的 API 变更。

检查实验设置是否公平，baseline 对比是否合理。

发现数据泄漏、指标计算错误等隐蔽问题。

代码审查，发现潜在边界条件和异常情况。

确保评测脚本正确实现，无实现偏差。

独立复现核心实验，验证结果可复现性。

+
+

6. 与 Claude 的配合策略

Section titled “6. 与 Claude 的配合策略”
+
flowchart TD
    subgraph 研究流程
        A(Idea 生成) --> B(项目设计)
        B --> C(代码实现)
        C --> D(实验验证)
        D --> E(论文写作)
    end

+
    subgraph 工具分工
        F(Claude Opus 4.6) --> A
        G(Claude Code 2.0) --> C
        H(GPT-5.2 Pro) --> D
        I(GPT-5.2 Instant) --> F
    end
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
任务推荐工具理由
项目架构设计Claude Opus 4.6长上下文，200K tokens
代码实现Claude Code 2.0SWE-bench 80.9%
细节审查GPT-5.2 ProARC-AGI 90%+，深度推理
快速问答GPT-5.2 Instant响应速度快
漏洞检测GPT-5.2 Thinking深度推理能力
文献调研Claude + Elicit自动化综述能力
+
+

7. 项目文档生成流程

Section titled “7. 项目文档生成流程”
+

7.1 步骤 1: 生成项目基准文档

Section titled “7.1 步骤 1: 生成项目基准文档”
+

将 idea 交给 GPT Pro/Thinking，让其生成可用的项目文档：

+
# 项目基准文档模板

+
## 0. 一页速览（30秒读懂）
[核心问题 + 核心贡献]

+
## 1. 研究问题与动机（Problem Statement）
- 解决什么问题？
- 为什么这个问题重要？

+
## 2. 相关工作地图（Related Work Map）
- 领域分类
- 主要方法
- 你的定位

+
## 3. 核心研究假设与可验证主张（Claims）
- Claim 1: [必须对应实验]
- Claim 2: [必须对应实验]

+
## 4. 形式化（Problem Formulation）
[数学形式化]

+
## 5. 系统设计（System Architecture）
[架构图 + 模块说明]

+
## 6. 学习目标（Learning Objective）
[损失函数 + 优化目标]

+
## 7. 实验与评测（Experiments）
- 主实验
- 消融实验
- 压力测试

+
## 8. 预期贡献（Expected Outcome）
- 论文贡献点
- 潜在影响

+
## 9. 风险与备选（Risks & Alternatives）
- 最大风险
- Plan B
+

7.2 步骤 2: 人工 Review

Section titled “7.2 步骤 2: 人工 Review”
+
    +
  • 主要看”claim-实验对齐”
    • +
  • baseline 是否足够强
    • +
  • 是否有致命假设
    • +
+

关键问题：

+
    +
  • 哪些段落最容易被审稿人抓住？
    • +
  • 哪些实验设计不够严谨？
    • +
  • 哪些 claim 缺证据？
    • +
+

7.3 步骤 3: 生成执行文档

Section titled “7.3 步骤 3: 生成执行文档”
+

再次交给 GPT Pro/Thinking，生成专门给 Claude Code 用的执行文档（更偏工程步骤与任务拆分）。

+

7.4 步骤 4: 迭代到稳定

Section titled “7.4 步骤 4: 迭代到稳定”
+

用这种方式迭代到文档稳定为止。

+
+

8. Scitex：自动化科研平台

Section titled “8. Scitex：自动化科研平台”
+

8.1 核心功能

Section titled “8.1 核心功能”
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功能说明
合成数据生成根据假设自动生成符合统计规律的合成数据
统计分析自动完成 ANOVA、效应量计算
可视化生成出版级图表（箱线图、散点图）
论文撰写LaTeX 模板自动填充
同行评审AI 模拟审稿人，自动生成 Rebuttal
+

8.2 工作流程

Section titled “8.2 工作流程”
+
flowchart LR
    A(研究假设) --> B(合成数据)
    B --> C(统计分析)
    C --> D(可视化)
    D --> E(论文撰写)
    E --> F(AI 评审)
    F --> G(自动修订)
+

8.3 适用场景

Section titled “8.3 适用场景”
+
    +
  • 快速原型验证（合成数据验证假设）
    • +
  • 人机协作科研
    • +
  • 教育与实践培训
    • +
+
+

最佳实践

Section titled “最佳实践”
+

GPT-5.2 核心优势：深度推理能力，特别适合发现隐藏假设和逻辑漏洞。

+

9.1 使用建议

Section titled “9.1 使用建议”
+
    +
  1. 不要用它写代码：Claude Code 在代码任务上更强
    2. +
  2. 用它做审查：让它找 bug、提质疑
    3. +
  3. 用它做分析：让它分析论文、发现逻辑问题
    4. +
  4. 多用它的 Thinking 模式：深度推理输出更可靠
    5. +
+
+

10. Book 实战增补（逐篇并入）

Section titled “10. Book 实战增补（逐篇并入）”
+

10.1 Kimi-K2.5 免费通道：作为 GPT/Claude 之外的成本缓冲层

Section titled “10.1 Kimi-K2.5 免费通道：作为 GPT/Claude 之外的成本缓冲层”
+

这部分整合自 book/土豪英伟达_Kimi_K2_5_免费用_API_支持OpenCode.md，核心价值不是“替代一切”，而是给工作流增加一条低成本可切换通道。

+

10.1.1 实操配置要点

Section titled “10.1.1 实操配置要点”
+
    +
  • 平台：NVIDIA Build（注册后获取 API Key）。
    • +
  • Base URL：https://integrate.api.nvidia.com/v1
    • +
  • Model ID：moonshotai/kimi-k2.5
    • +
+

10.1.2 推荐使用策略

Section titled “10.1.2 推荐使用策略”
+ + + + + + + + + + + + + + + + + + + + + +
任务类型建议模型层
快速草稿、批量改写、低风险总结Kimi-K2.5（成本优先）
高风险技术决策、关键代码审查GPT/Claude 强模型（质量优先）
大批量流水任务Kimi 先跑，再用强模型抽检
+

10.1.3 接入清单

Section titled “10.1.3 接入清单”
+
    +
  1. 在平台获取 API Key 并保存。
    2. +
  2. 在客户端填入 base-url + model-id + key。
    3. +
  3. 用 3 个固定测试 prompt 做烟雾测试（速度、稳定性、格式）。
    4. +
  4. 把它加入模型路由规则，作为“降本 fallback”。
    5. +
+

10.1.4 详细步骤（可直接执行）

Section titled “10.1.4 详细步骤（可直接执行）”
+
    +
  1. 进入 NVIDIA Build 平台创建 key，并记录创建时间与配额说明。
    2. +
  2. 在工具侧新增供应商： +
      +
    • base_url: https://integrate.api.nvidia.com/v1
      • +
    • model: moonshotai/kimi-k2.5
      • +
    • api_key: <你的key>
      • +
    +
    3. +
  3. 运行三条基准提示词： +
      +
    • 摘要任务（看压缩质量）
      • +
    • 结构化输出（看 JSON 稳定性）
      • +
    • 长文本改写（看一致性）
      • +
    +
    4. +
  4. 记录延迟、失败率、输出格式稳定性，写入模型路由文档。
    5. +
  5. 配置“降级策略”：主模型失败时自动回退到 Kimi。
    6. +
+

最小验证脚本（示意）

Section titled “最小验证脚本（示意）”
+
Terminal window
curl -sS https://integrate.api.nvidia.com/v1/chat/completions \
  -H "Authorization: Bearer $NVIDIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.5",
    "messages": [{"role":"user","content":"用三点总结RAG评测重点"}],
    "temperature": 0.2
  }'
+

10.1.5 文章细节补充（按原文落地）

Section titled “10.1.5 文章细节补充（按原文落地）”
+
10.1.5.1 A. 账号与密钥准备
Section titled “10.1.5.1 A. 账号与密钥准备”
+
    +
  1. 访问 NVIDIA Build 开发者平台并完成注册。
    2. +
  2. 在控制台创建 API Key，创建后立即复制保存。
    3. +
  3. 记录模型标识与基座 URL，后续统一写入配置中心。
    4. +
+
10.1.5.2 B. 在多工具中的统一接入
Section titled “10.1.5.2 B. 在多工具中的统一接入”
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    +
  • OpenCode/Coding CLI：新增一个可切换模型配置项；
    • +
  • 笔记或写作工具（如 Copilot 类）：复用同一套 key/base/model；
    • +
  • 脚本任务：通过环境变量统一注入，减少散落配置。
    • +
+

建议新增一个 models.md（或等价配置文件）记录：

+
    +
  • 供应商名称
    • +
  • base URL
    • +
  • model ID
    • +
  • 适用任务
    • +
  • 失败回退路径
    • +
+
10.1.5.3 C. 三段式验证（避免上线即翻车）
Section titled “10.1.5.3 C. 三段式验证（避免上线即翻车）”
+
    +
  1. 连通性验证：确认接口可通、响应码正常；
    2. +
  2. 能力验证：固定三组任务评估质量（摘要、结构化、长改写）；
    3. +
  3. 稳定性验证：连续跑 20~50 次，观察超时与格式漂移。
    4. +
+
10.1.5.4 D. 故障回退策略（建议默认开启）
Section titled “10.1.5.4 D. 故障回退策略（建议默认开启）”
+
    +
  • 主模型失败（超时/限流）→ 自动切 Kimi；
    • +
  • Kimi 输出不满足格式约束 → 再交给主模型做最终规范化；
    • +
  • 两者都失败 → 返回可重试错误而不是静默空结果。
    • +
+

10.1.6 风险提示

Section titled “10.1.6 风险提示”
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    +
  • 免费额度/策略可能变化，生产任务需保留备选供应商。
    • +
  • 同一任务跨模型切换时，需明确输出格式与验收标准，避免结果漂移。
    • +
\ No newline at end of file diff --git a/research/favicon.svg b/research/favicon.svg new file mode 100644 index 0000000..cba5ac1 --- /dev/null +++ b/research/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/research/figures/comparison/index.html b/research/figures/comparison/index.html new file mode 100644 index 0000000..1e0a0e5 --- /dev/null +++ b/research/figures/comparison/index.html @@ -0,0 +1,188 @@ + 专业绘图对比 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

专业绘图对比

1. 论文图表类型清单

Section titled “1. 论文图表类型清单”
+

从几千篇论文里提取”表达强”的图，按类型建一个参考库：

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1.1 方法结构图 (Architecture)

Section titled “1.1 方法结构图 (Architecture)”
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    +
  • 整体系统架构
    • +
  • 模块关系
    • +
  • 数据流向
    • +
+

1.2 流程图 (Pipeline)

Section titled “1.2 流程图 (Pipeline)”
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    +
  • 训练流程
    • +
  • 推理流程
    • +
  • 数据处理流程
    • +
+

1.3 训练/推理示意图

Section titled “1.3 训练/推理示意图”
+
    +
  • 算法步骤可视化
    • +
  • 时序关系
    • +
+

1.4 主结果表 + 消融图

Section titled “1.4 主结果表 + 消融图”
+
    +
  • 对比实验表格
    • +
  • 消融研究图表
    • +
+

1.5 失败案例可视化

Section titled “1.5 失败案例可视化”
+
    +
  • 边界情况
    • +
  • 错误分析
    • +
+
+

2. 工具详细对比

Section titled “2. 工具详细对比”
+

2.1 Mermaid.js

Section titled “2.1 Mermaid.js”
+

优点:

+
    +
  • 纯文本语法，易于版本控制
    • +
  • 支持多种图表类型
    • +
  • GitHub/GitLab 原生支持
    • +
+

缺点:

+
    +
  • 定制能力有限
    • +
  • 复杂图表难以实现
    • +
+

代码示例:

+
graph TD
    A(输入数据) --> B(特征提取)
    B --> C{决策}
    C -->|条件1| D(路径1)
    C -->|条件2| E(路径2)
    D --> F(输出)
    E --> F
+
+

2.2 PlantUML

Section titled “2.2 PlantUML”
+

优点:

+
    +
  • UML 标准化
    • +
  • 支持时序图、用例图等
    • +
  • 文本驱动，易于维护
    • +
+

缺点:

+
    +
  • 学习曲线较陡
    • +
  • 主要针对 UML 场景
    • +
+
+

2.3 D3.js

Section titled “2.3 D3.js”
+

优点:

+
    +
  • 高度可定制
    • +
  • 支持复杂交互
    • +
  • 强大的数据绑定能力
    • +
+

缺点:

+
    +
  • 学习成本高
    • +
  • 开发时间长
    • +
+

适用场景: 创新性的数据可视化，需要展示论文的独特洞见

+
+

2.4 draw.io

Section titled “2.4 draw.io”
+

优点:

+
    +
  • 完全免费
    • +
  • 支持 Git 集成
    • +
  • 丰富的图形库
    • +
+

缺点:

+
    +
  • 大型项目性能下降
    • +
  • 协作功能不如 Figma
    • +
+
+

2.5 Figma

Section titled “2.5 Figma”
+

优点:

+
    +
  • 专业级设计工具
    • +
  • 实时协作
    • +
  • 强大的组件系统
    • +
+

缺点:

+
    +
  • 需要学习成本
    • +
  • 免费版有功能限制
    • +
+

适用场景: 高质量插图，特别是需要多人协作时

+
+

3. 推荐工作流

Section titled “3. 推荐工作流”
+

3.1 阶段1: 快速原型

Section titled “3.1 阶段1: 快速原型”
+

使用 draw.io 或 Mermaid.js 快速搭建图表框架

+

3.2 阶段2: 精细化

Section titled “3.2 阶段2: 精细化”
+

对于关键图表，使用 Figma 进行精细化设计

+

3.3 阶段3: 代码生成

Section titled “3.3 阶段3: 代码生成”
+

对于数据可视化，使用 D3.js 或 Python (matplotlib/plotly)

+

3.4 阶段4: 整合

Section titled “3.4 阶段4: 整合”
+

使用 PPT 或 Figma 进行最终整合

\ No newline at end of file diff --git a/research/figures/design/index.html b/research/figures/design/index.html new file mode 100644 index 0000000..7d4a4f7 --- /dev/null +++ b/research/figures/design/index.html @@ -0,0 +1,332 @@ + 图表设计规范 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

图表设计规范

1. 图表设计规范

Section titled “1. 图表设计规范”
+

选定视觉语言后要定规范（否则全篇风格会散）

+
+

2. 配色方案

Section titled “2. 配色方案”
+

2.1 原则

Section titled “2.1 原则”
+

颜色尽量少（论文更稳），强调用粗细/虚实区分

+

2.2 推荐配色

Section titled “2.2 推荐配色”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
用途颜色HEX
主色蓝色#3B82F6
强调色橙色#F97316
辅助色绿色#10B981
中性色灰色#6B7280
+

2.3 顶会配色参考

Section titled “2.3 顶会配色参考”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
会议主色调特点
NeurIPS蓝色系专业、冷静
ICML紫色系学术、严谨
ICLR橙色系活力、创新
CVPR蓝绿色系视觉、科技
+
+

3. 字体选择

Section titled “3. 字体选择”
+

3.1 英文字体

Section titled “3.1 英文字体”
+ + + + + + + + + + + + + + + + + + + + + + + + + +
字体适用场景特点
Arial通用安全、清晰
Helvetica标题专业、现代
Times New Roman正文传统、学术
+

3.2 中文字体

Section titled “3.2 中文字体”
+ + + + + + + + + + + + + + + + + + + + +
字体适用场景特点
宋体正文传统、正式
黑体标题现代、清晰
+

3.3 统一原则

Section titled “3.3 统一原则”
+
    +
  • 全篇使用同一字体族
    • +
  • 图表字体与正文保持一致
    • +
  • 避免超过3种字体
    • +
+
+

4. 线条规范

Section titled “4. 线条规范”
+

4.1 线宽规范

Section titled “4.1 线宽规范”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
用途线宽样式
主要边界2px实线
次要边界1px实线
辅助线0.5px虚线
数据流1.5px箭头线
+

4.2 箭头样式

Section titled “4.2 箭头样式”
+
    +
  • 实心箭头：主要数据流
    • +
  • 空心箭头：辅助关系
    • +
  • 无箭头：包含关系
    • +
+
+

5. 标注规范

Section titled “5. 标注规范”
+

5.1 图例

Section titled “5.1 图例”
+
    +
  • 放置位置：右上角或底部
    • +
  • 字体：比正文小1-2号
    • +
  • 边框：可选，保持简洁
    • +
+

5.2 说明文字

Section titled “5.2 说明文字”
+
    +
  • 字体大小：不小于8pt（确保印刷可读）
    • +
  • 颜色：与主色调一致
    • +
  • 对齐：左对齐，便于阅读
    • +
+
+

6. 尺寸规范

Section titled “6. 尺寸规范”
+

6.1 期刊/会议要求

Section titled “6.1 期刊/会议要求”
+ + + + + + + + + + + + + + + + + + + + + + + + + +
类型单栏双栏
宽度3.5英寸7英寸
分辨率300 DPI300 DPI
格式PDF/EPSPDF/EPS
+

6.2 保持可读性

Section titled “6.2 保持可读性”
+
    +
  • 文字不小于8pt
    • +
  • 线条不小于0.5pt
    • +
  • 标注与元素留足够间距
    • +
+
+

7. 常见错误

Section titled “7. 常见错误”
+

7.1 ❌ 错误示范

Section titled “7.1 ❌ 错误示范”
+
    +
  • 颜色过多（超过5种）
    • +
  • 字体不一致
    • +
  • 线条粗细不统一
    • +
  • 箭头样式混乱
    • +
  • 标注过小难以阅读
    • +
+

7.2 ✅ 正确示范

Section titled “7.2 ✅ 正确示范”
+
    +
  • 使用2-3种主色
    • +
  • 统一字体和字号
    • +
  • 线条规范统一
    • +
  • 箭头清晰一致
    • +
  • 标注清晰易读
    • +
+
+

8. GPT 辅助流程

Section titled “8. GPT 辅助流程”
+
    +
  1. +

    GPT 根据论文 + 参考图生成：

    +
      +
    • “图的元素清单”（模块/箭头/标注）
      • +
    • “布局草案”（从左到右还是从上到下）
      • +
    • “提示词”（给绘图工具出初版草图）
      • +
    +
    2. +
  2. +

    最后用 PPT/Figma 统一绘制与整合

    +
    3. +
+

8.1 提示词模板

Section titled “8.1 提示词模板”
+
请为以下论文内容设计一张架构图，遵循学术规范：

+
[论文内容]

+
要求：
1. 元素清单：列出所有模块、箭头、标注
2. 布局：从左到右的数据流
3. 配色：使用2-3种颜色，符合学术规范
4. 线条：主要边界2px实线，辅助线1px虚线
5. 标注：清晰易读，字体不小于10pt
\ No newline at end of file diff --git a/research/figures/tools/index.html b/research/figures/tools/index.html new file mode 100644 index 0000000..43d4440 --- /dev/null +++ b/research/figures/tools/index.html @@ -0,0 +1,437 @@ + 自动化绘图工具 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

自动化绘图工具

1. PaperBanana (arXiv:2601.23265, 2026)

Section titled “1. PaperBanana (arXiv:2601.23265, 2026)”
+

1.1 核心价值

Section titled “1.1 核心价值”
+

PaperBanana 是首个专门为 AI 科学家设计的自动化学术插图生成系统。

+

论文: arXiv:2601.23265

+

1.2 核心功能

Section titled “1.2 核心功能”
+ + + + + + + + + + + + + + + + + + + + + +
功能说明
自动插图需求识别从论文文本自动识别需要可视化的部分
顶会风格生成生成符合 NeurIPS/ICML/ICLR 审美的初版
智能图表推荐根据内容推荐最适合的图表类型
+
+

2. 工具生态全景

Section titled “2. 工具生态全景”
+
flowchart TD
    subgraph ["原型阶段"]
        A[Mermaid.js] --> B(快速原型)
        C[draw.io] --> B
    end

+
    subgraph ["专业阶段"]
        B --> D[Figma]
        D --> E(专业插图)
    end

+
    subgraph ["自动化阶段"]
        F[PaperBanana] --> G(自动生成)
        G --> E
    end

+
    subgraph ["整合阶段"]
        E --> H(PPT 整合)
        H --> I(最终发布)
    end
+
+

3. 工具对比

Section titled “3. 工具对比”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
工具类型优势适用场景学习曲线
Mermaid.js文本驱动代码驱动、版本友好流程图、时序图低
PlantUMLUML标准化系统架构图中
D3.js自定义高度可定制创新可视化高
draw.io在线绘图免费、协作快速原型低
Figma协作设计专业级高质量插图中
PPT整合工具方便整合最终发布低
+
+

4. 工具选择决策树

Section titled “4. 工具选择决策树”
+
flowchart TD
    A(图表类型) --> B{是流程图/时序图?}
    A --> C{需要高度定制?}
    A --> D{团队协作?}
    A --> E{快速原型?}

+
    B -->|是| F[Mermaid.js]
    C -->|是| G[D3.js]
    D -->|是| H[Figma]
    E -->|是| I[draw.io]
    F --> J(初版)
    G --> K(精细化)
    H --> L(协作)
    I --> M(快速迭代)

+
    M --> N(PPT 整合)
+
+

5. GPT 辅助绘图流程

Section titled “5. GPT 辅助绘图流程”
+

5.1 阶段 1: 需求分析

Section titled “5.1 阶段 1: 需求分析”
+
你是一位专业的学术插图设计师。请分析以下论文内容：

+
["粘贴论文的方法部分"]

+
输出：
1. 需要可视化的核心概念
2. 概念之间的关系
3. 适合的图表类型建议
+

5.2 阶段 2: 元素清单

Section titled “5.2 阶段 2: 元素清单”
+
基于以下论文内容, 生成架构图的元素清单：

+
["论文内容"]

+
请输出：
1. 所有模块及其功能描述
2. 模块间的数据流向
3. 需要标注的关键信息
4. 建议的颜色方案
+

5.3 阶段 3: 布局建议

Section titled “5.3 阶段 3: 布局建议”
+
请为以下内容设计布局方案：

+
["元素清单"]

+
考虑：
- 从左到右还是从上到下？
- 中心辐射还是层次结构？
- 留白和平衡
+

5.4 阶段 4: Mermaid 代码生成

Section titled “5.4 阶段 4: Mermaid 代码生成”
+
flowchart LR
    subgraph ["输入处理"]
        A(原始数据) --> B(预处理)
    end

+
    subgraph ["核心模型"]
        B --> C(特征提取)
        C --> D(注意力模块)
        D --> E(输出生成)
    end

+
    subgraph ["损失函数"]
        F(主损失) --> G(多任务Loss)
        H(正则项) --> G
    end

+
    E --> I(最终输出)
+
+

6. 配色方案示例

Section titled “6. 配色方案示例”
+

6.1 AI 顶会风格

Section titled “6.1 AI 顶会风格”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
颜色用途HEX
背景图表背景#FFFFFF
主模块核心方法#10A37F
辅助模块对比方法#007AFF
数据流箭头连接#6B7280
高亮关键部分#AB68FF
+

6.2 暗色主题

Section titled “6.2 暗色主题”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
颜色用途HEX
背景图表背景#212121
主模块核心方法#10A37F
辅助模块对比方法#007AFF
数据流箭头连接#9CA3AF
高亮关键部分#AB68FF
+
+

7. 最佳实践

Section titled “7. 最佳实践”
+

整个论文使用统一的配色和字体方案

避免过多颜色, 每个颜色有明确含义

确保在黑白打印时也能清晰区分

每个图都有清晰的图例和说明

+
+

8. PaperBanana 论文要点补充（2601.23265）

Section titled “8. PaperBanana 论文要点补充（2601.23265）”
+

为响应“把论文内容并入现有章节”的要求, 这里补充 PaperBanana 原文中的关键信息（不上传 PDF 文件）：

+

8.1 研究动机

Section titled “8.1 研究动机”
+

论文明确指出：自动科研系统已能生成方法与实验, 但发表级插图仍是人工瓶颈。PaperBanana 的目标是把“论文图”从手工绘制转为可迭代自动流程。

+

8.2 方法框架

Section titled “8.2 方法框架”
+

PaperBanana 采用 agentic pipeline, 将插图生成拆为可控阶段：

+
    +
  1. 检索参考图与风格约束
    2. +
  2. 规划图的语义内容与视觉结构
    3. +
  3. 图像渲染与矢量生成
    4. +
  4. 自我批评与迭代修正
    5. +
+

8.3 评测设置

Section titled “8.3 评测设置”
+

论文引入 PaperBananaBench（292 个测试样例）, 覆盖方法图等常见学术插图场景, 用于评估自动生成质量与可用性。

+

8.4 工程启发（对本书工作流）

Section titled “8.4 工程启发（对本书工作流）”
+
    +
  • 图表要与方法叙事同源：由 method text 驱动, 而不是事后美化。
    • +
  • 优先保证“信息正确 + 可复现”, 再追求视觉高级感。
    • +
  • 生成后必须保留可编辑中间产物（SVG/分层素材）, 便于审稿期快速改图。
    • +
+

8.5 AutoFigure-edit 项目补充（根目录项目）

Section titled “8.5 AutoFigure-edit 项目补充（根目录项目）”
+

来自 AutoFigure-Edit-main.zip 的 README 还提供了一条很实用的“方法图工程化”路线：

+
    +
  • 从 method text 先生成草图 figure.png
    • +
  • 用 SAM3 做区域检测与框选（产出 samed.png + boxlib.json）
    • +
  • 对图元做去背景（RMBG-2.0）得到可复用 icon
    • +
  • 生成占位模板 template.svg 并可迭代优化
    • +
  • 最终组装成可编辑的 final.svg
    • +
+

这条链路和 PaperBanana 的共同点是：都强调中间产物可追溯, 便于审稿期快速改图。

+
+

9. Book 实战增补（逐篇并入）

Section titled “9. Book 实战增补（逐篇并入）”
+
+

本节并入 3 篇与”图表/视觉产物自动化”直接相关的文章，重点补齐从论文 Figure 到 PPT/视频转场的可执行流程。

+
+

9.1 1) PaperBanana：论文级 Figure 生成与润色

Section titled “9.1 1) PaperBanana：论文级 Figure 生成与润色”
+

来源：book/谷歌做了个论文专用版nano_banana_顶会级Figure直出.md

+

9.1.1 关键增量

Section titled “9.1.1 关键增量”
+
    +
  • Figure 目标从“好看”升级为“语义正确 + 学术审美一致”。
    • +
  • 支持两类核心产物：方法结构图（Methodology）与统计图（Statistical Plots）。
    • +
  • 支持从草图/初版图自动润色：重排层级、统一配色、强化箭头逻辑。
    • +
+

9.1.2 多 Agent 生产线（可复用）

Section titled “9.1.2 多 Agent 生产线（可复用）”
+
    +
  1. 检索参考图与领域范式；
    2. +
  2. 规划结构化图描述（模块、关系、层级）；
    3. +
  3. 按审美约束生成初稿；
    4. +
  4. 评论代理迭代纠错（语义一致性检查）；
    5. +
  5. 输出论文可用版与可编辑资产。
    6. +
+

9.1.3 实操结论

Section titled “9.1.3 实操结论”
+
    +
  • 统计图场景下，优先“AI 生成绘图代码再出图”，通常比直接生图更可控。
    • +
  • 生成完成后必须保留中间素材，避免审稿期改图返工。
    • +
+

9.1.4 详细步骤（PaperBanana 风格流程）

Section titled “9.1.4 详细步骤（PaperBanana 风格流程）”
+
    +
  1. 准备输入：方法段落、关键模块列表、模块关系表（A→B）。
    2. +
  2. 先产出“结构草稿”而不是终稿，确认语义无误后再美化。
    3. +
  3. 进入风格增强阶段：统一配色、字体、间距、箭头方向。
    4. +
  4. 对照正文逐项核验：术语一致、关系一致、无缺漏模块。
    5. +
  5. 导出两份：投稿图 + 可编辑源文件（SVG/分层）。
    6. +
+

9.1.5 文章细节补充（质量核验表）

Section titled “9.1.5 文章细节补充（质量核验表）”
+
    +
  • 语义一致：图中术语与正文完全一致；
    • +
  • 逻辑一致：箭头方向与流程关系无冲突；
    • +
  • 审美一致：配色、字号、间距统一；
    • +
  • 投稿可用：分辨率与版式满足会议模板要求。
    • +
+

9.2 2) PPT Skills：从静态页面到转场视频

Section titled “9.2 2) PPT Skills：从静态页面到转场视频”
+

来源：book/学会了PPTSkills_豆包Kimi都感觉不香了_但90_的人卡在第一步_Gemini_NanoBananaPro_API国内用不了_这篇帮你全解决（8个坑_国内可用）.md

+

9.2.1 可执行流程

Section titled “9.2.1 可执行流程”
+
    +
  1. 输入主题与约束（页数、风格、分辨率）；
    2. +
  2. 批量生成每页视觉稿；
    3. +
  3. 两两页面分析视觉差异；
    4. +
  4. 自动生成转场提示词；
    5. +
  5. 调用视频模型生成过渡片段；
    6. +
  6. 用 ffmpeg 合成完整演示视频。
    7. +
+

9.2.2 工程化建议

Section titled “9.2.2 工程化建议”
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    +
  • 优先“二次开发已有 skill”，避免从零造轮子。
    • +
  • 配置分层：官方 API 与第三方 API 兼容两套路径。
    • +
  • 对“连通性、鉴权、路径、依赖版本”做启动前自检，减少运行中断。
    • +
+

9.2.3 详细步骤（PPT Skills 国内可用改造）

Section titled “9.2.3 详细步骤（PPT Skills 国内可用改造）”
+
    +
  1. 复制原项目到新目录，避免直接污染上游版本。
    2. +
  2. 创建虚拟环境并安装依赖：
    3. +
+
Terminal window
python3 -m venv venv
source venv/bin/activate
pip install google-genai pillow python-dotenv -i https://pypi.org/simple
+
    +
  1. 复制配置模板并填写：
    2. +
+
Terminal window
cp .env.example .env
+
    +
  1. 先做 API 连接测试（返回 200 再继续主流程）。
    2. +
  2. 官方 SDK 不稳定时切 HTTP 直调，便于定位原始请求/响应。
    3. +
  3. 生成完成后再执行转场视频流水线，避免前置失败放大损失。
    4. +
+

9.2.4 文章细节补充（可复制命令顺序）

Section titled “9.2.4 文章细节补充（可复制命令顺序）”
+
Terminal window
# 初始化
cd <project>
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

+
# 生成静态页
python generate_ppt.py --plan plans/demo.md --style styles/vector-illustration.md --resolution 2K

+
# 生成转场视频（按项目脚本）
python generate_transition.py --input outputs/<run>/images --output outputs/<run>/videos
ffmpeg -f concat -safe 0 -i outputs/<run>/videos/list.txt -c copy outputs/<run>/full_ppt_video.mp4
+

9.2.5 常见问题排查（细化）

Section titled “9.2.5 常见问题排查（细化）”
+
    +
  1. API 连通失败：先用独立脚本验证请求，再跑主流程。
    2. +
  2. 模型返回空结果：检查模型名是否与供应商兼容。
    3. +
  3. 转场合成失败：检查 ffmpeg 是否安装、输入列表是否存在。
    4. +
  4. 分辨率不一致：统一生成参数，避免混用 2K/4K 导致拼接失败。
    5. +
+

9.2.6 高频坑位清单

Section titled “9.2.6 高频坑位清单”
+
    +
  • Base URL 误配（多拼路径导致 404）；
    • +
  • 运行环境不一致（系统 Python 与虚拟环境混用）；
    • +
  • 官方 SDK 报错不透明（必要时改 HTTP 直调便于排错）。
    • +
+

9.3 3) 图文一体化 Skill：文章生成 + 配图落地

Section titled “9.3 3) 图文一体化 Skill：文章生成 + 配图落地”
+

来源：book/Claude_Code_Skill_自动生成文章内容与配图.md

+

9.3.1 双 Skill 架构

Section titled “9.3.1 双 Skill 架构”
+
    +
  • article-generator：负责文章结构、SEO 信息与图片占位符。
    • +
  • image-generator：负责读取占位符上下文、生成 prompt、调用图像流水线。
    • +
+

9.3.2 8 步流水线（建议模板）

Section titled “9.3.2 8 步流水线（建议模板）”
+
    +
  1. 生成文章与占位图；
    2. +
  2. 提取占位符上下文；
    3. +
  3. 生成每图 prompt；
    4. +
  4. 批量生图；
    5. +
  5. 转 webp 压缩；
    6. +
  6. 上传对象存储（如 R2）；
    7. +
  7. 回填 CDN 链接；
    8. +
  8. 输出可发布版本。
    9. +
+

9.3.3 关键命令示例

Section titled “9.3.3 关键命令示例”
+
Terminal window
npm run generate-images <article-slug>
npm run compress-images <article-slug>
npm run upload-images <article-slug>
npm run replace-images <article-slug>
+

9.3.4 详细步骤（图文流水线）

Section titled “9.3.4 详细步骤（图文流水线）”
+
    +
  1. 用 article-generator 产出文章与占位图。
    2. +
  2. 运行 image-generator 读取占位并生成 prompt。
    3. +
  3. 依次执行四个命令：生图 → 压缩 → 上传 → 链接替换。
    4. +
  4. 每一步检查输出目录是否存在且非空。
    5. +
  5. 替换完成后抽查 3 处图片链接，确认 CDN 可访问。
    6. +
+

9.3.5 文章细节补充（目录规范建议）

Section titled “9.3.5 文章细节补充（目录规范建议）”
+
articles/<slug>.md
prompts/<slug>/*.md
images/<slug>/*.png
images/<slug>/*.webp
+

建议每一步都写入一个 status.json，用于中断后恢复：

+
    +
  • step
    • +
  • ok
    • +
  • updated_at
    • +
  • error
    • +
+

9.3.6 章节衔接说明

Section titled “9.3.6 章节衔接说明”
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这套方案与本章已有的 PaperBanana + AutoFigure-edit 形成互补：

+
    +
  • 论文 Figure 偏“科研表达规范化”；
    • +
  • 图文流水线偏“内容生产自动化”；
    • +
  • 两者都强调中间产物与可追溯性。
    • +
\ No newline at end of file diff --git a/research/idea/projects/index.html b/research/idea/projects/index.html new file mode 100644 index 0000000..95c49f7 --- /dev/null +++ b/research/idea/projects/index.html @@ -0,0 +1,392 @@ + 调研项目 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

调研项目

1. Sci-Reasoning 数据集

Section titled “1. Sci-Reasoning 数据集”
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1.1 核心价值

Section titled “1.1 核心价值”
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Sci-Reasoning 通过 LLM+Human Review的方法, 对 2023–2025 年 NeurIPS、ICML、ICLR 的 3819 篇 Oral/Spotlight 论文进行”学术谱系”建模。

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资源链接:

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flowchart TD
    A[3819 Papers] --> B[LLM Annotation]
    B --> C[Human Review]
    C --> D[15 Patterns]
    C --> E[Innovation Recipes]
    D --> F(科研创新规律)
    E --> F
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+

2. 15 种科研思维模式详解

Section titled “2. 15 种科研思维模式详解”
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诊断+重构：识别当前方法的局限性, 将限制转化为设计约束。

跨域融合：从相邻领域引入解决方案, 激活创新可能。

表示转化：替换问题的基本表示单元, 开辟新视角。

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2.1 完整模式列表

Section titled “2.1 完整模式列表”
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编号模式比例核心价值
P01Gap-Driven Reframing24.2%转化限制为设计约束
P02Cross-Domain Synthesis18.0%跨领域融合
P03Representation Shift10.5%表示转化
P04Modular Pipeline Composition4.2%模块化组合
P05Data & Evaluation Engineering5.4%数据与评测工程
P06Principled Probabilistic Modeling5.4%概率建模
P07Formal-Experimental Tightening6.7%理论实验紧密结合
P08Approximation Engineering4.9%可扩展性近似
P09Inference-Time Control2.4%推理时控制
P10Structural Inductive Bias5.1%结构归纳偏置
P11Multiscale Modeling1.4%多尺度建模
P12Mechanistic Decomposition1.9%机制分解
P13Adversary Modeling1.5%对手建模
P14Numerics & Systems Co-design1.4%数值与系统协同
P15Data-Centric Optimization2.1%以数据为中心
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+

3. Innovation Recipes（Innovation Recipes）

Section titled “3. Innovation Recipes（Innovation Recipes）”
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3.1 高价值组合

Section titled “3.1 高价值组合”
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flowchart LR
    A[Gap-Driven] -->|诊断+重构| B[Representation Shift]
    C[Cross-Domain] -->|导入+适配| D[Representation Shift]
    E[Gap-Driven] -->|诊断+借用| F[Cross-Domain]

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    B --> G("318次")
    D --> H("233次")
    F --> I("204次")
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组合模式出现次数策略
Gap-Driven + Representation Shift318”诊断+重构”
Cross-Domain + Representation Shift233”导入+适配”
Gap-Driven + Cross-Domain204”诊断+借用”
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+

4. 会议偏好分析

Section titled “4. 会议偏好分析”
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会议偏好模式写作建议
ICML形式化方法 (8.3%), 概率建模 (7.5%)增加理论分析和证明
ICLR表示创新 (11.8%), 基准测试 (8.5%)侧重表示分析和可视化
NeurIPS平衡跨学科覆盖强调方法的生物学合理性
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5. LLM 评估基准 (Hit@10)

Section titled “5. LLM 评估基准 (Hit@10)”
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这个评估衡量：给定前人工作, LLM 能否预测出研究方向的准确度。

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模型Hit@10 得分
Gemini 2.5 Pro49.35%
Claude Opus 442.86%
GPT-5.238.89%
Claude Sonnet 429.87%
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+

6. AI Scientist 系列

Section titled “6. AI Scientist 系列”
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6.1 完整对比

Section titled “6.1 完整对比”
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项目时间核心特点代码
The AI Scientist2024.08完整研究自动化SakanaAI
The AI Scientist-v22025无代码模板自动化-
ChemCrow2024化学研究 Agent (728+ 引用)-
Virtual-ScientistsACL 2025端到端科研协作InternScience
Kosmos2025自主数据驱动发现-
AI-Researcher2025完全自主研究系统-
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7. 实践建议

Section titled “7. 实践建议”
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7.1 如何借鉴 Sci-Reasoning

Section titled “7.1 如何借鉴 Sci-Reasoning”
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    +
  1. +

    构建调研库时做标签化

    +
      +
    • 每篇论文标注其”创新类型”
      • +
    • 便于后续 pattern mining
      • +
    +
    2. +
  2. +

    关注Innovation Recipes

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      +
    • 高频组合往往意味着”成熟赛道”
      • +
    • 低频组合可能有更大的创新空间
      • +
    +
    3. +
  3. +

    结合会议偏好

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    • ICML 侧重理论
      • +
    • ICLR 侧重表示
      • +
    • NeurIPS 平衡跨学科
      • +
    +
    4. +
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核心启示：创新不是玄学, 是可以被系统化分析和复制的模式。

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+

8. 根目录论文内容整合（不上传 PDF 本体）

Section titled “8. 根目录论文内容整合（不上传 PDF 本体）”
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8.1 论文一：Towards Execution-Grounded Automated AI Research (2601.14525)

Section titled “8.1 论文一：Towards Execution-Grounded Automated AI Research (2601.14525)”
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根据原文摘要与项目环境说明, 这项工作提出了一个 execution-grounded 的自动科研框架, 核心不是只生成“看起来合理”的 idea, 而是把 idea 真实落地执行并用反馈反哺下一轮搜索。

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关键点：

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  • 构建自动执行器：自动打补丁、提交代码、分配 GPU、回收训练日志。
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  • 设定两个可执行研究环境： +
      +
    • post-training：GRPO + MATH（Qwen2.5-Math-1.5B）
      • +
    • pre-training：nanoGPT + FineWeb
      • +
    +
    • +
  • 研究目标从“语言 plausibility”转为“执行后有效性”。
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  • 强调 execution feedback 对后续 idea 迭代的价值。
    • +
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8.2 论文二：Idea2Story (2601.20833)

Section titled “8.2 论文二：Idea2Story (2601.20833)”
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该工作把”research idea → 可写论文叙事”的过程做成了结构化流水线, 核心贡献是 pre-computation-driven 思路：尽量把文献理解和结构化表征前置, 降低在线大上下文推理成本与幻觉风险。

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关键点：

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  • 用知识图谱组织 Idea / Pattern / Domain / Paper 节点。
    • +
  • 通过模式选择、故事生成、反思修正, 产出完整 scientific narrative skeleton。
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  • 引入 anchored multi-agent review, 使打分更可审计、更可复现。
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  • 输出可直接进入写作阶段的结构化结果（title/abstract/problem/method/contrib/experiments）。
    • +
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8.3 并入现有工作流的建议

Section titled “8.3 并入现有工作流的建议”
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  1. 在 1.1 调研 阶段优先落地“可执行环境”, 避免只停留在概念验证。
    2. +
  2. 在 1.2 项目 阶段增加“叙事骨架检查”, 确保 idea 到论文表达不断层。
    3. +
  3. 让“执行反馈”与“叙事反馈”双闭环并行：一个校验有效性, 一个校验可写性。
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\ No newline at end of file diff --git a/research/idea/research/index.html b/research/idea/research/index.html new file mode 100644 index 0000000..0335fa6 --- /dev/null +++ b/research/idea/research/index.html @@ -0,0 +1,345 @@ + 调研方法 | Vibe Research - AI 科研指南 + Skip to content
Vibe Research Vibe Research - AI 科研指南

调研方法

1. 调研目标

Section titled “1. 调研目标”
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调研的目标不是”看了很多论文”，而是要产出能直接导向可写论文的结果：

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  1. 该方向到底在解决什么问题
    2. +
  2. 大家默认的设定/评测是什么
    3. +
  3. 现在的瓶颈在哪里
    4. +
  4. 哪些空白值得写
    5. +
  5. 你的 idea 能否形成可验证的 claim
    6. +
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2. 工具选择与分工

Section titled “2. 工具选择与分工”
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2.1 主力调研工具

Section titled “2.1 主力调研工具”
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工具适用场景
ChatGPT 5.2 Pro体系化综述、把一堆材料整理成”研究地图”、提炼问题定义与评价体系
Manus / GPT 5.2 Thinking抓漏洞（这篇论文到底新在哪里？有没有偷换设定？）
Gemini 3 Pro Deep Research补全资料、扩展相关工作、找被忽略的假设
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2.2 推荐分工

Section titled “2.2 推荐分工”
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  • Pro: 搭主框架（问题→方法→实验→结论）+ 产出”标准化笔记”
    • +
  • Thinking: 抓漏洞（创新点、评价公平性、潜在问题）
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  • Deep Research: 补引用与证据（相关工作漏了谁？还有哪些强 baseline？）
    • +
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3. 调研的产物清单

Section titled “3. 调研的产物清单”
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建议把调研结果固定成 6 份输出：

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  1. +

    Related Work Map（研究地图）

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    • 按”问题子类/数据模态/方法范式/训练范式/评测范式”分层
      • +
    +
    2. +
  2. +

    Benchmark & Metric Sheet（评测与指标表）

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    • 主流数据集/指标/协议/常见坑
      • +
    +
    3. +
  3. +

    Assumption Matrix（隐含假设矩阵）

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    • 每篇论文默认假设是什么？（资源、标注、算力、可访问信息等）
      • +
    +
    4. +
  4. +

    Failure Taxonomy（失败模式分类）

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    • 方法为什么失败？失败发生在什么条件下？
      • +
    +
    5. +
  5. +

    Gap List（研究空白清单）

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    • 空白不是”没人做”，而是”没人把它做成可验证主张 + 可复现实验”
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    +
    6. +
  6. +

    Claim Candidate Pool（候选主张池）

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    • 每个 claim 必须能写成一句话，并且对应至少一个主实验 + 一个消融
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    +
    7. +
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4. 调研流程

Section titled “4. 调研流程”
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4.1 定义边界 (Scope)

Section titled “4.1 定义边界 (Scope)”
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    +
  • 你要解决的”核心问题”一句话是什么？
    • +
  • 哪些工作算”同类问题”，哪些只是”相关但不同问题”？
    • +
+

4.2 建语料库 (Corpus)

Section titled “4.2 建语料库 (Corpus)”
+
    +
  • 每篇至少记录：任务定义、输入输出、数据集、指标、训练资源、关键 novelty、关键 baseline
    • +
+

4.3 标准化阅读

Section titled “4.3 标准化阅读”
+

不要自由笔记，要”字段化”：

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    +
  • Problem: 解决什么，设定是什么？
    • +
  • Key Idea: 一句话新意是什么？
    • +
  • Mechanism: 为什么有效（作者的因果叙事）？
    • +
  • Evidence: 哪些实验真正在支撑 claim？
    • +
  • Baselines: 对比是否公平？
    • +
  • Weakness: 最大漏洞是什么？
    • +
  • Reproduce: 复现难点在哪里？
    • +
+

4.4 对齐与冲突检测

Section titled “4.4 对齐与冲突检测”
+

多模型调研结果可能冲突，冲突往往就是”机会点”：

+
    +
  • 该方向究竟默认设定是什么？
    • +
  • 某些工作是不是在换设定？
    • +
  • 指标是不是被 hack 了？
    • +
  • 某些强 baseline 是否被刻意忽略？
    • +
+

4.5 产出 gap → 变成可写 idea

Section titled “4.5 产出 gap → 变成可写 idea”
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每个 gap 最终要落到可写论文的形式：

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    +
  • Claim（主张）
    • +
  • Setup（设定）
    • +
  • Method（方法）
    • +
  • Experiment（实验）
    • +
  • Expected Outcome（预期）
    • +
  • Risk（最大风险与备选方案）
    • +
+

5. 文献检索工具 (2025最新)

Section titled “5. 文献检索工具 (2025最新)”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
工具核心功能适用场景链接
Elicit1.25亿+论文库，自动化系统综述系统性文献调研elicit.com
Consensus AIAI驱动搜索，快速找证据快速验证假设consensus.app
Semantic ScholarAI提取论文含义，深度覆盖探索研究脉络semanticscholar.org
Scite智能引用追踪(支持/反驳/提及)评估论文影响力scite.ai
Connected Papers可视化论文关系图发现相关论文connectedpapers.com
ResearchRabbit论文推荐类似 Spotify持续追踪新工作researchrabbit.ai
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5.1 工作流建议

Section titled “5.1 工作流建议”
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    +
  1. 初步探索: Semantic Scholar + Connected Papers
    2. +
  2. 深度调研: Elicit (自动化筛选和数据提取)
    3. +
  3. 证据验证: Consensus AI + Scite
    4. +
  4. 持续追踪: ResearchRabbit (新论文推送)
    5. +
+

6. 多模型协作调研

Section titled “6. 多模型协作调研”
+

使用多个 LLM 并行可以交叉验证，避免单一模型的偏见。

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6.1 推荐配置

Section titled “6.1 推荐配置”
+ + + + + + + + + + + + + + + + + + + + + + + + + +
模型角色优势
Claude Opus 4.6主力分析师深度推理，长上下文 (200K)
GPT-5.2 Thinking漏洞检测者捕捉被忽略的假设
Gemini 2.5 Pro资料补充员强大的检索和整合能力
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6.2 协作模式

Section titled “6.2 协作模式”
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    +
  1. 分工阅读: 不同模型负责不同子领域
    2. +
  2. 交叉验证: 同一篇论文由多个模型独立分析
    3. +
  3. 冲突解决: 冲突点往往是研究空白
    4. +
  4. 综合汇总: 由最强模型整合所有发现
    5. +
+
+

7. 个人高通量选题法（补充）

Section titled “7. 个人高通量选题法（补充）”
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+

这部分来自 AI research.md 的个人实践段落，已融合为可执行版本，用于把”读很多论文”变成”筛出能发的 claim”。

+
+

7.1 三层过滤流程

Section titled “7.1 三层过滤流程”
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  1. 候选池层（广覆盖） +
      +
    • 先建立 2000+ 论文级别的候选池，按任务、数据模态、评测协议分桶。
      • +
    +
    2. +
  2. 可验证层（可落地） +
      +
    • 对每个候选方向补齐：主实验、消融、压力测试、失败预案。
      • +
    +
    3. +
  3. 可投稿层（可叙事） +
      +
    • 只保留能形成完整论证链的方向：问题价值 → 方法机制 → 实验证据 → 反质疑预案。
      • +
    +
    4. +
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7.2 快速打分模板（避免主观拍脑袋）

Section titled “7.2 快速打分模板（避免主观拍脑袋）”
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维度问题评分标准（1-5）
Novelty是否只是已有方法换壳？5 = 明确新设定或新机制
Executability两周内能否跑通主实验？5 = 依赖清晰、实现成本可控
Evaluability是否有公认基准和强 baseline？5 = 可直接复现实验协议
Narrative Fit能否写成完整论文故事？5 = 贡献/证据/风险闭环完整
+

建议只推进总分最高的 Top 1-3 个方向，降低“多线开坑”风险。

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Vibe Research Vibe Research - AI 科研指南

Vibe Research

从 Idea 到顶会投稿的完整 AI 科研操作系统
开始阅读 工具总览 完整工作流

研究工作流概览

Section titled “研究工作流概览”
+
flowchart LR
    subgraph Phase1["Phase 1: Idea"]
        A(文献调研) --> B(创新模式分析)
        B --> C(Gap 识别)
        C --> D(Claim 产出)
    end

+
    subgraph Phase2["Phase2:代码"]
        D --> E(项目设计)
        E --> F(Claude Code 构建)
        F --> G(实验验证)
    end

+
    subgraph Phase3["Phase3:写作"]
        G --> H(结构化写作)
        H --> I(图表制作)
        I --> J(审稿迭代)
    end

+
    Phase1 --> Phase2
    Phase2 --> Phase3

+
    classDef phase fill:#f8fafc,stroke:#10a37f,stroke-width:1.6px,color:#111827
    class Phase1,Phase2,Phase3 phase
+

快速价值

Section titled “快速价值”
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  • 🚀 用 14 周路线，把科研任务拆成可执行流水线。
  • 🧠 用多模型交叉审查，减少方向性失误和实验返工。
  • 🧪 用结构化实验与审稿闭环，提升投稿成功率。
  • 📊 用流程图与模板化文档，让团队协作更稳定。
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核心功能

Section titled “核心功能”
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Idea 生成

Section titled “Idea 生成”
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系统化的文献调研方法，使用多模型交叉验证，从2000+论文中发现可写的研究空白。

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核心方法：

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  • Related Work Map 构建
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  • Assumption Matrix 分析
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  • Gap List 产出
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  • Claim Candidate Pool
    • +
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代码实现

Section titled “代码实现”
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Claude Code、GPT-5.2系列、多Agent框架的最佳实践，高效构建实验系统。

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核心能力：

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  • 并行协作架构
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  • CLAUDE.md 团队记忆
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  • Sub-agents 交叉验证
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  • 自动化验证闭环
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论文图表

Section titled “论文图表”
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自动化绘图工具推荐，专业图表设计规范，让论文插图更具说服力。

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工具生态：

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  • PaperBanana 自动生成
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  • Mermaid 流程图
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  • D3.js 自定义可视化
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  • Figma 专业设计
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论文写作

Section titled “论文写作”
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论证链构建方法，多模型协作写作，Prism等新一代写作工具使用指南。

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写作方法：

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  • Claim → Evidence 论证链
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  • 多模型协作 tmux 配置
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  • 会议特定写作模板
    • +
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审稿迭代

Section titled “审稿迭代”
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结构化审稿流水线，会议特定审稿模板，Rebuttal写作策略。

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审稿流水线：

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    +
  • 10+ 轮自动审稿
    • +
  • TODO 驱动修改
    • +
  • 分类回应策略
    • +
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工具生态

Section titled “工具生态”
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Elicit、Zotero-MCP、Sci-Reasoning等最新工具，构建完整科研工具链。

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14 周完整时间线

Section titled “14 周完整时间线”
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周次阶段核心任务输出
Week 1-2调研Elicit 扫描 + Sci-Reasoning 分析Gap 清单
Week 3设计Claude Code 生成文档项目设计
Week 4-7实现Sub-agents 并行开发代码 + 实验
Week 8-9写作Prism + 图表生成初稿
Week 10审稿10+ 轮自动审稿修改版
Week 11投稿格式检查最终版
Week 12+Rebuttal如有需要录用
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适用场景

Section titled “适用场景”
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  • 研究生/博士生: 系统学习 AI 研究方法，提升论文产出质量
    • +
  • 工业界研究员: 快速掌握最新工具，提高研发效率
    • +
  • 独立研究者: 建立完整的科研工作流，从零开始发表顶会论文
    • +
+

从这里开始

Section titled “从这里开始”
+ +
+

本书持续更新中 · 视觉系统升级：2026-02-09 · 默认主题：浅色

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Vibe Research Vibe Research - AI 科研指南

科研圣经

科研圣经

Section titled “科研圣经”
+

本章节汇集零基础入门科研的核心知识，涵盖顶会顶刊对比、Idea生成方法、论文阅读流程与速成写作技巧。

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一、顶会顶刊投稿对比

Section titled “一、顶会顶刊投稿对比”
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1. 审稿人意见分类

Section titled “1. 审稿人意见分类”
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    +
  • Reject（拒稿）
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  • Major Revision（大修）
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  • Minor Revision（小修）
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  • Accept（接收）
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会议：一般只有 Reject（拒稿）和 Accept（接收）两种

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期刊：四种都有

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意见及应对方法见：知乎文章

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2. 审稿人提意见的意图

Section titled “2. 审稿人提意见的意图”
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类型意图
会议提意见为了拒绝文章
期刊审稿人提意见是为了改正文章，意见精确（跟哪些baseline进行对比、补充哪些评价指标、新增哪些数据集实验）
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3. 审稿人细致及专业程度

Section titled “3. 审稿人细致及专业程度”
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类型特点
会议很少有细节性的意见，质量参差不齐，不懂这个领域也可能是你的审稿人
期刊认真细致，专业
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4. 期刊和会议的优缺点

Section titled “4. 期刊和会议的优缺点”
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会议

Section titled “会议”
+

时间较短（顶会可能2-3月左右）

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CVPR-2026 时间线
Section titled “CVPR-2026 时间线”
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重要时间节点阶段
2025年10月OpenReview投稿网站开放作者注册 OpenReview
2025年10月OpenReview投稿网站开放论文提交
2025年11月摘要提交截止（晚上11:59 UTC-12）
2025年11月完整论文提交截止（晚上11:59 UTC-12）
2025年12月补充材料提交截止（晚上11:59 UTC-12）
2026年1月第一阶段拒稿通知
2026年2月作者反馈窗口期
2026年3月最终录用或拒稿通知（主要技术轨道）
2026年3月最终版（Camera-ready）文件提交（主要技术轨道）
2026年6月CVPR-2026正式举办
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AAAI-2026 时间线
Section titled “AAAI-2026 时间线”
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重要时间节点阶段
2025年6月16日OpenReview投稿网站开放作者注册
2025年6月25日OpenReview投稿网站开放论文提交
2025年7月25日摘要提交截止
2025年8月1日完整论文提交截止
2025年8月4日代码/附录材料截止
2026年9月8日第一轮结果出炉（首轮拒稿无rebuttal）
2025年10月2-8日rebuttal阶段
2025年11月3日最终录用通知
2025年11月13日最终版（Camera-ready）文件提交
2026年1月20-27日AAAI-2026正式举办
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期刊

Section titled “期刊”
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周期较长（顶刊可能8月到1年左右不等）

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LetPub 中科院一区 - 人工智能领域期刊查询结果

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5. 顶刊顶会目录

Section titled “5. 顶刊顶会目录”
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（1）2025年中科院新分区 1区 共53本期刊

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（2）CCF会议分类

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中国计算机学会推荐国际学术会议和期刊目录（2022更名版，2025年使用）.pdf

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6. 计算机可投期刊信息查询及会议截止时间

Section titled “6. 计算机可投期刊信息查询及会议截止时间”
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（1）期刊

Section titled “（1）期刊”
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LetPub 是一个提供最新SCI期刊影响因子查询及投稿分析系统的网站，可按照期刊名、研究方向、影响因子、收录情况等条件筛选和排序

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网站地址

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（2）会议

Section titled “（2）会议”
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CCF会议分类及截稿倒计时网站

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二、零基础入门科研如何想Idea

Section titled “二、零基础入门科研如何想Idea”
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1. 一篇论文诞生的整体流程图

Section titled “1. 一篇论文诞生的整体流程图”
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寻找领域内当前效果最好的论文（最好开源）
         ↓
看论文里展示的SOTA结果比较了哪些baseline
         ↓
顺着 baseline 与引用一路往前读
         ↓
在理论层面进行分析，找改进点
（哪些工作上不work，能否扩展）
         ↓
复现代码
         ↓
在代码层面进行分析，找缺陷
（运行速度慢、内存占用大等）
         ↓
改出自己的Idea
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举例：寻找领域内当前效果最好的那篇论文，最好开源→看论文里面展示的SOTA结果比较了哪些baseline，顺着 baseline 与引用一路往前读→在理论层面进行分析，找改进点，例如在哪些工作上是不work的，能否在这个领域进行一些扩展→复现代码→在代码层面进行分析，找缺陷，例如虽然某方法性能有提升，但是运行速度很慢、内存占用大，能否进行加速和优化→改出自己的点子。

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2. 寻找论文的渠道

Section titled “2. 寻找论文的渠道”
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（1）看最新出顶会中新中的论文

Section titled “（1）看最新出顶会中新中的论文”
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官网

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GitHub科研总结

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（2）大机构数据库

Section titled “（2）大机构数据库”
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（3）Paper with Code上面SOTA结果

Section titled “（3）Paper with Code上面SOTA结果”
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网址：https://paperswithcode.com/

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3. 建议部分

Section titled “3. 建议部分”
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（1）阶段划分

Section titled “（1）阶段划分”
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    +
  1. +

    资格达成期

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      +
    • 目标：满足学位授予的硬性成果指标（篇数、等级）
      • +
    • 策略：锁定 1–2 个垂直子领域，开展深度研究，尽量避免进入高曝光热门赛道，降低idea被抢占风险
      • +
    +
    2. +
  2. +

    影响拓展期

    +
      +
    • 目标：在完成资格后，利用已积累的模型、工具链与领域认知，向相邻学科或跨模态任务延伸，追求更高学术影响力
      • +
    +
    3. +
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（2）日常科研习惯

Section titled “（2）日常科研习惯”
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    +
  1. +

    输入端

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      +
    • 文献精读：每周 2 篇，输出结构化摘要（问题-方法-缺陷-可改进点）
      • +
    • 审稿意见逆向阅读：收集公开评审意见，提前预判可能被审稿人质疑点
      • +
    +

    例如：在 VLPose 这篇论文中，作者引入新模块的同时不可避免地引入了额外参数。为回应可能关于”性能提升是否仅源于参数增长”的质疑，作者在发表前通过对比实验设计，在参数量可控的前提下展示了其方法在性能上的显著优势，体现出结构设计本身的有效性。

    +

    论文链接

    +

    Table3(b)表反映的是在不同模型尺寸下，各微调方法需要训练的参数量，单位为 MB。

    +
    1. Visual Prompt Tuning（视觉提示微调）
    Section titled “1. Visual Prompt Tuning（视觉提示微调）”
    +
      +
    • 核心思想：不改动模型本体，仅在图像输入的”前面”加上可学习的”提示 embedding”
      • +
    • 就像在 Transformer 里加了一个”引导标签”，帮助模型适应新任务
      • +
    • 只训练这部分新加的 prompt，其余所有模型参数都 被冻结（frozen）
      • +
    • 这叫做参数高效的微调方法，很轻量，不会破坏原模型
      • +
    +
    2. Last Layer Tuning（最后一层微调）
    Section titled “2. Last Layer Tuning（最后一层微调）”
    +
      +
    • 传统做法：固定前面的模型结构，只微调最后一层（通常是预测层）
      • +
    • 相比全参数微调，它已经是”轻量”了，但仍然要训练模型的一部分原始参数
      • +
    +
    两种训练方式的不同点
    Section titled “两种训练方式的不同点”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    项目Visual Prompt TuningLast Layer Tuning
    是否改动原模型否 不改，只加东西是 改动最后一层
    可训练参数来源全是新加的 prompts原模型中的部分参数
    参数量通常更少较多
    泛化能力保留更强（原模型没动）中等（最后一层被改）
    结构可逆性可随时删除 prompts 恢复原模型改过原模型不可逆
    +

    结论是 VLPose 虽然只用了一点点额外的 prompt 参数（比 Visual Prompt 多，但远少于 Last Layer），但性能最好，结构还可逆，泛化也好。以高效的结构设计消解了对”参数堆砌”的潜在质疑。

    +
    2. +
  2. +

    输出端

    +
      +
    • 即时记录：手机备忘录等
      • +
    • 定期研讨：组会、会议、在线论坛等
      • +
    +
    3. +
+

4. 科研实操

Section titled “4. 科研实操”
+

4.1 入门大模型怎么选方向

Section titled “4.1 入门大模型怎么选方向”
+

目标：先搞清楚”能做多大的实验”

+

按资源情况划分：

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
手里的卡推荐任务为什么要这样做真实例子
资源多 ≥100 张 A100基于开源模型做大规模预训练、指令微调、强化学习卡多=数据吞吐大，能把基线刷高LLaMA-65B 原版就是 2048 张 A100 训的
资源中等 8 张 V100小领域微调卡不多不少，可以训”窄场景”专用模型纯语言领域：MoTCoder论文没有在humaneval或者mbpp这样的数据集上做验证，而是使用的Code Contest/apps：在专门做竞赛难度的编程数据集实验，数据量小但难度高。多模态领域：Lyra论文训练了一个视觉、语音、语言的多模态大模型，之前的多模态大模型在长音频和长视频上的效果不好，Lyra对应长视频音频的大海捞针任务做了提升
资源少 4 张 3090免训练（training-free）训练一次要一周，迭代慢；免训可直接推理，每轮迭代速度都很快，无需大量资源纯语言领域：Quick LLaMa：只做”长文本推理加速”，不改权重。视觉领域：VisionZIP：对图像的token做压缩
+

MoTCoder论文链接：https://arxiv.org/pdf/2312.15960

+

Lyra论文链接：https://arxiv.org/pdf/2412.09501

+

QuickLLaMA论文链接：https://arxiv.org/abs/2406.07528

+

VisionZIP论文链接：https://arxiv.org/pdf/2412.04467

+

4.2 如何判断一篇论文是否值得精读

Section titled “4.2 如何判断一篇论文是否值得精读”
+

值得精读 = 效果好 + 可复现

+
（1）效果好
Section titled “（1）效果好”
+

情况1：在某个Benchmark上面是SOTA

+
+

Benchmark（基准测试）是指一套标准化的测试任务、数据集和评估指标，用于公平、可重复地比较不同方法或系统的性能。

+
+

情况2：不是SOTA，但是现有SOTA是在它的基础上进行改进

+

可能出现这样的情况，假设某论文A提出一个基础模型，后续论文B在A的基础上加了一个”花哨的模块”成为SOTA，但这个模块在数据量小或任务简单时反而拖后腿。所以可能基于论文A的baseline去改进效果会更好。

+

情况3：某工作是现有SOTA的基础或它启发了现有SOTA

+

如果不知道某论文是否和现有SOTA有关，可从以下4个方面来判断该篇论文的质量：

+

方面1：大机构 > 小机构

+
常见的大机构类型
Section titled “常见的大机构类型”
+
    +
  1. +

    顶尖高校及实验室

    +
      +
    • 例如：MIT、Stanford、Tsinghua University等
      • +
    • 这些学校下的研究实验室（如MIT CSAIL、Stanford AI Lab）在学术圈具有很强影响力
      • +
    +
    2. +
  2. +

    知名科研院所

    +
      +
    • 例如：中国科学院、Max Planck Institute、Allen Institute for AI (AI2) 等
      • +
    +
    3. +
  3. +

    科技巨头的研究部门

    +
      +
    • 例如：Google Research（Gemini）、DeepMind、Microsoft Research（DeBERTa）、Meta AI（PyTorch、LLaMA）、OpenAI（GPT、CLIP）、NVIDIA Research（Diffusion Models）
      • +
    • 这些公司投入巨资，且产出大量SOTA研究成果
      • +
    +
    4. +
  4. +

    开源社区与合作研究组织

    + +
    5. +
+

方面2：简单 > 复杂

+

复杂论文可能只是某个模块在起作用，作者为了使这篇论文工作更丰富创新，可能会融合很多模块，导致论文难以理解。

+

方面3：清晰 > 繁琐

+

优先论文有公式推导、伪代码和图示。

+

方面4：开源可复现

+

（2）可复现

+

优先有公开源码的论文，训练数据和训练参数齐全，近期公开/维护

+

Benchmark 是什么？

Section titled “Benchmark 是什么？”
+

基本定义

+
    +
  • 在学术研究中，Benchmark 通常是： +
      +
    • 一个公开可用的 标准数据集（比如 COCO、ImageNet、HumanArt）
      • +
    • 配有 明确的评价指标（如 AP、Accuracy、F1-score）
      • +
    • 用于对不同模型/方法进行 公平比较
      • +
    +
    • +
+
举例
Section titled “举例”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
领域Benchmark 示例作用
图像分类ImageNet比谁分类准
人体姿态估计MS COCO / HumanArt比谁关键点预测准
自然语言处理GLUE / SuperGLUE比谁语言理解好
大模型能力MMLU / GSM8K比谁多任务泛化能力强
+

一个完整的 Benchmark 往往包括：

+ + + + + + + + + + + + + + + + + + + + + + + + + +
组件说明
数据集统一的数据输入（比如图片、文本）
标注统一的”正确答案”或ground truth
评价指标用来打分，比如AP、Recall等
测试协议比如是否允许使用额外数据、是否使用ground truth框等
+

4.3 看论文的流程

Section titled “4.3 看论文的流程”
+
（1）标题：大致知道是做什么方面的工作
Section titled “（1）标题：大致知道是做什么方面的工作”
+

译为：QuickLLaMa：用于大型语言模型的查询感知推理加速

+

通过QuickLLaMa：知道大致在做一件加速的事情

+
（2）摘要&示意图：大致知道这篇论文在做什么事情
Section titled “（2）摘要&示意图：大致知道这篇论文在做什么事情”
+

用户询问了关于哈利波特中很细节的问题，普通的Llama3无法给出回答，但是QuickLlama可以在30s内阅读完一本100k token的哈利波特并给出准确回答。

+

如果对这篇文章不感兴趣的话，可以去看下一篇文章了。

+
（3）深入看方法/架构示意图：知道这篇论文大致是在用什么框架/方式来实现这件事情
Section titled “（3）深入看方法/架构示意图：知道这篇论文大致是在用什么框架/方式来实现这件事情”
+
图示关键词解释
Section titled “图示关键词解释”
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
名称含义用途举例
Global Tokens (G)模型运行时始终存在的全局信息，如系统提示、任务说明等作为整个推理过程中每一步都可访问的公共信息；增强模型对任务和角色的理解系统提示：“你是一个图书问答助手”
Query Tokens (Q)用户输入的问题或查询句用来指导模型重点关注哪些记忆块（Memory Blocks）“哈利第一年结束时金妮指向了谁？“
Context Tokens (C)模型历史接收过的长文本内容，被拆分成很多 memory blocks 存入”记忆”中构成”记忆仓库”；供后续从中查找与 Query 或当前生成位置相关的上下文信息使用整本《哈利·波特》的文本内容被切分成块
Memory Blocks (B)上述 Context Tokens 划分后的一块块”笔记段落”用来支持查询感知（query-aware）的查找：哪些内容是现在需要调出来使用的Block 1 是”火车站送别”，Block 2 是”魔法课” 等
Query-related Blocks (R)与 Query 或 Current Token 高度相关的 memory blocks是从 Memory Blocks 中”筛选”出的重要内容，用于当前推理窗口当前提问与”火车站”段落相关，系统只加载这几段
Local Tokens (L)当前生成位置前的短距离上下文 token（相对于 Current Token）让模型知道”现在在哪”，并为相关性打分提供”当前位置的上下文""昨天张三来了，他…” 是生成”是”之前的 L
Current Token (H)当前要生成/预测的 token 的隐藏向量表示（注意力机制中的 query 向量）用于决定当前哪些 memory block 有用（用于 s(B, H) 打分）当前正要生成当前准备生成/预测的那个 token”他是___“的”是”这个词
Current Key-Value Cache (M)当前这一步模型使用的 key/value 信息组合，包含 G, Q, R, L 四部分作为 Attention 层的输入；也是每一步模型做决策时真正”看到”的全部上下文当前模型输入 = 全局说明 + 问题 + 相关段落 + 上下文
+

疑惑：先有的RAG和QuickLLaMa谁出现时间较早？它们有什么区别？

+

——>使用AI工具，用kimi k2查询得到以下对比表格：

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
维度RAG(2020年)QuickLLaMA（2024年）
核心机制在生成前，先检索外部文档，再让 LLM 结合检索结果生成答案在生成过程中，动态从长上下文中查找与查询相关的片段
数据来源通常是外部知识库（如 Wikipedia、向量数据库）是当前输入的长文本本身（如一本书、一篇长文）
是否需要训练需要训练检索器和生成器无需训练，可直接插入现有 LLM
使用场景回答知识密集型问题（如”2024 年奥运会举办城市”）回答长文本内部问题（如”哈利·波特中谁最后拿到魔杖？“）
检索粒度以文档或段落为单位以token 或记忆块为单位，更细粒度
+
（4）看实验表格结果：看这篇论文的方法比较的baseline有哪些，明确这论文的优势和提升是什么
Section titled “（4）看实验表格结果：看这篇论文的方法比较的baseline有哪些，明确这论文的优势和提升是什么”
+

图一第一列展示了，不同 benchmark 中的任务（多项选择问答、阅读理解题、对话摘要…）

+

第一行：方法对比；第二行：上下文窗口大小（Context Window）

+

该方法可以在长文本输入的Benchmark（基准测试）上取得很好的效果。

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
方法名称是否需要重新训练是否 Query-aware（查问题重点）推理速度显存占用记忆能力综合表现
LLaMA3-8B-1048K需要否慢极高很强（靠训练）精度高但代价大
StreamingLLM (Stream)不需要否快中等差（会忘）快但记不住长文本
InfLLM不需要否快低一定记忆力比 Stream 更强，但不够聪明
QLLM (QuickLLaMA)不需要是最快最低最强（精准查找）表现最全面优秀
+

图二展示了该方法在显存占用和时间上面和baseline的对比，Llama3都显存占用会随着token数量增加呈现指数增长，很快就会OOM超显存，但本文方法最多只使用了20b左右的显存，且时间增长是线性的。故有此结论，该方法相比于之前的baseline在性能、显存占用、速度都有提升。

+

如果对这篇文章不感兴趣的话，认为某个步骤写的不好，可以去看下一篇文章了

+
（5）进一步看表格内部的baseline：找baseline对应论文，用上面的流程再读一遍
Section titled “（5）进一步看表格内部的baseline：找baseline对应论文，用上面的流程再读一遍”
+

比如这里比较了英伟达的Stream LLM，去找这篇baseline对应的论文，用上面的流程再读一遍，把表格里面所有的baseline都搞懂，就差不多理解当前这篇论文了

+
（6）正文/代码：想有哪些地方可以改进，基于当前文章产生一个idea
Section titled “（6）正文/代码：想有哪些地方可以改进，基于当前文章产生一个idea”
+

4.4 代码跑通流程

Section titled “4.4 代码跑通流程”
+

（1）去 Papers with Code /github等搜索任务关键词。

+

（2）选有官方代码+有训练脚本+最近一年有更新的论文代码（有readme文件、环境版本号具体、参数详细、训练验证等使用教程清晰）。

+

（3）复现 = 配环境 → 下载数据 → 跑脚本 → 得到和作者差不多的分数。

+

详细流程：配置好代码环境和数据 → 理解代码运行逻辑（从主函数开始，哪些部分是数据处理，哪些是核心算法） → 基于哪些部分进行改进 → 对核心部分插入断点

+
阶段1 代码理解
Section titled “阶段1 代码理解”
+

例如：MoTCoder这篇论文中的模型，通过拆分子模块的方式来对代码问题进行求解，那么你可以输入给他一个例子，观察它是如何对输入问题进行子模块的拆分，从而理解这个模型的运行逻辑。

+

如果代码库提供可视化，可以基于可视化来帮助理解。

+
阶段2 在代码理解之上改进
Section titled “阶段2 在代码理解之上改进”
+

基于现有代码进行改进，比如观察到论文中的attention热力图对某些局部信息没有体现，你就可以新增一个模块来重点关注这些局部细节信息。经过你的新增模块之后，再次绘制热力图，发现之前没有被标注的细节信息被highlight，那么可以证明你新增的模块是有效的。

+

例子：论文TagCLIP新增了一个Trusty Learner模块，作者在论文中配了对应的可视化示意图，来展示该模块的作用，用以验证该模块的有效性。

+

论文研究问题：现有的基于对比语言-图像预训练（CLIP）的零样本语义分割方法在处理未见类别时存在显著的误分类问题，尤其是容易将未见类别与语义相似的已知类别混淆。

+

对于阶段2例子的详细解释

+
    +
  1. 研究问题：模型会把”没见过的东西”当成”见过的”
    2. +
+

研究的核心问题是：在图像分割中，模型需要给每个像素打上正确的类别标签。但很多模型只见过训练集里的”已知类别”（比如飞机、汽车、牛），一旦遇到”没见过的类别”（比如盆栽、电视、沙发），它常常会误认为这些新东西也是已知类别。比如，它可能会把”盆栽”误认为是”瓶子”，因为两者颜色或形状有点像。

+
    +
  1. 解决方法：让模型学会”这是不是我认识的东西”
    2. +
+

TagCLIP 的方法是增加了一个 “Trusty Token”，它的工作就像一个”可信度检测器”——先判断这个像素是不是已知类别。如果它觉得”不像是我认识的东西”，就会抑制模型给它贴上已知类别的标签。

+
    +
  1. 为什么要放 Fig.4？
    2. +
+

Fig.4 就是用来证明这个”可信度检测器”真的好用。

+
    +
  • 图里左两列显示，检测器对”已知类别”的反应很明显（像飞机、汽车这些它认识的东西被高亮）
    • +
  • 右一列显示，它对”未知类别”几乎不亮，说明它能有效忽略没见过的东西
    • +
+

简单说，这张图是”证据”，让读者一眼就能看出：

+
+

“看！我们的新方法确实能分清什么是它见过的，什么是它没见过的。“

+
+

4.5 怎么读代码

Section titled “4.5 怎么读代码”
+

目标：搞清楚”哪一段代码是我未来要改的地方”

+

流程：

+
情况1：普通github代码
Section titled “情况1：普通github代码”
+

整体框架粗读，重点部分代码亲自跑一遍（插断点），一行行去看其中的变量是如何变化的、变量的形状是什么。

+
情况2：代码库
Section titled “情况2：代码库”
+

比如在大模型训练中常见的代码库LLaMA-Factory

+

或者在视觉检测中常见的MMDetection，但这个代码库的封装很厉害，找变量入口和变化方式都很麻烦。因此这种类型的库不必层层debug，无需完全理解它的代码运作流程，会用即可。在使用说明里面看，如何添加一个新的模型、数据集等。

+

（1）粗读：

+

打开github仓库，先看 README.md，找到入口脚本（通常叫 main.py 或 train.py）。

+

（2）细读：

+

在核心函数里打断点。

+
    +
  1. IDE：用 VS Code 打开代码，F9 打断点，F5 启动调试
    2. +
  2. 断点：让程序跑到这行就暂停，方便查看变量值
    3. +
  3. 变量形状：print(x.shape)，例如 [32, 128, 768] 表示 batch=32、序列长 128、特征维度 768
    4. +
+

具体例子：在QuickLLaMA这篇文章中，它的核心框架是attention部分的实现，在代码中嵌套了方程来代替原本模型attention中的forward函数。除了论文中实现的QLLM之外，代码库还包含了这个方向的一系列论文，比如StreamLLM、LM-Infinite、InfLLM这些代码都是用同样的框架实现的，因此可以很容易比较他们之间的不同。

+

StreamLLM论文

+

LM-Infinite论文

+

4.6 改进现有SOTA

Section titled “4.6 改进现有SOTA”
+
从论文方法角度进行分析
Section titled “从论文方法角度进行分析”
+

效果好的论文，分成三类：

+

（1）效果好，方法创新——顶级论文

+

能否把这个方法用在新领域，比如Transformer出了之后——>做vision Transformer、Mamba出了之后——>做vision Mamba、Sam出了之后——>做3D Sam。

+
    +
  • 优点：如果是第一个把这个方法用在新领域的人，会受到很多关注
    • +
  • 缺点：竞争大，各大机构都在同台竞技
    • +
+

（2）效果好，方法简单

+
    +
  • 缺点：方法简单但改进有难度
    • +
+

法1：可以把一项工作尝试用在新领域

+

MOOD的这篇CVPR论文就是把掩码图像建模用在了异常检测方面。掩码图像建模方法在很多领域都证明了其有效性，但未在异常检测领域进行应用，可以实验证明该方法在异常检测的各个方向上是有效的。

+

法2：把这篇论文和其他论文的方法相结合（不只是ABC模块组合，也可以是A模块+B方法+C评价指标）

+

比如MOODv2就是把MOOD结合上新的预训练策略BEiTv2再结合新的异常检测指标ViM

+

（3）效果好，方法复杂

+

因为方法复杂，尝试把方法进行拆解，把某些模块替换成你觉得更好的模块或把某些模块简化

+
两种通用的方法
Section titled “两种通用的方法”
+

法1：找到现有方法在某些场景不work的缺陷，去掉现有方法成立的一些必要条件

+

现有方法缺陷：比如QuickLLaMa这篇，核心框架是：利用问题在长文本中进行关键信息的查找，有一种情况是，如果用户先输入的是长文本但是却不输入问题，在这种情况下可以改进这一点来形成新的文章。

+

核心思路就是：去掉现有方法成立的一些必要条件。

+

在新的场景下，这些现有方法就不work了，你就可以针对这种新场景来完成一篇论文。

+

法2：关注新出的Benchmark

+

比如HumanArt数据集是第一个在艺术领域上的姿态检测数据集，之前的人体姿态估计方法在这个数据集上的效果不好，于是出现了VLPose这个方法，在艺术检测这个数据集上和通用数据集上都做出了改进，形成一篇新论文。

+

法3：添加新的Benchmark、消融实验、Task

+

当idea已经证明可行，但性能不是SOTA时：

+
    +
  • +

    前提建议：选题阶段即对标SOTA

    +
    • +
  • +

    已验证有效但非SOTA性能：改进与发表策略

    +
    • +
+

（1）尝试和现有SOTA相结合

+

（2）无法结合SOTA时考虑：是否必须对比？

+

可能无需比较的情况：

+
    +
  • 情况1：是arxiv的预印本，还没有中稿
    • +
  • 情况2：是同期工作
    • +
+

必须比较的情况（想尽办法去证明你的工作是有价值的）：

+

方法1：添加新的 Benchmark（覆盖更多任务维度，提升普适性说服力）

+
    +
  • 某单一任务性能不如SOTA，但在其他10个任务上表现领先
    • +
  • 在多跳推理上优于SOTA，虽然单跳问题上稍逊
    • +
+

方法2：加新的消融实验（精细化对比同一设置下的表现）

+

原则：

+
    +
  • 比较对象限定为同等规模模型/方法
    • +
  • 若你贡献在于训练策略，仅需对比其他训练策略
    • +
  • 开源只和开源比，小贡献但有价值
    • +
+

方法3：添加新的task（做别人做不到或未做之事，强调独特性）

+
    +
  • 多模态模型实现视觉+语音+语言，领先仅支持视觉+语言的方法
    • +
  • 能处理2小时视频而非其他方法仅支持2秒视频
    • +
  • 引入新语言/场景/规模等独特任务设定
    • +
+

总结

+

若以上方法均尝试后性能仍无竞争力，该idea或许尚不成熟。

+

科研初期优先级建议：

+
    +
  • 效果优于创新：方法普通但效果好，也值得发表
    • +
  • 工程贡献也是贡献：通过数据工程、训练技巧取得SOTA，也会获得关注
    • +
+
+

例如：无需新颖方法，仅凭高质量数据 + 大参数量 + 微调策略也能发表高水平成果（如许多大模型训练类论文）。

+
+

4.8 怎么把论文的故事讲好

Section titled “4.8 怎么把论文的故事讲好”
+

目标：让审稿人快速相信”你的工作合理且有效”

+
解释为什么A+B是有效的
Section titled “解释为什么A+B是有效的”
+

（1）理论上

+

验证A+B > A，用公式推导进行证明（最有说服力，但很多情况下无法用理论求解）

+

（2）现象上

+

观察到了A中的一些现象，B这一模块可以弥补这一缺陷，为什么能够弥补，可以搭配一些可视化，与4.4 代码跑通流程阶段2 在代码理解之上改进这部分的原理是相同的

+

（3）工程上

+

把系统中的每个模块都相对应的比较同期模块，验证你的系统所采用的模块是最有效的。

+

例如 MOOD这篇论文比较了不同的预训练策略、不同的模型架构、不同的OOD检测指标，证明在这个系统中所使用的每一个模块都是最优的。

+

什么是OOD检测？

+

在现实世界中，很多AI系统面临这样的问题：它们在训练时只见过一部分”已知的数据”（称为In-Distribution（ID）数据），但测试或应用时可能会遇到”陌生的数据”（即Out-of-Distribution（OOD）数据）。OOD检测的任务就是要让AI系统学会识别出这些陌生的、没见过的数据，并避免错误地对它们做出判断。

+

（4）总结

+

把论文的形成过程讲清楚，包括整个系统的脉络、以及每个模块的作用、每个模块都要配合对应的消融实验来验证模块的有效性

+
+

三、怎么3天速成一篇论文

Section titled “三、怎么3天速成一篇论文”
+

工具

Section titled “工具”
+
    +
  • GPT-4o（英文） + DeepSeek（中文）
    • +
  • LaTeX格式论文在线编译网站：Overleaf
    • +
+

Overleaf知乎简介

+

应用举例

Section titled “应用举例”
+

（1）给图片写标题：向GPT输入：图片＋提示词（例如：给图片写英文latex格式的caption）

+

（2）写相关工作：相关工作的每篇论文摘要粘贴下来，输入给GPT可生成一个相关工作初稿

+

（3）介绍数据集：将数据集相关介绍输入给GPT生成data部分的段落

+

（4）组会汇报：做PPT可以使用DeepSeek：把一周工作内容输入给DeepSeek做PPT，将生成的表格直接粘贴到PPT中，提高工作效率

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arguments[2] : function() { + }; + var doc = void 0; + try { + var ifrWin = ifr.contentWindow; + doc = ifrWin.document; + if (!ifrWin || !doc) { + throw new Error("iframe inaccessible"); + } + } catch (e) { + errorFn(); + } + if (doc) { + successFn(doc); + } + } + }, { + key: "isIframeBlank", + value: function isIframeBlank(ifr) { + var bl = "about:blank", src = ifr.getAttribute("src").trim(), href = ifr.contentWindow.location.href; + return href === bl && src !== bl && src; + } + }, { + key: "observeIframeLoad", + value: function observeIframeLoad(ifr, successFn, errorFn) { + var _this = this; + var called = false, tout = null; + var listener = function listener2() { + if (called) { + return; + } + called = true; + clearTimeout(tout); + try { + if (!_this.isIframeBlank(ifr)) { + ifr.removeEventListener("load", listener2); + _this.getIframeContents(ifr, successFn, errorFn); + } + } catch (e) { + errorFn(); + } + }; + ifr.addEventListener("load", listener); + tout = setTimeout(listener, this.iframesTimeout); + } + }, { + key: "onIframeReady", + value: function onIframeReady(ifr, successFn, errorFn) { + try { + if (ifr.contentWindow.document.readyState === "complete") { + if (this.isIframeBlank(ifr)) { + this.observeIframeLoad(ifr, successFn, errorFn); + } else { + this.getIframeContents(ifr, successFn, errorFn); + } + } else { + this.observeIframeLoad(ifr, successFn, errorFn); + } + } catch (e) { + errorFn(); + } + } + }, { + key: "waitForIframes", + value: function waitForIframes(ctx, done) { + var _this2 = this; + var eachCalled = 0; + this.forEachIframe(ctx, function() { + return true; + }, function(ifr) { + eachCalled++; + _this2.waitForIframes(ifr.querySelector("html"), function() { + if (!--eachCalled) { + done(); + } + }); + }, function(handled) { + if (!handled) { + done(); + } + }); + } + }, { + key: "forEachIframe", + value: function forEachIframe(ctx, filter, each) { + var _this3 = this; + var end = arguments.length > 3 && arguments[3] !== void 0 ? arguments[3] : function() { + }; + var ifr = ctx.querySelectorAll("iframe"), open = ifr.length, handled = 0; + ifr = Array.prototype.slice.call(ifr); + var checkEnd = function checkEnd2() { + if (--open <= 0) { + end(handled); + } + }; + if (!open) { + checkEnd(); + } + ifr.forEach(function(ifr2) { + if (DOMIterator2.matches(ifr2, _this3.exclude)) { + checkEnd(); + } else { + _this3.onIframeReady(ifr2, function(con) { + if (filter(ifr2)) { + handled++; + each(con); + } + checkEnd(); + }, checkEnd); + } + }); + } + }, { + key: "createIterator", + value: function createIterator(ctx, whatToShow, filter) { + return document.createNodeIterator(ctx, whatToShow, filter, false); + } + }, { + key: "createInstanceOnIframe", + value: function createInstanceOnIframe(contents) { + return new DOMIterator2(contents.querySelector("html"), this.iframes); + } + }, { + key: "compareNodeIframe", + value: function compareNodeIframe(node, prevNode, ifr) { + var compCurr = node.compareDocumentPosition(ifr), prev = Node.DOCUMENT_POSITION_PRECEDING; + if (compCurr & prev) { + if (prevNode !== null) { + var compPrev = prevNode.compareDocumentPosition(ifr), after = Node.DOCUMENT_POSITION_FOLLOWING; + if (compPrev & after) { + return true; + } + } else { + return true; + } + } + return false; + } + }, { + key: "getIteratorNode", + value: function getIteratorNode(itr) { + var prevNode = itr.previousNode(); + var node = void 0; + if (prevNode === null) { + node = itr.nextNode(); + } else { + node = itr.nextNode() && itr.nextNode(); + } + return { + prevNode, + node + }; + } + }, { + key: "checkIframeFilter", + value: function checkIframeFilter(node, prevNode, currIfr, ifr) { + var key = false, handled = false; + ifr.forEach(function(ifrDict, i) { + if (ifrDict.val === currIfr) { + key = i; + handled = ifrDict.handled; + } + }); + if (this.compareNodeIframe(node, prevNode, currIfr)) { + if (key === false && !handled) { + ifr.push({ + val: currIfr, + handled: true + }); + } else if (key !== false && !handled) { + ifr[key].handled = true; + } + return true; + } + if (key === false) { + ifr.push({ + val: currIfr, + handled: false + }); + } + return false; + } + }, { + key: "handleOpenIframes", + value: function handleOpenIframes(ifr, whatToShow, eCb, fCb) { + var _this4 = this; + ifr.forEach(function(ifrDict) { + if (!ifrDict.handled) { + _this4.getIframeContents(ifrDict.val, function(con) { + _this4.createInstanceOnIframe(con).forEachNode(whatToShow, eCb, fCb); + }); + } + }); + } + }, { + key: "iterateThroughNodes", + value: function iterateThroughNodes(whatToShow, ctx, eachCb, filterCb, doneCb) { + var _this5 = this; + var itr = this.createIterator(ctx, whatToShow, filterCb); + var ifr = [], elements = [], node = void 0, prevNode = void 0, retrieveNodes = function retrieveNodes2() { + var _getIteratorNode = _this5.getIteratorNode(itr); + prevNode = _getIteratorNode.prevNode; + node = _getIteratorNode.node; + return node; + }; + while (retrieveNodes()) { + if (this.iframes) { + this.forEachIframe(ctx, function(currIfr) { + return _this5.checkIframeFilter(node, prevNode, currIfr, ifr); + }, function(con) { + _this5.createInstanceOnIframe(con).forEachNode(whatToShow, function(ifrNode) { + return elements.push(ifrNode); + }, filterCb); + }); + } + elements.push(node); + } + elements.forEach(function(node2) { + eachCb(node2); + }); + if (this.iframes) { + this.handleOpenIframes(ifr, whatToShow, eachCb, filterCb); + } + doneCb(); + } + }, { + key: "forEachNode", + value: function forEachNode(whatToShow, each, filter) { + var _this6 = this; + var done = arguments.length > 3 && arguments[3] !== void 0 ? arguments[3] : function() { + }; + var contexts = this.getContexts(); + var open = contexts.length; + if (!open) { + done(); + } + contexts.forEach(function(ctx) { + var ready = function ready2() { + _this6.iterateThroughNodes(whatToShow, ctx, each, filter, function() { + if (--open <= 0) { + done(); + } + }); + }; + if (_this6.iframes) { + _this6.waitForIframes(ctx, ready); + } else { + ready(); + } + }); + } + }], [{ + key: "matches", + value: function matches(element, selector) { + var selectors = typeof selector === "string" ? [selector] : selector, fn = element.matches || element.matchesSelector || element.msMatchesSelector || element.mozMatchesSelector || element.oMatchesSelector || element.webkitMatchesSelector; + if (fn) { + var match = false; + selectors.every(function(sel) { + if (fn.call(element, sel)) { + match = true; + return false; + } + return true; + }); + return match; + } else { + return false; + } + } + }]); + return DOMIterator2; + })(); + var Mark$1 = (function() { + function Mark3(ctx) { + classCallCheck(this, Mark3); + this.ctx = ctx; + this.ie = false; + var ua = window.navigator.userAgent; + if (ua.indexOf("MSIE") > -1 || ua.indexOf("Trident") > -1) { + this.ie = true; + } + } + createClass(Mark3, [{ + key: "log", + value: function log(msg) { + var level = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : "debug"; + var log2 = this.opt.log; + if (!this.opt.debug) { + return; + } + if ((typeof log2 === "undefined" ? "undefined" : _typeof(log2)) === "object" && typeof log2[level] === "function") { + log2[level]("mark.js: " + msg); + } + } + }, { + key: "escapeStr", + value: function escapeStr(str) { + return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&"); + } + }, { + key: "createRegExp", + value: function createRegExp(str) { + if (this.opt.wildcards !== "disabled") { + str = this.setupWildcardsRegExp(str); + } + str = this.escapeStr(str); + if (Object.keys(this.opt.synonyms).length) { + str = this.createSynonymsRegExp(str); + } + if (this.opt.ignoreJoiners || this.opt.ignorePunctuation.length) { + str = this.setupIgnoreJoinersRegExp(str); + } + if (this.opt.diacritics) { + str = this.createDiacriticsRegExp(str); + } + str = this.createMergedBlanksRegExp(str); + if (this.opt.ignoreJoiners || this.opt.ignorePunctuation.length) { + str = this.createJoinersRegExp(str); + } + if (this.opt.wildcards !== "disabled") { + str = this.createWildcardsRegExp(str); + } + str = this.createAccuracyRegExp(str); + return str; + } + }, { + key: "createSynonymsRegExp", + value: function createSynonymsRegExp(str) { + var syn = this.opt.synonyms, sens = this.opt.caseSensitive ? "" : "i", joinerPlaceholder = this.opt.ignoreJoiners || this.opt.ignorePunctuation.length ? "\0" : ""; + for (var index in syn) { + if (syn.hasOwnProperty(index)) { + var value = syn[index], k1 = this.opt.wildcards !== "disabled" ? this.setupWildcardsRegExp(index) : this.escapeStr(index), k2 = this.opt.wildcards !== "disabled" ? this.setupWildcardsRegExp(value) : this.escapeStr(value); + if (k1 !== "" && k2 !== "") { + str = str.replace(new RegExp("(" + this.escapeStr(k1) + "|" + this.escapeStr(k2) + ")", "gm" + sens), joinerPlaceholder + ("(" + this.processSynomyms(k1) + "|") + (this.processSynomyms(k2) + ")") + joinerPlaceholder); + } + } + } + return str; + } + }, { + key: "processSynomyms", + value: function processSynomyms(str) { + if (this.opt.ignoreJoiners || this.opt.ignorePunctuation.length) { + str = this.setupIgnoreJoinersRegExp(str); + } + return str; + } + }, { + key: "setupWildcardsRegExp", + value: function setupWildcardsRegExp(str) { + str = str.replace(/(?:\\)*\?/g, function(val) { + return val.charAt(0) === "\\" ? "?" : ""; + }); + return str.replace(/(?:\\)*\*/g, function(val) { + return val.charAt(0) === "\\" ? "*" : ""; + }); + } + }, { + key: "createWildcardsRegExp", + value: function createWildcardsRegExp(str) { + var spaces = this.opt.wildcards === "withSpaces"; + return str.replace(/\u0001/g, spaces ? "[\\S\\s]?" : "\\S?").replace(/\u0002/g, spaces ? "[\\S\\s]*?" : "\\S*"); + } + }, { + key: "setupIgnoreJoinersRegExp", + value: function setupIgnoreJoinersRegExp(str) { + return str.replace(/[^(|)\\]/g, function(val, indx, original) { + var nextChar = original.charAt(indx + 1); + if (/[(|)\\]/.test(nextChar) || nextChar === "") { + return val; + } else { + return val + "\0"; + } + }); + } + }, { + key: "createJoinersRegExp", + value: function createJoinersRegExp(str) { + var joiner = []; + var ignorePunctuation = this.opt.ignorePunctuation; + if (Array.isArray(ignorePunctuation) && ignorePunctuation.length) { + joiner.push(this.escapeStr(ignorePunctuation.join(""))); + } + if (this.opt.ignoreJoiners) { + joiner.push("\\u00ad\\u200b\\u200c\\u200d"); + } + return joiner.length ? str.split(/\u0000+/).join("[" + joiner.join("") + "]*") : str; + } + }, { + key: "createDiacriticsRegExp", + value: function createDiacriticsRegExp(str) { + var sens = this.opt.caseSensitive ? "" : "i", dct = this.opt.caseSensitive ? ["a\xE0\xE1\u1EA3\xE3\u1EA1\u0103\u1EB1\u1EAF\u1EB3\u1EB5\u1EB7\xE2\u1EA7\u1EA5\u1EA9\u1EAB\u1EAD\xE4\xE5\u0101\u0105", "A\xC0\xC1\u1EA2\xC3\u1EA0\u0102\u1EB0\u1EAE\u1EB2\u1EB4\u1EB6\xC2\u1EA6\u1EA4\u1EA8\u1EAA\u1EAC\xC4\xC5\u0100\u0104", "c\xE7\u0107\u010D", "C\xC7\u0106\u010C", "d\u0111\u010F", "D\u0110\u010E", "e\xE8\xE9\u1EBB\u1EBD\u1EB9\xEA\u1EC1\u1EBF\u1EC3\u1EC5\u1EC7\xEB\u011B\u0113\u0119", "E\xC8\xC9\u1EBA\u1EBC\u1EB8\xCA\u1EC0\u1EBE\u1EC2\u1EC4\u1EC6\xCB\u011A\u0112\u0118", "i\xEC\xED\u1EC9\u0129\u1ECB\xEE\xEF\u012B", "I\xCC\xCD\u1EC8\u0128\u1ECA\xCE\xCF\u012A", "l\u0142", "L\u0141", "n\xF1\u0148\u0144", "N\xD1\u0147\u0143", "o\xF2\xF3\u1ECF\xF5\u1ECD\xF4\u1ED3\u1ED1\u1ED5\u1ED7\u1ED9\u01A1\u1EDF\u1EE1\u1EDB\u1EDD\u1EE3\xF6\xF8\u014D", "O\xD2\xD3\u1ECE\xD5\u1ECC\xD4\u1ED2\u1ED0\u1ED4\u1ED6\u1ED8\u01A0\u1EDE\u1EE0\u1EDA\u1EDC\u1EE2\xD6\xD8\u014C", "r\u0159", "R\u0158", "s\u0161\u015B\u0219\u015F", "S\u0160\u015A\u0218\u015E", "t\u0165\u021B\u0163", "T\u0164\u021A\u0162", "u\xF9\xFA\u1EE7\u0169\u1EE5\u01B0\u1EEB\u1EE9\u1EED\u1EEF\u1EF1\xFB\xFC\u016F\u016B", "U\xD9\xDA\u1EE6\u0168\u1EE4\u01AF\u1EEA\u1EE8\u1EEC\u1EEE\u1EF0\xDB\xDC\u016E\u016A", "y\xFD\u1EF3\u1EF7\u1EF9\u1EF5\xFF", "Y\xDD\u1EF2\u1EF6\u1EF8\u1EF4\u0178", "z\u017E\u017C\u017A", "Z\u017D\u017B\u0179"] : ["a\xE0\xE1\u1EA3\xE3\u1EA1\u0103\u1EB1\u1EAF\u1EB3\u1EB5\u1EB7\xE2\u1EA7\u1EA5\u1EA9\u1EAB\u1EAD\xE4\xE5\u0101\u0105A\xC0\xC1\u1EA2\xC3\u1EA0\u0102\u1EB0\u1EAE\u1EB2\u1EB4\u1EB6\xC2\u1EA6\u1EA4\u1EA8\u1EAA\u1EAC\xC4\xC5\u0100\u0104", "c\xE7\u0107\u010DC\xC7\u0106\u010C", "d\u0111\u010FD\u0110\u010E", "e\xE8\xE9\u1EBB\u1EBD\u1EB9\xEA\u1EC1\u1EBF\u1EC3\u1EC5\u1EC7\xEB\u011B\u0113\u0119E\xC8\xC9\u1EBA\u1EBC\u1EB8\xCA\u1EC0\u1EBE\u1EC2\u1EC4\u1EC6\xCB\u011A\u0112\u0118", "i\xEC\xED\u1EC9\u0129\u1ECB\xEE\xEF\u012BI\xCC\xCD\u1EC8\u0128\u1ECA\xCE\xCF\u012A", "l\u0142L\u0141", "n\xF1\u0148\u0144N\xD1\u0147\u0143", "o\xF2\xF3\u1ECF\xF5\u1ECD\xF4\u1ED3\u1ED1\u1ED5\u1ED7\u1ED9\u01A1\u1EDF\u1EE1\u1EDB\u1EDD\u1EE3\xF6\xF8\u014DO\xD2\xD3\u1ECE\xD5\u1ECC\xD4\u1ED2\u1ED0\u1ED4\u1ED6\u1ED8\u01A0\u1EDE\u1EE0\u1EDA\u1EDC\u1EE2\xD6\xD8\u014C", "r\u0159R\u0158", "s\u0161\u015B\u0219\u015FS\u0160\u015A\u0218\u015E", "t\u0165\u021B\u0163T\u0164\u021A\u0162", "u\xF9\xFA\u1EE7\u0169\u1EE5\u01B0\u1EEB\u1EE9\u1EED\u1EEF\u1EF1\xFB\xFC\u016F\u016BU\xD9\xDA\u1EE6\u0168\u1EE4\u01AF\u1EEA\u1EE8\u1EEC\u1EEE\u1EF0\xDB\xDC\u016E\u016A", "y\xFD\u1EF3\u1EF7\u1EF9\u1EF5\xFFY\xDD\u1EF2\u1EF6\u1EF8\u1EF4\u0178", "z\u017E\u017C\u017AZ\u017D\u017B\u0179"]; + var handled = []; + str.split("").forEach(function(ch) { + dct.every(function(dct2) { + if (dct2.indexOf(ch) !== -1) { + if (handled.indexOf(dct2) > -1) { + return false; + } + str = str.replace(new RegExp("[" + dct2 + "]", "gm" + sens), "[" + dct2 + "]"); + handled.push(dct2); + } + return true; + }); + }); + return str; + } + }, { + key: "createMergedBlanksRegExp", + value: function createMergedBlanksRegExp(str) { + return str.replace(/[\s]+/gmi, "[\\s]+"); + } + }, { + key: "createAccuracyRegExp", + value: function createAccuracyRegExp(str) { + var _this = this; + var chars = "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\xA1\xBF"; + var acc = this.opt.accuracy, val = typeof acc === "string" ? acc : acc.value, ls = typeof acc === "string" ? [] : acc.limiters, lsJoin = ""; + ls.forEach(function(limiter) { + lsJoin += "|" + _this.escapeStr(limiter); + }); + switch (val) { + case "partially": + default: + return "()(" + str + ")"; + case "complementary": + lsJoin = "\\s" + (lsJoin ? lsJoin : this.escapeStr(chars)); + return "()([^" + lsJoin + "]*" + str + "[^" + lsJoin + "]*)"; + case "exactly": + return "(^|\\s" + lsJoin + ")(" + str + ")(?=$|\\s" + lsJoin + ")"; + } + } + }, { + key: "getSeparatedKeywords", + value: function getSeparatedKeywords(sv) { + var _this2 = this; + var stack = []; + sv.forEach(function(kw) { + if (!_this2.opt.separateWordSearch) { + if (kw.trim() && stack.indexOf(kw) === -1) { + stack.push(kw); + } + } else { + kw.split(" ").forEach(function(kwSplitted) { + if (kwSplitted.trim() && stack.indexOf(kwSplitted) === -1) { + stack.push(kwSplitted); + } + }); + } + }); + return { + "keywords": stack.sort(function(a, b) { + return b.length - a.length; + }), + "length": stack.length + }; + } + }, { + key: "isNumeric", + value: function isNumeric(value) { + return Number(parseFloat(value)) == value; + } + }, { + key: "checkRanges", + value: function checkRanges(array) { + var _this3 = this; + if (!Array.isArray(array) || Object.prototype.toString.call(array[0]) !== "[object Object]") { + this.log("markRanges() will only accept an array of objects"); + this.opt.noMatch(array); + return []; + } + var stack = []; + var last = 0; + array.sort(function(a, b) { + return a.start - b.start; + }).forEach(function(item) { + var _callNoMatchOnInvalid = _this3.callNoMatchOnInvalidRanges(item, last), start = _callNoMatchOnInvalid.start, end = _callNoMatchOnInvalid.end, valid = _callNoMatchOnInvalid.valid; + if (valid) { + item.start = start; + item.length = end - start; + stack.push(item); + last = end; + } + }); + return stack; + } + }, { + key: "callNoMatchOnInvalidRanges", + value: function callNoMatchOnInvalidRanges(range, last) { + var start = void 0, end = void 0, valid = false; + if (range && typeof range.start !== "undefined") { + start = parseInt(range.start, 10); + end = start + parseInt(range.length, 10); + if (this.isNumeric(range.start) && this.isNumeric(range.length) && end - last > 0 && end - start > 0) { + valid = true; + } else { + this.log("Ignoring invalid or overlapping range: " + ("" + JSON.stringify(range))); + this.opt.noMatch(range); + } + } else { + this.log("Ignoring invalid range: " + JSON.stringify(range)); + this.opt.noMatch(range); + } + return { + start, + end, + valid + }; + } + }, { + key: "checkWhitespaceRanges", + value: function checkWhitespaceRanges(range, originalLength, string) { + var end = void 0, valid = true, max = string.length, offset = originalLength - max, start = parseInt(range.start, 10) - offset; + start = start > max ? max : start; + end = start + parseInt(range.length, 10); + if (end > max) { + end = max; + this.log("End range automatically set to the max value of " + max); + } + if (start < 0 || end - start < 0 || start > max || end > max) { + valid = false; + this.log("Invalid range: " + JSON.stringify(range)); + this.opt.noMatch(range); + } else if (string.substring(start, end).replace(/\s+/g, "") === "") { + valid = false; + this.log("Skipping whitespace only range: " + JSON.stringify(range)); + this.opt.noMatch(range); + } + return { + start, + end, + valid + }; + } + }, { + key: "getTextNodes", + value: function getTextNodes(cb) { + var _this4 = this; + var val = "", nodes = []; + this.iterator.forEachNode(NodeFilter.SHOW_TEXT, function(node) { + nodes.push({ + start: val.length, + end: (val += node.textContent).length, + node + }); + }, function(node) { + if (_this4.matchesExclude(node.parentNode)) { + return NodeFilter.FILTER_REJECT; + } else { + return NodeFilter.FILTER_ACCEPT; + } + }, function() { + cb({ + value: val, + nodes + }); + }); + } + }, { + key: "matchesExclude", + value: function matchesExclude(el) { + return DOMIterator.matches(el, this.opt.exclude.concat(["script", "style", "title", "head", "html"])); + } + }, { + key: "wrapRangeInTextNode", + value: function wrapRangeInTextNode(node, start, end) { + var hEl = !this.opt.element ? "mark" : this.opt.element, startNode = node.splitText(start), ret = startNode.splitText(end - start); + var repl = document.createElement(hEl); + repl.setAttribute("data-markjs", "true"); + if (this.opt.className) { + repl.setAttribute("class", this.opt.className); + } + repl.textContent = startNode.textContent; + startNode.parentNode.replaceChild(repl, startNode); + return ret; + } + }, { + key: "wrapRangeInMappedTextNode", + value: function wrapRangeInMappedTextNode(dict, start, end, filterCb, eachCb) { + var _this5 = this; + dict.nodes.every(function(n, i) { + var sibl = dict.nodes[i + 1]; + if (typeof sibl === "undefined" || sibl.start > start) { + if (!filterCb(n.node)) { + return false; + } + var s = start - n.start, e = (end > n.end ? n.end : end) - n.start, startStr = dict.value.substr(0, n.start), endStr = dict.value.substr(e + n.start); + n.node = _this5.wrapRangeInTextNode(n.node, s, e); + dict.value = startStr + endStr; + dict.nodes.forEach(function(k, j) { + if (j >= i) { + if (dict.nodes[j].start > 0 && j !== i) { + dict.nodes[j].start -= e; + } + dict.nodes[j].end -= e; + } + }); + end -= e; + eachCb(n.node.previousSibling, n.start); + if (end > n.end) { + start = n.end; + } else { + return false; + } + } + return true; + }); + } + }, { + key: "wrapMatches", + value: function wrapMatches(regex, ignoreGroups, filterCb, eachCb, endCb) { + var _this6 = this; + var matchIdx = ignoreGroups === 0 ? 0 : ignoreGroups + 1; + this.getTextNodes(function(dict) { + dict.nodes.forEach(function(node) { + node = node.node; + var match = void 0; + while ((match = regex.exec(node.textContent)) !== null && match[matchIdx] !== "") { + if (!filterCb(match[matchIdx], node)) { + continue; + } + var pos = match.index; + if (matchIdx !== 0) { + for (var i = 1; i < matchIdx; i++) { + pos += match[i].length; + } + } + node = _this6.wrapRangeInTextNode(node, pos, pos + match[matchIdx].length); + eachCb(node.previousSibling); + regex.lastIndex = 0; + } + }); + endCb(); + }); + } + }, { + key: "wrapMatchesAcrossElements", + value: function wrapMatchesAcrossElements(regex, ignoreGroups, filterCb, eachCb, endCb) { + var _this7 = this; + var matchIdx = ignoreGroups === 0 ? 0 : ignoreGroups + 1; + this.getTextNodes(function(dict) { + var match = void 0; + while ((match = regex.exec(dict.value)) !== null && match[matchIdx] !== "") { + var start = match.index; + if (matchIdx !== 0) { + for (var i = 1; i < matchIdx; i++) { + start += match[i].length; + } + } + var end = start + match[matchIdx].length; + _this7.wrapRangeInMappedTextNode(dict, start, end, function(node) { + return filterCb(match[matchIdx], node); + }, function(node, lastIndex) { + regex.lastIndex = lastIndex; + eachCb(node); + }); + } + endCb(); + }); + } + }, { + key: "wrapRangeFromIndex", + value: function wrapRangeFromIndex(ranges, filterCb, eachCb, endCb) { + var _this8 = this; + this.getTextNodes(function(dict) { + var originalLength = dict.value.length; + ranges.forEach(function(range, counter) { + var _checkWhitespaceRange = _this8.checkWhitespaceRanges(range, originalLength, dict.value), start = _checkWhitespaceRange.start, end = _checkWhitespaceRange.end, valid = _checkWhitespaceRange.valid; + if (valid) { + _this8.wrapRangeInMappedTextNode(dict, start, end, function(node) { + return filterCb(node, range, dict.value.substring(start, end), counter); + }, function(node) { + eachCb(node, range); + }); + } + }); + endCb(); + }); + } + }, { + key: "unwrapMatches", + value: function unwrapMatches(node) { + var parent = node.parentNode; + var docFrag = document.createDocumentFragment(); + while (node.firstChild) { + docFrag.appendChild(node.removeChild(node.firstChild)); + } + parent.replaceChild(docFrag, node); + if (!this.ie) { + parent.normalize(); + } else { + this.normalizeTextNode(parent); + } + } + }, { + key: "normalizeTextNode", + value: function normalizeTextNode(node) { + if (!node) { + return; + } + if (node.nodeType === 3) { + while (node.nextSibling && node.nextSibling.nodeType === 3) { + node.nodeValue += node.nextSibling.nodeValue; + node.parentNode.removeChild(node.nextSibling); + } + } else { + this.normalizeTextNode(node.firstChild); + } + this.normalizeTextNode(node.nextSibling); + } + }, { + key: "markRegExp", + value: function markRegExp(regexp, opt) { + var _this9 = this; + this.opt = opt; + this.log('Searching with expression "' + regexp + '"'); + var totalMatches = 0, fn = "wrapMatches"; + var eachCb = function eachCb2(element) { + totalMatches++; + _this9.opt.each(element); + }; + if (this.opt.acrossElements) { + fn = "wrapMatchesAcrossElements"; + } + this[fn](regexp, this.opt.ignoreGroups, function(match, node) { + return _this9.opt.filter(node, match, totalMatches); + }, eachCb, function() { + if (totalMatches === 0) { + _this9.opt.noMatch(regexp); + } + _this9.opt.done(totalMatches); + }); + } + }, { + key: "mark", + value: function mark(sv, opt) { + var _this10 = this; + this.opt = opt; + var totalMatches = 0, fn = "wrapMatches"; + var _getSeparatedKeywords = this.getSeparatedKeywords(typeof sv === "string" ? [sv] : sv), kwArr = _getSeparatedKeywords.keywords, kwArrLen = _getSeparatedKeywords.length, sens = this.opt.caseSensitive ? "" : "i", handler = function handler2(kw) { + var regex = new RegExp(_this10.createRegExp(kw), "gm" + sens), matches = 0; + _this10.log('Searching with expression "' + regex + '"'); + _this10[fn](regex, 1, function(term, node) { + return _this10.opt.filter(node, kw, totalMatches, matches); + }, function(element) { + matches++; + totalMatches++; + _this10.opt.each(element); + }, function() { + if (matches === 0) { + _this10.opt.noMatch(kw); + } + if (kwArr[kwArrLen - 1] === kw) { + _this10.opt.done(totalMatches); + } else { + handler2(kwArr[kwArr.indexOf(kw) + 1]); + } + }); + }; + if (this.opt.acrossElements) { + fn = "wrapMatchesAcrossElements"; + } + if (kwArrLen === 0) { + this.opt.done(totalMatches); + } else { + handler(kwArr[0]); + } + } + }, { + key: "markRanges", + value: function markRanges(rawRanges, opt) { + var _this11 = this; + this.opt = opt; + var totalMatches = 0, ranges = this.checkRanges(rawRanges); + if (ranges && ranges.length) { + this.log("Starting to mark with the following ranges: " + JSON.stringify(ranges)); + this.wrapRangeFromIndex(ranges, function(node, range, match, counter) { + return _this11.opt.filter(node, range, match, counter); + }, function(element, range) { + totalMatches++; + _this11.opt.each(element, range); + }, function() { + _this11.opt.done(totalMatches); + }); + } else { + this.opt.done(totalMatches); + } + } + }, { + key: "unmark", + value: function unmark(opt) { + var _this12 = this; + this.opt = opt; + var sel = this.opt.element ? this.opt.element : "*"; + sel += "[data-markjs]"; + if (this.opt.className) { + sel += "." + this.opt.className; + } + this.log('Removal selector "' + sel + '"'); + this.iterator.forEachNode(NodeFilter.SHOW_ELEMENT, function(node) { + _this12.unwrapMatches(node); + }, function(node) { + var matchesSel = DOMIterator.matches(node, sel), matchesExclude = _this12.matchesExclude(node); + if (!matchesSel || matchesExclude) { + return NodeFilter.FILTER_REJECT; + } else { + return NodeFilter.FILTER_ACCEPT; + } + }, this.opt.done); + } + }, { + key: "opt", + set: function set$$1(val) { + this._opt = _extends({}, { + "element": "", + "className": "", + "exclude": [], + "iframes": false, + "iframesTimeout": 5e3, + "separateWordSearch": true, + "diacritics": true, + "synonyms": {}, + "accuracy": "partially", + "acrossElements": false, + "caseSensitive": false, + "ignoreJoiners": false, + "ignoreGroups": 0, + "ignorePunctuation": [], + "wildcards": "disabled", + "each": function each() { + }, + "noMatch": function noMatch() { + }, + "filter": function filter() { + return true; + }, + "done": function done() { + }, + "debug": false, + "log": window.console + }, val); + }, + get: function get$$1() { + return this._opt; + } + }, { + key: "iterator", + get: function get$$1() { + return new DOMIterator(this.ctx, this.opt.iframes, this.opt.exclude, this.opt.iframesTimeout); + } + }]); + return Mark3; + })(); + function Mark2(ctx) { + var _this = this; + var instance = new Mark$1(ctx); + this.mark = function(sv, opt) { + instance.mark(sv, opt); + return _this; + }; + this.markRegExp = function(sv, opt) { + instance.markRegExp(sv, opt); + return _this; + }; + this.markRanges = function(sv, opt) { + instance.markRanges(sv, opt); + return _this; + }; + this.unmark = function(opt) { + instance.unmark(opt); + return _this; + }; + return this; + } + return Mark2; + })); + } +}); + +// lib/highlight.ts +var import_mark = __toESM(require_mark(), 1); +var PagefindHighlight = class { + constructor(options = { + markContext: null, + highlightParam: "pagefind-highlight", + markOptions: { + className: "pagefind-highlight", + exclude: ["[data-pagefind-ignore]", "[data-pagefind-ignore] *"] + }, + addStyles: true + }) { + var _a, _b; + const { highlightParam, markContext, markOptions, addStyles } = options; + this.highlightParam = highlightParam ?? "pagefind-highlight"; + this.addStyles = addStyles ?? true; + this.markContext = markContext !== void 0 ? markContext : null; + this.markOptions = markOptions !== void 0 ? markOptions : { + className: "pagefind-highlight", + exclude: ["[data-pagefind-ignore]", "[data-pagefind-ignore] *"] + }; + (_a = this.markOptions).className ?? (_a.className = "pagefind__highlight"); + (_b = this.markOptions).exclude ?? (_b.exclude = [ + "[data-pagefind-ignore]", + "[data-pagefind-ignore] *" + ]); + this.markOptions.separateWordSearch = false; + this.highlight(); + } + getHighlightParams(paramName) { + const urlParams = new URLSearchParams(window.location.search); + return urlParams.getAll(paramName); + } + // Inline styles might be too hard to override + addHighlightStyles(className) { + if (!className) return; + const styleElement = document.createElement("style"); + styleElement.innerText = `:where(.${className}) { background-color: yellow; color: black; }`; + document.head.appendChild(styleElement); + } + createMarkInstance() { + if (this.markContext) { + return new import_mark.default(this.markContext); + } + const pagefindBody = document.querySelectorAll("[data-pagefind-body]"); + if (pagefindBody.length !== 0) { + return new import_mark.default(pagefindBody); + } else { + return new import_mark.default(document.body); + } + } + markText(instance, text) { + instance.mark(text, this.markOptions); + } + highlight() { + const params = this.getHighlightParams(this.highlightParam); + if (!params || params.length === 0) return; + this.addStyles && this.addHighlightStyles(this.markOptions.className); + const markInstance = this.createMarkInstance(); + this.markText(markInstance, params); + } +}; +window.PagefindHighlight = PagefindHighlight; +export { + PagefindHighlight as default +}; +/*! Bundled license information: + +mark.js/dist/mark.js: + (*!*************************************************** + * mark.js v8.11.1 + * https://markjs.io/ + * Copyright (c) 2014–2018, Julian Kühnel + * Released under the MIT license https://git.io/vwTVl + *****************************************************) +*/ diff --git a/research/pagefind/pagefind-modular-ui.css b/research/pagefind/pagefind-modular-ui.css new file mode 100644 index 0000000..9c6793e --- /dev/null +++ b/research/pagefind/pagefind-modular-ui.css @@ -0,0 +1,214 @@ +:root { + --pagefind-ui-scale: 0.8; + --pagefind-ui-primary: #034AD8; + --pagefind-ui-fade: #707070; + --pagefind-ui-text: #393939; + --pagefind-ui-background: #ffffff; + --pagefind-ui-border: #eeeeee; + --pagefind-ui-tag: #eeeeee; + --pagefind-ui-border-width: 2px; + --pagefind-ui-border-radius: 8px; + --pagefind-ui-image-border-radius: 8px; + --pagefind-ui-image-box-ratio: 3 / 2; + --pagefind-ui-font: system, -apple-system, ".SFNSText-Regular", + "San Francisco", "Roboto", "Segoe UI", "Helvetica Neue", + "Lucida Grande", sans-serif; +} + +[data-pfmod-hidden] { + display: none !important; +} + +[data-pfmod-suppressed] { + opacity: 0 !important; + pointer-events: none !important; +} + +[data-pfmod-sr-hidden] { + -webkit-clip: rect(0 0 0 0) !important; + clip: rect(0 0 0 0) !important; + -webkit-clip-path: inset(100%) !important; + clip-path: inset(100%) !important; + height: 1px !important; + overflow: hidden !important; + overflow: clip !important; + position: absolute !important; + white-space: nowrap !important; + width: 1px !important; +} + +[data-pfmod-loading] { + color: var(--pagefind-ui-text); + background-color: var(--pagefind-ui-text); + border-radius: var(--pagefind-ui-border-radius); + opacity: 0.1; + pointer-events: none; +} + +/* Input */ + +.pagefind-modular-input-wrapper { + position: relative; +} + +.pagefind-modular-input-wrapper::before { + background-color: var(--pagefind-ui-text); + width: calc(18px * var(--pagefind-ui-scale)); + height: calc(18px * var(--pagefind-ui-scale)); + top: calc(23px * var(--pagefind-ui-scale)); + left: calc(20px * var(--pagefind-ui-scale)); + content: ""; + position: absolute; + display: block; + opacity: 0.7; + -webkit-mask-image: url("data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12.7549 11.255H11.9649L11.6849 10.985C12.6649 9.845 13.2549 8.365 13.2549 6.755C13.2549 3.165 10.3449 0.255005 6.75488 0.255005C3.16488 0.255005 0.254883 3.165 0.254883 6.755C0.254883 10.345 3.16488 13.255 6.75488 13.255C8.36488 13.255 9.84488 12.665 10.9849 11.685L11.2549 11.965V12.755L16.2549 17.745L17.7449 16.255L12.7549 11.255ZM6.75488 11.255C4.26488 11.255 2.25488 9.245 2.25488 6.755C2.25488 4.26501 4.26488 2.255 6.75488 2.255C9.24488 2.255 11.2549 4.26501 11.2549 6.755C11.2549 9.245 9.24488 11.255 6.75488 11.255Z' fill='%23000000'/%3E%3C/svg%3E%0A"); + mask-image: url("data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12.7549 11.255H11.9649L11.6849 10.985C12.6649 9.845 13.2549 8.365 13.2549 6.755C13.2549 3.165 10.3449 0.255005 6.75488 0.255005C3.16488 0.255005 0.254883 3.165 0.254883 6.755C0.254883 10.345 3.16488 13.255 6.75488 13.255C8.36488 13.255 9.84488 12.665 10.9849 11.685L11.2549 11.965V12.755L16.2549 17.745L17.7449 16.255L12.7549 11.255ZM6.75488 11.255C4.26488 11.255 2.25488 9.245 2.25488 6.755C2.25488 4.26501 4.26488 2.255 6.75488 2.255C9.24488 2.255 11.2549 4.26501 11.2549 6.755C11.2549 9.245 9.24488 11.255 6.75488 11.255Z' fill='%23000000'/%3E%3C/svg%3E%0A"); + -webkit-mask-size: 100%; + mask-size: 100%; + z-index: 9; + pointer-events: none; +} + +.pagefind-modular-input { + height: calc(64px * var(--pagefind-ui-scale)); + padding: 0 calc(70px * var(--pagefind-ui-scale)) 0 calc(54px * var(--pagefind-ui-scale)); + background-color: var(--pagefind-ui-background); + border: var(--pagefind-ui-border-width) solid var(--pagefind-ui-border); + border-radius: var(--pagefind-ui-border-radius); + font-size: calc(21px * var(--pagefind-ui-scale)); + position: relative; + appearance: none; + -webkit-appearance: none; + display: flex; + width: 100%; + box-sizing: border-box; + font-weight: 700; +} + +.pagefind-modular-input::placeholder { + opacity: 0.2; +} + +.pagefind-modular-input-clear { + position: absolute; + top: calc(2px * var(--pagefind-ui-scale)); + right: calc(2px * var(--pagefind-ui-scale)); + height: calc(60px * var(--pagefind-ui-scale)); + border-radius: var(--pagefind-ui-border-radius); + padding: 0 calc(15px * var(--pagefind-ui-scale)) 0 calc(2px * var(--pagefind-ui-scale)); + color: var(--pagefind-ui-text); + font-size: calc(14px * var(--pagefind-ui-scale)); + cursor: pointer; + background-color: var(--pagefind-ui-background); + border: none; + appearance: none; +} + +/* ResultList */ + +.pagefind-modular-list-result { + list-style-type: none; + display: flex; + align-items: flex-start; + gap: min(calc(40px * var(--pagefind-ui-scale)), 3%); + padding: calc(30px * var(--pagefind-ui-scale)) 0 calc(40px * var(--pagefind-ui-scale)); + border-top: solid var(--pagefind-ui-border-width) var(--pagefind-ui-border); +} + +.pagefind-modular-list-result:last-of-type { + border-bottom: solid var(--pagefind-ui-border-width) var(--pagefind-ui-border); +} + +.pagefind-modular-list-thumb { + width: min(30%, + calc((30% - (100px * var(--pagefind-ui-scale))) * 100000)); + max-width: calc(120px * var(--pagefind-ui-scale)); + margin-top: calc(10px * var(--pagefind-ui-scale)); + aspect-ratio: var(--pagefind-ui-image-box-ratio); + position: relative; +} + +.pagefind-modular-list-image { + display: block; + position: absolute; + left: 50%; + transform: translateX(-50%); + font-size: 0; + width: auto; + height: auto; + max-width: 100%; + max-height: 100%; + border-radius: var(--pagefind-ui-image-border-radius); +} + +.pagefind-modular-list-inner { + flex: 1; + display: flex; + flex-direction: column; + align-items: flex-start; + margin-top: calc(10px * var(--pagefind-ui-scale)); +} + +.pagefind-modular-list-title { + display: inline-block; + font-weight: 700; + font-size: calc(21px * var(--pagefind-ui-scale)); + margin-top: 0; + margin-bottom: 0; +} + +.pagefind-modular-list-link { + color: var(--pagefind-ui-text); + text-decoration: none; +} + +.pagefind-modular-list-link:hover { + text-decoration: underline; +} + +.pagefind-modular-list-excerpt { + display: inline-block; + font-weight: 400; + font-size: calc(16px * var(--pagefind-ui-scale)); + margin-top: calc(4px * var(--pagefind-ui-scale)); + margin-bottom: 0; + min-width: calc(250px * var(--pagefind-ui-scale)); +} + +/* FilterPills */ + +.pagefind-modular-filter-pills-wrapper { + overflow-x: scroll; + padding: 15px 0; +} + +.pagefind-modular-filter-pills { + display: flex; + gap: 6px; +} + +.pagefind-modular-filter-pill { + display: flex; + justify-content: center; + align-items: center; + border: none; + appearance: none; + padding: 0 calc(24px * var(--pagefind-ui-scale)); + background-color: var(--pagefind-ui-background); + color: var(--pagefind-ui-fade); + border: var(--pagefind-ui-border-width) solid var(--pagefind-ui-border); + border-radius: calc(25px * var(--pagefind-ui-scale)); + font-size: calc(18px * var(--pagefind-ui-scale)); + height: calc(50px * var(--pagefind-ui-scale)); + cursor: pointer; + white-space: nowrap; +} + +.pagefind-modular-filter-pill:hover { + border-color: var(--pagefind-ui-primary); +} + +.pagefind-modular-filter-pill[aria-pressed="true"] { + border-color: var(--pagefind-ui-primary); + color: var(--pagefind-ui-primary); +} \ No newline at end of file diff --git a/research/pagefind/pagefind-modular-ui.js b/research/pagefind/pagefind-modular-ui.js new file mode 100644 index 0000000..6caacd6 --- /dev/null +++ b/research/pagefind/pagefind-modular-ui.js @@ -0,0 +1,8 @@ +(()=>{var w=Object.defineProperty;var b=(i,e)=>{for(var t in e)w(i,t,{get:e[t],enumerable:!0})};var f={};b(f,{FilterPills:()=>c,Input:()=>a,Instance:()=>p,ResultList:()=>o,Summary:()=>h});var r=class i{constructor(e){this.element=document.createElement(e)}id(e){return this.element.id=e,this}class(e){return this.element.classList.add(e),this}attrs(e){for(let[t,s]of Object.entries(e))this.element.setAttribute(t,s);return this}text(e){return this.element.innerText=e,this}html(e){return this.element.innerHTML=e,this}handle(e,t){return this.element.addEventListener(e,t),this}addTo(e){return e instanceof i?e.element.appendChild(this.element):e.appendChild(this.element),this.element}};var T=async(i=100)=>new Promise(e=>setTimeout(e,i)),a=class{constructor(e={}){if(this.inputEl=null,this.clearEl=null,this.instance=null,this.searchID=0,this.debounceTimeoutMs=e.debounceTimeoutMs??300,e.inputElement){if(e.containerElement){console.warn("[Pagefind Input component]: inputElement and containerElement both supplied. 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".repeat(Math.floor(10+Math.random()*e));return`
  • +
    +
    +

    ${i(30)}

    +

    ${i(40)}

    +
    +
    • `},y=(i,e)=>{let t=new r("li").class("pagefind-modular-list-result");if(e){let l=new r("div").class("pagefind-modular-list-thumb").addTo(t);i?.meta?.image&&new r("img").class("pagefind-modular-list-image").attrs({src:i.meta.image,alt:i.meta.image_alt||i.meta.title}).addTo(l)}let s=new r("div").class("pagefind-modular-list-inner").addTo(t),n=new r("p").class("pagefind-modular-list-title").addTo(s);return new r("a").class("pagefind-modular-list-link").text(i.meta?.title).attrs({href:i.meta?.url||i.url}).addTo(n),new r("p").class("pagefind-modular-list-excerpt").html(i.excerpt).addTo(s),t.element},E=i=>{if(!(i instanceof HTMLElement))return null;let e=window.getComputedStyle(i).overflowY;return 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p=class{constructor(e={}){this.__pagefind__=null,this.__initializing__=null,this.__searchID__=0,this.__hooks__={search:[],filters:[],loading:[],results:[]},this.components=[],this.searchTerm="",this.searchFilters={},this.searchResult={},this.availableFilters=null,this.totalFilters=null,this.options={bundlePath:e.bundlePath??u,mergeIndex:e.mergeIndex??[]},delete e.bundlePath,delete e.resetStyles,delete e.processResult,delete e.processTerm,delete e.debounceTimeoutMs,delete e.mergeIndex,delete e.translations,this.pagefindOptions=e}add(e){e?.register?.(this),this.components.push(e)}on(e,t){if(!this.__hooks__[e]){let s=Object.keys(this.__hooks__).join(", ");console.error(`[Pagefind Composable]: Unknown event type ${e}. Supported events: [${s}]`);return}if(typeof t!="function"){console.error(`[Pagefind Composable]: Expected callback to be a function, received ${typeof t}`);return}this.__hooks__[e].push(t)}triggerLoad(){this.__load__()}triggerSearch(e){this.searchTerm=e,this.__dispatch__("search",e,this.searchFilters),this.__search__(e,this.searchFilters)}triggerSearchWithFilters(e,t){this.searchTerm=e,this.searchFilters=t,this.__dispatch__("search",e,t),this.__search__(e,t)}triggerFilters(e){this.searchFilters=e,this.__dispatch__("search",this.searchTerm,e),this.__search__(this.searchTerm,e)}triggerFilter(e,t){this.searchFilters=this.searchFilters||{},this.searchFilters[e]=t,this.__dispatch__("search",this.searchTerm,this.searchFilters),this.__search__(this.searchTerm,this.searchFilters)}__dispatch__(e,...t){this.__hooks__[e]?.forEach(s=>s?.(...t))}async __clear__(){this.__dispatch__("results",{results:[],unfilteredTotalCount:0}),this.availableFilters=await 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Versuchen Sie eine der folgenden Suchen:",searching:"Suche f\xFCr [SEARCH_TERM]"},Ps={thanks_to:js,comments:Us,direction:Ds,strings:Is};var jt={};A(jt,{comments:()=>qs,default:()=>Ws,direction:()=>Bs,strings:()=>Vs,thanks_to:()=>Ls});var Ls="Liam Bigelow ",qs="",Bs="ltr",Vs={placeholder:"Search",clear_search:"Clear",load_more:"Load more results",search_label:"Search this site",filters_label:"Filters",zero_results:"No results for [SEARCH_TERM]",many_results:"[COUNT] results for [SEARCH_TERM]",one_result:"[COUNT] result for [SEARCH_TERM]",alt_search:"No results for [SEARCH_TERM]. Showing results for [DIFFERENT_TERM] instead",search_suggestion:"No results for [SEARCH_TERM]. Try one of the following searches:",searching:"Searching for [SEARCH_TERM]..."},Ws={thanks_to:Ls,comments:qs,direction:Bs,strings:Vs};var Ut={};A(Ut,{comments:()=>Gs,default:()=>Zs,direction:()=>Js,strings:()=>Ys,thanks_to:()=>Ks});var Ks="Pablo Villaverde ",Gs="",Js="ltr",Ys={placeholder:"Buscar",clear_search:"Limpiar",load_more:"Ver m\xE1s resultados",search_label:"Buscar en este sitio",filters_label:"Filtros",zero_results:"No se encontraron resultados para [SEARCH_TERM]",many_results:"[COUNT] resultados encontrados para [SEARCH_TERM]",one_result:"[COUNT] resultado encontrado para [SEARCH_TERM]",alt_search:"No se encontraron resultados para [SEARCH_TERM]. Mostrando en su lugar resultados para [DIFFERENT_TERM]",search_suggestion:"No se encontraron resultados para [SEARCH_TERM]. Prueba una de las siguientes b\xFAsquedas:",searching:"Buscando [SEARCH_TERM]..."},Zs={thanks_to:Ks,comments:Gs,direction:Js,strings:Ys};var Dt={};A(Dt,{comments:()=>Qs,default:()=>el,direction:()=>xs,strings:()=>$s,thanks_to:()=>Xs});var Xs="Mikel Larreategi ",Qs="",xs="ltr",$s={placeholder:"Bilatu",clear_search:"Garbitu",load_more:"Kargatu emaitza gehiagi",search_label:"Bilatu",filters_label:"Iragazkiak",zero_results:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat",many_results:"[COUNT] emaitza [SEARCH_TERM] bilaketarentzat",one_result:"Emaitza bat [COUNT] [SEARCH_TERM] bilaketarentzat",alt_search:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat. [DIFFERENT_TERM] bilaketaren emaitzak erakusten",search_suggestion:"Ez dago emaitzarik [SEARCH_TERM] bilaketarentzat. Saiatu hauetako beste bateikin:",searching:"[SEARCH_TERM] bilatzen..."},el={thanks_to:Xs,comments:Qs,direction:xs,strings:$s};var It={};A(It,{comments:()=>nl,default:()=>ll,direction:()=>rl,strings:()=>sl,thanks_to:()=>tl});var tl="Ali Khaleqi Yekta ",nl="",rl="rtl",sl={placeholder:"\u062C\u0633\u062A\u062C\u0648",clear_search:"\u067E\u0627\u06A9\u0633\u0627\u0632\u06CC",load_more:"\u0628\u0627\u0631\u06AF\u0630\u0627\u0631\u06CC \u0646\u062A\u0627\u06CC\u062C \u0628\u06CC\u0634\u062A\u0631",search_label:"\u062C\u0633\u062A\u062C\u0648 \u062F\u0631 \u0633\u0627\u06CC\u062A",filters_label:"\u0641\u06CC\u0644\u062A\u0631\u0647\u0627",zero_results:"\u0646\u062A\u06CC\u062C\u0647\u200C\u0627\u06CC \u0628\u0631\u0627\u06CC [SEARCH_TERM] \u06CC\u0627\u0641\u062A \u0646\u0634\u062F",many_results:"[COUNT] \u0646\u062A\u06CC\u062C\u0647 \u0628\u0631\u0627\u06CC [SEARCH_TERM] \u06CC\u0627\u0641\u062A \u0634\u062F",one_result:"[COUNT] \u0646\u062A\u06CC\u062C\u0647 \u0628\u0631\u0627\u06CC [SEARCH_TERM] \u06CC\u0627\u0641\u062A \u0634\u062F",alt_search:"\u0646\u062A\u06CC\u062C\u0647\u200C\u0627\u06CC \u0628\u0631\u0627\u06CC [SEARCH_TERM] \u06CC\u0627\u0641\u062A \u0646\u0634\u062F. \u062F\u0631 \u0639\u0648\u0636 \u0646\u062A\u0627\u06CC\u062C \u0628\u0631\u0627\u06CC [DIFFERENT_TERM] \u0646\u0645\u0627\u06CC\u0634 \u062F\u0627\u062F\u0647 \u0645\u06CC\u200C\u0634\u0648\u062F",search_suggestion:"\u0646\u062A\u06CC\u062C\u0647\u200C\u0627\u06CC \u0628\u0631\u0627\u06CC [SEARCH_TERM] \u06CC\u0627\u0641\u062A \u0646\u0634\u062F. \u06CC\u06A9\u06CC \u0627\u0632 \u062C\u0633\u062A\u062C\u0648\u0647\u0627\u06CC \u0632\u06CC\u0631 \u0631\u0627 \u0627\u0645\u062A\u062D\u0627\u0646 \u06A9\u0646\u06CC\u062F:",searching:"\u062F\u0631 \u062D\u0627\u0644 \u062C\u0633\u062A\u062C\u0648\u06CC [SEARCH_TERM]..."},ll={thanks_to:tl,comments:nl,direction:rl,strings:sl};var Pt={};A(Pt,{comments:()=>al,default:()=>cl,direction:()=>ol,strings:()=>ul,thanks_to:()=>il});var il="Valtteri Laitinen ",al="",ol="ltr",ul={placeholder:"Haku",clear_search:"Tyhjenn\xE4",load_more:"Lataa lis\xE4\xE4 tuloksia",search_label:"Hae t\xE4lt\xE4 sivustolta",filters_label:"Suodattimet",zero_results:"Ei tuloksia haulle [SEARCH_TERM]",many_results:"[COUNT] tulosta haulle [SEARCH_TERM]",one_result:"[COUNT] tulos haulle [SEARCH_TERM]",alt_search:"Ei tuloksia haulle [SEARCH_TERM]. N\xE4ytet\xE4\xE4n tulokset sen sijaan haulle [DIFFERENT_TERM]",search_suggestion:"Ei tuloksia haulle [SEARCH_TERM]. Kokeile jotain seuraavista:",searching:"Haetaan [SEARCH_TERM]..."},cl={thanks_to:il,comments:al,direction:ol,strings:ul};var Lt={};A(Lt,{comments:()=>fl,default:()=>ml,direction:()=>dl,strings:()=>hl,thanks_to:()=>_l});var _l="Nicolas Friedli ",fl="",dl="ltr",hl={placeholder:"Rechercher",clear_search:"Nettoyer",load_more:"Charger plus de r\xE9sultats",search_label:"Recherche sur ce site",filters_label:"Filtres",zero_results:"Pas de r\xE9sultat pour [SEARCH_TERM]",many_results:"[COUNT] r\xE9sultats pour [SEARCH_TERM]",one_result:"[COUNT] r\xE9sultat pour [SEARCH_TERM]",alt_search:"Pas de r\xE9sultat pour [SEARCH_TERM]. Montre les r\xE9sultats pour [DIFFERENT_TERM] \xE0 la place",search_suggestion:"Pas de r\xE9sultat pour [SEARCH_TERM]. Essayer une des recherches suivantes:",searching:"Recherche [SEARCH_TERM]..."},ml={thanks_to:_l,comments:fl,direction:dl,strings:hl};var qt={};A(qt,{comments:()=>gl,default:()=>bl,direction:()=>El,strings:()=>Rl,thanks_to:()=>pl});var pl="Pablo Villaverde ",gl="",El="ltr",Rl={placeholder:"Buscar",clear_search:"Limpar",load_more:"Ver m\xE1is resultados",search_label:"Buscar neste sitio",filters_label:"Filtros",zero_results:"Non se atoparon resultados para [SEARCH_TERM]",many_results:"[COUNT] resultados atopados para [SEARCH_TERM]",one_result:"[COUNT] resultado atopado para [SEARCH_TERM]",alt_search:"Non se atoparon resultados para [SEARCH_TERM]. Amosando no seu lugar resultados para [DIFFERENT_TERM]",search_suggestion:"Non se atoparon resultados para [SEARCH_TERM]. Probe unha das seguintes pesquisas:",searching:"Buscando [SEARCH_TERM]..."},bl={thanks_to:pl,comments:gl,direction:El,strings:Rl};var Bt={};A(Bt,{comments:()=>Cl,default:()=>Ml,direction:()=>kl,strings:()=>Sl,thanks_to:()=>Tl});var Tl="Nir Tamir ",Cl="",kl="rtl",Sl={placeholder:"\u05D7\u05D9\u05E4\u05D5\u05E9",clear_search:"\u05E0\u05D9\u05E7\u05D5\u05D9",load_more:"\u05E2\u05D5\u05D3 \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA",search_label:"\u05D7\u05D9\u05E4\u05D5\u05E9 \u05D1\u05D0\u05EA\u05E8 \u05D6\u05D4",filters_label:"\u05DE\u05E1\u05E0\u05E0\u05D9\u05DD",zero_results:"\u05DC\u05D0 \u05E0\u05DE\u05E6\u05D0\u05D5 \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA \u05E2\u05D1\u05D5\u05E8 [SEARCH_TERM]",many_results:"\u05E0\u05DE\u05E6\u05D0\u05D5 [COUNT] \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA \u05E2\u05D1\u05D5\u05E8 [SEARCH_TERM]",one_result:"\u05E0\u05DE\u05E6\u05D0\u05D4 \u05EA\u05D5\u05E6\u05D0\u05D4 \u05D0\u05D7\u05EA \u05E2\u05D1\u05D5\u05E8 [SEARCH_TERM]",alt_search:"\u05DC\u05D0 \u05E0\u05DE\u05E6\u05D0\u05D5 \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA \u05E2\u05D1\u05D5\u05E8 [SEARCH_TERM]. \u05DE\u05D5\u05E6\u05D2\u05D5\u05EA \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA \u05E2\u05D1\u05D5\u05E8 [DIFFERENT_TERM]",search_suggestion:"\u05DC\u05D0 \u05E0\u05DE\u05E6\u05D0\u05D5 \u05EA\u05D5\u05E6\u05D0\u05D5\u05EA \u05E2\u05D1\u05D5\u05E8 [SEARCH_TERM]. \u05E0\u05E1\u05D5 \u05D0\u05D7\u05D3 \u05DE\u05D4\u05D7\u05D9\u05E4\u05D5\u05E9\u05D9\u05DD \u05D4\u05D1\u05D0\u05D9\u05DD:",searching:"\u05DE\u05D7\u05E4\u05E9 \u05D0\u05EA [SEARCH_TERM]..."},Ml={thanks_to:Tl,comments:Cl,direction:kl,strings:Sl};var Vt={};A(Vt,{comments:()=>yl,default:()=>wl,direction:()=>vl,strings:()=>Hl,thanks_to:()=>Al});var Al="Amit Yadav ",yl="",vl="ltr",Hl={placeholder:"\u0916\u094B\u091C\u0947\u0902",clear_search:"\u0938\u093E\u092B \u0915\u0930\u0947\u0902",load_more:"\u0914\u0930 \u0905\u0927\u093F\u0915 \u092A\u0930\u093F\u0923\u093E\u092E \u0932\u094B\u0921 \u0915\u0930\u0947\u0902",search_label:"\u0907\u0938 \u0938\u093E\u0907\u091F \u092E\u0947\u0902 \u0916\u094B\u091C\u0947\u0902",filters_label:"\u092B\u093C\u093F\u0932\u094D\u091F\u0930",zero_results:"\u0915\u094B\u0908 \u092A\u0930\u093F\u0923\u093E\u092E [SEARCH_TERM] \u0915\u0947 \u0932\u093F\u090F \u0928\u0939\u0940\u0902 \u092E\u093F\u0932\u093E",many_results:"[COUNT] \u092A\u0930\u093F\u0923\u093E\u092E [SEARCH_TERM] \u0915\u0947 \u0932\u093F\u090F \u092E\u093F\u0932\u0947",one_result:"[COUNT] \u092A\u0930\u093F\u0923\u093E\u092E [SEARCH_TERM] \u0915\u0947 \u0932\u093F\u090F \u092E\u093F\u0932\u093E",alt_search:"[SEARCH_TERM] \u0915\u0947 \u0932\u093F\u090F \u0915\u094B\u0908 \u092A\u0930\u093F\u0923\u093E\u092E \u0928\u0939\u0940\u0902 \u092E\u093F\u0932\u093E\u0964 \u0907\u0938\u0915\u0947 \u092C\u091C\u093E\u092F [DIFFERENT_TERM] \u0915\u0947 \u0932\u093F\u090F \u092A\u0930\u093F\u0923\u093E\u092E \u0926\u093F\u0916\u093E \u0930\u0939\u093E \u0939\u0948",search_suggestion:"[SEARCH_TERM] \u0915\u0947 \u0932\u093F\u090F \u0915\u094B\u0908 \u092A\u0930\u093F\u0923\u093E\u092E \u0928\u0939\u0940\u0902 \u092E\u093F\u0932\u093E\u0964 \u0928\u093F\u092E\u094D\u0928\u0932\u093F\u0916\u093F\u0924 \u0916\u094B\u091C\u094B\u0902 \u092E\u0947\u0902 \u0938\u0947 \u0915\u094B\u0908 \u090F\u0915 \u0906\u091C\u093C\u092E\u093E\u090F\u0902:",searching:"[SEARCH_TERM] \u0915\u0940 \u0916\u094B\u091C \u0915\u0940 \u091C\u093E \u0930\u0939\u0940 \u0939\u0948..."},wl={thanks_to:Al,comments:yl,direction:vl,strings:Hl};var Wt={};A(Wt,{comments:()=>Nl,default:()=>jl,direction:()=>zl,strings:()=>Ol,thanks_to:()=>Fl});var Fl="Diomed ",Nl="",zl="ltr",Ol={placeholder:"Tra\u017Ei",clear_search:"O\u010Disti",load_more:"U\u010Ditaj vi\u0161e rezultata",search_label:"Pretra\u017Ei ovu stranicu",filters_label:"Filteri",zero_results:"Nema rezultata za [SEARCH_TERM]",many_results:"[COUNT] rezultata za [SEARCH_TERM]",one_result:"[COUNT] rezultat za [SEARCH_TERM]",alt_search:"Nema rezultata za [SEARCH_TERM]. Prikazujem rezultate za [DIFFERENT_TERM]",search_suggestion:"Nema rezultata za [SEARCH_TERM]. Poku\u0161aj s jednom od ovih pretraga:",searching:"Pretra\u017Eujem [SEARCH_TERM]..."},jl={thanks_to:Fl,comments:Nl,direction:zl,strings:Ol};var Kt={};A(Kt,{comments:()=>Dl,default:()=>Ll,direction:()=>Il,strings:()=>Pl,thanks_to:()=>Ul});var Ul="Adam Laki ",Dl="",Il="ltr",Pl={placeholder:"Keres\xE9s",clear_search:"T\xF6rl\xE9s",load_more:"Tov\xE1bbi tal\xE1latok bet\xF6lt\xE9se",search_label:"Keres\xE9s az oldalon",filters_label:"Sz\u0171r\xE9s",zero_results:"Nincs tal\xE1lat a(z) [SEARCH_TERM] kifejez\xE9sre",many_results:"[COUNT] db tal\xE1lat a(z) [SEARCH_TERM] kifejez\xE9sre",one_result:"[COUNT] db tal\xE1lat a(z) [SEARCH_TERM] kifejez\xE9sre",alt_search:"Nincs tal\xE1lat a(z) [SEARCH_TERM] kifejez\xE9sre. Tal\xE1latok mutat\xE1sa ink\xE1bb a(z) [DIFFERENT_TERM] kifejez\xE9sre",search_suggestion:"Nincs tal\xE1lat a(z) [SEARCH_TERM] kifejez\xE9sre. Pr\xF3b\xE1ld meg a k\xF6vetkez\u0151 keres\xE9sek egyik\xE9t:",searching:"Keres\xE9s a(z) [SEARCH_TERM] kifejez\xE9sre..."},Ll={thanks_to:Ul,comments:Dl,direction:Il,strings:Pl};var Gt={};A(Gt,{comments:()=>Bl,default:()=>Kl,direction:()=>Vl,strings:()=>Wl,thanks_to:()=>ql});var ql="Nixentric",Bl="",Vl="ltr",Wl={placeholder:"Cari",clear_search:"Bersihkan",load_more:"Muat lebih banyak hasil",search_label:"Telusuri situs ini",filters_label:"Filter",zero_results:"[SEARCH_TERM] tidak ditemukan",many_results:"Ditemukan [COUNT] hasil untuk [SEARCH_TERM]",one_result:"Ditemukan [COUNT] hasil untuk [SEARCH_TERM]",alt_search:"[SEARCH_TERM] tidak ditemukan. Menampilkan hasil [DIFFERENT_TERM] sebagai gantinya",search_suggestion:"[SEARCH_TERM] tidak ditemukan. Coba salah satu pencarian berikut ini:",searching:"Mencari [SEARCH_TERM]..."},Kl={thanks_to:ql,comments:Bl,direction:Vl,strings:Wl};var Jt={};A(Jt,{comments:()=>Jl,default:()=>Xl,direction:()=>Yl,strings:()=>Zl,thanks_to:()=>Gl});var Gl="Cosette Bruhns Alonso, Andrew Janco ",Jl="",Yl="ltr",Zl={placeholder:"Cerca",clear_search:"Cancella la cronologia",load_more:"Mostra pi\xF9 risultati",search_label:"Cerca nel sito",filters_label:"Filtri di ricerca",zero_results:"Nessun risultato per [SEARCH_TERM]",many_results:"[COUNT] risultati per [SEARCH_TERM]",one_result:"[COUNT] risultato per [SEARCH_TERM]",alt_search:"Nessun risultato per [SEARCH_TERM]. Mostrando risultati per [DIFFERENT_TERM] come alternativa.",search_suggestion:"Nessun risultato per [SEARCH_TERM]. Prova una delle seguenti ricerche:",searching:"Cercando [SEARCH_TERM]..."},Xl={thanks_to:Gl,comments:Jl,direction:Yl,strings:Zl};var Yt={};A(Yt,{comments:()=>xl,default:()=>ti,direction:()=>$l,strings:()=>ei,thanks_to:()=>Ql});var Ql="Tate",xl="",$l="ltr",ei={placeholder:"\u691C\u7D22",clear_search:"\u30AF\u30EA\u30A2",load_more:"\u6B21\u3092\u8AAD\u307F\u8FBC\u3080",search_label:"\u3053\u306E\u30B5\u30A4\u30C8\u3092\u691C\u7D22",filters_label:"\u30D5\u30A3\u30EB\u30BF",zero_results:"[SEARCH_TERM]\u306E\u691C\u7D22\u306B\u4E00\u81F4\u3059\u308B\u60C5\u5831\u306F\u3042\u308A\u307E\u305B\u3093\u3067\u3057\u305F",many_results:"[SEARCH_TERM]\u306E[COUNT]\u4EF6\u306E\u691C\u7D22\u7D50\u679C",one_result:"[SEARCH_TERM]\u306E[COUNT]\u4EF6\u306E\u691C\u7D22\u7D50\u679C",alt_search:"[SEARCH_TERM]\u306E\u691C\u7D22\u306B\u4E00\u81F4\u3059\u308B\u60C5\u5831\u306F\u3042\u308A\u307E\u305B\u3093\u3067\u3057\u305F\u3002[DIFFERENT_TERM]\u306E\u691C\u7D22\u7D50\u679C\u3092\u8868\u793A\u3057\u3066\u3044\u307E\u3059",search_suggestion:"[SEARCH_TERM]\u306E\u691C\u7D22\u306B\u4E00\u81F4\u3059\u308B\u60C5\u5831\u306F\u3042\u308A\u307E\u305B\u3093\u3067\u3057\u305F\u3002\u6B21\u306E\u3044\u305A\u308C\u304B\u306E\u691C\u7D22\u3092\u8A66\u3057\u3066\u304F\u3060\u3055\u3044",searching:"[SEARCH_TERM]\u3092\u691C\u7D22\u3057\u3066\u3044\u307E\u3059"},ti={thanks_to:Ql,comments:xl,direction:$l,strings:ei};var Zt={};A(Zt,{comments:()=>ri,default:()=>ii,direction:()=>si,strings:()=>li,thanks_to:()=>ni});var ni="Seokho Son ",ri="",si="ltr",li={placeholder:"\uAC80\uC0C9\uC5B4",clear_search:"\uBE44\uC6B0\uAE30",load_more:"\uAC80\uC0C9 \uACB0\uACFC \uB354 \uBCF4\uAE30",search_label:"\uC0AC\uC774\uD2B8 \uAC80\uC0C9",filters_label:"\uD544\uD130",zero_results:"[SEARCH_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC \uC5C6\uC74C",many_results:"[SEARCH_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC [COUNT]\uAC74",one_result:"[SEARCH_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC [COUNT]\uAC74",alt_search:"[SEARCH_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC \uC5C6\uC74C. [DIFFERENT_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC",search_suggestion:"[SEARCH_TERM]\uC5D0 \uB300\uD55C \uACB0\uACFC \uC5C6\uC74C. \uCD94\uCC9C \uAC80\uC0C9\uC5B4: ",searching:"[SEARCH_TERM] \uAC80\uC0C9 \uC911..."},ii={thanks_to:ni,comments:ri,direction:si,strings:li};var Xt={};A(Xt,{comments:()=>oi,default:()=>_i,direction:()=>ui,strings:()=>ci,thanks_to:()=>ai});var ai="",oi="",ui="ltr",ci={placeholder:"Rapu",clear_search:"Whakakore",load_more:"Whakauta \u0113tahi otinga k\u0113",search_label:"Rapu",filters_label:"T\u0101tari",zero_results:"Otinga kore ki [SEARCH_TERM]",many_results:"[COUNT] otinga ki [SEARCH_TERM]",one_result:"[COUNT] otinga ki [SEARCH_TERM]",alt_search:"Otinga kore ki [SEARCH_TERM]. 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b/research/pagefind/pagefind.js @@ -0,0 +1,6 @@ +const pagefind_version="1.4.0";let wasm_bindgen;(function(){const __exports={};let script_src;if(typeof document!=='undefined'&&document.currentScript!==null){script_src=new URL("UNHANDLED",location.href).toString()}let wasm=undefined;let WASM_VECTOR_LEN=0;let cachedUint8Memory0=null;function getUint8Memory0(){if(cachedUint8Memory0===null||cachedUint8Memory0.byteLength===0){cachedUint8Memory0=new Uint8Array(wasm.memory.buffer)}return cachedUint8Memory0}const cachedTextEncoder=(typeof TextEncoder!=='undefined'?new TextEncoder('utf-8'):{encode:()=>{throw Error('TextEncoder not available')}});const encodeString=(typeof cachedTextEncoder.encodeInto==='function'?function(arg,view){return cachedTextEncoder.encodeInto(arg,view)}:function(arg,view){const buf=cachedTextEncoder.encode(arg);view.set(buf);return{read:arg.length,written:buf.length}});function passStringToWasm0(arg,malloc,realloc){if(realloc===undefined){const buf=cachedTextEncoder.encode(arg);const ptr=malloc(buf.length,1)>>>0;getUint8Memory0().subarray(ptr,ptr+buf.length).set(buf);WASM_VECTOR_LEN=buf.length;return ptr}let len=arg.length;let ptr=malloc(len,1)>>>0;const mem=getUint8Memory0();let offset=0;for(;offset0x7F)break;mem[ptr+offset]=code}if(offset!==len){if(offset!==0){arg=arg.slice(offset)}ptr=realloc(ptr,len,len=offset+arg.length*3,1)>>>0;const view=getUint8Memory0().subarray(ptr+offset,ptr+len);const ret=encodeString(arg,view);offset+=ret.written;ptr=realloc(ptr,len,offset,1)>>>0}WASM_VECTOR_LEN=offset;return ptr}let cachedInt32Memory0=null;function getInt32Memory0(){if(cachedInt32Memory0===null||cachedInt32Memory0.byteLength===0){cachedInt32Memory0=new Int32Array(wasm.memory.buffer)}return cachedInt32Memory0}const cachedTextDecoder=(typeof TextDecoder!=='undefined'?new TextDecoder('utf-8',{ignoreBOM:true,fatal:true}):{decode:()=>{throw Error('TextDecoder not available')}});if(typeof TextDecoder!=='undefined'){cachedTextDecoder.decode()};function getStringFromWasm0(ptr,len){ptr=ptr>>>0;return cachedTextDecoder.decode(getUint8Memory0().subarray(ptr,ptr+len))}__exports.request_indexes=function(ptr,query){let deferred2_0;let deferred2_1;try{const retptr=wasm.__wbindgen_add_to_stack_pointer(-16);const ptr0=passStringToWasm0(query,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len0=WASM_VECTOR_LEN;wasm.request_indexes(retptr,ptr,ptr0,len0);var r0=getInt32Memory0()[retptr/4+0];var r1=getInt32Memory0()[retptr/4+1];deferred2_0=r0;deferred2_1=r1;return getStringFromWasm0(r0,r1)}finally{wasm.__wbindgen_add_to_stack_pointer(16);wasm.__wbindgen_free(deferred2_0,deferred2_1,1)}};__exports.filters=function(ptr){let deferred1_0;let deferred1_1;try{const retptr=wasm.__wbindgen_add_to_stack_pointer(-16);wasm.filters(retptr,ptr);var r0=getInt32Memory0()[retptr/4+0];var r1=getInt32Memory0()[retptr/4+1];deferred1_0=r0;deferred1_1=r1;return getStringFromWasm0(r0,r1)}finally{wasm.__wbindgen_add_to_stack_pointer(16);wasm.__wbindgen_free(deferred1_0,deferred1_1,1)}};__exports.request_filter_indexes=function(ptr,filters){let deferred2_0;let deferred2_1;try{const retptr=wasm.__wbindgen_add_to_stack_pointer(-16);const ptr0=passStringToWasm0(filters,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len0=WASM_VECTOR_LEN;wasm.request_filter_indexes(retptr,ptr,ptr0,len0);var r0=getInt32Memory0()[retptr/4+0];var r1=getInt32Memory0()[retptr/4+1];deferred2_0=r0;deferred2_1=r1;return getStringFromWasm0(r0,r1)}finally{wasm.__wbindgen_add_to_stack_pointer(16);wasm.__wbindgen_free(deferred2_0,deferred2_1,1)}};__exports.enter_playground_mode=function(ptr){const ret=wasm.enter_playground_mode(ptr);return ret>>>0};__exports.request_all_filter_indexes=function(ptr){let deferred1_0;let deferred1_1;try{const retptr=wasm.__wbindgen_add_to_stack_pointer(-16);wasm.request_all_filter_indexes(retptr,ptr);var r0=getInt32Memory0()[retptr/4+0];var r1=getInt32Memory0()[retptr/4+1];deferred1_0=r0;deferred1_1=r1;return getStringFromWasm0(r0,r1)}finally{wasm.__wbindgen_add_to_stack_pointer(16);wasm.__wbindgen_free(deferred1_0,deferred1_1,1)}};function passArray8ToWasm0(arg,malloc){const ptr=malloc(arg.length*1,1)>>>0;getUint8Memory0().set(arg,ptr/1);WASM_VECTOR_LEN=arg.length;return ptr}__exports.init_pagefind=function(metadata_bytes){const ptr0=passArray8ToWasm0(metadata_bytes,wasm.__wbindgen_malloc);const len0=WASM_VECTOR_LEN;const ret=wasm.init_pagefind(ptr0,len0);return ret>>>0};__exports.search=function(ptr,query,filter,sort,exact){let deferred4_0;let deferred4_1;try{const retptr=wasm.__wbindgen_add_to_stack_pointer(-16);const ptr0=passStringToWasm0(query,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len0=WASM_VECTOR_LEN;const ptr1=passStringToWasm0(filter,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len1=WASM_VECTOR_LEN;const ptr2=passStringToWasm0(sort,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len2=WASM_VECTOR_LEN;wasm.search(retptr,ptr,ptr0,len0,ptr1,len1,ptr2,len2,exact);var r0=getInt32Memory0()[retptr/4+0];var r1=getInt32Memory0()[retptr/4+1];deferred4_0=r0;deferred4_1=r1;return getStringFromWasm0(r0,r1)}finally{wasm.__wbindgen_add_to_stack_pointer(16);wasm.__wbindgen_free(deferred4_0,deferred4_1,1)}};__exports.load_index_chunk=function(ptr,chunk_bytes){const ptr0=passArray8ToWasm0(chunk_bytes,wasm.__wbindgen_malloc);const len0=WASM_VECTOR_LEN;const ret=wasm.load_index_chunk(ptr,ptr0,len0);return ret>>>0};__exports.add_synthetic_filter=function(ptr,filter){const ptr0=passStringToWasm0(filter,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len0=WASM_VECTOR_LEN;const ret=wasm.add_synthetic_filter(ptr,ptr0,len0);return ret>>>0};__exports.set_ranking_weights=function(ptr,weights){const ptr0=passStringToWasm0(weights,wasm.__wbindgen_malloc,wasm.__wbindgen_realloc);const len0=WASM_VECTOR_LEN;const ret=wasm.set_ranking_weights(ptr,ptr0,len0);return ret>>>0};__exports.load_filter_chunk=function(ptr,chunk_bytes){const ptr0=passArray8ToWasm0(chunk_bytes,wasm.__wbindgen_malloc);const len0=WASM_VECTOR_LEN;const ret=wasm.load_filter_chunk(ptr,ptr0,len0);return ret>>>0};async function __wbg_load(module,imports){if(typeof Response==='function'&&module instanceof Response){if(typeof WebAssembly.instantiateStreaming==='function'){try{return await WebAssembly.instantiateStreaming(module,imports)}catch(e){if(module.headers.get('Content-Type')!='application/wasm'){console.warn("`WebAssembly.instantiateStreaming` failed because your server does not serve wasm with `application/wasm` MIME type. Falling back to `WebAssembly.instantiate` which is slower. Original error:\n",e)}else{throw e}}}const bytes=await module.arrayBuffer();return await WebAssembly.instantiate(bytes,imports)}else{const instance=await WebAssembly.instantiate(module,imports);if(instance instanceof WebAssembly.Instance){return{instance,module}}else{return instance}}}function __wbg_get_imports(){const imports={};imports.wbg={};return imports}function __wbg_init_memory(imports,maybe_memory){}function __wbg_finalize_init(instance,module){wasm=instance.exports;__wbg_init.__wbindgen_wasm_module=module;cachedInt32Memory0=null;cachedUint8Memory0=null;return wasm}function initSync(module){if(wasm!==undefined)return wasm;const imports=__wbg_get_imports();__wbg_init_memory(imports);if(!(module instanceof WebAssembly.Module)){module=new WebAssembly.Module(module)}const instance=new WebAssembly.Instance(module,imports);return __wbg_finalize_init(instance,module)}async function __wbg_init(input){if(wasm!==undefined)return wasm;if(typeof input==='undefined'&&typeof script_src!=='undefined'){input=script_src.replace(/\.js$/,'_bg.wasm')}const imports=__wbg_get_imports();if(typeof input==='string'||(typeof Request==='function'&&input instanceof Request)||(typeof URL==='function'&&input instanceof URL)){input=fetch(input)}__wbg_init_memory(imports);const{instance,module}=await __wbg_load(await input,imports);return __wbg_finalize_init(instance,module)}wasm_bindgen=Object.assign(__wbg_init,{initSync},__exports)})();var u8=Uint8Array;var u16=Uint16Array;var u32=Uint32Array;var fleb=new u8([0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0,0]);var fdeb=new u8([0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,0,0]);var clim=new u8([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);var freb=function(eb,start){var b=new u16(31);for(var i2=0;i2<31;++i2){b[i2]=start+=1<>>1|(i&21845)<<1;x=(x&52428)>>>2|(x&13107)<<2;x=(x&61680)>>>4|(x&3855)<<4;rev[i]=((x&65280)>>>8|(x&255)<<8)>>>1}var x;var i;var hMap=function(cd,mb,r){var s=cd.length;var i2=0;var l=new u16(mb);for(;i2>>rvb]=sv}}}}else{co=new u16(s);for(i2=0;i2>>15-cd[i2]}}}return co};var flt=new u8(288);for(i=0;i<144;++i)flt[i]=8;var i;for(i=144;i<256;++i)flt[i]=9;var i;for(i=256;i<280;++i)flt[i]=7;var i;for(i=280;i<288;++i)flt[i]=8;var i;var fdt=new u8(32);for(i=0;i<32;++i)fdt[i]=5;var i;var flrm=hMap(flt,9,1);var fdrm=hMap(fdt,5,1);var max=function(a){var m=a[0];for(var i2=1;i2m)m=a[i2]}return m};var bits=function(d,p,m){var o=p/8|0;return(d[o]|d[o+1]<<8)>>(p&7)&m};var bits16=function(d,p){var o=p/8|0;return(d[o]|d[o+1]<<8|d[o+2]<<16)>>(p&7)};var shft=function(p){return(p+7)/8|0};var slc=function(v,s,e){if(s==null||s<0)s=0;if(e==null||e>v.length)e=v.length;var n=new(v.BYTES_PER_ELEMENT==2?u16:v.BYTES_PER_ELEMENT==4?u32:u8)(e-s);n.set(v.subarray(s,e));return n};var ec=["unexpected EOF","invalid block type","invalid length/literal","invalid distance","stream finished","no stream handler",,"no callback","invalid UTF-8 data","extra field too long","date not in range 1980-2099","filename too long","stream finishing","invalid zip data"];var err=function(ind,msg,nt){var e=new Error(msg||ec[ind]);e.code=ind;if(Error.captureStackTrace)Error.captureStackTrace(e,err);if(!nt)throw e;return e};var inflt=function(dat,buf,st){var sl=dat.length;if(!sl||st&&st.f&&!st.l)return buf||new u8(0);var noBuf=!buf||st;var noSt=!st||st.i;if(!st)st={};if(!buf)buf=new u8(sl*3);var cbuf=function(l2){var bl=buf.length;if(l2>bl){var nbuf=new u8(Math.max(bl*2,l2));nbuf.set(buf);buf=nbuf}};var final=st.f||0,pos=st.p||0,bt=st.b||0,lm=st.l,dm=st.d,lbt=st.m,dbt=st.n;var tbts=sl*8;do{if(!lm){final=bits(dat,pos,1);var type=bits(dat,pos+1,3);pos+=3;if(!type){var s=shft(pos)+4,l=dat[s-4]|dat[s-3]<<8,t=s+l;if(t>sl){if(noSt)err(0);break}if(noBuf)cbuf(bt+l);buf.set(dat.subarray(s,t),bt);st.b=bt+=l,st.p=pos=t*8,st.f=final;continue}else if(type==1)lm=flrm,dm=fdrm,lbt=9,dbt=5;else if(type==2){var hLit=bits(dat,pos,31)+257,hcLen=bits(dat,pos+10,15)+4;var tl=hLit+bits(dat,pos+5,31)+1;pos+=14;var ldt=new u8(tl);var clt=new u8(19);for(var i2=0;i2>>4;if(s<16){ldt[i2++]=s}else{var c=0,n=0;if(s==16)n=3+bits(dat,pos,3),pos+=2,c=ldt[i2-1];else if(s==17)n=3+bits(dat,pos,7),pos+=3;else if(s==18)n=11+bits(dat,pos,127),pos+=7;while(n--)ldt[i2++]=c}}var lt=ldt.subarray(0,hLit),dt=ldt.subarray(hLit);lbt=max(lt);dbt=max(dt);lm=hMap(lt,lbt,1);dm=hMap(dt,dbt,1)}else err(1);if(pos>tbts){if(noSt)err(0);break}}if(noBuf)cbuf(bt+131072);var lms=(1<>>4;pos+=c&15;if(pos>tbts){if(noSt)err(0);break}if(!c)err(2);if(sym<256)buf[bt++]=sym;else if(sym==256){lpos=pos,lm=null;break}else{var add=sym-254;if(sym>264){var i2=sym-257,b=fleb[i2];add=bits(dat,pos,(1<>>4;if(!d)err(3);pos+=d&15;var dt=fd[dsym];if(dsym>3){var b=fdeb[dsym];dt+=bits16(dat,pos)&(1<tbts){if(noSt)err(0);break}if(noBuf)cbuf(bt+131072);var end=bt+add;for(;bt>3&1)+(flg>>4&1);zs>0;zs-=!d[st++]);return st+(flg&2)};var gzl=function(d){var l=d.length;return(d[l-4]|d[l-3]<<8|d[l-2]<<16|d[l-1]<<24)>>>0};function gunzipSync(data,out){return inflt(data.subarray(gzs(data),-8),out||new u8(gzl(data)))}var td=typeof TextDecoder!="undefined"&&new TextDecoder();var tds=0;try{td.decode(et,{stream:true});tds=1}catch(e){}var gz_default=gunzipSync;var calculate_excerpt_region=(word_positions,excerpt_length)=>{if(word_positions.length===0){return 0}let words=[];for(const word of word_positions){words[word.location]=words[word.location]||0;words[word.location]+=word.balanced_score}if(words.length<=excerpt_length){return 0}let densest=words.slice(0,excerpt_length).reduce((partialSum,a)=>partialSum+a,0);let working_sum=densest;let densest_at=[0];for(let i2=0;i2densest){densest=working_sum;densest_at=[i2]}else if(working_sum===densest&&densest_at[densest_at.length-1]===i2-1){densest_at.push(i2)}}let midpoint=densest_at[Math.floor(densest_at.length/2)];return midpoint};var build_excerpt=(content,start,length,locations,not_before,not_from)=>{let is_zws_delimited=content.includes("\u200B");let fragment_words=[];if(is_zws_delimited){fragment_words=content.split("\u200B")}else{fragment_words=content.split(/[\r\n\s]+/g)}for(let word of locations){if(fragment_words[word]?.startsWith(``)){continue}fragment_words[word]=`${fragment_words[word]}`}let endcap=not_from??fragment_words.length;let startcap=not_before??0;if(endcap-startcapendcap){start=endcap-length}if(start{const anchors=fragment.anchors.filter((a)=>/h\d/i.test(a.element)&&a.text?.length&&/\S/.test(a.text)).sort((a,b)=>a.location-b.location);const results=[];let current_anchor_position=0;let current_anchor={title:fragment.meta["title"],url:fragment.url,weighted_locations:[],locations:[],excerpt:""};const add_result=(end_range)=>{if(current_anchor.locations.length){const relative_weighted_locations=current_anchor.weighted_locations.map((l)=>{return{weight:l.weight,balanced_score:l.balanced_score,location:l.location-current_anchor_position}});const excerpt_start=calculate_excerpt_region(relative_weighted_locations,desired_excerpt_length)+current_anchor_position;const excerpt_length=end_range?Math.min(end_range-excerpt_start,desired_excerpt_length):desired_excerpt_length;current_anchor.excerpt=build_excerpt(fragment.raw_content??"",excerpt_start,excerpt_length,current_anchor.locations,current_anchor_position,end_range);results.push(current_anchor)}};for(let word of fragment.weighted_locations){if(!anchors.length||word.location=anchors[0].location){next_anchor=anchors.shift()}let anchored_url=fragment.url;try{const url_is_fq=/^((https?:)?\/\/)/.test(anchored_url);if(url_is_fq){let fq_url=new URL(anchored_url);fq_url.hash=next_anchor.id;anchored_url=fq_url.toString()}else{if(!/^\//.test(anchored_url)){anchored_url=`/${anchored_url}`}let fq_url=new URL(`https://example.com${anchored_url}`);fq_url.hash=next_anchor.id;anchored_url=fq_url.toString().replace(/^https:\/\/example.com/,"")}}catch(e){console.error(`Pagefind: Couldn't process ${anchored_url} for a search result`)}current_anchor_position=next_anchor.location;current_anchor={title:next_anchor.text,url:anchored_url,anchor:next_anchor,weighted_locations:[word],locations:[word.location],excerpt:""}}}add_result(anchors[0]?.location);return results};var asyncSleep=async(ms=100)=>{return new Promise((r)=>setTimeout(r,ms))};var isBrowser=typeof window!=="undefined"&&typeof document!=="undefined";var PagefindInstance=class{constructor(opts={}){this.version=pagefind_version;this.backend=wasm_bindgen;this.decoder=new TextDecoder("utf-8");this.wasm=null;this.basePath=opts.basePath||"/pagefind/";this.primary=opts.primary||false;if(this.primary&&!opts.basePath){this.initPrimary()}if(/[^\/]$/.test(this.basePath)){this.basePath=`${this.basePath}/`}if(isBrowser&&window?.location?.origin&&this.basePath.startsWith(window.location.origin)){this.basePath=this.basePath.replace(window.location.origin,"")}this.baseUrl=opts.baseUrl||this.defaultBaseUrl();if(!/^(\/|https?:\/\/)/.test(this.baseUrl)){this.baseUrl=`/${this.baseUrl}`}this.indexWeight=opts.indexWeight??1;this.excerptLength=opts.excerptLength??30;this.mergeFilter=opts.mergeFilter??{};this.ranking=opts.ranking;this.highlightParam=opts.highlightParam??null;this.loaded_chunks={};this.loaded_filters={};this.loaded_fragments={};this.raw_ptr=null;this.searchMeta=null;this.languages=null}initPrimary(){if(isBrowser&&typeof import.meta.url!=="undefined"){let derivedBasePath=import.meta.url.match(/^(.*\/)pagefind.js.*$/)?.[1];if(derivedBasePath){this.basePath=derivedBasePath}else{console.warn(["Pagefind couldn't determine the base of the bundle from the import path. Falling back to the default.","Set a basePath option when initialising Pagefind to ignore this message."].join("\n"))}}}defaultBaseUrl(){let default_base=this.basePath.match(/^(.*\/)_?pagefind/)?.[1];return default_base||"/"}async options(options2){const opts=["basePath","baseUrl","indexWeight","excerptLength","mergeFilter","highlightParam","ranking"];for(const[k,v]of Object.entries(options2)){if(k==="mergeFilter"){let filters2=this.stringifyFilters(v);let ptr=await this.getPtr();this.raw_ptr=this.backend.add_synthetic_filter(ptr,filters2)}else if(k==="ranking"){await this.set_ranking(options2.ranking)}else if(opts.includes(k)){if(k==="basePath"&&typeof v==="string")this.basePath=v;if(k==="baseUrl"&&typeof v==="string")this.baseUrl=v;if(k==="indexWeight"&&typeof v==="number")this.indexWeight=v;if(k==="excerptLength"&&typeof v==="number")this.excerptLength=v;if(k==="mergeFilter"&&typeof v==="object")this.mergeFilter=v;if(k==="highlightParam"&&typeof v==="string")this.highlightParam=v}else{console.warn(`Unknown Pagefind option ${k}. Allowed options: [${opts.join(", ")}]`)}}}async enterPlaygroundMode(){let ptr=await this.getPtr();this.raw_ptr=this.backend.enter_playground_mode(ptr)}decompress(data,file="unknown file"){if(this.decoder.decode(data.slice(0,12))==="pagefind_dcd"){return data.slice(12)}data=gz_default(data);if(this.decoder.decode(data.slice(0,12))!=="pagefind_dcd"){console.error(`Decompressing ${file} appears to have failed: Missing signature`);return data}return data.slice(12)}async set_ranking(ranking){if(!ranking)return;let rankingWeights={term_similarity:ranking.termSimilarity??null,page_length:ranking.pageLength??null,term_saturation:ranking.termSaturation??null,term_frequency:ranking.termFrequency??null};let ptr=await this.getPtr();this.raw_ptr=this.backend.set_ranking_weights(ptr,JSON.stringify(rankingWeights))}async init(language,opts){await this.loadEntry();let index=this.findIndex(language);let lang_wasm=index.wasm?index.wasm:"unknown";this.loadedLanguage=language;let resources=[this.loadMeta(index.hash)];if(opts.load_wasm===true){resources.push(this.loadWasm(lang_wasm))}await Promise.all(resources);this.raw_ptr=this.backend.init_pagefind(new Uint8Array(this.searchMeta));if(Object.keys(this.mergeFilter)?.length){let filters2=this.stringifyFilters(this.mergeFilter);let ptr=await this.getPtr();this.raw_ptr=this.backend.add_synthetic_filter(ptr,filters2)}if(this.ranking){await this.set_ranking(this.ranking)}}async loadEntry(){try{let entry_response=await fetch(`${this.basePath}pagefind-entry.json?ts=${Date.now()}`);let entry_json=await entry_response.json();this.languages=entry_json.languages;this.loadedVersion=entry_json.version;this.includeCharacters=entry_json.include_characters??[];if(entry_json.version!==this.version){if(this.primary){console.warn(["Pagefind JS version doesn't match the version in your search index.",`Pagefind JS: ${this.version}. Pagefind index: ${entry_json.version}`,"If you upgraded Pagefind recently, you likely have a cached pagefind.js file.","If you encounter any search errors, try clearing your cache."].join("\n"))}else{console.warn(["Merging a Pagefind index from a different version than the main Pagefind instance.",`Main Pagefind JS: ${this.version}. Merged index (${this.basePath}): ${entry_json.version}`,"If you encounter any search errors, make sure that both sites are running the same version of Pagefind."].join("\n"))}}}catch(e){console.error(`Failed to load Pagefind metadata: +${e?.toString()}`);throw new Error("Failed to load Pagefind metadata")}}findIndex(language){if(this.languages){let index=this.languages[language];if(index)return index;index=this.languages[language.split("-")[0]];if(index)return index;let topLang=Object.values(this.languages).sort((a,b)=>b.page_count-a.page_count);if(topLang[0])return topLang[0]}throw new Error("Pagefind Error: No language indexes found.")}async loadMeta(index){try{let compressed_resp=await fetch(`${this.basePath}pagefind.${index}.pf_meta`);let compressed_meta=await compressed_resp.arrayBuffer();this.searchMeta=this.decompress(new Uint8Array(compressed_meta),"Pagefind metadata")}catch(e){console.error(`Failed to load the meta index: +${e?.toString()}`)}}async loadWasm(language){try{const wasm_url=`${this.basePath}wasm.${language}.pagefind`;let compressed_resp=await fetch(wasm_url);let compressed_wasm=await compressed_resp.arrayBuffer();const final_wasm=this.decompress(new Uint8Array(compressed_wasm),"Pagefind WebAssembly");if(!final_wasm){throw new Error("No WASM after decompression")}this.wasm=await this.backend(final_wasm)}catch(e){console.error(`Failed to load the Pagefind WASM: +${e?.toString()}`);throw new Error(`Failed to load the Pagefind WASM: +${e?.toString()}`)}}async _loadGenericChunk(url,method){try{let compressed_resp=await fetch(url);let compressed_chunk=await compressed_resp.arrayBuffer();let chunk=this.decompress(new Uint8Array(compressed_chunk),url);let ptr=await this.getPtr();this.raw_ptr=this.backend[method](ptr,chunk)}catch(e){console.error(`Failed to load the index chunk ${url}: +${e?.toString()}`)}}async loadChunk(hash){if(!this.loaded_chunks[hash]){const url=`${this.basePath}index/${hash}.pf_index`;this.loaded_chunks[hash]=this._loadGenericChunk(url,"load_index_chunk")}return await this.loaded_chunks[hash]}async loadFilterChunk(hash){if(!this.loaded_filters[hash]){const url=`${this.basePath}filter/${hash}.pf_filter`;this.loaded_filters[hash]=this._loadGenericChunk(url,"load_filter_chunk")}return await this.loaded_filters[hash]}async _loadFragment(hash){let compressed_resp=await fetch(`${this.basePath}fragment/${hash}.pf_fragment`);let compressed_fragment=await compressed_resp.arrayBuffer();let fragment=this.decompress(new Uint8Array(compressed_fragment),`Fragment ${hash}`);return JSON.parse(new TextDecoder().decode(fragment))}async loadFragment(hash,weighted_locations=[],search_term){if(!this.loaded_fragments[hash]){this.loaded_fragments[hash]=this._loadFragment(hash)}let fragment=await this.loaded_fragments[hash];fragment.weighted_locations=weighted_locations;fragment.locations=weighted_locations.map((l)=>l.location);if(!fragment.raw_content){fragment.raw_content=fragment.content.replace(//g,">");fragment.content=fragment.content.replace(/\u200B/g,"")}if(!fragment.raw_url){fragment.raw_url=fragment.url}fragment.url=this.processedUrl(fragment.raw_url,search_term);const excerpt_start=calculate_excerpt_region(weighted_locations,this.excerptLength);fragment.excerpt=build_excerpt(fragment.raw_content,excerpt_start,this.excerptLength,fragment.locations);fragment.sub_results=calculate_sub_results(fragment,this.excerptLength);return fragment}fullUrl(raw){if(/^(https?:)?\/\//.test(raw)){return raw}return`${this.baseUrl}/${raw}`.replace(/\/+/g,"/").replace(/^(https?:\/)/,"$1/")}processedUrl(url,search_term){const normalized=this.fullUrl(url);if(this.highlightParam===null){return normalized}let individual_terms=search_term.split(/\s+/);try{let processed=new URL(normalized);for(const term of individual_terms){processed.searchParams.append(this.highlightParam,term)}return processed.toString()}catch(e){try{let processed=new URL(`https://example.com${normalized}`);for(const term of individual_terms){processed.searchParams.append(this.highlightParam,term)}return processed.toString().replace(/^https:\/\/example\.com/,"")}catch(e2){return normalized}}}async getPtr(){while(this.raw_ptr===null){await asyncSleep(50)}if(!this.raw_ptr){console.error("Pagefind: WASM Error (No pointer)");throw new Error("Pagefind: WASM Error (No pointer)")}return this.raw_ptr}stringifyFilters(obj={}){return JSON.stringify(obj)}stringifySorts(obj={}){let sorts=Object.entries(obj);for(let[sort,direction]of sorts){if(sorts.length>1){console.warn(`Pagefind was provided multiple sort options in this search, but can only operate on one. Using the ${sort} sort.`)}if(direction!=="asc"&&direction!=="desc"){console.warn(`Pagefind was provided a sort with unknown direction ${direction}. Supported: [asc, desc]`)}return`${sort}:${direction}`}return``}async filters(){let ptr=await this.getPtr();let filters2=this.backend.request_all_filter_indexes(ptr);let filter_array=JSON.parse(filters2);if(Array.isArray(filter_array)){let filter_chunks=filter_array.filter((v)=>v).map((chunk)=>this.loadFilterChunk(chunk));await Promise.all([...filter_chunks])}ptr=await this.getPtr();let results=this.backend.filters(ptr);return JSON.parse(results)}async preload(term,options2={}){await this.search(term,{...options2,preload:true})}async search(term,options2={}){options2={verbose:false,filters:{},sort:{},...options2};const log=(str)=>{if(options2.verbose)console.log(str)};log(`Starting search on ${this.basePath}`);let start=Date.now();let ptr=await this.getPtr();let filter_only=term===null;term=term??"";let exact_search=/^\s*".+"\s*$/.test(term);if(exact_search){log(`Running an exact search`)}let trueLanguage=null;try{trueLanguage=Intl.getCanonicalLocales(this.loadedLanguage)[0]}catch(err2){}const term_chunks=[];let segments;if(trueLanguage&&typeof Intl.Segmenter!=="undefined"){const segmenter=new Intl.Segmenter(trueLanguage,{granularity:"grapheme"});segments=[...segmenter.segment(term)].map(({segment})=>segment)}else{segments=[...term]}for(const segment of segments){if(this.includeCharacters?.includes(segment)){term_chunks.push(segment)}else if(!/^\p{Pd}|\p{Pe}|\p{Pf}|\p{Pi}|\p{Po}|\p{Ps}$/u.test(segment)){term_chunks.push(segment.toLocaleLowerCase())}}term=term_chunks.join("").replace(/\s{2,}/g," ").trim();log(`Normalized search term to ${term}`);if(!term?.length&&!filter_only){return{results:[],unfilteredResultCount:0,filters:{},totalFilters:{},timings:{preload:Date.now()-start,search:Date.now()-start,total:Date.now()-start}}}let sort_list=this.stringifySorts(options2.sort);log(`Stringified sort to ${sort_list}`);const filter_list=this.stringifyFilters(options2.filters);log(`Stringified filters to ${filter_list}`);let index_resp=this.backend.request_indexes(ptr,term);let index_array=JSON.parse(index_resp);let filter_resp=this.backend.request_filter_indexes(ptr,filter_list);let filter_array=JSON.parse(filter_resp);let chunks=index_array.filter((v)=>v).map((chunk)=>this.loadChunk(chunk));let filter_chunks=filter_array.filter((v)=>v).map((chunk)=>this.loadFilterChunk(chunk));await Promise.all([...chunks,...filter_chunks]);log(`Loaded necessary chunks to run search`);if(options2.preload){log(`Preload \u2014 bailing out of search operation now.`);return null}ptr=await this.getPtr();let searchStart=Date.now();let result=this.backend.search(ptr,term,filter_list,sort_list,exact_search);log(`Got the raw search result: ${result}`);let{filtered_counts,total_counts,results,unfiltered_total,search_keywords}=JSON.parse(result);let resultsInterface=results.map((result2)=>{let weighted_locations=result2.l.map((l)=>{let loc={weight:l.w/24,balanced_score:l.s,location:l.l};if(l.v){loc.verbose={word_string:l.v.ws,length_bonus:l.v.lb}}return loc});let locations=weighted_locations.map((l)=>l.location);let res={id:result2.p,score:result2.s*this.indexWeight,words:locations,data:async()=>await this.loadFragment(result2.p,weighted_locations,term)};if(result2.params){res.params={document_length:result2.params.dl,average_page_length:result2.params.apl,total_pages:result2.params.tp}}if(result2.scores){res.scores=result2.scores.map((r)=>{return{search_term:r.w,idf:r.idf,saturating_tf:r.b_tf,raw_tf:r.r_tf,pagefind_tf:r.p_tf,score:r.s,params:{weighted_term_frequency:r.params.w_tf,pages_containing_term:r.params.pct,length_bonus:r.params.lb}}})}return res});const searchTime=Date.now()-searchStart;const realTime=Date.now()-start;log(`Found ${results.length} result${results.length == 1 ? "" : "s"} for "${term}" in ${Date.now() - searchStart}ms (${Date.now() - start}ms realtime)`);let response={results:resultsInterface,unfilteredResultCount:unfiltered_total,filters:filtered_counts,totalFilters:total_counts,timings:{preload:realTime-searchTime,search:searchTime,total:realTime}};if(search_keywords){response.search_keywords=search_keywords}return response}};var Pagefind=class{constructor(options2={}){this.backend=wasm_bindgen;this.primaryLanguage="unknown";this.searchID=0;this.primary=new PagefindInstance({...options2,primary:true});this.instances=[this.primary];this.init(options2?.language)}async options(options2){await this.primary.options(options2)}async enterPlaygroundMode(){await this.primary.enterPlaygroundMode()}async init(overrideLanguage){if(isBrowser&&document?.querySelector){const langCode=document.querySelector("html")?.getAttribute("lang")||"unknown";this.primaryLanguage=langCode.toLocaleLowerCase()}await this.primary.init(overrideLanguage?overrideLanguage:this.primaryLanguage,{load_wasm:true})}async mergeIndex(indexPath,options2={}){if(this.primary.basePath.startsWith(indexPath)){console.warn(`Skipping mergeIndex ${indexPath} that appears to be the same as the primary index (${this.primary.basePath})`);return}let newInstance=new PagefindInstance({primary:false,basePath:indexPath});this.instances.push(newInstance);while(this.primary.wasm===null){await asyncSleep(50)}await newInstance.init(options2.language||this.primaryLanguage,{load_wasm:false});delete options2["language"];await newInstance.options(options2)}mergeFilters(filters2){const merged={};for(const searchFilter of filters2){for(const[filterKey,values]of Object.entries(searchFilter)){if(!merged[filterKey]){merged[filterKey]=values;continue}else{const filter=merged[filterKey];for(const[valueKey,count]of Object.entries(values)){filter[valueKey]=(filter[valueKey]||0)+count}}}}return merged}async filters(){let filters2=await Promise.all(this.instances.map((i2)=>i2.filters()));return this.mergeFilters(filters2)}async preload(term,options2={}){await Promise.all(this.instances.map((i2)=>i2.preload(term,options2)))}async debouncedSearch(term,options2,debounceTimeoutMs){const thisSearchID=++this.searchID;this.preload(term,options2);await asyncSleep(debounceTimeoutMs);if(thisSearchID!==this.searchID){return null}const searchResult=await this.search(term,options2);if(thisSearchID!==this.searchID){return null}return searchResult}async search(term,options2={}){let search2=await Promise.all(this.instances.map((i2)=>i2.search(term,options2)));const filters2=this.mergeFilters(search2.map((s)=>s.filters));const totalFilters=this.mergeFilters(search2.map((s)=>s.totalFilters));const results=search2.map((s)=>s.results).flat().sort((a,b)=>b.score-a.score);const timings=search2.map((s)=>s.timings);const unfilteredResultCount=search2.reduce((sum,s)=>sum+s.unfilteredResultCount,0);let response={results,unfilteredResultCount,filters:filters2,totalFilters,timings};if(search2[0].search_keywords){response.search_keywords=search2[0].search_keywords}return response}};var pagefind=void 0;var initial_options=void 0;var init_pagefind=()=>{if(!pagefind){pagefind=new Pagefind(initial_options??{})}};var options=async(new_options)=>{if(pagefind){await pagefind.options(new_options)}else{initial_options=new_options}};var init=async()=>{init_pagefind()};var destroy=async()=>{pagefind=void 0;initial_options=void 0};var mergeIndex=async(indexPath,options2)=>{init_pagefind();return await pagefind.mergeIndex(indexPath,options2)};var search=async(term,options2)=>{init_pagefind();return await pagefind.search(term,options2)};var debouncedSearch=async(term,options2,debounceTimeoutMs=300)=>{init_pagefind();return await pagefind.debouncedSearch(term,options2,debounceTimeoutMs)};var preload=async(term,options2)=>{init_pagefind();return await pagefind.preload(term,options2)};var filters=async()=>{init_pagefind();return await pagefind.filters()};export{debouncedSearch,destroy,filters,init,mergeIndex,options,preload,search} \ No newline at end of file diff --git a/research/pagefind/wasm.en.pagefind b/research/pagefind/wasm.en.pagefind new file mode 100644 index 0000000..e49ad34 Binary files /dev/null and b/research/pagefind/wasm.en.pagefind differ diff --git a/research/pagefind/wasm.unknown.pagefind b/research/pagefind/wasm.unknown.pagefind new file mode 100644 index 0000000..e3f5520 Binary files /dev/null and b/research/pagefind/wasm.unknown.pagefind differ diff --git a/research/review/structured/index.html b/research/review/structured/index.html new file mode 100644 index 0000000..6131c67 --- /dev/null +++ b/research/review/structured/index.html @@ -0,0 +1,247 @@ + 结构化审稿 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    结构化审稿

    1. 审稿流水线概览

    Section titled “1. 审稿流水线概览”
    +
    flowchart TD
        A(初稿完成) --> B(多模型并行审稿)
        B --> C(交叉验证意见)
        C --> D(按优先级排序)
        D --> E(批量修复)
        E --> F(验证修复)
        F --> G{通过?}
        G -->|否| B
        G -->|是| H(定稿)
    
+
        classDef key fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827
        class A,H key
    +
    +

    2. 审稿输出要结构化

    Section titled “2. 审稿输出要结构化”
    +

    审稿必须回答以下关键问题：

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    问题重要性
    哪条 claim 缺证据？🔴 高
    哪个实验最关键但没做？🔴 高
    哪个 baseline 必须补？🔴 高
    哪段叙事最像空话？🟡 中
    哪个地方最容易被说不新？🔴 高
    +
    +

    3. 审稿输出模板

    Section titled “3. 审稿输出模板”
    +
    # 审稿意见
    
+
    ## 1. 缺证据的 Claim
    
+
    | Claim | 问题 | 需要补充 |
    |-------|------|---------|
    | XXX机制是关键创新 | 没有消融实验 | 去掉XXX后的性能对比 |
    | 显著优于SOTA | 评价指标不公平 | 统一指标后重新对比 |
    
+
    ## 2. 缺失的实验
    
+
    | 实验类型 | 现状 | 需要补充 |
    |---------|------|---------|
    | 压力测试 | 无 | 低资源/噪声/分布外测试 |
    | 消融实验 | 不完整 | 每个模块的独立贡献 |
    
+
    ## 3. 缺失的 Baseline
    
+
    | 方法 | 年份 | 问题 | 需要 |
    |------|------|------|------|
    | YYY方法 | 2024 | 最新强baseline | 补充对比 |
    
+
    ## 4. 空话段落
    
+
    | 位置 | 问题 | 建议 |
    |------|------|------|
    | 3.2节第二段 | 显著提升无数据 | 补充具体数字或删除 |
    
+
    ## 5. 创新性质疑预案
    
+
    | 可能的质疑 | 应对策略 |
    |-----------|---------|
    | 只是简单组合 | 强调发现的XXX insight |
    | 不公平对比 | 补充公平对比实验 |
    +
    +

    4. TODO 转化模板

    Section titled “4. TODO 转化模板”
    +

    每条意见对应一个改动 + 验收标准：

    +
    # TODO List
    
+
    ## 紧急（提交前必须完成）
    
+
    - [ ] 补消融实验
      - 内容: 去掉模块 X
      - 验收: 指标下降 ≥ 5%
    
+
    - [ ] 补公平对比
      - 内容: 训练步数对齐 + 算力对齐
      - 验收: 与 YYY 方法公平对比
    
+
    - [ ] 补压力测试
      - 内容: 低资源/噪声/分布外测试
      - 验收: 每个场景至少 1 组实验
    
+
    ## 重要（提升质量）
    
+
    - [ ] 重写3.2节
      - 内容: 添加具体数据或删除空洞表述
      - 验收: 每段有具体数字支撑
    
+
    - [ ] 增加与ZZZ的对比讨论
      - 内容: 强调我们的 XXX insight
      - 验收: 明确说明区别和优势
    
+
    ## 可选（锦上添花）
    
+
    - [ ] 补充人类评估实验
    - [ ] 增加可视化分析
    +
    +

    5. 多模型审稿配置

    Section titled “5. 多模型审稿配置”
    +

    5.1 模型分工

    Section titled “5.1 模型分工”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    模型侧重点Prompt 重点
    Claude Opus 4.6整体逻辑叙事连贯性、创新性
    GPT-5.2 Pro技术细节方法正确性、实验设计
    Gemini Pro可复现性代码完整性、数据可用性
    +

    5.2 审稿 Prompt

    Section titled “5.2 审稿 Prompt”
    +
    你是一位 ["会议"] 的资深审稿人。请严格审查以下论文：
    
+
    ["粘贴论文内容"]
    
+
    请从以下角度审查：
    
+
    1. **创新性 (Novelty)**
       - 主要贡献是什么？
       - 与之前工作有何不同？
    
+
    2. **技术正确性 (Technical Soundness)**
       - 方法是否正确？
       - 理论是否成立？
    
+
    3. **实验完整性 (Experiments)**
       - Baseline 是否足够？
       - 消融实验是否完整？
    
+
    4. **写作质量 (Writing)**
       - 逻辑是否清晰？
       - 表述是否准确？
    
+
    请用以下格式输出：
    
+
    ---
    ## Overall Score: (1-5)
    
+
    ## Strengths:
    - ...
    
+
    ## Weaknesses:
    1. ["严重"] ...
    2. ["中等"] ...
    3. ["轻微"] ...
    
+
    ## Detailed Comments:
    ...
    ---
    +
    +

    6. 迭代循环

    Section titled “6. 迭代循环”
    +

    6.1 10+ 轮迭代流程

    Section titled “6.1 10+ 轮迭代流程”
    +
    flowchart TD
        A(第1轮审稿) --> B(整理意见)
        B --> C(按优先级排序)
        C --> D(选择 Top 3-5 问题)
        D --> E(批量修复)
        E --> F(验证修复)
        F --> G{满意?}
        G -->|否| A
        G -->|是| H(进入下一轮)
    +

    6.2 迭代原则

    Section titled “6.2 迭代原则”
    +
      +
    1. 不要贪多：每轮只修复 3-5 条
      2. +
    2. 优先级排序：先解决最致命的
      3. +
    3. 验证修复：修复后重新审稿确认
      4. +
    4. 记录进度：保留修改历史
      5. +
    +

    6.3 审稿闭环增强（补充）

    Section titled “6.3 审稿闭环增强（补充）”
    +

    来自 AI research.md 与 vibe 并集经验的补充做法：

    +
      +
    • 每轮审稿输出后先做 Issue 聚类（证据不足 / 实验缺口 / 叙事问题 / 格式问题）
      • +
    • 每类只挑 Top 1-2 个高影响问题进入当轮修复，避免“全面开花但都不深”
      • +
    • 对每个修复项明确“验证证据”字段（新增实验、修改段落、补引用）
      • +
    • 保留一份 审稿→修复映射表，方便 Rebuttal 时直接引用
      • +
    +
    +

    7. 常见问题与应对

    Section titled “7. 常见问题与应对”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    问题应对策略
    审稿意见冲突以技术细节为准
    无法复现的实验补充复现步骤
    创新性被质疑增加对比实验
    实验设计被质疑解释设计理由
    写作被吐槽精修语言表达
    +
    +

    8. 审稿检查清单

    Section titled “8. 审稿检查清单”
    +

    8.1 提交前检查

    Section titled “8.1 提交前检查”
    +
      +
    • 所有 claim 都有实验支撑
      • +
    • 强 baseline 都已对比
      • +
    • 消融实验完整
      • +
    • 压力测试覆盖边界情况
      • +
    • 引用格式正确
      • +
    • 语言表达清晰
      • +
    • 图表清晰可读
      • +
    • 补充材料完整
      • +
    \ No newline at end of file diff --git a/research/review/templates/index.html b/research/review/templates/index.html new file mode 100644 index 0000000..128db62 --- /dev/null +++ b/research/review/templates/index.html @@ -0,0 +1,275 @@ + 会议审稿模板 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    会议审稿模板

    1. 会议特定审稿模板

    Section titled “1. 会议特定审稿模板”
    +

    不同会议有不同的审稿标准，以下是定制化模板要点：

    +
    +

    2. 审稿标准对比

    Section titled “2. 审稿标准对比”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    会议审稿重点常见质疑
    NeurIPS理论创新 + 实验验证”神经科学合理性？""SOTA 对比？“
    ICML数学严谨 + 理论分析”证明是否完整？""设定是否合理？“
    ICLR表示理解 + 可视化”真正学到了什么？""可视化？“
    CVPR实验完整性”消融是否充分？""数据集是否公平？“
    IROS系统实现 + 真实场景”硬件实现？""真实机器人验证？“
    ACL/EMNLP语言学洞察 + 评估”人类评估？""定性分析？“
    +
    +

    3. NeurIPS 审稿模板

    Section titled “3. NeurIPS 审稿模板”
    +

    3.1 评分标准

    Section titled “3.1 评分标准”
    +
      +
    • Originality: 问题/方法/设定的创新性
      • +
    • Significance: 对领域的影响
      • +
    • Quality: 技术正确性和实验完整性
      • +
    • Clarity: 写作和展示质量
      • +
    +

    3.2 常见质疑

    Section titled “3.2 常见质疑”
    +
      +
    1. +

      “神经科学合理性在哪里？”

      +
        +
      • 回应：引用相关神经科学文献
        • +
      +
      2. +
    2. +

      “与现有方法X的区别是什么？”

      +
        +
      • 回应：创建对比表格
        • +
      +
      3. +
    3. +

      “为什么只在这些数据集上测试？”

      +
        +
      • 回应：解释数据集选择的合理性
        • +
      +
      4. +
    +
    +

    4. ICML 审稿模板

    Section titled “4. ICML 审稿模板”
    +

    4.1 评分标准

    Section titled “4.1 评分标准”
    +
      +
    • Relevance: 是否符合会议主题
      • +
    • Technical Correctness: 数学是否正确
      • +
    • Novelty: 方法或理论的创新性
      • +
    • Clarity: 表述是否清晰
      • +
    +

    4.2 常见质疑

    Section titled “4.2 常见质疑”
    +
      +
    1. +

      “理论分析不够完整”

      +
        +
      • 回应：补充定理和证明
        • +
      +
      2. +
    2. +

      “设定是否合理？”

      +
        +
      • 回应：论证假设的合理性
        • +
      +
      3. +
    3. +

      “理论结果与实验的关系不清晰”

      +
        +
      • 回应：明确解释理论如何指导实验设计
        • +
      +
      4. +
    +
    +

    5. ICLR 审稿模板

    Section titled “5. ICLR 审稿模板”
    +

    5.1 评分标准

    Section titled “5.1 评分标准”
    +
      +
    • Technical Quality: 方法的正确性
      • +
    • Novelty: 创新性
      • +
    • Reproducibility: 可复现性
      • +
    • Presentation: 展示质量
      • +
    • Insightfulness: 洞察深度
      • +
    +

    5.2 常见质疑

    Section titled “5.2 常见质疑”
    +
      +
    1. +

      “真正学到了什么表示？”

      +
        +
      • 回应：添加表示可视化分析
        • +
      +
      2. +
    2. +

      “可视化不够”

      +
        +
      • 回应：添加更多图表
        • +
      +
      3. +
    3. +

      “为什么这个表示有效？”

      +
        +
      • 回应：添加理论或实验分析
        • +
      +
      4. +
    +
    +

    6. CVPR 审稿模板

    Section titled “6. CVPR 审稿模板”
    +

    6.1 评分标准

    Section titled “6.1 评分标准”
    +
      +
    • Originality: 创新性
      • +
    • Quality: 技术质量
      • +
    • Clarity: 清晰度
      • +
    • Significance: 重要性
      • +
    +

    6.2 常见质疑

    Section titled “6.2 常见质疑”
    +
      +
    1. +

      “消融是否充分？”

      +
        +
      • 回应：添加每个模块的消融
        • +
      +
      2. +
    2. +

      “数据集是否公平？”

      +
        +
      • 回应：确保训练/测试分离
        • +
      +
      3. +
    3. +

      “SOTA 对比是否完整？”

      +
        +
      • 回应：补充最新方法的对比
        • +
      +
      4. +
    +
    +

    7. IROS 审稿模板

    Section titled “7. IROS 审稿模板”
    +

    7.1 评分标准

    Section titled “7.1 评分标准”
    +
      +
    • Originality: 创新性
      • +
    • Technical Quality: 技术质量
      • +
    • Presentation: 展示质量
      • +
    • Hardware/Real-world: 硬件实现或真实场景验证
      • +
    +

    7.2 常见质疑

    Section titled “7.2 常见质疑”
    +
      +
    1. +

      “硬件实现了吗？”

      +
        +
      • 回应：描述硬件设置
        • +
      +
      2. +
    2. +

      “真实机器人验证了吗？”

      +
        +
      • 回应：添加真实实验结果
        • +
      +
      3. +
    3. +

      “仿真到实际的差距？”

      +
        +
      • 回应：讨论 sim-to-real 问题
        • +
      +
      4. +
    \ No newline at end of file diff --git a/research/scripts/mermaid-init.js b/research/scripts/mermaid-init.js new file mode 100644 index 0000000..c6cbf3c --- /dev/null +++ b/research/scripts/mermaid-init.js @@ -0,0 +1,248 @@ +// Mermaid initialization for Starlight + Expressive Code +(function () { + 'use strict'; + + const SCRIPT_VERSION = '20260209e'; + const SOURCE_SELECTOR = '.sl-markdown-content pre[data-language="mermaid"]'; + const RENDER_CLASS = 'mermaid-diagram'; + const HIDDEN_CLASS = 'mermaid-source-hidden'; + const THEME_OBSERVER_FLAG = '__vrMermaidThemeObserver'; + let mermaidModule = null; + let isRendering = false; + let needsRerender = false; + + function isDarkTheme() { + return document.documentElement.dataset.theme === 'dark'; + } + + function getSourceRoot(block) { + return block.closest('.expressive-code') || block; + } + + function normalizeMermaidCode(rawText) { + return String(rawText || '') + .replace(/\u007f/g, '\n') + .replace(/\u001f/g, '\n') + .replace(/[\u2028\u2029]/g, '\n') + .replace(/\u00a0/g, ' ') + .replace(/\r\n?/g, '\n') + .replace(/[\u200B-\u200D\uFEFF]/g, '') + .replace(/[\x00-\x08\x0B\x0C\x0E-\x1E]/g, '') + .split('\n') + .map((line) => line.replace(/\s+$/g, '')) + .join('\n') + .trim(); + } + + function fixLegacySubgraphSyntax(code) { + let autoSubgraphId = 0; + const lines = code.split('\n').map((line) => { + const legacyParen = line.match(/^(\s*)subgraph\s+([A-Za-z_][\w-]*)\s+\((.+)\)\s*$/); + if (legacyParen) { + const [, indent, id, title] = legacyParen; + return `${indent}subgraph ${id}["${String(title).replace(/"/g, '\\"')}"]`; + } + + const bracketOnly = line.match(/^(\s*)subgraph\s+\[(.+)\]\s*$/); + if (bracketOnly) { + autoSubgraphId += 1; + const [, indent, rawTitle] = bracketOnly; + const trimmed = String(rawTitle).trim(); + const title = /^".*"$/.test(trimmed) ? trimmed : `"${trimmed.replace(/"/g, '\\"')}"`; + return `${indent}subgraph SG_AUTO_${autoSubgraphId}[${title}]`; + } + + const quoteOnly = line.match(/^(\s*)subgraph\s+"(.+)"\s*$/); + if (quoteOnly) { + autoSubgraphId += 1; + const [, indent, title] = quoteOnly; + return `${indent}subgraph SG_AUTO_${autoSubgraphId}["${String(title).replace(/"/g, '\\"')}"]`; + } + + return line; + }); + + return lines + .join('\n') + .replace(/subgraph\s+([A-Za-z_][\w-]*)\s+\(([^\n\)]*)\)/g, (_, id, title) => { + return `subgraph ${id}["${String(title).replace(/"/g, '\\"')}"]`; + }); + } + + function finalizeMermaidCode(rawText) { + const normalized = normalizeMermaidCode(rawText); + return fixLegacySubgraphSyntax(normalized); + } + + function getCodeFromEcLines(block) { + const lineNodes = block.querySelectorAll('.ec-line .code'); + if (!lineNodes.length) return ''; + return Array.from(lineNodes) + .map((node) => node.textContent || '') + .join('\n'); + } + + function getCodeFromCopyButton(block) { + const button = block.closest('.expressive-code')?.querySelector('.copy button[data-code]'); + return button?.dataset.code || ''; + } + + function extractMermaidSource(block) { + const fromEcLines = getCodeFromEcLines(block); + if (fromEcLines.trim()) return finalizeMermaidCode(fromEcLines); + + const fromCopy = getCodeFromCopyButton(block); + if (fromCopy.trim()) return finalizeMermaidCode(fromCopy); + + const codeNode = block.querySelector('code'); + const fallback = codeNode ? codeNode.textContent || codeNode.innerText || '' : ''; + return finalizeMermaidCode(fallback); + } + + function cleanupRenderedDiagrams() { + document.querySelectorAll(`.${RENDER_CLASS}[data-vr-mermaid="true"]`).forEach((node) => node.remove()); + document.querySelectorAll(SOURCE_SELECTOR).forEach((block) => { + const root = getSourceRoot(block); + root.classList.remove(HIDDEN_CLASS); + root.style.display = ''; + block.removeAttribute('data-vr-mermaid-rendered'); + }); + } + + function buildErrorFallback(message, source) { + const wrapper = document.createElement('div'); + wrapper.className = RENDER_CLASS; + wrapper.dataset.vrMermaid = 'true'; + wrapper.style.borderColor = 'rgba(239, 68, 68, 0.45)'; + + const title = document.createElement('div'); + title.textContent = `[mermaid] render failed: ${message}`; + title.style.cssText = 'color:#b91c1c;font-weight:600;margin-bottom:8px;'; + wrapper.appendChild(title); + + const pre = document.createElement('pre'); + pre.textContent = source; + pre.style.cssText = 'white-space:pre-wrap;word-break:break-word;max-height:320px;overflow:auto;margin:0;'; + wrapper.appendChild(pre); + + return wrapper; + } + + async function ensureMermaid() { + if (mermaidModule) return mermaidModule; + const mod = await import('https://cdn.jsdelivr.net/npm/mermaid@10.9.1/dist/mermaid.esm.min.mjs'); + mermaidModule = mod.default || mod; + return mermaidModule; + } + + async function doRender() { + console.log('[mermaid] init script', SCRIPT_VERSION); + const mermaid = await ensureMermaid(); + mermaid.initialize({ + startOnLoad: false, + securityLevel: 'loose', + theme: isDarkTheme() ? 'dark' : 'base', + }); + + cleanupRenderedDiagrams(); + const blocks = Array.from(document.querySelectorAll(SOURCE_SELECTOR)); + if (!blocks.length) return; + + console.log('[mermaid] Found', blocks.length, 'diagrams'); + + let ok = 0; + let failed = 0; + for (let index = 0; index < blocks.length; index += 1) { + const block = blocks[index]; + const sourceRoot = getSourceRoot(block); + const source = extractMermaidSource(block); + if (!source) continue; + + const wrapper = document.createElement('div'); + wrapper.className = RENDER_CLASS; + wrapper.dataset.vrMermaid = 'true'; + + const host = document.createElement('div'); + host.className = 'mermaid'; + host.textContent = source; + wrapper.appendChild(host); + sourceRoot.insertAdjacentElement('afterend', wrapper); + + try { + await mermaid.run({ nodes: [host] }); + sourceRoot.classList.add(HIDDEN_CLASS); + sourceRoot.style.display = 'none'; + block.dataset.vrMermaidRendered = 'true'; + ok += 1; + } catch (error) { + wrapper.remove(); + sourceRoot.classList.remove(HIDDEN_CLASS); + sourceRoot.style.display = ''; + const fallback = buildErrorFallback(error?.message || String(error), source); + sourceRoot.insertAdjacentElement('afterend', fallback); + console.warn(`[mermaid] Diagram ${index + 1} render failed:`, error?.message || error); + failed += 1; + } + } + + console.log('[mermaid] render done:', { ok, failed, total: blocks.length }); + } + + async function renderAllDiagrams() { + if (isRendering) { + needsRerender = true; + return; + } + + isRendering = true; + try { + do { + needsRerender = false; + await doRender(); + } while (needsRerender); + } catch (error) { + console.error('[mermaid] Load failed:', error); + } finally { + isRendering = false; + } + } + + function observeThemeChanges() { + if (window[THEME_OBSERVER_FLAG]) return; + + let themeTimer = null; + const observer = new MutationObserver((mutations) => { + for (const mutation of mutations) { + if (mutation.type === 'attributes' && mutation.attributeName === 'data-theme') { + if (themeTimer) clearTimeout(themeTimer); + themeTimer = setTimeout(() => { + renderAllDiagrams(); + }, 80); + break; + } + } + }); + + observer.observe(document.documentElement, { + attributes: true, + attributeFilter: ['data-theme'], + }); + + window[THEME_OBSERVER_FLAG] = observer; + } + + function bootMermaid() { + renderAllDiagrams(); + observeThemeChanges(); + } + + document.addEventListener('astro:after-swap', bootMermaid); + + if (document.readyState === 'loading') { + document.addEventListener('DOMContentLoaded', bootMermaid, { once: true }); + } else { + bootMermaid(); + } + + window.renderMermaidDiagrams = renderAllDiagrams; +})(); diff --git a/research/sitemap-0.xml b/research/sitemap-0.xml new file mode 100644 index 0000000..91b5a55 --- /dev/null +++ b/research/sitemap-0.xml @@ -0,0 +1 @@ +https://adongwanai.github.io/AgentGuide/researchhttps://adongwanai.github.io/AgentGuide/research/code/agents/https://adongwanai.github.io/AgentGuide/research/code/claude/https://adongwanai.github.io/AgentGuide/research/code/gpt/https://adongwanai.github.io/AgentGuide/research/figures/comparison/https://adongwanai.github.io/AgentGuide/research/figures/design/https://adongwanai.github.io/AgentGuide/research/figures/tools/https://adongwanai.github.io/AgentGuide/research/idea/projects/https://adongwanai.github.io/AgentGuide/research/idea/research/https://adongwanai.github.io/AgentGuide/research/library/root-assets/https://adongwanai.github.io/AgentGuide/research/review/structured/https://adongwanai.github.io/AgentGuide/research/review/templates/https://adongwanai.github.io/AgentGuide/research/tools/overview/https://adongwanai.github.io/AgentGuide/research/tools/workflow/https://adongwanai.github.io/AgentGuide/research/writing/collaboration/https://adongwanai.github.io/AgentGuide/research/writing/methods/https://adongwanai.github.io/AgentGuide/research/writing/prism/https://adongwanai.github.io/AgentGuide/research/writing/rebuttal/ \ No newline at end of file diff --git a/research/sitemap-index.xml b/research/sitemap-index.xml new file mode 100644 index 0000000..64df5c6 --- /dev/null +++ b/research/sitemap-index.xml @@ -0,0 +1 @@ +https://adongwanai.github.io/AgentGuide/research/sitemap-0.xml \ No newline at end of file diff --git a/research/tools/overview/index.html b/research/tools/overview/index.html new file mode 100644 index 0000000..4427749 --- /dev/null +++ b/research/tools/overview/index.html @@ -0,0 +1,400 @@ + 工具总览 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    工具总览

    1. 工具选择矩阵

    Section titled “1. 工具选择矩阵”
    +
    flowchart TD
        subgraph ["调研"]
            A[Elicit] --> B(文献检索)
            C[Sci-Reasoning] --> D(创新分析)
        end
    
+
        subgraph ["实现"]
            E[Claude Code] --> F(代码生成)
            G[LangGraph] --> H(工作流编排)
        end
    
+
        subgraph ["写作"]
            I[Prism] --> J(论文写作)
            K[PaperBanana] --> L(图表生成)
        end
    
+
        subgraph ["审稿"]
            M[Claude + GPT-5.2] --> N(多模型审稿)
        end
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    任务阶段推荐工具备选方案选择理由
    文献检索ElicitConsensus, Semantic Scholar1.25亿+论文, 自动化综述
    文献管理Zotero-MCPMendeley, PapersClaude 集成, 语义搜索
    代码生成Claude CodeGPT-5.2, Codex200K上下文, 80.9% SWE-bench
    Agent编排LangChain/LangGraphCrewAI, AutoGen灵活工作流, 强大社区
    论文写作PrismOverleafGPT-5.2 集成, LaTeX 原生
    审稿模拟Claude + GPT-5.2Gemini Pro多模型交叉验证
    画图PaperBanana + MermaidD3.js, Figma自动化 + 版本控制
    +
    +

    2. 工具能力对比

    Section titled “2. 工具能力对比”
    +

    2.1 LLM 能力对比

    Section titled “2.1 LLM 能力对比”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    模型上下文SWE-benchARC-AGI适用场景
    Claude Opus 4.6200K80.9%-长文档、代码
    GPT-5.2 Pro--90%+复杂推理
    GPT-5.2 Thinking---深度分析
    Gemini 2.5 Pro1M+-49.35%超长文档
    +

    2.2 文献检索工具对比

    Section titled “2.2 文献检索工具对比”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    工具论文数特色价格
    Elicit1.25亿+自动化综述免费+付费
    Semantic Scholar2亿+AI驱动免费
    Consensus-证据搜索免费+付费
    Scite-引用质量付费
    +
    +

    3. 快速参考

    Section titled “3. 快速参考”
    +

    3.1 常用命令速查

    Section titled “3.1 常用命令速查”
    +
    Terminal window
    # Claude Code
    npm run dev          # 启动开发服务器
    astro build          # 构建项目
    astro preview        # 预览构建结果
    
+
    # MCP
    mcp list             # 列出已安装的 MCP 服务器
    mcp install zotero   # 安装 Zotero-MCP
    
+
    # 文献检索
    elicit search "topic"  # Elicit 搜索
    +

    3.2 关键链接汇总

    Section titled “3.2 关键链接汇总”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    资源链接
    Sci-Reasoninghttps://github.com/AmberLJC/Sci-Reasoning
    The AI Scientisthttps://github.com/SakanaAI/AI-Scientist
    Elicithttps://elicit.com
    Prismhttps://openai.com/prism/
    claude-scientific-skillshttps://github.com/K-Dense-AI/claude-scientific-skills
    LangChainhttps://github.com/langchain-ai
    LlamaIndexhttps://github.com/run-llama
    +
    +

    4. 根目录项目/论文内容并入说明

    Section titled “4. 根目录项目/论文内容并入说明”
    +

    以下内容来自你根目录的项目与论文文件, 已并入现有章节（未上传 PDF 文件本体）：

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    来源并入内容已落地章节
    2601.14525 + Automated-AI-Researcherexecution-grounded 自动科研执行闭环（idea→patch→run→feedback）idea/projects, code/agents
    2601.20833 + Idea2Paperpre-computation + KG + anchored review 的叙事生成路线idea/projects
    2601.23265 + PaperBanana自动学术插图框架、基准与工程策略figures/tools
    AutoFigure-edit README方法图到可编辑 SVG 的工程流水线实践figures/tools
    +

    4.1 额外说明

    Section titled “4.1 额外说明”
    +
      +
    • AI research.md 的增量内容已融合到现有章节正文（idea / code / figures / writing / review / tools），未新增独立来源页。
      • +
    • vibe 三文档已先做并集去重，再融合到现有 2. 代码实现 及相关工作流章节。
      • +
    • 严格来源映射表见 library/root-assets 的“严格映射清单（来源 → 现有章节）”。
      • +
    +
    +

    5. Book 实战增补（逐篇并入）

    Section titled “5. Book 实战增补（逐篇并入）”
    +

    5.1 1) VSCode Agent Skills：从启用到复用

    Section titled “5.1 1) VSCode Agent Skills：从启用到复用”
    +

    来源：book/在_VScode_中玩转_Agent_Skills（科研工作者必备skill推荐_一定要收藏）.md

    +

    5.1.1 详细步骤（VSCode 路线）

    Section titled “5.1.1 详细步骤（VSCode 路线）”
    +
      +
    1. 打开设置并启用 chat.useAgentSkills。
      2. +
    2. 在项目内创建目录：.github/skills/<skill-name>/。
      3. +
    3. 创建 SKILL.md，写清触发条件、输入输出、执行步骤。
      4. +
    4. 按需添加资源文件（示例、脚本、模板）。
      5. +
    5. 在会话中通过任务描述触发，验证 skill 是否被自动加载。
      6. +
    +

    5.1.2 复用策略

    Section titled “5.1.2 复用策略”
    +
      +
    • 优先复用社区技能库，再做本地改造；
      • +
    • 改造时优先替换路径、命名、验证命令；
      • +
    • 先用小样本测试，再扩展到完整任务。
      • +
    +

    5.1.3 文章细节补充（VSCode 实操顺序）

    Section titled “5.1.3 文章细节补充（VSCode 实操顺序）”
    +
      +
    1. 先创建一个最小 skill（仅 1 个步骤），验证可触发。
      2. +
    2. 再添加 workflow-example 与脚本资源，验证可读取。
      3. +
    3. 最后引入社区 skill 并本地改造，避免一次导入过大难排错。
      4. +
    +

    5.1.4 推荐社区资源（与原文一致）

    Section titled “5.1.4 推荐社区资源（与原文一致）”
    +
      +
    • awesome-copilot（社区技能集合）
      • +
    • anthropics/skills（官方参考）
      • +
    • K-Dense-AI/claude-scientific-skills（科研场景）
      • +
    +

    5.2 2) 三 CLI 联动：Claude + Codex + Gemini

    Section titled “5.2 2) 三 CLI 联动：Claude + Codex + Gemini”
    +

    来源：book/老金开源_Claude4_5_Codex5_1_Gemini3_3大顶级CLI终极联动整合技巧_省钱_高质量_快_.md

    +

    5.2.1 详细步骤（最小可跑版）

    Section titled “5.2.1 详细步骤（最小可跑版）”
    +
      +
    1. 安装三个 CLI：Claude Code、Codex CLI、Gemini CLI。
      2. +
    2. 配置 API 与认证（优先图形化管理，避免手工环境变量错误）。
      3. +
    3. 先从 Command 模式启动：分析 → 生成 → 审查。
      4. +
    4. 任务稳定后升级到 Skill 模式（保存中间结果与重试机制）。
      5. +
    5. 长周期协作用 MCP 打通上下文，再按需引入 Subagent 与 Hooks。
      6. +
    +

    5.2.2 五种编排方式对照

    Section titled “5.2.2 五种编排方式对照”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    方式适用风险
    Command快速、一次性任务复用差、可观测性弱
    Skill多步骤稳定流程初期配置成本较高
    MCP长会话与系统互联服务器与权限管理复杂
    Subagent并行提速token 成本上升
    Hooks自动质检误触发与调试复杂
    +

    5.2.3 常见坑提醒

    Section titled “5.2.3 常见坑提醒”
    +
      +
    • CLI 订阅/授权失效；
      • +
    • 代理/网络导致认证失败；
      • +
    • sandbox 权限不足导致写入失败；
      • +
    • 多模型切换后输出格式漂移。
      • +
    +

    5.2.4 文章细节补充（三CLI联动最小命令）

    Section titled “5.2.4 文章细节补充（三CLI联动最小命令）”
    +
    Terminal window
    # 1) 安装
    npm i -g @anthropic-ai/claude-code
    npm i -g @openai/codex
    npm i -g @google/gemini-cli
    
+
    # 2) 鉴权（示意，按各自官方流程）
    gemini auth login
    codex auth login
    
+
    # 3) 在 Claude 内先跑 Command 模式
    /ai-team "实现一个最小登录接口并附测试"
    +

    5.2.5 五编排方式的选型建议（更细）

    Section titled “5.2.5 五编排方式的选型建议（更细）”
    +
      +
    • 30 分钟内一次性任务：Command
      • +
    • 反复执行的标准流程：Skill
      • +
    • 需要跨系统持久上下文：MCP
      • +
    • 模块可并行且预算允许：Subagent
      • +
    • 固定时机自动质检：Hooks
      • +
    \ No newline at end of file diff --git a/research/tools/workflow/index.html b/research/tools/workflow/index.html new file mode 100644 index 0000000..d778d48 --- /dev/null +++ b/research/tools/workflow/index.html @@ -0,0 +1,630 @@ + 完整工作流 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    完整工作流

    1. 完整研究时间线

    Section titled “1. 完整研究时间线”
    +
    gantt
        title 14周顶会论文发表时间线
        dateFormat  YYYY-MM-DD
        section 调研
        Elicit扫描       :a1, 2026-02-10, 5d
        Sci-Reasoning    :a2, after a1, 5d
        Gap清单产出      :a3, after a2, 4d
        section 设计
        项目文档         :b1, after a3, 5d
        实验计划         :b2, after b1, 3d
        section 实现
        核心代码         :c1, after b2, 14d
        主实验           :c2, after c1, 7d
        消融实验         :c3, after c2, 5d
        section 写作
        初稿             :d1, after c3, 10d
        图表             :d2, after d1, 5d
        section 审稿
        自动审稿         :e1, after d2, 5d
        迭代修改         :e2, after e1, 5d
        section 投稿
        最终版           :f1, after e2, 3d
        提交             :f2, 2026-05-20, 1d
    +
    +

    2. Week 1-2: 调研阶段

    Section titled “2. Week 1-2: 调研阶段”
    +

    2.1 目标

    Section titled “2.1 目标”
    +

    产出结构化的研究地图和空白清单

    +

    2.2 详细工作流

    Section titled “2.2 详细工作流”
    +
    flowchart LR
        A(Elicit 扫描) --> B[Semantic Scholar]
        B --> C[Sci-Reasoning]
        C --> D(整理研究地图)
        D --> E(产出 Gap 清单)
    
+
        classDef key fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827
        class A,C,E key
    +

    2.3 具体任务

    Section titled “2.3 具体任务”
    +
      +
    1. 使用 Elicit 扫描领域关键词（1-2天）
      2. +
    2. 使用 Semantic Scholar 探索研究脉络（1-2天）
      3. +
    3. 使用 Sci-Reasoning 分析创新模式（2-3天）
      4. +
    4. 整理研究地图（2-3天）： +
        +
      • 问题子类分类
        • +
      • 方法范式分类
        • +
      • 评测基准整理
        • +
      +
      5. +
    +

    2.4 产出

    Section titled “2.4 产出”
    + + + + + + + + + + + + + + + + + + + + + +
    产出物说明
    Research Gap 清单10-20个候选方向
    Benchmark & Metric Sheet主流数据集和指标
    Assumption Matrix隐含假设分析
    +
    +

    3. Week 3: 项目设计

    Section titled “3. Week 3: 项目设计”
    +

    3.1 目标

    Section titled “3.1 目标”
    +

    确定研究问题和实验计划

    +

    3.2 详细工作流

    Section titled “3.2 详细工作流”
    +
    flowchart TD
        A(Claude Code 生成文档) --> B(GPT-5.2 审查)
        B --> C(人工Review)
        C --> D(确定实验计划)
    
+
        classDef review fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827
        classDef audit fill:#eff6ff,stroke:#2563eb,stroke-width:1.5px,color:#1e3a8a
        class A review
        class B,C audit
    +

    3.3 具体任务

    Section titled “3.3 具体任务”
    +
      +
    1. 生成项目文档（使用 Claude Code, 2-3天）
      2. +
    2. 审查文档（使用 GPT-5.2 Pro, 1天）： +
        +
      • claim-实验是否对齐？
        • +
      • baseline 是否足够强？
        • +
      • 是否有致命假设？
        • +
      +
      3. +
    3. 确定实验计划（1天）： +
        +
      • 主实验设计
        • +
      • 消融实验清单
        • +
      • 压力测试场景
        • +
      +
      4. +
    +

    3.4 产出

    Section titled “3.4 产出”
    + + + + + + + + + + + + + + + + + + + + + +
    产出物说明
    项目文档20-30页详细文档
    实验计划主实验+消融+压力测试
    里程碑时间表每周任务分解
    +
    +

    4. Week 4-7: 代码实现 + 实验

    Section titled “4. Week 4-7: 代码实现 + 实验”
    +

    4.1 目标

    Section titled “4.1 目标”
    +

    完成实现和主要实验

    +

    4.2 团队配置

    Section titled “4.2 团队配置”
    +
    flowchart TD
        subgraph ["开发团队"]
            A[Architect x1] --> E(核心模块)
            B[Implementer x2] --> E
            C[Tester x1] --> F(测试评测)
        end
    
+
        subgraph ["质量保障"]
            F --> G(自动汇总)
        end
    
+
        classDef role fill:#ecfeff,stroke:#0ea5e9,stroke-width:1.5px,color:#0f172a
        class A,B,C role
    +

    4.3 具体任务

    Section titled “4.3 具体任务”
    +
      +
    1. 并行开发（Claude Code, 2-3周）
      2. +
    2. 实验执行（1周）： +
        +
      • 主实验：与 baseline 对比
        • +
      • 消融实验：每个模块贡献
        • +
      • 压力测试：边界条件
        • +
      +
      3. +
    3. 结果汇总（2-3天）
      4. +
    +

    4.4 产出

    Section titled “4.4 产出”
    + + + + + + + + + + + + + + + + + + + + + +
    产出物说明
    可复现代码库完整的实验代码
    实验结果主表+消融+分析
    复现指南便于他人复现
    +
    +

    5. Week 8-9: 论文写作

    Section titled “5. Week 8-9: 论文写作”
    +

    5.1 目标

    Section titled “5.1 目标”
    +

    完成论文初稿

    +

    5.2 写作流程

    Section titled “5.2 写作流程”
    +
    flowchart LR
        A(Claude Opus 大纲) --> B(多实例并行写作)
        B --> C(PaperBanana 图表)
        C --> D(多模型协作完善)
    
+
        classDef writing fill:#f8fafc,stroke:#10a37f,stroke-width:1.5px,color:#111827
        class A,B,C writing
    +

    5.3 具体任务

    Section titled “5.3 具体任务”
    +
      +
    1. 生成大纲（Claude Opus 4.6, 1天）
      2. +
    2. 并行写作（多实例, 5-7天）
      3. +
    3. 图表制作（2-3天）
      4. +
    +

    5.4 产出

    Section titled “5.4 产出”
    + + + + + + + + + + + + + + + + + + + + + +
    产出物说明
    论文初稿8-10页正文
    所有图表方法图、结果图
    补充材料额外实验、细节
    +
    +

    6. Week 10: 审稿迭代

    Section titled “6. Week 10: 审稿迭代”
    +

    6.1 目标

    Section titled “6.1 目标”
    +

    通过多轮审稿迭代提升论文质量

    +

    6.2 审稿配置

    Section titled “6.2 审稿配置”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    轮次模型侧重点
    第1轮Claude Opus 4.6整体逻辑
    第2轮GPT-5.2 Pro技术细节
    第3轮Gemini Pro可复现性
    +

    6.3 具体任务

    Section titled “6.3 具体任务”
    +
      +
    1. 多模型并行审稿（2天）
      2. +
    2. 交叉验证意见（1天）
      3. +
    3. 批量修改（2天）
      4. +
    +

    6.4 产出

    Section titled “6.4 产出”
    + + + + + + + + + + + + + + + + + + + + + +
    产出物说明
    稳定投稿版本准备提交
    审稿意见记录历史迭代
    修改说明响应记录
    +
    +

    7. Week 11: 投稿

    Section titled “7. Week 11: 投稿”
    +

    检查清单

    Section titled “检查清单”
    +
      +
    • 格式符合会议要求
      • +
    • 页数在限制内
      • +
    • 补充材料完整
      • +
    • 作者信息正确
      • +
    • 代码/数据链接有效
      • +
    +
    +

    8. Week 12+: Rebuttal

    Section titled “8. Week 12+: Rebuttal”
    +

    Rebuttal 策略

    Section titled “Rebuttal 策略”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    意见类型应对策略
    可修复的承诺修改, 补充说明
    误解的礼貌澄清, 补充证据
    不同意但合理讨论, 引用支持文献
    不同意的坚定反驳, 有理有据
    +
    +

    9. 时间线总览

    Section titled “9. 时间线总览”
    +
    Week 1-2:  ██████████████  调研
    Week 3:    ██████          项目设计
    Week 4-7: █████████████████████████  代码+实验
    Week 8-9: ██████████████  写作
    Week 10:  ██████████████  审稿迭代
    Week 11:  ████            投稿
    Week 12+: ██████████████  Rebuttal（如需要）
    +

    执行闭环强化（补充）

    Section titled “执行闭环强化（补充）”
    +

    在 Week 4-10 阶段建议显式加入 execution-grounded 子循环：

    +
    flowchart LR
        A(Idea) --> B(Patch)
        B --> C(Run)
        C --> D(Feedback)
        D --> A
    +
      +
    • Idea：提出可验证改动（必须绑定指标）
      • +
    • Patch：最小可执行改动（便于回滚）
      • +
    • Run：在固定环境里跑通训练/评测
      • +
    • Feedback：记录收益/失败模式并更新下一轮计划
      • +
    +

    这样做可以把“灵感驱动”转换为“证据驱动”，减少无效迭代。

    +
    +

    10. 每日任务模板

    Section titled “10. 每日任务模板”
    +

    工作日（周一至周五）

    Section titled “工作日（周一至周五）”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    时间任务时长
    09:00-10:00计划与回顾1h
    10:00-12:00核心工作2h
    14:00-17:00深度工作3h
    19:00-20:00总结整理1h
    +

    周末

    Section titled “周末”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    时间任务时长
    10:00-12:00论文写作2h
    14:00-17:00审稿迭代3h
    19:00-20:00周总结1h
    +
    +

    核心原则：工具是辅助, 核心是你的研究洞察力。让 AI 帮你处理重复性工作, 把时间留给真正的思考。

    +
    +

    11. Book 实战增补（逐篇并入）

    Section titled “11. Book 实战增补（逐篇并入）”
    +
    +

    本节整合 6 篇工作流与 Skill 工程化文章，形成“规范定义 → 任务拆解 → 执行闭环 → 验证交付”的统一流程。

    +
    +

    1) Skill 开发完全指南：从可用到可复用

    Section titled “1) Skill 开发完全指南：从可用到可复用”
    +

    来源：book/Claude_Code_Skill_开发完全指南_从入门到精通.md

    +

    结构化实践

    Section titled “结构化实践”
    +
      +
    • Skill 本质是标准作业流程（SOP），核心目标是稳定复现而非一次性发挥。
      • +
    • 目录按复杂度递进：最小可用 SKILL.md → 标准工作流（workflow/ + scripts/ + config/）。
      • +
    • 执行者分工：可确定任务交脚本，不确定任务交 Agent，协调任务交主会话。
      • +
    +

    关键约束

    Section titled “关键约束”
    +
      +
    • 能脚本化就脚本化，减少 token 消耗；
      • +
    • Subagent 并行控制节奏（分轮执行 + /compact）；
      • +
    • 每一步都有验证点与可恢复状态（progress.json）。
      • +
    +

    详细步骤（Skill 从 0 到 1）

    Section titled “详细步骤（Skill 从 0 到 1）”
    +
      +
    1. 先做最小版：只建 SKILL.md，跑通单步骤任务。
      2. +
    2. 任务稳定后再拆 workflow/stepNN-*.md。
      3. +
    3. 把可确定操作下沉到 scripts/，并统一 JSON 返回。
      4. +
    4. 为每步定义检查点（文件存在、格式正确、字段完整、业务通过）。
      5. +
    5. 增加断点恢复文件，确保中断后可继续执行。
      6. +
    +

    文章细节补充（执行者决策树）

    Section titled “文章细节补充（执行者决策树）”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    任务类型推荐执行者原因
    API 调用、文件转换、批处理脚本快、稳、低 token
    需要判断/生成/归纳Subagent语义处理更强
    轻协调与流程推进主 Agent全局可控
    +

    建议：默认先脚本，只有脚本无法覆盖时再交 Agent。

    +

    2) Skill 最佳实践：文档驱动与需求模板

    Section titled “2) Skill 最佳实践：文档驱动与需求模板”
    +

    来源：book/Claude_Code_Skill_最佳实践与创建规范.md

    +

    三件必做

    Section titled “三件必做”
    +
      +
    1. 全局约束写入 CLAUDE.md；
      2. +
    2. 输出样例放入参考资产（结果参照）；
      3. +
    3. 需求清单模板化（输入、输出、约束、验收）。
      4. +
    +

    工程建议

    Section titled “工程建议”
    +
      +
    • Skill 粒度控制在“单一职责 + 可组合”；
      • +
    • 强制 Plan → Execute → Review 循环；
      • +
    • assets/ 作为素材库直取直用，减少上下文消耗。
      • +
    +

    详细步骤（需求模板实装）

    Section titled “详细步骤（需求模板实装）”
    +
      +
    1. 新建 requirements.md，固定十项：目标、输入、输出、约束、验收等。
      2. +
    2. 缺失信息先问清，不允许“边做边猜”。
      3. +
    3. 先用模板生成首版 Skill，再做两轮真实任务回测。
      4. +
    4. 将回测问题反写到模板，持续迭代。
      5. +
    +

    文章细节补充（十项模板最小集合）

    Section titled “文章细节补充（十项模板最小集合）”
    +
      +
    1. Skill 名称与触发条件；
      2. +
    2. 场景与边界；
      3. +
    3. 必填输入与可选输入；
      4. +
    4. 输出类型与文件结构；
      5. +
    5. 主流程与分支流程；
      6. +
    6. 资源依赖（脚本/参考/资产）；
      7. +
    7. 技术与业务约束；
      8. +
    8. 自动/手动验证方式；
      9. +
    9. 参考实现与链接；
      10. +
    10. 优先级与扩展计划。
      11. +
    +

    3) Skill Creator MVP：先约束行为，再让 AI 写代码

    Section titled “3) Skill Creator MVP：先约束行为，再让 AI 写代码”
    +

    来源：book/我是如何用_Skill_Creator_把一个真实项目拆成一整套_Agent_Skills_的（MVP_实战）.md

    +

    MVP 六件套（按项目按需启用）

    Section titled “MVP 六件套（按项目按需启用）”
    +
      +
    • product-design.skill
      • +
    • frontend-dev.skill
      • +
    • backend-dev.skill
      • +
    • api-contract.skill
      • +
    • testing.skill
      • +
    • deployment.skill
      • +
    +

    核心思想：Skill 要服从项目现实，不存在的模块不生成对应技能。

    +

    详细步骤（MVP 六件套落地）

    Section titled “详细步骤（MVP 六件套落地）”
    +
      +
    1. 扫描仓库判断是否存在前后端/测试/部署。
      2. +
    2. 仅为存在模块创建 Skill，其他跳过。
      3. +
    3. api-contract 先行，前后端执行都以接口契约为准。
      4. +
    4. testing 明确“什么算完成”，避免功能完成但不可验收。
      5. +
    +

    文章细节补充（各 Skill 的禁止项）

    Section titled “文章细节补充（各 Skill 的禁止项）”
    +
      +
    • product-design：不写实现代码；
      • +
    • frontend-dev：不擅自引新库/改工程配置；
      • +
    • backend-dev：不改架构与数据库主结构；
      • +
    • api-contract：只定义契约，不写实现；
      • +
    • testing：不跳过失败用例；
      • +
    • deployment：只记录当前真实发布方式。
      • +
    +

    4) Superpowers + Planning-with-Files：流程与记忆分层

    Section titled “4) Superpowers + Planning-with-Files：流程与记忆分层”
    +

    来源：book/大佬的Claude_Code是神器你的像玩具_Superpowers___Planning_with_Files的Skills组合拳让_Coding_Agent效率翻倍.md

    +

    组合策略

    Section titled “组合策略”
    +
      +
    • Superpowers：负责“流程纪律”（brainstorm → plan → execute → verify）。
      • +
    • Planning-with-Files：负责“外部记忆”（task_plan.md、progress.md、knowledge.md）。
      • +
    +

    推荐闭环

    Section titled “推荐闭环”
    +
      +
    1. 先澄清目标、受众、约束；
      2. +
    2. 写入结构化计划与验收；
      3. +
    3. 执行时持续记录进度；
      4. +
    4. 结束前跑验证并补齐证据。
      5. +
    +

    详细步骤（Superpowers + Planning Files）

    Section titled “详细步骤（Superpowers + Planning Files）”
    +
      +
    1. 触发 brainstorm 收集关键约束（目标、对象、时间、边界）。
      2. +
    2. 产出计划并写入 task_plan.md。
      3. +
    3. 每完成子任务更新 progress.md（含失败记录）。
      4. +
    4. 关键决策沉淀到 knowledge.md，便于新会话接续。
      5. +
    5. 交付前执行 verification，保存验证证据。
      6. +
    +

    文章细节补充（命令顺序）

    Section titled “文章细节补充（命令顺序）”
    +
    /superpowers:brainstorm
    /superpowers:write-plan
    /superpowers:execute-plan
    # 收尾执行 verification-before-completion
    +

    建议每次执行后同步三文件：

    +
      +
    • task_plan.md（目标与步骤）
      • +
    • progress.md（过程与阻塞）
      • +
    • knowledge.md（决策与约束）
      • +
    +

    5) 规范驱动开发：拒绝“直接开写”

    Section titled “5) 规范驱动开发：拒绝“直接开写””
    +

    来源：book/拒绝屎山代码_让你的_Claude_code_具备类似高级架构师的全局规划与规范执行能力_附实操_.md

    +

    章节增补要点

    Section titled “章节增补要点”
    +
      +
    • AI 本质执行规格，不是自动理解业务语境。
      • +
    • 需前置限定：技术栈、目录结构、实现边界、阶段目标。
      • +
    • 将“计划文档、执行记录、验证命令”纳入硬门槛，避免伪完成。
      • +
    +

    详细步骤（规范先行）

    Section titled “详细步骤（规范先行）”
    +
      +
    1. 在 CLAUDE.md 固定四类限制：技术栈、目录、边界、验收。
      2. +
    2. 任务开始先生成 PLAN，不允许直接进入编码。
      3. +
    3. 每完成一个任务强制跑测试与审查。
      4. +
    4. 未通过验证禁止标记完成。
      5. +
    +

    文章细节补充（强约束检查项）

    Section titled “文章细节补充（强约束检查项）”
    +
      +
    • 是否存在“先写后想”的越序行为；
      • +
    • 是否缺少测试与验证命令；
      • +
    • 是否改动了禁止目录或越界模块；
      • +
    • 是否提供可复验结果（日志/测试报告）。
      • +
    +

    6) feature-dev + ralph-loop：规划引擎与长循环执行

    Section titled “6) feature-dev + ralph-loop：规划引擎与长循环执行”
    +

    来源：book/用_Claude_Code_搭建_从需求到交付_的自动化工作流.md

    +

    双引擎分工

    Section titled “双引擎分工”
    +
      +
    • feature-dev：跑 7 阶段（Discovery → Review → Summary）。
      • +
    • ralph-loop：针对单任务持续迭代，直到达到 completion promise。
      • +
    +

    实战模板

    Section titled “实战模板”
    +
    feature-dev 负责：方向、架构、验收标准
    ralph-loop 负责：持续执行、测试打磨、问题收敛
    +

    适用场景

    Section titled “适用场景”
    +
      +
    • 大特性拆解：先 feature-dev，再 ralph-loop 跑实现细节。
      • +
    • 测试补齐：让 ralph-loop 专注覆盖率与边界用例收敛。
      • +
    +

    详细步骤（双引擎协作）

    Section titled “详细步骤（双引擎协作）”
    +
      +
    1. 先用 feature-dev 产出设计与实施计划。
      2. +
    2. 把计划转成 ralph-loop 的阶段任务与完成短语。
      3. +
    3. 设置 --max-iterations，避免无限循环。
      4. +
    4. 循环结束后回到 feature-dev 做最终 Review + Summary。
      5. +
    +

    文章细节补充（组合使用模板）

    Section titled “文章细节补充（组合使用模板）”
    +
      +
    1. feature-dev 产出：DESIGN.md + IMPLEMENTATION_PLAN.md + QUALITY_CRITERIA.md。
      2. +
    2. ralph-loop 读取上述文档并分阶段执行。
      3. +
    3. 每轮循环必须输出：完成内容、失败原因、下一轮计划。
      4. +
    4. 达到 completion-promise 才允许结束循环。
      5. +
    +

    本章统一检查清单（新增）

    Section titled “本章统一检查清单（新增）”
    +
      +
    • 是否先完成需求澄清与计划文档？
      • +
    • 是否为关键步骤定义了验证标准？
      • +
    • 是否记录了执行进度与中断恢复点？
      • +
    • 是否把复用能力沉淀为 Skill/命令，而非临时对话？
      • +
    \ No newline at end of file diff --git a/research/writing/collaboration/index.html b/research/writing/collaboration/index.html new file mode 100644 index 0000000..21a9316 --- /dev/null +++ b/research/writing/collaboration/index.html @@ -0,0 +1,229 @@ + 多模型协作写作 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    多模型协作写作

    1. 多模型协作概览

    Section titled “1. 多模型协作概览”
    +
    flowchart TD
        subgraph ["写作流程"]
            A(大纲生成) --> B(分段写作)
            B --> C(一致性检查)
            C --> D(整合优化)
        end
    
+
        subgraph ["模型分工"]
            E[Claude Opus 4.6] --> A
            F[Claude Code] --> B
            G[GPT-5.2 Pro] --> C
            H[Gemini Pro] --> C
        end
    +
    +

    2. AI-research-SKILLs

    Section titled “2. AI-research-SKILLs”
    +

    “20-ml-paper-writing”模块制定了严格规则：绝不凭记忆生成引用, 强制 5 步：

    +
    flowchart LR
        A(检索) --> B(双源验证)
        B --> C(获取官方 BibTeX)
        C --> D(核对摘要)
        D --> E(写入)
    +

    覆盖 AI 科研全流程的 77 项技能（20 个技术板块）。

    +
    +

    3. tmux 工作流配置

    Section titled “3. tmux 工作流配置”
    +

    让 Claude / GPT-5.2 / Gemini Pro 分工协作：

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    tmux 实例负责内容模型选择协作方式
    Instance 1Related WorkClaude Code文献梳理与对比
    Instance 2Method 写作Claude Code方法描述与形式化
    Instance 3实验描述GPT-5.2 Pro实验设计和结果
    Instance 4一致性校对Gemini Pro术语、符号统一
    +
    +

    4. 协作写作流程

    Section titled “4. 协作写作流程”
    +

    4.1 阶段 1: 大纲生成

    Section titled “4.1 阶段 1: 大纲生成”
    +

    使用 Claude Opus 4.6 生成长大纲（200K 上下文）

    +
    # 论文大纲模板
    
+
    ## 1. Introduction (1.5页)
    - 研究问题
    - 核心挑战
    - 你的贡献（3-4点）
    
+
    ## 2. Related Work (2页)
    - 领域分类
    - 主要方法
    - 你的定位
    
+
    ## 3. Method (4页)
    - 问题形式化
    - 核心方法
    - 技术细节
    
+
    ## 4. Experiments (3页)
    - 实验设置
    - 主结果
    - 消融实验
    
+
    ## 5. Conclusion (0.5页)
    - 总结
    - 局限
    - 未来方向
    +

    4.2 阶段 2: 分段写作

    Section titled “4.2 阶段 2: 分段写作”
    +

    各实例并行处理不同章节：

    +
    Terminal window
    # tmux 窗口布局
    # =================
    # 窗口 1: Related Work
    # 窗口 2: Method
    # 窗口 3: Experiments
    # 窗口 4: Introduction/Conclusion
    # 窗口 5: 整合校对
    +

    4.3 阶段 3: 一致性检查

    Section titled “4.3 阶段 3: 一致性检查”
    +

    使用专门实例校对一致性：

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    检查项工具说明
    术语一致性Claude Code统一术语使用
    符号一致性GPT-5.2 Pro数学符号检查
    引用格式Gemini ProBibTeX 格式
    +

    4.4 阶段 4: 整合优化

    Section titled “4.4 阶段 4: 整合优化”
    +

    由最强模型整合所有内容, 确保叙事流畅。

    +
    +

    5. 术语表管理

    Section titled “5. 术语表管理”
    +

    5.1 术语表示例

    Section titled “5.1 术语表示例”
    +
    # 术语表 (GLOSSARY.md)
    
+
    | 中文 | 英文 | 缩写 | 定义 |
    |------|------|------|------|
    | 注意力机制 | Attention Mechanism | Attn | ... |
    | 多头注意力 | Multi-Head Attention | MHA | ... |
    | 自注意力 | Self-Attention | SA | ... |
    +

    5.2 共享术语表工作流

    Section titled “5.2 共享术语表工作流”
    +
    flowchart TD
        A(创建术语表) --> B(推送到共享仓库)
        B --> C(各实例拉取最新)
        C --> D(写作时引用)
        D --> E(发现新术语)
        E --> B
    +

    5.3 使用建议

    Section titled “5.3 使用建议”
    +

    单一文件管理所有术语, 避免分散

    使用 Git 追踪变更, 可回滚

    各实例定期更新术语表

    建立术语决策流程

    +
    +

    6. 会议特定建议

    Section titled “6. 会议特定建议”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    会议偏好写作建议
    NeurIPS神经科学结合强调方法的生物学合理性
    ICML理论基础增加理论分析和证明
    ICLR表示学习侧重表示分析和可视化
    CVPR实验完整性充分的消融和对比实验
    ACL/EMNLP语言学洞察增加定性分析和案例分析
    +
    +

    7. 常见写作误区

    Section titled “7. 常见写作误区”
    +

    ❌ “像论文但没信息” - 缺乏实质贡献

    ❌ 过度声明 - claim 超出实验支持

    ❌ 忽略关键 baseline - 对比不充分

    ❌ 叙事不清 - 逻辑跳跃

    +
    +

    8. 协作检查清单

    Section titled “8. 协作检查清单”
    +
      +
    • 术语表已创建并同步
      • +
    • 各章节使用统一格式
      • +
    • 引用已验证
      • +
    • 符号系统一致
      • +
    • 叙事逻辑连贯
      • +
    • 实验claim对齐
      • +
    \ No newline at end of file diff --git a/research/writing/methods/index.html b/research/writing/methods/index.html new file mode 100644 index 0000000..b93f3cd --- /dev/null +++ b/research/writing/methods/index.html @@ -0,0 +1,207 @@ + 写作方法 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    写作方法

    1. 写作核心原则

    Section titled “1. 写作核心原则”
    +

    写作最核心的是：让审稿人相信你的 claim。

    +

    核心流程：论证链 → Outline → 文字生成

    +
    +

    2. 构建论证链（不写正文）

    Section titled “2. 构建论证链（不写正文）”
    +

    先把论文的”论证链”写出来：

    +
    flowchart TD
        A[Claim 1] --> B(需要哪些证据?)
        A --> C(哪些是必要的?)
        A --> D(哪些是锦上添花?)
    
+
        B --> E(主实验)
        B --> F(消融实验)
        B --> G(压力测试)
    +

    2.1 论证链示例

    Section titled “2.1 论证链示例”
    +
    Claim 1: 我们的方法在低资源场景下优于baseline
    ├── 主实验: 在3个数据集上对比, 指标提升X%
    ├── 消融: 验证每个模块的贡献
    └── 压力测试: 资源减少10%/50%/90%时的表现
    
+
    Claim 2: 方法的关键创新是XXX机制
    ├── 消融: 去掉XXX后性能下降Y%
    ├── 可视化: 展示XXX激活模式
    └── 理论分析: XXX为什么有效
    +
    +

    3. 写 Outline（只写要点）

    Section titled “3. 写 Outline（只写要点）”
    +

    每小节回答一个问题：

    + + + + + + + + + + + + + + + + + + + + + +
    问题含义
    这节要说服审稿人什么？核心主张
    审稿人可能质疑什么？潜在反驳
    我用什么证据挡住？实验支撑
    +

    3.1 Outline 模板

    Section titled “3.1 Outline 模板”
    +
    # 1. Introduction
    
+
    ## 要回答的问题
    - 这个问题为什么重要？
    - 现有方法有什么问题？
    - 我们的核心贡献是什么？
    
+
    ## 可能的质疑
    - "这个问题真的重要吗？"
      → 回答：用具体应用场景和数据说明
    - "你们只是简单的组合吧？"
      → 回答：强调我们发现的XXX insight
    
+
    ## 关键证据
    - 实验1: 在3个标准数据集上提升X%
    - 实验2: 验证了XXX机制的必要性
    +
    +

    4. 生成具体文字

    Section titled “4. 生成具体文字”
    +

    最后再让模型生成具体文字：

    +

    好处：

    +
      +
    • 不会空谈
      • +
    • 每个观点都有支撑
      • +
    • 逻辑清晰
      • +
    +
    +

    5. MCP 工具与 Skills

    Section titled “5. MCP 工具与 Skills”
    +

    5.1 MCP工具（文献搜索与访问）

    Section titled “5.1 MCP工具（文献搜索与访问）”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    工具功能适用场景
    Zotero-MCP直接访问本地文献库文献管理、引用分析
    Perplexity MCPDeep Research 模式技术调研、综述写作
    ClickHouse MCP数据库集成大数据分析
    Zen MCP研究工作流流程自动化
    Paper Search MCP多数据库搜索快速获取新论文
    +

    5.2 Skills工具（科研分析与写作）

    Section titled “5.2 Skills工具（科研分析与写作）”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    工具技能数量特色
    claude-scientific-skills140+即用型技能, 多领域覆盖
    claude-scientific-writer-专注科研写作
    Content-Research-Writer-长文协作写作
    20-ml-paper-writing77项完整 ML 论文写作流程
    +

    5.3 GitHub 资源

    Section titled “5.3 GitHub 资源”
    + +
    +

    6. 写作检查清单

    Section titled “6. 写作检查清单”
    +

    每个 claim 都有对应的实验支撑

    对比了所有强 baseline

    叙事流畅, 无逻辑跳跃

    全文使用统一的术语

    引用格式正确, 来源可靠

    图表有清晰的图例和说明

    \ No newline at end of file diff --git a/research/writing/prism/index.html b/research/writing/prism/index.html new file mode 100644 index 0000000..2e28e5d --- /dev/null +++ b/research/writing/prism/index.html @@ -0,0 +1,256 @@ + Prism 编辑器 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    Prism 编辑器

    1. Prism 概述

    Section titled “1. Prism 概述”
    +

    Prism 是 OpenAI 推出的 LaTeX 原生科研写作工作区, 发布于 2026年1月27日。

    +

    核心优势：GPT-5.2 原生集成, 无需插件即可获得最强 AI 写作辅助。

    +
    +

    2. 核心特性

    Section titled “2. 核心特性”
    +
    flowchart TD
        subgraph ["核心能力"]
            A(LaTeX 原生) --> B(无缝迁移)
            C(GPT-5.2 集成) --> D(智能辅助)
            E(实时协作) --> F(多人编辑)
        end
    
+
        subgraph ["写作支持"]
            G(中英文支持) --> H(双语写作)
            I(公式图表) --> J(可视化)
            K(引用管理) --> L(智能引用)
        end
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    特性说明
    LaTeX 原生无缝迁移现有 Overleaf 项目
    GPT-5.2 集成最强 AI 写作辅助
    实时协作多人同时编辑
    中英文支持双语写作无障碍
    公式图表内置可视化工具
    引用管理智能文献引用
    +
    +

    3. Prism vs Overleaf

    Section titled “3. Prism vs Overleaf”
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    维度PrismOverleaf
    AI 集成GPT-5.2 原生需外部插件
    实时协作✓✓
    中文支持原生需配置
    定价免费（个人）有限制
    模板库基础模板丰富模板
    编译速度快快
    +
    +

    4. 写作工作流

    Section titled “4. 写作工作流”
    +

    4.1 阶段 1: 项目设置

    Section titled “4.1 阶段 1: 项目设置”
    +
    Terminal window
    # 创建新项目
    1. 访问 openai.com/prism/
    2. 点击 "New Project"
    3. 选择论文模板 (NeurIPS/ICML/ICLR)
    4. 配置基础设置
    +

    4.2 阶段 2: AI 辅助写作

    Section titled “4.2 阶段 2: AI 辅助写作”
    +
    % 使用 AI 辅助生成内容
    % 在 Prism 中, 选中文本并点击 "Ask AI"
    
+
    \section{Method}
    我们的方法基于 Transformer 架构,
    引入了一种新颖的 XXX 机制。
    
+
    % AI 建议扩展：
    % 基于 Transformer 架构, 我们提出了一种
    % 自适应注意力机制 (Adaptive Attention),
    % 能够根据输入动态调整注意力分布。
    +

    4.3 阶段 3: 协作编辑

    Section titled “4.3 阶段 3: 协作编辑”
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    功能操作
    实时同步自动保存到云端
    评论讨论Cmd+Click 添加评论
    版本历史查看历史版本
    任务分配@mention 协作者
    +
    +

    5. 中英文写作支持

    Section titled “5. 中英文写作支持”
    +

    5.1 混合写作

    Section titled “5.1 混合写作”
    +
    % 中文摘要 + 英文正文
    
+
    \begin{abstract}
    本文提出了一种新颖的 XXX 方法,
    在多个基准数据集上取得了显著的性能提升。
    \end{abstract}
    
+
    \section{Introduction}
    Deep learning has achieved remarkable success...
    % 可随时切换语言
    +

    5.2 翻译辅助

    Section titled “5.2 翻译辅助”
    +
    Prism 内置翻译功能：
    1. 选中中文段落
    2. 点击 "Translate to English"
    3. AI 生成学术英文版本
    4. 可选择保留/修改
    +
    +

    6. 引用管理

    Section titled “6. 引用管理”
    +

    6.1 智能引用

    Section titled “6.1 智能引用”
    +
    % Prism 会自动推荐相关引用
    
+
    我们的方法与 XXX[1] 有显著不同。
    
+
    % AI 建议：
    % 相比之前的工作[2,3], 我们的方法...
    +

    6.2 引用格式

    Section titled “6.2 引用格式”
    + + + + + + + + + + + + + + + + + + + + + +
    会议格式
    NeurIPS\cite
    ICML\citep
    ICLR\cite
    +
    +

    7. 使用场景

    Section titled “7. 使用场景”
    +

    AI 辅助生成大纲、扩展段落、优化表达

    快速定位、修改、验证修改内容

    多人实时编辑, 版本管理

    中英文互译, 保持学术表达

    自动格式化, 符合会议要求

    自动验证引用完整性

    +
    +

    8. 快速开始

    Section titled “8. 快速开始”
    +
      +
    1. 访问官网: openai.com/prism
      2. +
    2. 创建账户: 使用 OpenAI 账户登录
      3. +
    3. 新建项目: 选择论文模板
      4. +
    4. 开始写作: 使用 GPT-5.2 辅助
      5. +
    +
    +

    9. 最佳实践

    Section titled “9. 最佳实践”
    +

    建议：先用 AI 生成初稿, 再人工精修, 确保内容的准确性和创新性。

    +
      +
    1. 大纲先行：让 AI 生成结构化大纲
      2. +
    2. 分段写作：逐章节完善, 避免大段重写
      3. +
    3. 及时保存：利用版本历史回溯
      4. +
    4. 引用验证：提交前检查引用完整性
      5. +
    5. 格式检查：确认符合会议模板
      6. +
    \ No newline at end of file diff --git a/research/writing/rebuttal/index.html b/research/writing/rebuttal/index.html new file mode 100644 index 0000000..95a48db --- /dev/null +++ b/research/writing/rebuttal/index.html @@ -0,0 +1,158 @@ + Rebuttal 策略 | Vibe Research - AI 科研指南 + Skip to content
    Vibe Research Vibe Research - AI 科研指南

    Rebuttal 策略

    1. Rebuttal 策略

    Section titled “1. Rebuttal 策略”
    +
    +

    2. 分类回应

    Section titled “2. 分类回应”
    +

    2.1 可修复的

    Section titled “2.1 可修复的”
    +

    策略: 明确承诺修改，给出具体方案

    +

    模板:

    +
    感谢审稿人的建议。我们将按以下方式修改：
    
+
    1. 补充消融实验（详见附件图X）
    2. 增加与方法Y的对比
    3. 重写第3节以更清晰地表达...
    
+
    我们已将这些修改在附件中标注。
    +

    2.2 误解的

    Section titled “2.2 误解的”
    +

    策略: 礼貌澄清，补充说明

    +

    模板:

    +
    感谢审稿人的关注。我们想澄清以下几点：
    
+
    1. 关于XXX：我们的方法确实做了...
    2. 关于YYY：这可能是一个误解，实际上是...
    
+
    我们在文中已添加更清晰的说明。
    +

    2.3 不同意但合理的

    Section titled “2.3 不同意但合理的”
    +

    策略: 讨论并引用支持文献

    +

    模板:

    +
    审稿人提出了一个很好的观点。我们同意...，
    
+
    但我们选择这种方法的原因是...
    
+
    相关文献[1,2]也支持这种做法。
    
+
    我们会在文中增加相关讨论。
    +

    2.4 不同意的

    Section titled “2.4 不同意的”
    +

    策略: 坚定但有理有据地反驳

    +

    模板:

    +
    我们尊重审稿人的观点，但不同意...
    
+
    原因是...（实验证据/理论分析/文献支持）
    
+
    我们的方法在这一方面确实有优势...
    +
    +

    3. 常见意见应对模板

    Section titled “3. 常见意见应对模板”
    +

    3.1 缺少消融实验

    Section titled “3.1 缺少消融实验”
    +
    感谢审稿人的建议。我们已在补充材料中添加了消融实验[表X]，
    显示去掉模块A后性能下降Y%，证实了其必要性。
    +

    3.2 对比不公平

    Section titled “3.2 对比不公平”
    +
    我们已确保所有方法使用相同的训练设置（学习率、batch size等），
    详见补充材料[实验设置]。
    
+
    我们注意到方法Y的原始设置与我们的有所不同，
    为了公平对比，我们也用他们的设置重新运行了实验。
    +

    3.3 创新性不足

    Section titled “3.3 创新性不足”
    +
    与[ABC方法]不同，我们关注的是X场景下的Y问题，
    这在之前工作中未被探索。
    
+
    我们的贡献在于：
    1. 发现了Z现象
    2. 提出了W方法
    3. 在N个数据集上验证了有效性
    
+
    相关工作[附表]详细列出了区别。
    +

    3.4 缺少某篇文献

    Section titled “3.4 缺少某篇文献”
    +
    感谢审稿人指出这篇重要文献。我们已将其加入相关工作并讨论：
    
+
    - 该方法与我们的相似之处：...
    - 关键区别：...
    - 我们的方法如何扩展了它：...
    +

    3.5 结果不够显著

    Section titled “3.5 结果不够显著”
    +
    我们认为结果的显著性在于：
    
+
    1. 在X场景下（之前未被探索），我们的方法优于baseline
    2. 消融显示每个模块都有贡献
    3. 在Y个数据集上一致有效
    
+
    虽然绝对提升不大，但在...场景下很有价值。
    +
    +

    4. Rebuttal 成功要素

    Section titled “4. Rebuttal 成功要素”
    +
      +
    1. +

      感谢审稿人（即使意见尖锐）

      +
      2. +
    2. +

      具体修改措施（不是泛泛而谈）

      +
      3. +
    3. +

      实验数据支持（新增结果）

      +
      4. +
    4. +

      保持专业和礼貌

      +
      5. +
    +
    +

    5. Rebuttal 结构模板

    Section titled “5. Rebuttal 结构模板”
    +
    # Rebuttal
    
+
    ## 概述
    
+
    感谢所有审稿人的宝贵意见。我们已认真考虑每条意见，
    并准备了相应的修改和补充实验。
    
+
    ## 对审稿人A的回应
    
+
    ### 意见1: ...
    感谢审稿人的建议。[具体回应]
    
+
    ### 意见2: ...
    [回应...]
    
+
    ## 对审稿人B的回应
    
+
    ### 意见1: ...
    [回应...]
    
+
    ## 对审稿人C的回应
    
+
    ### 意见1: ...
    [回应...]
    
+
    ## 主要修改总结
    
+
    1. 补充了X个消融实验
    2. 增加了与方法Y的对比
    3. 重写了第Z节以更清晰
    
+
    修改后的全文已附后/已在附件中标注。
    
+
    ## 附件
    
+
    - 补充实验结果
    - 修改后的全文（标注版）
    \ No newline at end of file