Rename project to CodeScout and simplify README

Updated project name and removed sections related to the previous project description, objectives, and workstreams.

Author: neubig

README.md

@@ -1,92 +1,3 @@
-# Agentic Code Search OSS
+# CodeScout
 
 An open-source implementation of a low-latency agent for code localization.
-
-- **Repository:** `https://github.com/All-Hands-AI/agentic-code-search-oss`
-- **Slack:** `#agentic-code-search-oss` (All-Hands-AI workspace)
-
-## 1. Problem Statement
-
-LLM-based coding agents are bottlenecked by context retrieval. They are often slow and inefficient at finding the correct files and code snippets to edit in a large repository. This project builds a small, fast, specialized agent to solve the **code localization** problem.
-
-## 2. Objective
-
-The primary goal is to **minimize the latency of code localization**. The secondary goal is to maintain high precision.
-
-Success will be measured by:
-
-- **Latency:** Time to identify target code locations.
-- **Precision:** Percentage of identified locations that are correct.
-- **Recall:** Percentage of all correct locations that were identified.
-
-## 3. Technical Plan
-
-The approach is to train a small language model using Reinforcement Learning (RL) on a standardized benchmark.
-
-1. **Benchmark Environment:** `SWE-Gym` will be used for training and evaluation, as it provides realistic software engineering tasks with executable environments.
-
-2. **Reward Signal:** The evaluation logic from the `Agentless` project will be used as the "verifiable reward" mechanism. The agent is rewarded for correctly identifying the files and lines that require edits.
-
-3. **RL Framework:** The agent will be trained using an RL framework. `SkyRL` and `AReaL` are the primary candidates.
-
-4. **Model:** A small, efficient language model (e.g., `Qwen3-0.6B`) will be fine-tuned for the localization task to ensure low inference latency.
-
-5. **Tooling Strategy:** The agent will use a set of tools to navigate the codebase. The focus is on:
-    - **Diverse Tool Calls:** Implementing and evaluating tools beyond `grep`, such as Abstract Syntax Tree (AST) parsers for structural code analysis.
-    - **Parallel Tool Calling:** Architecting the agent to execute multiple search queries simultaneously to reduce the number of sequential steps.
-
-## 4. Workstreams & Next Steps
-
-The project is broken down into the following workstreams:
-
-- **Workstream 1: Evaluation & RL Environment**
-
-  - **Task:** Set up the core training environment by integrating the `Agentless` validator with `SWE-Gym`. This will provide the foundation for an RL training loop.
-
-- **Workstream 2: Tooling**
-
-  - **Task:** Research, implement, and evaluate different tool calls (e.g., AST-based search, advanced regex, semantic search).
-  - **Task:** Design and implement an architecture that supports parallel execution of these tools.
-
-- **Workstream 3: Reinforcement Learning**
-
-  - **Task:** Implement and run training loops using a selected RL framework (e.g., `SkyRL`, `AReaL`).
-  - **Task:** Experiment with reward shaping and policy optimization to improve agent performance.
-
-- **Future Considerations:**
-  - Investigating question-answering tasks using datasets like `CodeSearchNet`.
-  - Analyzing successful agent trajectories to improve learning.
-
-## 5. Contribution
-
-This is a community-driven project.
-
-1. Join the `#agentic-code-search-oss` channel on the All-Hands-AI Slack.
-2. Check the GitHub Issues for open tasks.
-3. Attend the weekly meetings to sync on progress (details in the Slack channel).
-
-## 6. Resources
-
-- **Primary Inspiration:** [Cognition AI's SWE-grep Blog Post](https://cognition.ai/blog/swe-grep)
-- **Core Components:**
-  - [SWE-Gym (Environment)](https://github.com/SWE-Gym/SWE-Gym)
-  - [Agentless (Reward Validator)](https://github.com/OpenAutoCoder/Agentless)
-  - [SkyRL (RL Framework)](https://github.com/NovaSky-AI/SkyRL)
-  - [AReaL (RL Framework)](https://github.com/inclusionAI/AReaL)
-- **Relevant Research & Projects:**
-  - [NVIDIA Nemotron-CORTEXA](https://github.com/NVIDIA/Nemotron-CORTEXA)
-  - [LocAgent: Graph-Guided LLM Agents](https://arxiv.org/abs/2503.09089)
-  - [SWE-Fixer: Open-Source LLMs for Issue Resolution](https://arxiv.org/abs/2501.05040)
-- **Datasets:**
-  - [CodeSearchNet](https://github.com/github/CodeSearchNet)
-  - [SWE-Fixer-Train-110K](https://huggingface.co/datasets/internlm/SWE-Fixer-Train-110K)
-- **Training Parallel Tool Calling:**
-  - [SWE-Grep](https://cognition.ai/blog/swe-grep): Forcing parallel tool calling during training (8 tools in parallel per step)
-  - [LLMCompiler](https://arxiv.org/abs/2405.17438): Using a "compiler" idea to orchestrate parallel tool calling during training; could be an overall kill for just searching tasks.
-  - [Divide-Then-Aggregate](https://aclanthology.org/2025.acl-long.1401.pdf): Another similar training method for parallel tool calling.
-  - [KAT](https://skywork.ai/blog/kat-models-parallel-tool-calling-ai-coding-agents/): Some good practices for parallel tool calling.
-  - Overall, this space is relatively unexplored.
-  - Finally, this parallel tool calling thing is related to the idea of "multi-agent" framework:
-    - [M1-Parallel](https://arxiv.org/abs/2507.08944): runs multiple multi-agent teams in parallel
-    - [ToolFlow](https://arxiv.org/abs/2410.18447): multiple agents to synthesize the training data
-