[Klaud Cold] Simplify agentic synthetic-AL checklist item: drop engine-specific knobs / 精简 agentic 合成 AL 清单条目：移除引擎特定参数

docs/PR_REVIEW_CHECKLIST.md

@@ -20,7 +20,7 @@ As a PR reviewer and CODEOWNER, I have reviewed this and have:
 - [ ] Verified that this PR has passed PR validation. Please link to GitHub Action workflow that shows this.
 - [ ] Verified that this PR passes evals.  Please link to GitHub Action workflow that shows this.
 - [ ] Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
-- [ ] For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance — SGLang via `SGLANG_SIMULATE_ACC_LEN` (with `SGLANG_SIMULATE_ACC_METHOD: match-expected` and `SGLANG_SIMULATE_ACC_TOKEN_MODE: real-draft-token`), vLLM via `--speculative-config` with `"rejection_sample_method": "synthetic"` and `"synthetic_acceptance_length"` — and that the acceptance-length value exactly matches the committed golden AL curve in [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) for that model, thinking mode, and `num_speculative_tokens`. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
+- [ ] For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
 - [ ] Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
 - [ ] If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet as supported by vLLM/SGLang community maintainers
 - [ ] If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.

docs/PR_REVIEW_CHECKLIST_zh.md

@@ -22,7 +22,7 @@ As a PR reviewer and CODEOWNER, I have reviewed this and have:
 - [ ] Verified that this PR has passed PR validation. Please link to GitHub Action workflow that shows this.
 - [ ] Verified that this PR passes evals.  Please link to GitHub Action workflow that shows this.
 - [ ] Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
-- [ ] For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance — SGLang via `SGLANG_SIMULATE_ACC_LEN` (with `SGLANG_SIMULATE_ACC_METHOD: match-expected` and `SGLANG_SIMULATE_ACC_TOKEN_MODE: real-draft-token`), vLLM via `--speculative-config` with `"rejection_sample_method": "synthetic"` and `"synthetic_acceptance_length"` — and that the acceptance-length value exactly matches the committed golden AL curve in [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) for that model, thinking mode, and `num_speculative_tokens`. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
+- [ ] For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
 - [ ] Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
 - [ ] If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet as supported by vLLM/SGLang community maintainers
 - [ ] If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.
@@ -43,7 +43,7 @@ Signed: `FILL_IN_GITHUB_USERNAME`
 3. 已确认该 PR 通过了 PR 验证，并附上能证明这一点的 GitHub Action 工作流链接。
 4. 已确认该 PR 通过了 evals（准确性评测），并附上能证明这一点的 GitHub Action 工作流链接。
 5. 已确认投机解码（speculative decoding）PR 使用 chat template，使接受长度（AL）分布与真实场景对齐。
-6. 对 agentic 工作负载：已确认投机解码配置（EAGLE / MTP / draft 模型）启用了模拟合成接受（simulated synthetic acceptance）—— SGLang 通过 `SGLANG_SIMULATE_ACC_LEN`（配合 `SGLANG_SIMULATE_ACC_METHOD: match-expected` 与 `SGLANG_SIMULATE_ACC_TOKEN_MODE: real-draft-token`），vLLM 通过 `--speculative-config` 中的 `"rejection_sample_method": "synthetic"` 与 `"synthetic_acceptance_length"` —— 且接受长度（AL）取值与 [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) 中该模型、thinking 模式与 `num_speculative_tokens` 对应的已提交黄金 AL 曲线完全一致。提交可选择任意受支持的 draft 长度，但不得替换为其他接受目标。
+6. 对 agentic 工作负载：已确认投机解码配置（EAGLE / MTP / draft 模型）启用了模拟合成接受（simulated synthetic acceptance），且接受长度（AL）取值来自 [golden_al_distribution/](https://github.com/SemiAnalysisAI/InferenceX/tree/main/golden_al_distribution) 中该模型、thinking 模式与 draft 长度对应的已提交黄金 AL 曲线。提交可选择任意受支持的 draft 长度，但不得替换为其他接受目标。
 7. 已确认模型架构未被基准测试 hack 更改 — 例如在不原生支持的模型上使用 `--hf-overrides` 每 x 层跳过 indexer。一般规则：不接受减少模型架构 FLOPs 的优化；让同样的计算跑得更快没有问题；更低精度的 FLOPs 也可以，前提是该配置通过私有 evals。北极星原则：只使用在意准确性的客户在生产中实际使用的优化。
 8. 如果公司声称在其硬件上将 vLLM/SGLang 作为一等 LLM 推理引擎支持，已确认相应 vLLM 提交使用上游 [vLLM docker 仓库](https://hub.docker.com/u/vllm)、SGLang 提交使用上游 [lmsysorg docker 仓库](https://hub.docker.com/u/lmsysorg)。唯一例外：新硬件（如 MI455X UALoE72、Vera Rubin NVL72、Rubin NVL8 等），以及经 vLLM/SGLang 社区维护者确认上游尚未从根本上支持的新模型架构。
 9. 如果公司声称在其硬件上将 vLLM/SGLang 作为一等上游 in-tree LLM 推理引擎支持，已确认相应 vLLM/SGLang 提交先于其他框架（TRT-LLM、ATOM 等）完成。例外情形同上。