vLLM SOTA Humanize Loop

Overview

Use this skill when the user names a model and wants the vLLM serving path to autonomously keep improving until it matches or beats the best reproducible SGLang or TensorRT-LLM result in the same target environment.

This workflow has two durable parts:

1. A fixed baseline phase that must be completed once before any code patching.
2. One Humanize RLCR loop that owns gap decision, profiling, required layer/kernel deep dive, vLLM patching, optional NCU evidence, and real-model revalidation.

Do not split the campaign into a pre-loop profiling phase plus a later patch loop. After the fixed benchmark exists, Phase 2 gap decisions, Phase 3 profiling, llm-pipeline-analysis, kernel evidence, and code changes all belong inside the same model-level RLCR loop.