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In 11 model-benchmark comparisons, FAPO wins with non-overlapping mean $\\pm$ trial-standard-deviation ranges, and the mean FAPO-GEPA gain is +14.1 pp. In the six","title":"FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines","url":"https://arxiv.org/abs/2606.19605","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.19605v1 Announce Type: cross \nAbstract: Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain. We present FAPO (Fully Autonomous Prompt Optimization), a framework that lets Claude Code optimize an LLM pipeline inside a standardized codebase. FAPO evaluates a pipeline, inspects intermediate steps, diagnoses failures, proposes scoped changes, and validates variants repeatedly to optimize against a score function. 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