FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
- Published
- Source
- arXiv
- Paper number
- 464
- Field
- Software Engineering
- arXiv ID
- 2606.19605
Key points
- The Claude Code-based optimization loop follows a pipeline of evaluation, intermediate-step analysis for failure attribution, scoped change proposal, safety review, and verification.
- The prompt-first strategy prioritizes prompt edits and escalates to chain-structure or parameter changes only when attribution identifies a structural bottleneck.
- It represents the pipeline as a stateful graph built with LangGraph, and it ensures reproducibility through tenant-isolated workspaces.
- Across 6 benchmarks and 3 models, meaning 18 comparisons, GEPA wins 15 times with an average gain of 14.1 percentage points, and it wins all 6 structure-change comparisons on HoVer and IFBench with an average gain of 33.8 percentage points.
- On the security task CTIBench-RCM, it improves GPT-5 by 4.0 points, Foundation-Sec-8B-Instruct by 7.1 points, and Foundation-Sec-8B-Reasoning by 2.0 points.
- A step-attribution subagent separates prompt-editable issues from structural bottlenecks, which prevents unnecessary structural changes.
Paper links
External research summaries. These are not HDATF publications or measured product results.