Hugging Face Daily PapersSiwei Wu, Jincheng Ren, Yizhi Li1 min readpaperadvanced
ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement
Summary
ModularRSI proposes a contrastive, modular approach to evolve agent harnesses by independently updating five functional modules and aggregating evidence across 2,000 benchmark‑disjoint tasks. Experiments on TB2.0 and SWE‑Bench Verified show consistent gains on unseen tasks and cross‑model transfer.
- The harness is decomposed into Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection, each evolved within a restricted scope.
- Contrastive analysis of successful vs. failed trajectories across 2k external tasks isolates recurring behavioral deficiencies.
- Benchmark‑disjoint evolution prevents overfitting to evaluation suites, improving generalization.
- Evolved harnesses achieve measurable performance gains on unseen in‑domain and cross‑domain tasks in TB2.0 and SWE‑Bench Verified.
Engineers building LLM‑driven coding agents need a systematic way to improve their control harnesses without overfitting, and ModularRSI offers a modular, evidence‑driven solution.
7/10
