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HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offs

Zhuoshi Pan, ZhiYuan Feng, Yukuo Cen, Honglin Lin, Junru Lu, Zhongxia Chen, Shuo Yang, H. Vicky Zhao, Jie Tang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26770 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Harness の開発は、能力のある AI エージェントを構築する上で重要な役割を果たします。最近のアプローチは、リクルシブ自己改善 (RSI) を通じてハネスを改善しますが、そのパフォーマンスの向上はしばしばより高い実行コストを伴います。 HarnessPro を導入し、タスクパフォーマンスを向上させながら実行コストを削減するためのラベルフリーメソッドであるハネスを導入します。この設定では、LLM 生成テストを使用してハネスを評価します。専門エージェントは、実行経路を診断し、異なるパフォーマンスコストの優先順位で更新を計画し、それらを候補ハネスとして実装します。ハネスプロは、単

Machine translation (Meta M2M100); the English original follows.

Abstract

Harness development plays a crucial role in building capable AI agents. Recent approaches improve harnesses through recursive self-improvement (RSI), but their performance gains often come with higher execution costs. We introduce HarnessPro, a label-free method for evolving harnesses to improve task performance while reducing execution cost. In this setting, candidate harnesses are evaluated using LLM-generated tests without access to ground-truth solutions or official tests. Specialized agents diagnose execution trajectories, plan updates with different performance-cost priorities, and implement them as candidate harnesses. Rather than returning a single harness, HarnessPro uses normalized weighted squared-distance selection to provide cost-focused (lite), balanced (medium), and performance-focused (heavy) harnesses for different deployment budgets. Selected harnesses enter the next iteration, where execution feedback guides further updates. Our experiments cover Terminal-Bench 2, SWE-Bench Pro, and ProgramBench, using GPT-5.5 with Codex and GLM-5.2 with Claude Code. Across these benchmarks using GPT-5.5 with Codex, the heavy mode of HarnessPro achieves an average pass-rate gain of 2.0 percentage points and an average per-task cost reduction of 26.1% relative to RHO. This advantage also holds when using GLM-5.2 with Claude Code. Code is available at https://github.com/pzs19/HarnessPro

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Topics: Computer science, Task (project management), Selection (genetic algorithm), Software deployment, Plan (archaeology), Code (set theory)

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