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HarnessPro improves AI agent performance and cuts costs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HarnessPro, a novel method for improving AI agent harnesses by recursively enhancing their performance while simultaneously reducing execution costs. This label-free approach utilizes LLM-generated tests and specialized agents to diagnose and plan updates, offering 'lite', 'medium', and 'heavy' harness configurations tailored to different budget constraints. Experiments on benchmarks like SWE-Bench Pro demonstrated that HarnessPro, using models such as GPT-5.5 with Codex, can achieve significant gains in pass rates alongside substantial cost reductions. AI

IMPACT This method could lead to more efficient and cost-effective AI agents for various tasks.

RANK_REASON The cluster contains a research paper detailing a new method for AI agent development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HarnessPro improves AI agent performance and cuts costs

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The cluster contains a research paper detailing a new method for AI agent development. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhuoshi Pan, ZhiYuan Feng, Yukuo Cen, Honglin Lin, Junru Lu, Zhongxia Chen, Shuo Yang, H. Vicky Zhao, Jie Tang ·

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

    arXiv:2610.26770v2 Announce Type: replace-cross 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 i…

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