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Evo^2 framework co-evolves AI agent weights and harnesses for improved performance

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Evo$^2$, a novel framework designed to enhance language model agents through co-evolution of their weights and harnesses. This approach connects weight updates with harness improvements, which define how an agent interacts with prompts, tools, and feedback. Evo$^2$ utilizes a dual-role policy for harness updates and guides weight training with harness modifications and their outcomes. Experiments show Evo$^2$ significantly outperforms existing methods on coding benchmarks, achieving 82.1% on Polyglot and 48.3% on SWE-bench Verified, and can improve proprietary models like GPT-5 mini. AI

IMPACT Introduces a novel co-evolutionary approach for AI agents, potentially improving performance and transferability across models and tasks.

RANK_REASON Academic paper detailing a new AI framework and its experimental results. [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 →

Evo^2 framework co-evolves AI agent weights and harnesses for improved performance

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Academic paper detailing a new AI framework and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chejian Xu, Shujian Zhang, Wenxuan Zhou, John Lambert, Beck Chen, Bo Li, Andrew Hard, Rajiv Mathews ·

    Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution

    arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improvements. Existing approaches typically pursue these directions separately: some me…

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