HarnessBank framework enhances AI agent performance through self-evolution

Researchers have introduced HarnessBank, a new framework designed to improve the performance of AI agents by automatically evolving their surrounding 'harness' components, such as prompts and tools. This system uses a dual-agent approach, with one agent diagnosing failures and generating new harness configurations, and another verifying these evolutions. HarnessBank maintains a 'gene bank' of high-performing harnesses, recombining and screening them to avoid common issues like overfitting and search collapse. Experiments across seven benchmarks demonstrated performance improvements ranging from 5.1% to 15.4%, indicating the effectiveness of model-specific harness evolution. AI

IMPACT Enhances AI agent capabilities by providing a structured method for self-improvement and harness evolution.

RANK_REASON The cluster is based on an arXiv preprint detailing a new research framework for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• HarnessBank
• Hugging Face
• large-language models
• ScienceCast
• Xiaotian Luo

• paper
• model release

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