LLM agents learn to improve their own harnesses autonomously

Researchers have developed a novel method called Self-Harness, which enables LLM-based agents to autonomously improve their own operating harnesses. This process involves identifying model-specific failure patterns, generating harness modifications tailored to these failures, and validating the edits through regression testing. When applied to various benchmarks and diverse LLM families, Self-Harness consistently enhanced performance, demonstrating significant gains in pass rates and addressing specific bottlenecks in artifact handling, runtime control, and state retrieval. AI

IMPACT This research could lead to more adaptable and efficient LLM agents that can self-optimize their interaction with environments, reducing the need for manual engineering.

RANK_REASON The cluster describes a research paper detailing a new method for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• AppWorld
• GLM-5
• Hangfan Zhang
• MiniMax M2.5
• Qwen3.5 35B A3B
• Self-Harness
• SWE-bench Verified
• Terminal Bench 2.0

• paper
• model release

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