arXiv — cs.AI preprintsInternational5 October 2026
Rethinking the Evaluation of Harness Evolution for Agents
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2607.12227v3 Announce Type: replace Abstract: Harness evolution is an iterative search procedure that repeatedly evaluates and revises candidate harnesses used for LLM agents using task feedback. We revisit the evaluation of such automatic harness evolution procedures and identify two fundamental issues in the protocol. First, prior work does not compare these approaches with simple task-level search baselines under matched feedback and inference budgets. Second, prior work searches for harness configurations using verification signals (e.g., unit test cases) drawn from the same benchmar
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching5 October 2026
- Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses5 October 2026
- The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?5 October 2026
- Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation5 October 2026
- Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents5 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.