Year: 2026
Type: preprint
Abstract: Self-evolving agents can improve without updating model weights by iteratively optimizing the harness based on evaluation feedback. Existing methods are largely candidate-centric: they record evolution outcomes (which agent variants have been produced and how they perform), rather than evolution progress (which issues, i.e., recurring failure patterns, have been fixed and which remain). Such methods pose two difficulties for cumulative harness evolution. First, repair context is scattered across candidate records and must be reconstructed every round, which leads to inefficient, redundant, or misdirected revisions. Second, because candidates are evaluated as a whole, partial fixes across different agents are difficult to combine, while ineffective changes persist alongside beneficial ones. To address these problems, we formulate issue-centric agent optimization, which explicitly carries forward evolutionary progress through a persistent issue state. We instantiate this paradigm in ADIAS, a harness-evolution framework with two key components. An Issue Manager maintains the issue state with stable issue identities, lifecycle status, supporting evidence, and intervention-outcome histories. An Issue-Guided Optimizer proposes repair targets from the issue state and performs focused harness revision. On five interactive benchmarks, ADIAS outperforms the strongest baseline by 25.2% on average and achieves consistent gains across four backbone models. Controlled ablations show that removing the Issue Manager or the Issue-Guided Optimizer reduces performance by up to 40.7%.
Source: arXiv (Cornell University)
Institution: University of Cambridge
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7202139312