MESH-Harness: Self-Improving Agent Harnesses via Bandit-Guided Compositional Evolution

2026-10-04

AI Summary

This study addresses the challenge of optimizing agent frameworks under limited evaluation budgets by proposing an efficient self-improvement method that freezes model weights and optimizes only the framework. Through modular design and combinatorial evolution, it overcomes enumeration bottlenecks while synergizing module-level reuse with feedback-driven optimization via full-covariance LinUCB-guided search, mixed-init coordinate ascent, and validation-trajectory-driven code iteration. Experimental results demonstrate that the proposed approach significantly outperforms the Meta-Harness baseline across multiple tasks, reducing testing costs by 44.2% and total costs by 14.6%.

Abstract

An agent harness is the code that organizes context, maintains state, and coordinates tool calls for a language model. We study how to improve the harness under a limited evaluation budget while keeping model weights fixed. Our method, MESH-Harness, organizes each harness into functional modules with explicit role-specific interfaces, allowing alternative implementations of each module to be substituted and recombined. It uses shared module representations and full-covariance LinUCB to score candidate combinations based on predicted performance and exploration value. Mixed-start coordinate ascent selects complete configurations for evaluation without enumerating the combinatorial space. Validation traces then guide local code edits, and the resulting candidates are incorporated into fixed-capacity role-specific pools for subsequent recombination. On text tasks, retrieval-augmented mathematical reasoning, code generation, and interactive scientific tasks, MESH-Harness outperforms Meta-Harness by 5.70, 7.01, 2.00, and 5.00 points, respectively, under matched candidate-evaluation budgets. Iterative harness optimization improves MESH-Harness by 5.63-7.79 points over its first-round configurations. For the reported configurations, aggregate test-time cost is 44.2% lower than that of Meta-Harness, while total cost including search is 14.6% lower. These results show that combining module-level design reuse with feedback-driven compositional search can systematically improve agent harnesses while keeping overall optimization cost under control.

Problem

• agent harness
• compositional optimization
• limited evaluation budget
• self-improving agents

Innovation

• Compositional Evolution
• LinUCB
• Agent Harness
• Modular Architecture
• Coordinate Ascent