SkillHEX: Improving Agent Skills via Hypothesis-Driven Autonomous Exploration and Exploitation

2026-08-06

AI Summary

This work addresses the challenge that large language model agents face during testing—namely, limited interaction budgets and the absence of validation sets, which hinder effective skill evolution under sparse rewards and multifactorial confounding. To overcome this, the authors propose SkillHEX, a framework that leverages hypothesis-driven self-verification to generate executable tests, transforming falsifiable failure hypotheses into diagnostic dense rewards without requiring additional environment interactions. Coupled with evidence-guided persistent branch tree search, SkillHEX dynamically balances exploration and exploitation to avoid local optima. Evaluated on 87 tasks from SkillsBench, the method achieves average pass rates of 55.9% and 57.9% using GPT-5.3-Codex and Claude Opus 4.7, respectively, within only five iterations, significantly outperforming existing self-evolution approaches.

Abstract

Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment. Real-world deployments thus require autonomous, on-demand skill evolution at test time, constrained by limited interaction budgets and a lack of training or validation sets. This setting introduces a severe sparse reward challenge, where outcomes conflate multiple latent failure causes. Under such ambiguity, existing methods that greedily refine a single incumbent skill are particularly vulnerable to an exploitation trap, allowing early misdiagnoses to exhaust limited trials along unproductive trajectories. To address this, we introduce SkillHEX, a closed-loop framework coupling hypothesis-driven self-verification with evidence-guided tree search. SkillHEX translates falsifiable failure hypotheses into executable tests, producing diagnostic evidence as dense reward without additional environment attempts. This evidence guides a search over persistent skill-revision branches, dynamically balancing the exploitation of supported edits with the exploration of plausible alternatives. Evaluated on 87 tasks from SkillsBench, SkillHEX outperforms existing self-evolving methods and achieves an average pass rate of 55.9% and 57.9% using GPT-5.3-Codex and Claude Opus 4.7 under a five-iteration budget, respectively.

Problem

• autonomous skill evolution
• sparse reward
• hypothesis-driven exploration
• limited interaction budget
• skill refinement

Innovation

• hypothesis-driven exploration
• autonomous skill evolution
• evidence-guided tree search
• dense reward from self-verification
• closed-loop skill refinement