Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization

Authors: Xuanqi Zhang, Ruinan Jin, Running Yang, Yuxuan Zhang, Minghui Chen, Wenlong Deng, Xiaoxiao Li

Publication: arXiv preprint, 2026

Overview

Likelihood-Only Tool Scoring (LOTS) measures how removing an observed tool output changes the likelihood of an agent’s answer. It accumulates these measurements across recurring tasks to maintain a useful tool set. Experiments examine improvements over time, reductions in tool context, and transfer of learned tool selections between models.