EvoTool: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection

Researchers propose EvoTool, a new framework that optimizes AI agent tool-use policies through evolutionary algorithms rather than traditional gradient-based methods. The system decomposes agent policies into four modules and uses blame attribution and targeted mutations to improve performance, showing over 5-point improvements on benchmarks.