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    <abstract>LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging dueto delayed supervision and the difficulty ofcredit assignment in long-horizon trajectories.Existing optimization approaches tend to beeither monolithic, which are prone to entangling behaviors, or single-aspect, which ignorecross-module error propagation. To addressthese limitations, we propose EVOTOOL, a self-evolving framework that optimizes a modulartool-use policy via a gradient-free evolutionary paradigm. EVOTOOL decomposes agent’stool-use policy into four modules, includingPlanner, Selector, Caller, and Synthesizer, anditeratively improves them through three mechanisms. Trajectory-Grounded Blame Attribution uses diagnostic traces to localize failuresto a specific module. Feedback-Guided Targeted Mutation then edits only that modulevia natural-language critique. Diversity-AwarePopulation Selection preserves complementarycandidates to ensure solution diversity. Acrossfour diverse benchmarks, EVOTOOL outperforms strong baselines by over 5 points on bothGPT-4.1 and Qwen3-8B, while achieving superior efficiency and transferability.</abstract>
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%0 Conference Proceedings
%T EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection
%A Yang, Shuo
%A Han, Caren
%A Ma, Xueqi
%A Li, Yan
%A Ghasemi Madani, Mohammad Reza
%A Hovy, Eduard
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-evotool
%X LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging dueto delayed supervision and the difficulty ofcredit assignment in long-horizon trajectories.Existing optimization approaches tend to beeither monolithic, which are prone to entangling behaviors, or single-aspect, which ignorecross-module error propagation. To addressthese limitations, we propose EVOTOOL, a self-evolving framework that optimizes a modulartool-use policy via a gradient-free evolutionary paradigm. EVOTOOL decomposes agent’stool-use policy into four modules, includingPlanner, Selector, Caller, and Synthesizer, anditeratively improves them through three mechanisms. Trajectory-Grounded Blame Attribution uses diagnostic traces to localize failuresto a specific module. Feedback-Guided Targeted Mutation then edits only that modulevia natural-language critique. Diversity-AwarePopulation Selection preserves complementarycandidates to ensure solution diversity. Acrossfour diverse benchmarks, EVOTOOL outperforms strong baselines by over 5 points on bothGPT-4.1 and Qwen3-8B, while achieving superior efficiency and transferability.
%U https://aclanthology.org/2026.acl-long.2016/
%P 43553-43572

Markdown (Informal)

[EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection](https://aclanthology.org/2026.acl-long.2016/) (Yang et al., ACL 2026)

• EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection (Yang et al., ACL 2026)

ACL

• Shuo Yang, Caren Han, Xueqi Ma, Yan Li, Mohammad Reza Ghasemi Madani, and Eduard Hovy. 2026. EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43553–43572, San Diego, California, United States. Association for Computational Linguistics.