AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning
Abstract
Large Audio Language Models (LALMs) excel at perception but struggle with complex reasoning requiring precise acoustic measurements. While external tools can extract fine-grained features like exact tempo or pitch, effective integration remains challenging: naively using all tools causes information overload, while prompt-based selection fails to assess context-dependent utility. To address this, we propose AuTAgent (Audio Tool Agent), a reinforcement learning framework that learns when and which tools to invoke. By employing a sparse-feedback training strategy with a novel Differential Reward mechanism, the agent learns to filter out irrelevant tools and invokes external assistance only when it yields a net performance gain over the base model. Experimental results confirm that AuTAgent complements the representation bottleneck of LALMs by providing verifiable acoustic evidence. It improves accuracy by 4.20% / 6.20% and 9.80% / 8.00% for open-source and closed-source backbones on the MMAU Test-mini and the MMAR benchmarks, respectively. In addition, further experiments demonstrate exceptional transferability. We highlight the complementary role of external tools in augmenting audio model reasoning.
Cite
@article{arxiv.2602.13685,
title = {AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning},
author = {Siqian Tong and Xuan Li and Yiwei Wang and Baolong Bi and Yujun Cai and Shenghua Liu and Yuchen He and Chengpeng Hao},
journal= {arXiv preprint arXiv:2602.13685},
year = {2026}
}