aiqbench analysis
EChO-Agent addresses critical limitations in Large Audio Language Models by introducing a modular framework that explicitly plans, executes tools, and verifies evidence within audio segments. This approach significantly improves accuracy on complex reasoning tasks compared to standard reinforcement learning or tool-augmented prompting methods alone. The system's ability to self-verify its own processing of relevant audio data represents a meaningful step forward in reliable machine listening applications.
EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
arXiv:2606.15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning. Reinforcement learning and tool-augmented prompting can help models better relate questions to audio but lack a reliable way to understand, integrate, and self-verify audio segments. To address this gap, we present EChO-Agent, a modular agent framework that reformulates complex audio QA as a planning, tool execution, evidence integration, and answer verification workflow. Experiments on MMAR benchmark show EChO-Agent improves both accuracy and rubric scores over baseline and ablation studies show evidence integration is the key factor.
Abstract:While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning. Reinforcement learning and tool-augmented prompting can help models better relate questions to audio but lack a reliable way to understand, integrate, and self-verify audio segments. To address this gap, we present EChO-Agent, a modular agent framework that reformulates complex audio QA as a planning, tool execution, evidence integration, and answer verification workflow. Experiments on MMAR benchmark show EChO-Agent improves both accuracy and rubric scores over baseline and ablation studies show evidence integration is the key factor.
5 pages, 2 figures. Accepted by Interspeech 2026
Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
arXiv:2606.15141 [eess.AS]
(or arXiv:2606.15141v1 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2606.15141 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 215 words