EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
On the map: LLM reasoning → Audio-language model reasoning
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 29 references that are in the map. First posted on arXiv 13 Jun 2026.
Builds on
22 of 29 references.
Closest ancestors · 4
Same direction, newest first
- 2026The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents15 Feb
- 2025Qwen2.5-Omni Technical Report26 Mar
- 2025Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model13 Jan
- 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark24 Oct
Other references · 18
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
- 2023Let's Verify Step by Stepcited by 3,598
- 2020Shortcut Learning in Deep Neural Networkscited by 1,928
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
- 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 591
- 2023SALMONN: Towards Generic Hearing Abilities for Large Language Modelscited by 492
- 2025Qwen3-Omni Technical Reportcited by 451
- 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
6 more
- 2023Faithful Chain-of-Thought Reasoningcited by 285
- 2024AnyGPT: Unified Multimodal LLM with Discrete Sequence Modelingcited by 213
- 2023Listen, Think, and Understandcited by 206
- 2024Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilitiescited by 183
- 2025Baichuan-Omni-1.5 Technical Reportcited by 107
- 2025OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesiscited by 10
Built on by
Citing papers per year: 2026: 1.
1 paper.
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Close in wording, no citation either way
- 2026Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning
- 2026Multi-Source Evidence Fusion for Audio Question Answering
- 2025Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards
- 2025CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challenges
- 2026AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning
- 2025Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
- 2026AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
- 2026Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models