Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning

• URL: http://arxiv.org/abs/2602.11909v1
• Date: Thu, 12 Feb 2026 13:06:34 GMT
• Title: Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning
• Authors: Daiqing Wu, Xuan Zhang, Dongbao Yang, Jiashu Yao, Longfei Chen, Qingsong Liu, Sicheng Zhao, Can Ma, Yangyang Kang, Yu Zhou,
• Abstract summary: Current efforts replicate text-based reasoning by contextualizing audio content through a one-time encoding.<n>We propose audio-interleaved reasoning to break through this bottleneck.<n>We present Echo, a LALM capable of dynamically re-listening to audio in demand during reasoning.
• Score: 39.264735719707154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through a one-time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio-interleaved reasoning to break through this bottleneck. It treats audio as an active reasoning component, enabling sustained audio engagement and perception-grounded analysis. To instantiate it, we introduce a two-stage training framework, first teaching LALMs to localize salient audio segments through supervised fine-tuning, and then incentivizing proficient re-listening via reinforcement learning. In parallel, a structured data generation pipeline is developed to produce high-quality training data. Consequently, we present Echo, a LALM capable of dynamically re-listening to audio in demand during reasoning. On audio comprehension benchmarks, Echo achieves overall superiority in both challenging expert-level and general-purpose tasks. Comprehensive analysis further confirms the efficiency and generalizability of audio-interleaved reasoning, establishing it as a promising direction for advancing audio comprehension. Project page: https://github.com/wdqqdw/Echo.

Related papers

• Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation [30.42124709340273]
  We identify three key barriers: limited large-scale audio-text corpora, insufficient caption diversity, and lack of systematic exploration and evaluation.<n>Our results demonstrate that audio-language pretraining yields competitive, transferable representations.<n>These findings establish audio-language pretraining as a viable pathway toward general-purpose audio representations.
  arXiv  Detail & Related papers  (2025-11-20T19:17:35Z)
• SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models [18.802543558300044]
  We present SightSound-R1, a cross-modal distillation framework that transfers advanced reasoning from a stronger LVLM teacher to a weaker LALM student.<n>Results show that SightSound-R1 improves LALM reasoning performance both in the in-domain AVQA test set and in unseen auditory scenes and questions.
  arXiv  Detail & Related papers  (2025-09-19T06:39:39Z)
• AudioStory: Generating Long-Form Narrative Audio with Large Language Models [87.23256929520743]
  AudioStory is a framework that integrates large language models with text-to-audio systems to generate structured, long-form audio narratives.<n>It employs LLMs to decompose complex narrative queries into temporally ordered sub-tasks with contextual cues.<n>Extensive experiments show the superiority of AudioStory on both single-audio generation and narrative audio generation, surpassing prior TTA baselines in both instruction-following ability and audio fidelity.
  arXiv  Detail & Related papers  (2025-08-27T17:55:38Z)
• ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing [47.14083940177122]
  ThinkSound is a novel framework that enables stepwise, interactive audio generation and editing for videos.<n>Our approach decomposes the process into three complementary stages: semantically coherent, interactive object-centric refinement, and targeted editing.<n> Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics.
  arXiv  Detail & Related papers  (2025-06-26T16:32:06Z)
• From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data [55.2480439325792]
  Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs.<n>These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks.<n>We propose a data generation framework that produces contrastive-like training data, designed to enhance ALLMs' ability to differentiate between present and absent sounds.
  arXiv  Detail & Related papers  (2025-05-26T16:08:41Z)
• Probing Audio-Generation Capabilities of Text-Based Language Models [5.4211188445379825]
  This research investigates the extent to which Large Language Models can be prompted to generate audio.<n>We employ a three-tier approach, progressively increasing the complexity of audio generation.<n>Our findings reveal that while LLMs can generate basic audio features, their performance deteriorates as the complexity of the audio increases.
  arXiv  Detail & Related papers  (2025-05-04T23:46:01Z)
• Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models [91.11904427660043]
  We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks.<n>We train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning.<n>Our findings stress the core of structured CoT training in advancing audio reasoning.
  arXiv  Detail & Related papers  (2025-03-04T06:18:34Z)
• Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models [0.9285295512807729]
  The Audio Question Answering (AQA) task includes audio event classification, audio captioning, and open-ended reasoning.<n>LALMs excel in general audio understanding, but are limited in temporal reasoning.<n>This paper addresses these challenges and limitations in audio temporal reasoning.
  arXiv  Detail & Related papers  (2024-09-10T05:26:53Z)
• Exploring the Role of Audio in Video Captioning [59.679122191706426]
  We present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. We propose new local-global fusion mechanisms to improve information exchange across audio and video.
  arXiv  Detail & Related papers  (2023-06-21T20:54:52Z)
• AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head [82.69233563811487]
  Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. We propose a multi-modal AI system named AudioGPT, which complements LLMs with foundation models to process complex audio information.
  arXiv  Detail & Related papers  (2023-04-25T17:05:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.