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Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
LALMsAudio ComprehensionAudio-Interleaved Reasoning
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 informative audio segments through supervised fine-tuning, and then incentivizing proficient revisiting 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 revisiting audio segments 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. We commit to releasing the model, code, and data.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Echo, a Large Audio Language Model employing audio-interleaved reasoning with reinforcement learning to overcome information bottlenecks in audio comprehension. Within the taxonomy, Echo occupies the 'Audio-Interleaved Reasoning with Reinforcement Learning' leaf, which currently contains only this paper as a sibling. This positioning suggests a relatively sparse research direction focused specifically on RL-driven dynamic audio revisiting, contrasting with the broader 'Audio-Interleaved Reasoning and Dynamic Audio Engagement' branch that encompasses four distinct subcategories addressing various forms of iterative audio processing.

The taxonomy reveals neighboring work in adjacent leaves: 'Audio Chain-of-Thought Reasoning with Acoustic Tools' explores linguistic reasoning augmented by acoustic tool access, while 'Interleaved Audio-Text Token Generation for Streaming' addresses low-latency interactions through token interleaving. The 'Interleaved Instruction Tuning for Semantic Reasoning' leaf focuses on training strategies that embed audio tokens within prompts. Echo's RL-based approach to incentivizing audio segment revisiting differentiates it from these neighboring directions, which emphasize tool integration, streaming efficiency, or prompt-level interleaving without explicit reinforcement signals. The taxonomy's scope notes clarify that static single-pass encoding methods fall outside this branch entirely.

Among thirty candidates examined, the audio-interleaved reasoning format shows no clear refutation across ten candidates, suggesting relative novelty in this specific formulation. However, the two-stage training framework and the Echo model with structured data generation each face one refutable candidate among ten examined. This indicates that while the core reasoning paradigm appears less explored, certain training methodologies and data generation strategies have precedent in the limited search scope. The contribution-level statistics suggest the audio-interleaved reasoning concept itself may represent the most distinctive element, whereas implementation details overlap more substantially with prior work.

Based on the top-thirty semantic matches and taxonomy structure, Echo appears to occupy a sparsely populated niche within audio-language modeling. The analysis covers a focused subset of the literature, primarily capturing recent work in dynamic audio reasoning and multimodal integration. The limited search scope means that broader surveys or domain-specific benchmarks outside the semantic neighborhood may contain additional relevant precedents not reflected in these statistics.

Taxonomy

Core-task Taxonomy Papers
13
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: audio comprehension via audio-interleaved reasoning. The field encompasses methods that enable language models to process and reason about audio inputs, often by integrating audio representations directly into multimodal architectures. The taxonomy reveals several main branches: Audio-Interleaved Reasoning and Dynamic Audio Engagement focuses on interactive and iterative reasoning strategies that allow models to refine understanding through multiple passes or reinforcement-based feedback; End-to-End Multimodal Audio-Language Models emphasizes unified architectures that jointly handle audio and text without separate preprocessing pipelines; Audio Encoding and Representation for Language Models addresses how raw audio signals are transformed into embeddings suitable for language model consumption; Audio Comprehension Evaluation and Reasoning Assessment develops benchmarks and metrics to measure understanding quality; and Multitask Audio Processing with Shared Architectures explores parameter-efficient designs that handle diverse audio tasks within a single framework. Representative works such as Qwen Omni[1] and VITA Audio[5] illustrate end-to-end integration, while approaches like Interleaved Instruction Tuning[9] and Acoustic Prompt Tuning[7] highlight different strategies for aligning audio with language model reasoning. A particularly active line of work centers on dynamic engagement mechanisms that enable models to iteratively refine their audio interpretations, contrasting with static encoding approaches that process audio in a single forward pass. Echo[0] sits within the Audio-Interleaved Reasoning with Reinforcement Learning cluster, emphasizing adaptive reasoning strategies that leverage feedback to improve comprehension over time. This positions it closely alongside Thinking with Sound[3] and Step Audio[4], which similarly explore stepwise or reflective reasoning patterns, yet Echo[0] distinguishes itself by incorporating reinforcement learning signals to guide the interleaving process. In contrast, works like VITA Audio Fast[12] and PolyAudio[11] prioritize efficiency and broad task coverage, trading off some iterative refinement for faster inference. The central tension across these branches involves balancing the depth of reasoning—achieved through multiple audio-interleaved passes—against computational cost and the need for robust evaluation frameworks that can capture nuanced audio understanding beyond surface-level transcription.

Claimed Contributions

Audio-interleaved reasoning format

The authors introduce a new reasoning format that treats audio as active components rather than static context, allowing LALMs to dynamically re-listen to audio segments during reasoning. This approach overcomes the information bottleneck of one-time audio encoding and enables sustained engagement with audio throughout the reasoning process.

10 retrieved papers
Two-stage training framework

The authors develop a training framework that first uses supervised fine-tuning to teach LALMs to localize salient audio segments, then applies reinforcement learning to refine the model's ability to strategically re-listen to multiple audio segments during reasoning.

10 retrieved papers
Can Refute
Echo model and structured data generation pipeline

The authors present Echo, a large audio language model that instantiates audio-interleaved reasoning by proactively re-listening to relevant audio segments. They also develop a structured data generation pipeline that produces curated training data with audio-grounded questions, answers, and chains of thought.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Audio-interleaved reasoning format

The authors introduce a new reasoning format that treats audio as active components rather than static context, allowing LALMs to dynamically re-listen to audio segments during reasoning. This approach overcomes the information bottleneck of one-time audio encoding and enables sustained engagement with audio throughout the reasoning process.

Contribution

Two-stage training framework

The authors develop a training framework that first uses supervised fine-tuning to teach LALMs to localize salient audio segments, then applies reinforcement learning to refine the model's ability to strategically re-listen to multiple audio segments during reasoning.

Contribution

Echo model and structured data generation pipeline

The authors present Echo, a large audio language model that instantiates audio-interleaved reasoning by proactively re-listening to relevant audio segments. They also develop a structured data generation pipeline that produces curated training data with audio-grounded questions, answers, and chains of thought.

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