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
Venue: ICLR 2026 Poster (2026)
Keywords: LALMs
Audio Comprehension
Audio-Interleaved Reasoning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce Echo, a Large Audio Language Model with audio-interleaved reasoning that dynamically revisits audio segments, achieving superior performance in complex audio comprehension.

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. Project page: https://github.com/wdqqdw/Echo.

Published as a conference paper at ICLR 2026
ECHO: TOWARDS ADVANCED AUDIO COMPREHEN- SION VIA AUDIO-INTERLEAVER REASONING
Daiqing Wu1.3,7 Xuan Zhang
Dongbao Yang* Jiashu Yaod
Longfei Chen Yu Zhou
Qingsong Liu
Sicheng Zhao
Can Ma
Yangyang Kang 3,m
1 "IE Chinese Academy of Scienees
ZZhejiang University "ByteDance China
VVCIP & TMCC & DISSec, College of Computer Science, Nankai University
$ "School of Information Science and Technology. ShanghaiTech University
'Department of Psychological and Cognitive Sciences. Tsinghua University
" "University of Chinese Academy of Sciences
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ABSTRACT
The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through one-time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio-interleaved reason- ing to break through this bottleneck. It treats audio an active reasoning compo- nent, enabling sustained audio engagement and instantiate it, we introduce a two-stage training framework, first teaching LALMs to localize salient audio segments through supervised fine-tuning, and then in- centivizing proficient re-listening via reinforcement learning. parallel, a struc- tured 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 advanc- ing audio comprehension. Project page: https://gihub..oo/wwqqggv//
analysis. To
INTRODUCTION
Audio serves as a primary medium for the expression, transmission, and reception of information in human communication (Hinton et al., 2012). As such, the capability comprehend audio has become an essential objective in the development of embodied intelligence, as well as key pursuit within the broader landscape of artificial general intelligence (Pfeifer Bongard, 2006). Through integration of audio encoders with large language models (LLMs), large audio language models (LALMs) have evolved as a prominent paradigm for this goal. Notable systems, such as Salmonn (Tang al., 2024) and Qweno-Audio (Chu et al., 2024), exemplify this trend, demonstrating superior performance across fundamental audio tasks, including speech recognition (Panayotov et al., 2015), sound classification (Gong et al., 2022), and music analysis (Agostinelli et al., 2023).
Recently, reasoning capabilities in LLMs have witnessed rapid growth (Huang & Chang, 2022). By breaking down complex problems into intermediate steps, LLMs progressively derive solutions through a sequence of incremental results (Wei et al., 2022), leading to substantial improvements in previous challenging domains such as mathematics and coding (Liu et al., 2024; Hui et al., 2024). This development has spurred research on LALMs, motivating a shift from basic audio perception toward advanced audio comprehension, which entails nuanced interpretation and reasoning over real-world compound audio. Leveraging prompt engineering (Ma et al., 2025b), supervised fine- tuning (Xie et al., 2025), or reinforcement learning (Li et al., 2025a), a close wave of research succeeds in replicating text reasoning in LALMs. These approaches typically rely coding to contextualize audio content, after which reasoning unfolds exclusively in the text modality (Su et al., 2025), format we refer to audio-conditioned text reasoning. While this represents significant milestone for LALMs, it inherently suffers from the gap between heterogeneous modal- ities. Unlike symbolic text, audio constitutes continuous signal that carries more diverse and
one-time en-

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Figure 1: Comparison between (a) and (b) compares attention allocated by the LALM to prompt, response, and audio tokens during reasoning, averaged over 100 samples. By switching from audio-conditionel text reasoning to audio-interleaved reasoning, LALM places significantly higher attention focus on the audio tokens (A+140%), thereby leading to more meaningful and traceable audio analysis.
text reasoning and indio-interleaved reasoning.
fine-grained information. Consequently, one-time encoding imposes a severe information bottle- namk-n the difficulty of preserving subtle audio details and reconstructing them from heavily compressed embeddings. This limitation risks LALMs overlooking salient details during reasoning, especially in realistic environments characterized by diversified and overlapping auditory sources.
In contrast, human auditory cognition involves eyclic re-listening of salient acoustic segments, driven by the interaction between auditory working memory and top-down attentional control (Grif- fiths & Warren, 2002; Oberauer & Hein, 2012). This mechanism enables humans to iteratively refine internal auditory representations and achieve more grounded and accurate pocision-making (Bronkhorst, 2015; Nobre & Van Ede, 2018). Inspired by this mechanism, we introduce a new reasoning format termed audio-interleaved reasoning. It treats audio as active reasoning com- ponents rather than static contexts, granting LALMs direct access to acoustic content as needed. Consequently, LALMs overcome the previous information bottleneck and sustain deeper engage- ment with audio during reasoning. Figure provides an intuitive comparison of the two reasoning formats. Under audio-conditioned text reasoning (a), LALM allocates non-trivial share of atten- tion (greater than 10%) to audio tokens only within the first 25 generation steps, after which this proportion rapidly falls below 5%. Conversely, indio-interleaved reasoning (b) stabilizes elevated attention to audio tokens (10%-14%) throughout the 25-200) steps. This sustained integration of auditory information enables the LALM to produce more logically coherent thoughts and more ac- curate responses to complex audio, as exemplified on the left.
To equip LALMs with audio-interleaved reasoning, we introduce a two-stage training framework. begins with Qwen2.5-Ommn (7B) (Xu et al., 2025), which is first enhanced through supervised fine- tuning to localize salient audio segments during reasoning. This yields a cold-start model capable of referencing specific temporal intervals via structured <seg> tag pairs and producing reason- ing steps grounded in the corresponding audio segments. We denote this intermediate format as audio-grounded reasoning, which explicitly attends to the audio multiple times while remaining text-confined. In the second stage, we begin by directly interleaving raw audio tokens into the rea- soning of the cold-start model. Each time segment tag pair is decoded, the generation is paused. The corresponding audio tokens are then inserted immediately after the tag. forming an augmented context for subsequent generation. This extends reasoning into a genuinely multimodal process, which is further refined through reinforcement learning. Guided by verifiable reward signals, the model attains advanced proficiency in strategically re-listening to multiple audio segments, exploit- ing fine- fineinraieed information available in the raw audio to emulate human-like reasoning patterns.
Alongside this framework, we devise a structured data generation pipeline. It leverages audio datasets with available temporal metadata to produce tightly audio-grounded questions, answers, and chains of thoughts (CoTs). This process yields a curated collection of 75.9k audio question- answering Audio-QA) samples with CoTs and 21.9k samples without, covering diverse audio cate- gories and varying levels of difficulty. Building on this resource, we present Echo, a LALM that instantiates audio-interleaved reasoning by proactively re-listening relevant audio segments
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Published as a conference paper at ICLR 2026
during the reasoning process. When evaluated against other LALMs, Echo demonstrates superior overall performance on audio comprehension benchmarks (MMAR (Ma et al., 2025c), MMAU- mini, and MMAU (Sakshi et al., 2024)) that emphasize fine-grained interpretation and expert-level reasoning. even surpassing advanced proprietary systems such as GPT-4o (Hurst et al., 2024) and Gemini-2.0-Flash (Google, 2025). In summary, the contributions of this paper threefold.
Emulating human cognitive processes, we propose audio-interleaved reasoning, which re- gards audio active components rather than static context. This formulation promotes sustained audio engagement, leading to more meaningful thoughts and precise answers.
Supported by the two-stage training framework and data generation pipeline, we present Echo, a LALM that demonstrates exceptional expertise in dynamically localizing and re- listening to informative audio segments during reasoning.
Systematic evaluation of Echo on three benchmarks comprehensively validates its effec- tiveness and efficiency, underscoring the viability and potential audio-interleaved rea- soning for advancing audio comprehension of LALMs.
RELATED WORK
2.1 LARGE AUDIO LANGUAGE MODEL
The success of LLMs constitutes pivotal advance in artificial intelligence, catalyzing the emergence and growth of LALMS that leverage LLMS as the central orchestrator for general audio comprehen- sion. Early systems such as AudioGPT (Huang et al., 2024) and HuggingGPT (Shen et al., 2023) mainly adopt cascaded approach, which transcribes audio signals into text before feeding them into the LLM. To alleviate acoustic information loss, subsequent LALMs directly inject audio features into LLMs, typically by mapping them into the LLM embedding space via linear projection. This design has given rise to a wide spectrum of models, ranging from academic pioneers (Zhang et al., 2023; Deshmukh et al., 2023; Tang et al., 2024; Chu et al., 2023) to advanced commercial systems (Comanici et al., 2025; Goel et al., 2025; Wu et al., 2025a; Dinkel et al., 2025).
As expectations for LALMS evolve, research emphasis has increasingly turned toward handling the complexities real-world compound audio. This trend is reflected in the recent emergence of more challenging audio benchmarks (Yang et al., 2024; Gao et al., 2024; Ma et al., 2025a: Ye et al., 2025). In particular, MMAU (Sakshi et al., 2024) and MMAR (Ma et al., 2025c) introduce intricate test cases that demand expertized interpretation and deliberate reasoning. aiming to quantify more sophisticated, human-like auditory intelligence. Building on these challenges, comprehensive evaluations across diverse tasks demonstrate that while existing LALMs achieve proficiency on basic perception tasks, they still face considerable limitations when confronted with expert-level audio questions, highlighting the pressing need for further advancement of audio comprehension.
2.2 MULTIMODAL. CHAIN-OF- TRAIN-OF-THOUGHT REASONING
The ability to perform CoT reasoning was first recognized (Kojima et al., 2022) and developed (Wang & Zhou, 2024) within LLMs and later extended to multimodal scenarios (Wang al., 2025). In LALMs, Audio-CoT (Ma et al., 2025b) pioncers this direction through prompt engineering, Sub- sequent Audio-Reasoner (Xie et al., 2025) employs supervised fine-tuning to instill structured reasoning pathway, adhering to the sequence of planning, caption, reasoning, and summary before deriving the final response. More recently, growing body of work (Li et al., 2025a; Wen al., 2025; Xing et al., 2025; Rouditchenko et al., 2025; Wu et al., 2025b) has shifted attention toward reinforcement learning, incentivizing reasoning abilities LALMMS with verifiable rewards.
Despite recent prosperity, current LALMss remain largely confined to audio-conditioned text reason- ing. This format separates multimodal perception from the reasoning process, thereby imposing an intrinsic ceiling on the integration of nuanced multimodal cues. A similar bottleneck has recently prompted an evolution the reasoning formats of large vision language models (LVLMs). Moving beyond "thinking about images" toward "thinking with images" (Su et al., 2025). modern LVLMs now seamlessly incorporate spatial references (Fan et al., 2025) or even raw image patches (Zheng al., 2025; Zhang al., 2025; Jiang et al., 2025) directly into their reasoning. This represents
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Input:
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Output:
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(b). Supervised Fine-Tuning
(c). Cold-Start Model
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Figure 2: Summarized illustration of the training framework. The base model (a) is first enabled to localize and reference audio segments via SFT (b). The obtained cold-start model (c) then equipped with audio-interleaved reasoning via inference adaptation (d): the inference process is paused whenever segment tags are encountered, and the corresponding raw audio segments are inserted afterwards before resuming. Subsequently, RL (c) is applied to further endow the model with competence in flexible audio invocation and accurate responding.
revolutionary step toward human-like reasoning (Larkin & Simon, 1987) and serves as a key inspi- ration for our work. Unlike images, audio is inherently temporal and can be naturally localized and segmented via timestamps. Capitalizing on this property, we propose audio-interleaved reasoning. an intuitive mechanism that empowers LALMs to dynamically localize salient audio segments and iteratively re-listen them during reasoning. Through this formulation, we seek to catalyze an analogous evolution in LALMs: from "thinking about audio" to "thinking with audio".
3 ECHO
In this section, we provide a comprehensive introduction to Echo, detailing its two-stage training framework (Figure 2) and the accompanying data generation pipeline (Figure 3).
3.1 TRAINING FRAMEWORK
The First Stage: Supervised Fine-tuning (SFT). The training begins with a base model initialized from pre-trained Qwen2.5-Omm. (7B) (Xu al., 2025), denoted as Tg. When tasked with audio reasoning, the model demonstrates a persistent tendency to rely on natural language reasoning, being reluctant to incorporate specific audio segments to produce andio-grounded reasoning (Section C). Since this capability is crucial for localizing informative segments in indio-interleaved reasoning, we first endow the model with it through SFT.
Let the dataset be denoted as DSFT {x,, vt )).Y Ji which consists of N high-quality Audio-QA pairs. Each sample (æ,, v/") includes a multimodal input 2; = (A, q), consisting of an audio A and a question q, and a golden-standard response y [u], Vi text composed of an audio aadii-grounded CoT c enclosed in a <think> tag pair and a ground-truth answer within <answer> tag pair. The CoT is saturated with references to informative audio segments, with each structured as <seg>starl timestamp, end timestamp</ seg>>. Each reference is deliberately preceded by a justification for its invocation and followed by fine-grained and tightly associated analysis. Base model Te is subsequently optimized to proactively refer to informative audio segments and perform focused analysis during reasoning, by mimicking the demonstrations from Dsrt under the following cross-entropy objective:
-V, = (c, a), which is n-length
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Figure 3: Overview of the data generation pipeline. It begins with an audio dataset containing fine- grained temporal metadata. For each audio data, Qwen2.5-Omni is employed derive a structured caption encompassing comprehensive descriptions, speech content, and musical elements. This information, along with the temporal metadata, is then fed into DeepSeek-RI (Guo al., 2025) for synthesizing QA TAT triplets. The synthesized triplets undergo further filtering based on the quality of Audio- QA and CoT, and are subsequently appended into two separate datasets for SFT and RL.
The Second Stage: Reinforcement Learning (RL). Upon acquisition of the cold-start model, still denoted as T. we first adapt its inference mechanism to activate audio-interleave reasoning. Given an input r; (A, q). the model performs inference until a <seg> tag pair is detected. Let and e represent the start and end timestamps encapsulated within the tag pair; the corresponding audio segment Ag:e is clipped from the initial audio A. This segment, along with the current text output o, forms an augmented input sequence x = (ay @ o @ Age). which is fed back into the model to resume inference. This process repeats iteratively until <eos> token is generated. The pseudocode is presented in Section D. Despite inheriting the capability to process multi-audio inputs from the base model, the cold-start model lacks sufficient competence in handling interleaved audio-text sequences. It restricts the model's effective attending to nearby audio segments during reasoning, obscuring the production of meaningful analysis coupled with iineggrained perception. To address this issue, we employ RL to facilitate more effective inference sampling (Yue et al., 2025), thereby unlocking the potential of interleavee reasoning.
In reward design, several augmentations are applied to the standard format and accuracy rewards to accommodate the new reasoning format. Specifically, the base format reward Rooraat assigns 0.5 points to responses that correctly use the encapsulation tags. Based on empirical observations (Section J), we introduce an additional consistency reward Reonsist to encourage semantic continuity after closing a <seg> tag pair. This reward checks whether the next token of </ /eg>> is capital letter (indicating the start of new sentence) or the token (suggesting continued segment reference or early termination of reasoning). A penalty of -0.1 is applied for either case, with the maximum cumulation to -0.5. On the other hand, the base accuracy reward Race allocates 0.5 points for answers that match the ground truth. Inspired by Zheng al. (2025), we introduce segment reward Reey to incentivize audio segment re-listening. This reward grants an additional 0.5 points if the response refers to least one segment and the answer is correct, and 0 points otherwise. In summary, the total reward for a response T is computed as:
R(T) = Roomat (7) + Raonsise + Race (T) + Reeg(T) (T).
Subsequently, Group Relative Policy Optimization (Shao et al., 2024) is employed for policy upda- tion. Given an input x, the algorithm begins by sampling G' candidate responses {Fi f T.. }a} from the old policy model Told- The advantage of response Ty is then computed as the normalized re- ward across the mini-batch: Ag = (R(Tg) mean(R)) /std(()) The cold-start model To is updated to minimize PPO-style (Schulman et al., 2017) elipped surrogate objective:
1 G
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Table I: Accuracy comparison of advanced LALMs on MMAR. "Avg" denotes the macro-average accuracy across all audio types. Abbreviations: Sd = Sound, Ms = Music, Sp = Speech. Bold, underline, and wavy underline indicate the best, second-best, and third-best results, respectively.
Single Modality (%)
Mixed-Modality (%)
Model
Avg (%)
Sd
Ms
Sp
Sd-Ms
Sd-Sp
Ms-Sp
Sd.Ms Sp
Random Guess
29.39
25,88
31.48
25.00
29.30
31.10
28.13
28.61
Open Source Base LALMSs
SALMONN (lang 2024)
30,30
31.07
34,69
9.09
34.86
35.37
41.67
31.01
Qwen-Audee (Chu al., 2023)
8.4B
27.88
20.39
22.11
9.09
25.23
25.61
20.83
21.59
GAMA (Ghoshh et al., 2024)
7B
29.09
24.27
27.89
27.27
24.77
28.05
12.50
24.83
Qwen2-Audio (Chu al., 2024)
8.4B
33.33
24.27
32.31
9.09
31.19
30.49
25.00
26.53
Omnii 1.5 (Li et 2025t)
41.21
33.01
40.48
36.36
48.62
39.02
41.67
40,05
Audio- madio-Flamingo-222 2 (Gihosh al., 2025)
3B
24.85
17.48
20.75
18.18
26.61
23.17
8.33
19.91
Qwen2.5- (Xu et ol., 2025)
7B
58.79
40.78
59,86
54,55
61.93
67.07
58.33
$7.33
Proprictary LALMS
MOPT-4-minii-uiee (Hurst al., 2024)
38.79
35,92
58.84
45.45
60.09
$7.32
50.00
49.49
GPT-4--Addi (Hurst al., 2024)
53.94
50.97
70.41.
63.64
72.48
62.20
75.00
64.09
Gemini-2.0-Fins (Google, 2025)
61.21
50.97
72.11
81.82
72.48
65.85
70.83
67,90
2omini-2.5-Pro (Comanici et al., 2025)
66.06
57.77
73.13
81.82
31,10
69.51
70.83
70.03
Adapted LALMs
Audio-CoT (Ma al., 2025b)
8.4B
35.76
25.24
34.01
9.09
30.73
30.49
37.50
28.97
Audio-Reasoner Reasoner (Xic et al., 2025)
8.4B
43.64
33.50
32.99
45.45
42.66
31.71
25.00
36,42
Ounii- R1 (Rouditchenko et al., 2025)
2025)
7B
67.30
51.50
64.30
55.50
70.20
64.60
70,80
63.46
Audio Thinker (Wi al., 2025b)
7B
68.48
53.88
64,29
72.73
71.56
73.17
66.67
67.25
Echo (Ours)
67.27
60.68
69.39
81.82
69.72
74.39
66.67
69.99
3.2 DATA GENERATION PIPELINE
Existing Audio-QA training datasets, such AVQA (Yang et al., 2022) and Coltho-AQA (Lipping et al., 2022), are generally designed without a strong emphasis on task difficulty and lack golden- propose
standard CoTs that incorporate fine- treeggraied audio references. To address this limitation, a data generation pipeline that leverages the advanced reasoning capabilities of LLMs to synthesize challenging Audio-QA data and corresponding audio-grounded CoT's for use in our RL and SFT. Figure presents an overview of the pipeline.
We begin by selecting audio datasets enriched with available temporal metadata (in practice, AudioSet-Strong (Hershey et al., 2021) and MusicBench (Melechovsky et al., 2024)), and employ Qwen2.5-0mni convert audio into textual descriptions. Each audio undergoes three independent procedures: once to generate a comprehensive caption, once to transcribe potential speech, and once to extract possible music elements. Combined with the temporal metadata, these outputs form vivid textual simulation of the original audio. Based on this simulation, we prompt DeepSeek-RI (Guo et al., 2025) to automatically synthesize QA-CoT triplets (refer to Section E for prompt usage). Benefiting from the advanced reasoning I.LM, this process yields QA pairs that require fine-grained temporal analysis of the audio content, along with corresponding CoTs that follow a step-by-step reasoning process grounded in <seg> tag pairs to derive answers from the questions.
To further ensure the rationality and complexity of the Audio-QA pairs, as well as the faithfulness and logical coherence of the CoTs, the synthesized triplets are subjected to rigorous re re-evaluation by DeepSeek-PR RI under detailed quality criteria. Triplets deemed to contain both high-quality Audio- QA and CoT are retained for the SFT dataset, while those with only high-quality Audio-QA preserved without CoT and assigned to the RI. dataset; all others are discarded. To further enrich the RL data, we perform direct quality evaluation for Audio-Qr QA pairs from AVQA and select an additional 10k high-quality samples. In total, we obtain 75.9t high-quality Audio-QA samples with COT, denoted EAQA-SFT (Echo Audio-QA for SFT), and 21.9k Audio-QA samples without Cот, denoted as EAQA-RL. More comprehensive statistics are presented in Section F.
4 EXPERIMENTS
Implementations. For SFT, we use learning rate of 5c-6 and a batch size of 16, freezing the audio encoder throughout training. The model is trained for one epoch on the EAQA-SFT dataset. For RL, adopt learning rate of 1e-6, batch size of 64, mini-batch size of 32, KL-coefficient of 0.04, and 8 rollouts per query. The training proceeds for one epoch on the EAQA-RL dataset. During inference, Echo performs audio-interleaved reasoning following the identical adaptation detailed in

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Published as a conference paper at ICLR 2026
Table 2: Accuracy comparison of advanced LALMs on MMAU-min and MMAU. "Avg" denotes the macro-average accuracy across three audio types.
MMAU-mini
MMAU (%)
Model
Size
Sd
Ms
Sp
Avg
Sd
Ms
Sp
Avg
Random Guess
26.72
24.55
26.72
26.00
25.73
26.53
25.50
25.92
Open
Source Base
LALLSs
SALMONN (Tang et al,, 2024)
13B
41.14
37. 13
26.43
34,90
42.10
37.83
28.77
36.23
GAMA (Ghosh et al., 2024)
7B
31.83
17.71
12.91
20.82
30.73
17.33
16.97
21.68
Qwen2-Audio (Cho
2024)
8.4B
67.27
56.29
55.26
59.61
61.17
55.67
55.37
57.40
Owen2.5-Omni (Xu et al., 2025)
7B
78.10
65.90
70.60
71.53
76.77
67.33
68.90
71.00
Audio (Ding et al., 2025)
8.2B
75.68
66.77
62.
68.20
70.70
65.93
56.57
64.40
DeSTA2.5 1.2.5-AAndi Andia (Iu al., 2025)
8B
70.27
56.29
71.47
66.01
66.83
57.10
71.94
65.29
Mudio-Flamingo (Goel al., 2025)
8.2R
79.58
66.77
66.37
70.91
75.83
74.47
66.97
72.42
Proprictary LALMs
GPT-4 Audio (Hurst et al. 2024)
64.56
56.29
66.67
62.51
63,20
49.93
69.33
60.82
Gemini-2.0-HHe 2.0-ha (Google, 2025)
71.17
65.27
75.08
70.51
68.93
$9.30
72.87
67.03
Gemini- P.5-Pro (Comanici el al., 2025)
75.08
68.26
71.47
71.60
70.63
64.77
72.67
69.36
Step-Andio-2 (Wu 2025a)
84.04
73.56
75.15
27.58
80.60
72.75
73.86
Adapted LALMS
Audio Reasuner (Xie et al., 2025)
8.4B
67.87
69.16
66.07
67.70
67.27
61.53
62.53
63.78
Omni- R1 (Rouditchenko et al., 2025)
7B
81.70
73.40
76.00
77.03
78.30
70.80
75.80
74.97
Thinker (Wo al., 2025b)
7B
82.58
74.55
76.88
78.00
79.03.
70.53
76.6
75.39
Echo (Ours)
7B
86.49
76.35
78.38
80.41
79.62
72.33
77.87
76.61
Section 3.1 (Figure 2 (d)). We consider the answer correct only if the content within the <answer> tag pair matches exactly with the ground-truth ignoring case and special characters.
Evaluations. Experiments are conducted on three popular Audio-QA benchmarks: MMAR (Ma al., 2025c), MMAU 1505.1 and MMAU-mini (v05, 15.25) (Sakshi et al., 2024), all which are tailored for evaluating advanced audio comprehension. MMAR comprises 1 multi-disciplinarg tasks requiring expert-level reasoning. spanning diverse audio types including speech, music, sound, and mixed-type content. MMAU and MMAU-mini place greater emphasis on general audio under- standing, containing 9k and 1k tasks, respectively. They cover the evaluation across 27 distinct skills and three levels of task difficulty, each necessitating different degrees of reasoning over speech, mu- sic, and sound. Accuracy is employed as the primary evaluation metric across all benchmarks.
4.1 MAIN RESULTS
Comparison on MMAR (Table 1). When faced with reasoning-intensive audio tasks, most open- source base LALMs fail to demonstrate significant advantages over random guessing, revealing a pronounced gap between basic audio perception and advanced audio comprehension. In contrast, LALMS specifically adapted for the latter exhibit notable improvements. Collectively, Echo achieves the best average accuracy among open-source and adapted LALMs, even surpassing advanced pro- prietary LALMs such GPT-4o-Audio and Gemini-2.09 2.a-hh.hh. These results underscore the benefit of decomposing complex audio inputs into simpler segments during reasoning, establishing audio- interleaved reasoning as a promising solution for challenging real-world scenarios.
audio comprehension tasks, Echo also delivers competitive results across various types of audio inputs, achieving higher average accuracy (+2.41 % on MMAU-mini and +1.22% on MMAU) than other open-source and proprietary LALMs. This outcome demonstrates that deeper engagement with audio content pro- vides an inherent advantage for LALMs to produce innception-groundee analysis and reliable re- sponses, confirming the generalizability of audio-interleaved reasoning in broader scenarios.
Comparison on MMAU-mini and MMAU (Table 2). On
4.2 ANALYTICAL RESULTS
Effectiveness of the Proposed Training Framework (Table 3: A-B--C-D), From A A-B, SFT results in 4.97% accuracy gain, 72% increase average output length, and a corresponding 73% rise in reasoning latency. This effect stems primarily from the data distribution of EAQA-SFT, whose CoT annotations are produced by an advanced LLM and reflect a more elaborate, coherent, and audio- grounndd reasoning path. Adapting the inference format (B-C) initially leads to a performance drop, as the model not yet accustomed to interleaved audio-text text inputs. However, subsequent RL (C-D) effectively mitigates this gap, with outcome-
audio-interleaved reasoning

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Published as a conference paper at ICLR 2026
Table 3: Comparison of average accuracy, response length, and reasoning latency MMAR of models obtained from different training recipes. All inferences are obtained under a fixed prompt (Section E.2) and carried out on single NVIDIA A100 GPU using the vLLM engine (Kwon al., 2023). The proposed framework leading to Echo is represented by A C-D.
MMAR (Avg)
Model
SFT Data
RL Data
Reasoning Format
Acc (%)
Length (word)
Latency (8)
A
Base Model
Audio-C Conditioned Text
$1.80
67.95
1.18
B
Cold-Start Model
EAQA-SFT
Audio-Grounded
56.77
117.06
2.04
Unadpted RL Model
EAQA SFT
EAQA RI.
Audio-Guun Grounded
64.63
98.12
1.97
C
Cold-Start Model
EAQA-SFT
indio-Interleaved
52.26
101.73
2.16
Fcho D
EAQA SFT
EAQA RI
Audio- lateo-Interreaved
69.99
107.40
2.12
D'
Echo-AVQA
SAQA-SFT
AVQA
Intel-enterleaved
67.58
120.18
2.37
E
Direct RL Model
EAQA RL
Audio Canii-Conditionedd Text
63.15
104.42
2,06
E' Direct RL Model
AVQA
Madio-Conditioned Text
59.51
111.14
2.11
(a). Accuracy Roward
(b). Format Consistency Reward
(c). # Segment Response
<40
140
142
[4)
035
033
11.2
223
102
114
(d). Average Segment Duration (s)
(e). Overlap Ratio among Segments
(f). PPO KL Divergence
035
500 Nog Cigy Son Ray
1.0
335
350
2.33
2.01
150 250 380
255
Figure 4: Evolvement of (a,b): reward components, (c,d,e): segment-associated statistics, and (f): model divergence during the RL process. (e) evaluates the temporal overlap among segments within each response. (ff measures the distribution discrepancy between the policy and the reference model.
based supervision ultimately boosting the average accuracy to peak of 69.99%. Overall, these results validate the efficacy of our training framework, highlighting its role in unlocking the sub- stantial potential of audio-interleaved reasoning.
Comparison of Reasoning Format (Table 3: E-BB-DD). Lines B' and E represent models em- ploying audio-grounded and audio-conditioned text reasoning formats, respectively, both subjected to the same RL process Echo (D). Specifically, B' bypasses the inference adaptation from B to C, while E omits SFT, thus preserving the base model's original reasoning format. Along the tra- jectory E-B'-+D, accuracy improves with increased audio involvement, underscoring the intrinsic benefit of audio-interleaved reasoning beyond data effects. Importantly, output length and inference latency remain comparable across formats, indicating that audio-interleaved reasoning incurs only marginal computational trade-off, which guarantees its overall efficiency.
Impact of RL Data (Table D E-+E).. Replacing the RL data from the 21.9k samples in EAQA-RL with 40.4k samples from AVQA results in consistent decline in average accuracy across reasoning formats. It demonstrates that EAQA-RL delivers both superior annotation quality and more appropriately leveled challenges, thereby providing more effective supervision for incen- tivizing reasoning capabilities in LALMs.
Training Dynamics of RL (Figure 4). As training progresses, the accuracy reward (a) exhibits fluctuating yet upward trend, and the format and consistency rewards (b) rapidly converge near their upper bounds. This demonstrates the effectiveness of verifiable rewards in both accuracy enhance- ment and format calibration. The efficacy of the segment reward evident in segment-associated statistics: responses stabilize at invoking about 1.9 segments (c) with an average duration of 3.0s (d), and segment overlap (e) remains low around 0. These patterns indicate a healthy training process, reasoning with minimally overlapping
in which the model consistently employs segments. Finally, the PPO KL divergence (f) stays near zero, confirming that gradient clipping and the KI. penalty effectively constrain the policy within trusted region and ensure stability.
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Published as a conference paper at ICLR 2026
Basa Modal
Cold-Srart Model
Echo
Disconant Ematon Interppett (Speach)
100.0 (+352.0)
100
(+17.3)
(+20.5)
Multi Speaker Roie Mapping (Speech))
986.3
Emettinnal Tonn Interpoet wwwprpattiooooo (Mfust))
90.3
(+9..)
79.4 (+5.9)
Tempurel Event Reasoning (Sound)
Event Event-Baed Based Sound Receoning (Soune)
(+20.0)
Ambient Sound Interar interoretation (Sound)
(+24.6)
05.4
Acoustic Scone Reazoning (Sound) (Sound)
(+18.8)
Accuracy (%)
80
Mustral Genra Reszoning (Music)
70.a
60.0
(+16.7)
Emation Summarisstin
Temporal Reasaning (Music)
(+14.3)
73.8
73.5
72.8
70
63
63.0
63.5
54.2
54.2
$o.8
40-
20-
Figure 5: Progression of fine- graeegg cognitive abilities from the base model, to the cold-start model, and finally to Echo. The evaluation selects tasks from 10 representative skills, annotated in MMAU- mini as core requirements for LALMs, encompassing reasoning over speech, music, and sound.
Skill- Wise Evolution on MMAU-mini (Figure 5). A consistent enhancement is observed across 10 representative skills from the base model to Echo. The most substantial improvements occur in tasks associated with cognitive skills for speech and sound, exemplified by +37.0% on multi-speaker role mapping. +20.8% on event-based sound reasoning, and +20.5 on emotion state summariza- tion. These tasks demand precise temporal localization and fine-grained audio perception, which are inherently strengthened by the format of audio-interleaved reasoning. Notable progress is also achieved in music-related skills. Though less dramatic, Echo attains consistent accuracy improve- ments exceeding 5%, demonstrating its competence in interpreting temporally dependent chord, melody, and genre. Collectively, these results confirm the broad effectiveness of audio-interleaved reasoning across diverse skill dimensions.
Segment Coverage (Figure 6). Echo demon- strates a strong proficiency in dynamically re- listening to audio segments. On 1000 tasks from
Normaltte ad Frequency
Sugmant (rrasnt 75.0% Resporses)
fed Segmant (Present 29 d% Respereasi
Oiher Segmerts Pracentin 11.7% Reqparee
MMAR, re-isteess to at least one audio segment in 99.4% of responses, two segments in 78.0%, and three or more in 28.4%. The re-accessed seg- ments are distributed relatively evenly across the audio timeline, with modest bias toward the first half. Interestingly, while EAQA-SFT annotations constrained to the first 10 seconds due to lim- itations in source temporal metadata, 78.4% of MMAR audios extend beyond this range (further analyzed in Section K). Echo nevertheless identifies informative segments well past the 10-second mark, underscoring its ability generalize segment localization across varying audio lengths.
80
200
Relative Position in Audio (9)
Figure 6: Distribution of segments coverage over relative audio positions Echo's audio- interleaved reasoning to MMAR tasks.
5 CONCLUSION AND DISCUSSION
In this paper, we propose audio-interleaved reasoning, a new reasoning format that breaks through the information bottleneck inherent in conventional audio-conditioned text reasoning, offering more human-aligned and flexible approach to audio comprehension. Building on it, we introduce a two-stage training framework consisting of SFT and RL, along with a data generation pipeline that produces accompanying EAQA-SFT and EAQA-RL datasets. These components collectively enable the development of Echo, LALM equipped with audio-interleaved reasoning. Through extensive experiments, we demonstrate Echo's 's overall superior performance on audio comprehension tasks requiring expert-level reasoning. Further analyses reveal that this format promotes deeper audio engagement and enables accurate fine-grained perception, while incurring only minimal computa- tional overhead, Overall, the results validate interleveeeeeeved reasoning as an effective path toward addressing challenging real-world scenarios and highlight its potential for future research.
Despite these advances, the current implementation in Echo remains relatively straightforward. There is considerable room for refinement through more customized audio re-listening or fine- grained supervision. For instance, while reaccessing original audio segments imitates human cog- nition, LALMS could potentially support more advanced manipulations, such slow playback or

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Published as a conference paper at ICLR 2026
isolating specific frequency bands. Moreover, the CoT annotations in EAQA-SFT are automatically generated from fixed temporal metadata without human heuristics, and EAQA-RL similarly lacks detailed supervision over segment selection and usage. Explorations these aspects could further unlock the potential of audio-interleaved reasoning.
6 ACKNOWLEDGEMENT
This work is supported by the National Natural Science Foundation of China (Grant No. 624063 & 62376266 & 62571294 & 62441614) and the Beijing Natural Science Foundation (L252009).
ETHICS STATEMENT
This work advances the audio comprehension capabilities of LALMs by introducing a new reason- ing format, a training framework, and corresponding datasets. The datasets are constructed from publicly available sources in compliance with their usage licenses, and no new data are collected. However, we note that the use of LLM (DeepSeek-RI) in the data generation process may inadver- tently propagate biases inherent its underlying training data. Specifically, as revealed by Mar- janović et al. (2025). DeepSeck-RI exhibits systematic "rumination", characterized by repeatedly revisiting similar problem framings rather than exploring diverse reasoning trajectories. The same study also highlights that DeepSeek-RI may generate harmful unsafe reasoning traces, which could, in turn, be unintentionally distilled into downstream models during training. Consequently, for downstream in real-world applications, the dataset should undergo more rigorous processing to identify and mitigate such biases, thereby reducing potential negative impacts.
8 REPRODUCIBILITY STATEMENT
We provide detailed description of our training framework (Section 3.1, Section D, Section E), data generation pipeline (Section 3.2, Section F), and evaluation settings (Section Section G) to support the reproducibility of this research. We have released the training code and scripts in
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Pinci Yang. Xin Wang. Xuguang Duan, Hong Chen, Runze Hou, Cong Jin, and Wenwu Zhu. Avqa: A dataset for audio-visual question answering videos. In ACM MM, pp. 3480-33000 03900-3-111-- 2022.
Qian Yang, Jin Xu, Wennui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv. Zhou Zhao, Chang Zhou, et al. Air-bench: Benchmarking large audio-language models via generative comprehension. In ACL, pp. 1979-1998, 2024.
Qilang Ye, Wei Zeng, Meng Liu, Jie Zhang, Yupeng Hu, Zitong Yu, and Yu Zhou, When eyes and ears disagree: Can milms discern audio-visual confusion? arXiv preprint ar 2iv::5511.10000 2025.
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang. Shiji Song, and Gao Huang. Does re- inforcement learning really incentivize reasoning capacity ilms beyond the base model? arXiv preprint arXiv:2504. 13837, 2025.
Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, and Xipeng Qiu. Speechgpt: Empowering large language models with intrinsic cross-modal conversational abil- ities. In Findings of EMNLP, pp. 15757-15773, 2023.
Xintong Zhang, Zhi Gao. Bofei Zhang, Pengxiang Li, Xiaowen Zhang, Yang Liu, Tao Yuan, Yuwei Wu, Yunde Jia, Song-Chun Zhu, and Qing Li. Chain-of-focus: Adaptive visual search and zoom- ing for multimodal reasoning via RL. arXiv preprint arXiv:2505. 2025.
Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, et al. Swift: a scalable lightweight infrastructure for fine-tuning. In AAAI, volume 39, pp. 29733-29735, 2025.
Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing" thinking with images" via reinforcement learning. arXiv preprint arXiv:2505. 14362, 2025.
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A
APPENDIX MENU
Section B Case Analysis of Different Reasoning Formats.
Section C-Reasoning Habits of Base Model.
Section D-Pseudocode of Audio-Interleaved Reasoning.
Section E-Prompt Usage.
Section -.E.QA--FFTEEAAA-RR.
Section G-More Implementation Details.
Section H-Details of Compared Baselines.
Section I- Quantitative Analysis of Re-lestened Segments.
Section J-Ablation Study of Reward Design.
Section K- K-Generrlizability to Long Audio.
Section L- Limitatiion Analysis.
Section M M-More Case Visulization.
Section N-Claim of LLM Usage.
B CASE ANALYSIS OF DIFFERENT REASONING FORMATS
Audio-QA
posswardinnteoauiin osrssttaeessoorrttt youneskspayyaiin
Audio-OA
Flaying poier. Playing maniong Playeng cheza.
What well passward??
Ihik slap- ty-slep.
julliotijio
setaal scceraroo this?
Think step-ty-step
Wet the silcs Audio Condittond Text Reasoning
Base Model:
Responso
Base Model: Audio Conditioned Text Reasoning
Response
neef fiss" oes, But
options are "aaspwerdd is repuired" firs"
Well soune canda shufling and genaric noundo
lit ds shtiing aed from
order softew tnsd
they/n madjing
you have in ouner cortee rivt io get the passoieet and you ve maddy done thair then it sawets Bos answor posewordia requieaf its sounge. rightt Aryway, guacs the
the repact snundts and lobly mevred the garwis dynomize la line being placecior picted up
Answer Playing mahoong
slous?
Cold-Start Model: Audio-Grounded Reasoning
Response
Cold- Colr-Start Model:
Audio-Grounded Reasoning
Responsa
The contare disine sourda shufft carda isu, 4asge3.0, 0. 0.24begg)) 2 lassing speech
3.
eleeaant
Al the beginninng of the conversntine, the first szenker wida for the will preewces daage3 1, Bicckegh the socreed speaker dasatr stxtes 'you howe in onder cotho which siimeddssedy
games where cards are dhuffed and playee inteout with the gamee "The coneetenttien ioppriiingggg and tharpingg may carts beings girggla siced on
hancs moving The apeech indicalza plapera Whis combissiion of cand sounde andiiteraatiio rokee dronnly powne cantt garss
cuppuccino
1sgge13.
3. B-lleeg, Later in saegeS 4.4 8, f-veer,, when first speuker auto again, the aocondispaate reporrs yyu hown nnder ootine frs"" This contruuee empisace cofoee catering pulprrass
Answer. Playing X
getting passsoent
Answer: wwdorreetoorrrooooooee
Audio-Intarleave Reasoning
Response
Echo: Audio-interlsaved Reasoning
Response
Althe bepintrg of madio sauged.0 3. 1.0hbage ① hear a disinct Buunp, follood by woman speaking. This remedille sugaeab an interction incling ploskal deject
Frxk saaged.1, 3.34isege:
soflo Bnd woman csto for the sesapert and second apooker
aep peree caag-1..
5.30laap
2.0xisag> srothar pocharttump heard The
tnumps inlloued by
snying you howe to onder collos toe" wter the mon snlwing expuircino and ingu agar.
spoech ahrungly resembkes Ie difiiiic c stotts of des being moved in game lii maitinn..
This procss also repusted maliipic treephoot sudo, aaci do 84
Whlls other sounda are preaent the aecsemor distred percosaive events and infenperre sed disine comnel of makjonn games insiking mitiile plapers.
5.205eegPP (8,
10.841age [I]
Answer youha nothoweecoodeerofeeent
/
Answer Playing mahjong /
Figure 7: Intuitive comparison of reasoning formats on two representative MMAR tasks.
Figure 7 compares responses generated by different models, each employing distinct reasoning format. The audio-conditioned text reasoning produced by the base model is entirely confined to natural language analysis, offering limited and superficial references to the audio content and failing to provide sufficient justification. After SFT, the cold-start model learns to generate audio-grounde reasoning that includes <seg> tag pairs along with traceable analyses. However, this mode remains essentially text-based and continues to overlook certain audio cues, occasionally leading the reason- ing chain astray (as in the right case). In contrast, Echo's audio-interleaved reasoning dynamically inserts actual audio segments into the reasoning process. This facilitates deeper audio engagement and ultimately yields correct outcomes in both cases through logically coherent and perceptually grounded analysis.
C REASONING HABITS OF BASE MODEL
Figure& 8 illustrates attempts to instruct the base model using different prompt templates to elicit audio-grouu grounded reasoning. However, the base model consistently fails to adhere to these instructions and is reluctant to reference specific audio segments via timestamps. These results motivate our use of SFT to first endow the base model with this capability.
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dillboin Audio
Determine productng sounnd the mudio Question
[Audio], [Question] Choose the answer from: [Choices). Think step-by- stop. Think as cemorchenztvely as pcosible in point point way, For each poinr. refer to the specilic zegments using limestamps with <segp.. Answer the format of stinee...tinniesaaa.. "a..o................. "...as.... slanswe..
Parnoi
[Audio]: [Qusestonn] Choese the ansver from: (Choices). Think slep-by- stop. Answer the format <think <thinke sanswers... cranswar.
chirk Parrol bird that can minic human speech <think car wwwee--errrnnneeeeeeee
Prompt Template
thhnkooonddeen ng the contest, the sound leely produced by bird. The options are Paroot and Owt, Farrots are known for thei" abiliy to mimic human speech, which fite the description of the audio. Ows are not known for mimicking human speech. sthinic senswer-Parrtchaae Parse--knseerr
[Audio]. (Question) Chouse the from: (Choices). Think with
thinking.
Answer
Baso Model:
<thinc There sounds
speaking, The
sounds consistent throughout the The woman
[Audio]. [Question] Chocse the answer from: [Cholces]. Think stop-by- step. Divide the audio into segments to analysis Answar the format <thnks... <thnnk--nnswwr.... <lanswerr>
[Audio], [Question]] Choose the answer from: (Cholces). Think stop by slap. Rater to the spacficc audio segments while thinking, and indiaade the correspanding limescapp.. Answer in the format of sthini.... <think>> <answer>... <tanswer"
<thikk-The audoo contains bind sound, a human voloe, and mechanical voice. The bird sound is consistont througout the audio, and the human voise and mechanical voice are presvent at aiees The bir sound is the most consistent and prominent sound, is lieet the source of the sound. sound. Atthiris canswer-Parat-iee Paron-ionswer
<thini>Thhre sounds
chirping
spaskingg
The man alco speaking in bind-lkee marner mannen <aineesasssee Parrordaasswer
Figure 8: Responses of the base model under different prompt templates.
D PSEUDOCODE OF AUDIO-INTERLEAVED REASONING
Algorithm presents the pseudocode iadio-interleaved reasoning.
Algorithm Inference Adaptation of Audio- Interleavedd Reasoning
Require: Trained model Te, input sequence 2; (A, 4)
Ensure: Final output sequence o
1: 0
2: A 4 A
3: 22
4: repeat
5: Ô Ô (2(x)) (Perform inference until <seg> or <eos>}
6:
Append to
7: if <seg> tag pair is detected in then
8:
Extract start timestamp and end timestamp e from tags
9:
Age - clip( Afill 8, E)
10:
3 Ô + Asse (Append output and audio segment]
11: else
12:
@ {Termiinte if no segment tag)
13:
end if
14:
until ó contains <eos> token
15:
return
E PROMPT USAGE
E. DATA CONSTRUCTION
Audio Information Extraction: Comprehensive Caption
Describe the audio as comprehensively as possible.
Audio Information Extraction: Speech Transcription
Give some information about the speech, such as the text content of the speech, the emotion of the speaker, the gender of the speaker, and the spoken language, only if the speech is present this audio.
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Audio Information Extraction: Musical Elements
Give some information about the music, such as music genre and music instruments, only music is present in this audio.
Audio-QA CoT Synthesis
You are given simulated information about acoustic events in audio clip, including: Al: A description the audio. A2: Information about speech (only present), including possible transcript, emotion, and speaker gender. A3: Information about music (only if present), including genre and instruments. A4: list of key audio segments where major sound events occur (with start and end timestamps). A5: The duration of the audio.
These are used to simulate your understanding as if you had listened to the actual audio. You MUST treat them as your internal interpretation of "the audio", and you MUST NOT reference AI-A5 or "the audio description" explicitly in your output.
Your goal is to generate challenging question-aie --s--a--swaaaaaeee chain-of-thouugh (CoT) triplets the following format: [QUESTION, : "A question that requires listening to the audio answer accurately.". [MULTICCOOIC]] [a, b, c, d], [ANSWER]: the correct answer, [COT]: [think]....yorr reasoning htw.://hink/aaswwrr..////aaaa
REQUIREMENTS:
Question Design: The question must be answerable only by listening to the audio. should require non-trivial inference, going beyond surface-level perception. The question may either involve explicit temporal framing (e.g., asking what happened first, last, or at the same time) be temporally neutral (e.g., asking about specific sound, event, or emotion). However, the answer must require temporal reasoning-suce as identifying the order of events, comparing the lengths of different segments, detecting overlaps in time.
2. Multiple Choice Options: The correct answer must be unambiguously supported by the audio. The incorrect answers must plausible, but clearly incorrect when grounded the audio.
Chain-of-Thought (CoT): The reasoning process must be natural, fluent, and well- structured. Avoid using bullet points numbering. Each time specific piece of evidence from the audio discussed, the relevant audio segment must explicitly referenced first, using the format s.7>/seg>). For For example: "In <seg>0.4, 2. 3.55//egs the speaker raises their voice, suggesting urgency."
end serd_sscond///ee /eeg> (e.g., <seg>3 3,
1. ///////>>>> we can infer that a dog is barking in the back- ground." Each CoT must include least one segment reference, unless the provided seg- ments all span the entire audio duration. General reasoning steps not tied to any specific moment do not require segment tags.
"By listening to <seg>0.3
4. Use of Simulated Inputs (A1-A5): Treat A1-A5 as your internal hearing/understanding of the audio. Refer to any sounds or events by saying they occurred "in the audio". You MUST NOT refer Al-A5 or "the audio description" external annotations.
Example:
A1: The audio contains a speech across a range of times, siren sound, and breathing.
A2: There is speech in Mandarin with male speaker, expressing Searful mood. The speech content is more than copper are falling down here.
A3: There no music this audio.
24: eetrong_eventste speech, man speaking"), | ("time_rarngeeeeeeee range": "0.0s 2.0s", "Label": "Wind"), ["time_range", range": "1.7s 22. "label": "Tick"), time range": "2.1s 10.0s", "lasel": "Nind" ("time range": "2.4s 2.6s", "label": "Tick"), ("""),((ttt ("time_range"; "3.0s 3.1s", label"; "Tap"), ("time_range": "3.25 3.4s", "label": "Tick"), ("time_range": "3.6s 4.1s", "label": "Surface contact"), (" Lime_range"; "4.1s 4.9s", "label*: "Male speech, speaking
[["timerrange:: "0.0s - 1.4s", "label": "Male
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"}, raaime_range"; "4.9s 5. 5.7s", "label"; "Breathing"), time range"; "5.9s 6.0s", "label*: "Tick"I, ("time range": "6.0s - 6.7s", "label": "Male speech, man speaking"), (" time range": *6.3s - 10.0s", "label": "Emergency vehicle"), (" time_range": *6.9 7.4s", "label*; "Male speech, man speaking "}, r"time_range"; "9.2s 9.98", "label"; "Male speech, man speaking")]}
A5: 10.0
Output:
[QUESTION_TEXX : How long after the surface contact sound ends does the man next speak?,
[MULTI_CHCICE]: [0 seconds (immediately after), 0.3 seconds, 0.8 seconds, 1.2 seconds),
[ANSWER]: 0 seconds ( (immediately after),
[cor]: [think] <seg>3.6, 4.1</seg2, the surface contact occurs, ending at 4.1 seconds. Immediately after, in <seg>4.1, 4.9</ seg?, the man begins speaking again. Since the speech starts at the exact moment the surface contact ends, there is temporal gap between the two events. Therefore, the speaks inmodiately after the surface contact ends. [/think] [answer]0 seconds (imnediatcly after) [/answer),
Input Format:
A1:
[DESCRIPTION]
A2:
[SPEECH]
A3:
[MUSIC]
A4: [SEGMENTS]
A5:
[DURATION]
Do not output contents other than the question-answes answerr th (CoT) triplets.
Audio-QA CoT Filtering
You are given simulated information about acoustic events an audio clip, including: A1: A description of the audio. A2: Information about speech (only if present), including possible transcript, emotion, and speaker gender. A3: Information about music (only present), including genre and instruments. A4: A list of key audio segments where major sound events occur (with start and end timestamps). A5: The duration of the audio. Your task is to evaluate the quality of the following Question- Choices Ponices-Answer of Thought (CoT) quadruplet based on the provided acoustic attributes (Al-A5).
Evaluation Criteria:
Question and Answer Quality
Necessity of Audio Information: Does answering this question require listening to or analyzing the provided audio information (A1-A5)?
Answer Plausibility: Is the correct answer (A) the only plausible choice among the given options?
Ambiguity: Are there other choices that could be correct based on the given infor- mation? yes, the QA invalid.
CoT Quality
Does the CoT mention any information that is not present in A1-A5? If yes, consider the reasoning flawed.
Grounding:
Coherence: Is the CoT logically coherent, step-by-step, and does it naturally lead to the given answer?
Fluency: Is the reasoning presented in clear and natural English, without redun- dancy or contradiction?
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Audio Info:
Al: [DESCRIPTION]
A2: [SPERCH]
A3: [MUSIC]
A4: [SEGMENTS]
A5:
[DURATION]
Output Format:
IQA valid] Yes/No, [COT valid] Yes/No Do not output any reason.
E.2 EVALUATION
Standard prompt template for evaluation of LALMS
[QUESTION] Choose the answer from [CHOICES]. Think step-by-step. Refer to the specific audio segments while thinking, and indicate the corresponding timestamps. Answer in the format of <think>... </.ttiikkk/.>>>>>>>. <ankser>>. wtt.eeee.....wwe........ </answer>.
F EAQA-SFT&EAQA-RR
F.1 STATISTICS
Table 4: Comprehensive statistics EAQA-SFT, EAQA-RL, and AVQA.
Avg Audio Length
Require Temporal Analysis
Include CoT Annolation
Average
Dataset
Sample
Data Source
# Choices
CoT Length
SellinSSe-Sttungg (79.8%)
4 (99.5%)
EAQA-SFT
75,862
9.85s
words
MusicBench (20.2%)
> (0.5%)
AudioSet- Strong (7.5%)
2 (10%)
EAQA-RL RL
21,900
AVQA (46.8%)
9.86s
(5%)
MusicBench (45.7%)
(85%)
AVQA
40,425
AVQA (100%)
9.96s
(100%)
Table presents detailed statistics of the training datasets, including our constructed EAQA-SFT, EAQA-RI,, and the widely adopted AVQA dataset. Benefiting from the temporal metadata of the source data and the advanced reasoning capability of DeepSeek-RI, both EAQA-SFT and EAQA- RL emphasize the fine-grained analysis of temporal clues. Additionally, EAQA-SFT is annotated with high-quality CoT data, with an average length of 87.5 words. EAQA-RL designed with varied number of choices to cover tasks of multiple difficulty levels.
F.2 MITIGATION OF NOISES IN THE SOURCE METADATA
The temporal metadata in AudioSet-Strong and MusieBench inevitably contains annotation noise, which can propagate into the synthesized datasets and affect model training. Our data annotation pipeline reduces such noise through two complementary mechanisms. First, during the synthesis of QA-CoT triplets, the LLM (DeepSeek-RI) R1)) is provided with audio information from multiple sources, including temporal metadata and predictions from Qwen2.5-Omni. These sources implic- itly cross-validate one another, allowing the LLM to assess the relative reliability of different cues. As illustrated Figure 9, while the temporal metadata labels the audio segment as containing music and three sound effects, Sowen2.5Ommi detects music and questions the third sound effect. When integrating these signals, the LLM synthesizes QA-CoT triplets only from information consistently supported across sources, thereby reducing the influence of uncertain temporal metadata. This im- plicit cross-valls validation mechanism promotes a more selective use of temporal annotations, thereby mitigating the propagation of noise into synthesized data.
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Temporal Metadata
Question
10.0 Music
10.0] game
What the tomporai gup between the swcord daaincc aauudd whec??
[5.G. 6-4] Sound effect
(7.6. 5.5] Sound aflect
Audio
Jr.S, 10.0 Sound offect
Cholces Answer
DV
Validation
Cross
1.2 secondee 7.6 awcoorafs)
The sudio combraation video game sound effecte. There as distinct "witoosh" or whisling sound middle, faliowed a brief interuptiinn esplosiin
CoT
first sccond sound offect The find acund elleet
Audio Information Extraction
DeepSook-R1
-sogge. &. 6.4- 6.4-iogg- meaning ands 6-4 sectandl The swaand dieined sound effect ssans ister of 1..5. This moons the gop betwoer effect execity 7.6 664 andonds, 0.6. 7.8 seconda don't align with thess specilic event boundaetee.
Qwen2.5 5-Omni
Audio Description
Prompt
There no spoedh the aucio.
Cenerale chaillnning GA-OoT
Speech Transcription
tiplets 2
There is to music in the audio.
Musical Analysis
Figure 9: Audio information from multiple sources acts as implicit cross validaaan for each other.
Prompt
Quasttio
Evausia the quality of the Kudio-GAA Cnl based onteria: 1... 2...
In which quarter of the audio does the engin sound reach it highust intamally?
| Audio
Temporal Metadata [0. 10.0j Purp
Choicas Answer Trird 7.5 seconds. 7.6-10.0 seconds)
The audio features the engine startin, accelenaing revving.
Information Extraction
Audio Description
The aurtlo compane single pumo aound sparning Its entrre duration, as heard hout Keegod.n tiiiee Hinewww: wudko spcotficlly plasse with coradann saund. The sound reaching it high est intensily would logicully occur during Foe revving phaes alter scce prograssion ruturally places internily
There speec' in the audio.
ReepSe
Tren2.5-Ommi
Speech Transcription
ongito starting, distinc
There is no music the audio.
Musical Analysis
Judgemmett Invalid
Invalid
loely
Figure 10: Re-evaluating process explicitly filters out identifiable hallucinated answers and CoTs.
Second, the LLM performs an additional round of re-evaluation to further improve the quality of the synthesized training data. In this stage, temporal metadata, audio information extracted from Qwen2.5-Omni, and the previously synthesized QA-CoT triplets are jointly provided to the LLM, allowing it to reassess each sample with full contextual evidence. Through explicit prompting, the LLM is instructed verify answer plausibility, identify ambiguous choices, and detect unsupported or hallucinated CoT steps. As a result, implausible QA pairs and hallucinated CoT's are filtered out. As exemplified in Figure 10, an initial QA pair requires the model to determine sound intensity based on clues absent from both the temporal metadata and the audio information, leading to an implausible answer and ungrounded analysis in the COT. By filtering similar cases, this stage further suppresses annotation noise and hallucinated reasoning in the final training sets.
Through these refinements, the noise present in the final EAQA-SFT and EAQA-RL datasets is substantially reduced. This improvement is empirically supported by the results in Table 3: the transitions (A-B) and (C+D) show clear performance gains on MMAR before and after training, and the comparison between D and D' further demonstrates the consistent advantage of EAQA-RL over the commonly used AVQA dataset.
F.3 INFLUENCE OF SYNTHESIZER, RE-EVALUATOR, AND AUDIO EXTRACTOR
Table 5: Influence of substituting DeepSeek-RI R1 by other pop-performed LLMs (Seed- 1.6- 66-Thinking (Seed, 2025) and GPT-5 (OpenAl, 2025)) for QA-CoT synthesizing and re-evaluating.
Average CoT Length
MMAR Acc (%)
MMAU-mini
Synthesizer
Re-evaluator
Datasets
Sample
Acc (%)
EAQA-SFT
75,862
87.5 words
DeepSeek-R1
DeepSeek-R1
69.99
80.41
EAQA-RI.
21,900
EAQA-SFT-Seed
77,693
103.3 words
Seed-1.6 1.-TThinkin
DeepSeek-RI
68.51
78.78
EAQA-RL-Seed
13,840
EAQA-SFE SFT-GPT
54,523
82.2 words
DeepSeck-R1
GPT-5
69.86
80.10
EAQA-RI-GPT
15,325
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Table 5 compares the effects of using different LLMs to construct the training data. To ensure a fair evaluation, we maintain the identical training configuration across all SFT and RL experi- ments introduced in Section 4 and Section G. Although substituting either the synthesizer or the re-evaluator leads to variations in sample numbers and CoT lengths, the downstream model per- formance remains largely consistent, which validates the stability and robustness of the proposed data generation pipeline. Notably, both variants yield a substantial reduction in the amount of RL data, yet this reduction leads only marginal performance drops. This pattern suggests potential trade-off between data quality and quantity: stricter filtering may increase the overall reliability of the retained samples, thereby enabling more data-efficient optimization.
Table 6: Influence of substituting Owen2.5-Ommn by other popular LALMs (Kimi-Audio (Ding ct al., 2025) and Qwen2- (Chu et al., 2024)) for aduio information extraction.
Average CoT Length
MMAR
MMAU-mini
Audio Extractor
Datasets
# Sample
Ace (%)
Ace (%)
EAQA-SFT
75,862
87.5 words
Qwen2.5-Omi 5-Omni
69.99
80.41
EAQA-RL
21,900
EAQA-SFT-Kimm
83,387
94.5 words
Kimi-Audio
68.82
78.90
EAQQA-RI-Kimi
19,569
EAQA -AQA-SSFT-Owen2 69,094
86.3 words
Qwen2-Audio
63.91
75.41
RAQA-RL-Qwent 15,769
Table 6 reports results from a similar setup, comparing the impact of different audio extractors. Un- like earlier observations, the choice of audio extractor Icads to pronounced differences in data qual- ity, as evidenced by downstream model performance. This underscores the pivotal role of the audio extractor in the data generation pipeline and suggests that may currently constitute bottleneck. From a broader perspective, improving the fidelity of audio information through stronger extractors effectively reduces labeling noise originating from temporal metadata. This result provides empiri- cal support for the cross- validation mechanism described in Section F.2, offering additional insight into how the proposed pipeline mitigates noise in temporal annotations.
F.4 INFLUENCE OF THE RE-EVALUATION PROCESS
Table 7: Ablation of the re-evaluation process.
SFT Data
Data
MMAR
MMAU-mini
# Sample
Description
Sample
Description
Acc (%)
Aco(%)
75,862
Re-evaluated as High Quality
21,900
Re-evaluated as High Quality
69.99
80.41
111,868
All Data Except for RL Data
21,900
Re-evaluated as High Quality
64.67
76. 19
75,862
Re-evaluated High Quality 21,900
Re-evaluated as Low Quality
67.03
77.40
Table provides a quantitative analysis of how the re-evaluation process influences downstream model performance. The first row represents the setting in which both the SFT and RL datasets consist exclusively of samples judged as high-quality during re-evaluation, and the model trained under this condition achieves the best overall performance. When low-quality samples, those that would ordinarily be filtered out, are introduced into the SFT data, downstream performance drops markedly. This result confirms the effectiveness of the re-evaluation stage in suppressing CoT noise and hallucinations. Substituting the RI. data with samples identified as low-quality (we adopt this configuration mainly because scaling RL data substantially increases training cost) also leads to a decline in performance, though the degradation is less pronounced than in the SFT case. This may be attributed to the fact that the constructed QA samples generally contain fewer hallucinations and less noise compared to QA-CoT triplets.
G MORE IMPLEMENTATION DETAILS
Following the experimental setup described in Section 4, SFT is implemented using the ms-swift engine (Zhao et al., 2025), with a linear warm-up schedule applied during the first 5% of training steps. The RL process is conducted using the VERL engine (Sheng al., 2025), with the sampling
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temperature for rollouts set 1.0. Model inference during rollout is performed using the vLLM engine (Kwon et al., 2023). For evaluation, unified prompt template from Section E.2 is adopted across all experiments, along with decoding temperature of 0.7. Line A in Table 3 represents our reproduced results, which are not entirely consistent with the results fetched directly from the original paper and reported in Table 2.
H DETAILS OF COMPARED BASELINES
In this section, provide detailed introduction to current adapted LALMS to offer com- prehensive view of the field.
Audio-CoT (Ma et al., 2025b) represents pioneering effort in exploring the reasoning capabilities of LALMs. It investigates three distinct CoT methods without modifying the model's parameters: 1). providing few-shot demonstrations; 2). adding the prompt "let's think step-by-step"; and 3). generating an audio caption before reasoning. Following Sakshi et al. (2024). we report results using the second approach in Table with Qwen2-Audio (Chu et al., 2024) employed the base LALM.
Audio-Reasoner (Xie et al., 2025) advances the reasoning capabilities of LALMS from a data- centric perspective. It utilizes Gemini-2.0-Flaan and Gemini-2.0-Par to construct CoТА,, a large- seale dataset comprising million Audio-QA samples with corresponding COT. The CoT anno- tations in CoTA follow structured pathway, including planning, captioning, reasoning, and sum- marization. Audio-Reasonee is obtained by performing SFT Qwen2- Audioo on this dataset. We did not adopt CoTA as the SFT dataset because its CoT annotations do not include <seg> tag pairs or accompanying segment-level analysis, which are essential for the development of the subsequent audio-interleaved reasoning.
Omni-R1 (Rouditchenko et al., 2025) explores the impact of RL on LALMs. Although it does not specifically emphasize audio reasoning, demonstrates that outcome-superrised GRPO is highly effective in optimizing LALMs. Notably, it shows that RL on pure Text-QA tasks can significantly enhance LALMs' performance on audio reasoning tasks. The study also introduces pipeline for constructing QA pairs from raw audio data, which serves as an inspiration for our proposed data generation approach. In our comparison, tuning Qwen2.5-Omni on its generated VGGT-GPT dataset.
report its top-performing model, obtained by fine-
Audio-Thinker (Wu al., 2025b), a very recent work, further advances the application of RL in LALMs. It guides the model to modulate its reasoning patterns during the GRPO process in response to tasks of varying difficulty levels, while employing an -ua-jdd- mechanism to provide explicit supervision signals for reasoning quality. Building upon Qwen2.5-Omin it achieves state-of-the-aa performance across MMAU, MMAR, and AIR benchmarks.
I QUANTITATIVE ANALYSIS OF RE-LISTENED SEGMENTS
Audio-QA
How many women are singing the audio? Choose the answer from:
Think slep- by-slop.
0.0
2.0
10.0
23.3
1.1
6.5
10.2
1.5
3.6
11.5
26.4
Audio-interleaved Reasoning
Response
Predicted (Re-listead] Segments
Segments
Ustorinn the firs that the main fomall vocals Thon. beginning. 3.0ge-0.0 dleary devieeg. the femm le contimuss voice is zoll prominent Howeverr <5og-9 8, 12.4-begg- male volce joins and 12.7. 16 18.20leeg,, he bezomesi the man singer. No other farmat w vo cse ane haard beae track Thersfore, only one woman is eodiusively singing.
fomalo
10U=0.73>=00.5 V
loU=0.73==005
the men tune. Lakw, nauga4.2 2. & Seigge her
P2 A2 leU-0.80==00
A2 loU-0.0000a -0000000000
loU-0.5
A3 loU 60.0000555
A3
1oU=0.60=:0.5
1oU-40.5
Relistened
Temporal Information
Segment Precision Value 315
Segment Coverage
Segments:
30 in
4.2
0.9
12.7
0.0
Sog.enttttnntmmm Analysis:
Pan-Segment Analysis:
coego 1. 1-teegr,
Later, <sego-422 B.Ecteego. volce prominant.
singing meleay.
Figure 11: Illustration of how segment precision and segment coverage are computed for a sample, and how per-segment analysis is extracted for segment.
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1.1 SEGMENT PRECISION AND SEGMENT COVERAGE
To quantitatively evaluate the accuracy of Echo in locating salient segments, we first introduce two sample-level evaluation metrics: segment precision and segment coverage. Both metries rely on the Intersection over Union (IoU) between the predicted (re-listened) segments and the annotated segments. For two segments A Aszes and Asgreg where 81, 82 denote start times and e,, e2 denote end times, the IoU is calculated as:
1(81, (82, e2) (s11, e1 U( S2, 62)
IoU A Asseyj A Asgrog)
(4)
Based on this, segment precision is defined as the proportion of predicted segments in sample that have an IoU 2 p with at least one annotated segment, reflecting the localization accuracy. Let the predicted segments for sample be P (pI. (pI.P2..... pr. and the set of annotated segments be A [41, a2....,.am..
am) Segment precision at threshold is defined as:
| (v()) = PP| {p: 1 E A,, loU(p,, .5) 2 p}}-
(5)
Conversely, segment coverage is defined as the proportion of annotated segments that have an IoU 2 P with at least one predicted segment, indicating how well the predicted segments cover the annotated segments. Segment coverage at threshold is defined as:
1 { A E P, loU(a; P5) 2 p}/- A
C.(p)
(6)
Figure 11 illustrates the computation process of these metrics with an example. Based on these metries, we conduct a comprehensive evaluation of the model's proficiency localizing audio seg- ments using both our manually annotated and existing temporal metadata. In the absence of com- parable baselines, we compute the average segment precision and segment coverage for both the cold-start model and Echo across three subsets: randomly selected samples, correctly answered samples, and incorrectly answered samples. This analysis offers insight into how RL influences segment-localization ability and how the quality of this localization, in turn, impacts the correctness of the final responses.
Table 8: Comparison of average segment precision and segment coverage on random, correctly answered, and incorreetly answered samples from MMAR
p.
p=0.5
Model
Split
P.
C,
P.
C2
Random
88.32
70.62
81.21
Cold-Start Model
Correctly Answered
91.03
78.29
83.85
67.75
Incorrectly Answered
86.79
63.85
78.19
52.32
Random
92.47
73.22
84.41
62.04
Echo
Correctly Answered
94.25
79.26
85,38
68.98
Incorrectly Answered
91.84
67.99
82.35
56.93
On MMAR, we select 50 correctly answered samples, 50 incorrectly answered samples, and 50 non-overlapping random samples, and manually annotate the audio segments that are critical for answering each question. These human annotations serve as the reference for evaluating average segment precision and segment coverage, with the results summarized in Table 8.
Echo achieves high performance on both metrics, indicating strong proficiency in segment local- ization. For correctly answered samples, Echo consistently exceeds performance on incorrect samples in both precision and coverage, revealing a clear positive correlation between accurate seg- ment localization and effective audio comprehension. The gap in segment coverage particularly notable, suggesting that some model errors arise not from mislocalizing segments but from failing to retrieve essential regions of the audio.
The cold-start model exhibits localization ability that is already comparable Echo's, though RL still provides measurable gains. This pattern suggests that outcome- rewards implicitly im- prove segment-grounddng behavior, even without explicit supervision on segment localization.
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Table 9: Comparison of average segment precision and segment coverage on samples from Audio- QA constructed upon AudioGrounding (Xu et al., 2021).
p=
p=0.3
Model
Split
P.
C,
C₂
All 500 Samples
84.37
60.27
77.67
51.89
Cold-Start Model
Correctly Answered
90.53
66.70
55.01
Incorrectly Answered
75.02
53.16
75.04
45.77
All 500 Samples
89.34
64.83
81.23
55.58
Echo
Correctly Answered
91.65
69.71
83.88
59.75
Incorrectly Answered
86.19
60.04
80.19
51.86
Since manual annotations inevitably introduce subjectivity, we further validate our conclusions us- ing existing temporal metadata. The AudioGrounding dataset (Xu al., 2021), originally designed for the text-to-audio grounding task, contains manually annotated sound event timestamps with rel- atively low noise. However, it does provide QA pairs by default, generation pipeline to construct 500 QA samples from its audio and event annotations.
apply proposed data
In addition overall evaluating all constructed samples, also sample 50 correctly answered and 50 incorrectly answered cases, with results reported in Table 9. The overall trend is consistent with our earlier findings, though all metrics are slightly lower, especially segment coverage. This decline occurs because the segment labels in AudioGrounding are not filtered for task relevance, thus including segments unrelated the QA task, which reduces measured coverage. Despite such differences, the consistent pattern across datasets validates our analysis.
1.2 VALIDITY OF PER-SEGMNNT ANALYSIS
To assess the plausibility of the model's analysis for each referenced segment, adopt LLM-as- a-judge evaluation. For each segment reference, we first extract the full sentence in which it appears (as exemplified in Figure 11). This sentence, together with the corresponding Audio-QA pair and the model's complete response, is then provided to an advanced LALM (Gemini-2.5-PP) in our implementation). The model is instructed evaluate whether the analysis expressed the sentence is relevant to the content of the referenced audio segment and whether it contributes meaningfully to the final answer. The prompt used for this assessment shown below.
Segment-level Analysis Plausibility Evaluation
You are given the following inputs for evaluating the plausibility of a model's 's segment-leve analysis:
$1: The full sentence in which referenced audio segment appears. S2: The corresponding Audio-QA -A pair (question, choices, and correct answer). S3: The model's complete response for that Audio-QA instance.
Your task is to assess whether the analysis expressed in S1 relevant to the referenced audio segment and whether it meaningfully contributes to deriving the correct answer.
Evaluation Criteria:
Relevance: Whether the analysis in SI directly related to the content of the referenced audio segment.
Contribution: Whether the analysis provides meaningful support for the final an- swer.
Output Format:
Relevance: Yes/No, Contribation: Yes/No
Do not provide explanations or reasoning.
We treat both relevance and contribution as segment-level metrics, assigning each binary judgment (1 or 0). These metrics are computed on the segments drawn from the same 150 MMAR samples used in Table and the aggregated results are reported in Table 10.
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Table 10: Relevance and contribution of per-segment analysis on MMAR.
Model
Split
Relevance
Contribution
Random
86.00
72.00
Cold-Start Model
Correctly Answered
86.00
78.00
Incorretly Answered
82.00
64.00
Random
94.00
84.00
Echo
Correctly Answered
94.00
88.00
Incoreetly Answered
92.00
72.00
The results indicate that, regardless of whether the final answer is correct or incorrect, Echo con- sistently produces highly relevant segment-level analyses, substantially outperforming the cold-start model. This highlights the advantage of andio-interleaeed reasoning over an initial audio-grovn -rouuddd approach: re-listening to key segments enables Echo to deliver more accurate and context-aware interpretations. In contrast, the contribution metric exhibits a clear separation between correctly and incorrectly answered samples. Together with the findings from Section I.1, this pattern suggests potential failure mode: Echo may sometimes anchor its reasoning on suboptimal segments. Even when the analysis of such segments is itself accurate, their weak alignment with the question can still mislead the final decision.
Overall, Echo attains high performance both relevance and contribution, confirming that the model is capable of producing precise and reliable per-segment analyses as intended.
J ABLATION STUDY OF REWARD DESIGN
J.1 IMPACT OF CURRENT REWARD COMPONENTS
Table II: Comparison of models undergoing RL of different reward formulation. "Include" evalu- ates whether a response contains any segment reference. "Coverage" measures the non-overlapping total duration of all segment references in each response and their proportion relative to the audio duration.
MMAR (Avg)
Reward Formulation
Segment
Acc (%)
Length (word)
Include (%)
Count
Coverage (s)((%)
A Accuracy + Format
67.84
77.27
86.40
1.62
3.94 23.32
B Accuracy + Format + Consistency
68.07
113.37
88.50
2.11
4.59 27.15
C Accuracy + Format + Segment
68.22
86.58
98.70
2.11
4.94 /9.42
D
Accuracy + Format + Consistency Segment
69. 99
107.40
99.40
2.25
4.17 /25.08
Audio-QA
Audio-GA qu]]biiijn
Gn a plann,
In locadiin indoore censicoms?
Indorr. Custcons
alop-
In scenaric does sound
Think stop-
Choose the answo from:
On ana
Echo (without Segment Reward)
Response
Echo (without Consistency Reward)
Response
The sadio containe distnotibeet inng sounds and mediaaiams that typ colly occur trside noom.
The ausio contains cww.ooooniuuoooo back roise and make ce sparking The beokground mytheii quelity suggedtin
The soomants singingle wocom porteedlt by conaiteen bookgroant nocle that the accundic: ratural soundil audio ladis such demants ta. No cuidoor-ppps sounda liie dinda waather
Specifially <ueg> Answer On a pich
1.3rokago
occur. Thua the undoaae sounds and mate acouslica suppest the szone a incoors. Annwer inctors
X
Echo (with Consistency Reward)
Response
baginring aucko speciiially aaggeo. D.bbaeg we bend chappng,
Echo (with Segment Reward)
Response
ousdor
4ccheeg Dil the sounds of wind and car presing are dasrty ausite The prosence of tinds and wirdd pugpeess an open nullhoor ssace whin the peexing confime landikssed seiiing envirommntt Thise slo moments contaned- tais sounds, wind. and can- atronlly indicate ousdoor Serrost- el aoomerto ralher than dosecioff apuce passilll lik thip or ban. Therstore, the scane doucttbe the sudio livey occuning plans.
susio conraina sounds wird howding, both which typicaity asacciated with
ge
aucillis, and -5ege4,2, 4,2 10.0-r [[s, dog howling cleatly heard. These putzior
eemeetss presorr throughout ausio indioring the womanin singing nutliie whewre the
pardiet
Answer: Cundoons
Answer: On a plain
Figure 12: Qualitative visualization of reward components' impact.
Table 11 and Figure 12 examine the impact of different reward components during RL. In the right case of Figure 12, the model trained without the consistency reward exhibits unstable behavior after re- re-listening to audio segments, frequently skipping analytical steps and directly generating answers, leading to unreliable reasoning traces. comparison, the model trained with the consistency re- ward produces coherent segment- wise analysis and derives conclusions in structured, step-by-step
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Audio-OA
sound the audio, Choose the arewer from
Rubot.
Audio-QA |iilioni
to 1il audio lrers an dli movil or new movis Choose the anower from
New movis
Think slep- Sy-eeep
Reooter,
Parok.
tryetep.
Old movel.
Audio Content
Audio Content
voculicadara its ability to minil human speach and other dislistt sounds. The dog jince-ol sounds includos
exckainde
crial child ad The dhild ungently etanes can't owins" worning which is repeated The soutt oreely adio the chilste age
dilp tegki
sonwersstion
Nighyphoned, aucttined, and wartling whisee Hill charaderibic of bidd's natural call inslucing souns. the and of the dlip., Intaaian unima and ene scerante acundi such au: dews, rescrant low-
risan) trmeedonlly tolkwinng rg this exchangs. there a very bud muttien rpieety Inckrating that
spench
high
sdmiiting
"Docter; "Secoed"" "Whal color "'Oanng" "repasted pear
interto puatted lanoed chep rik
loreuta methost instrusion This (oloned by the chastic sounds fanniic spleshing and Frushing g weker signll rig tig ckiicts
messengy loms or gong sound rupel successio of quaddig. cheppnn, aqusebting revass, Hight
stagglo Aenkttr the last,
cris out diotoss, "Heip lsint owinn" auul chilc
wovering
the danger and stating his reient for the child to leams Welll learn. Everybocy.."
dianlosing
chudile sound sugpesling g the minriry of humnn laughter.
Echo: Rollout #1
Response coors. Later ssogu18 Beitogg and
Echo: Rollout #1
Response
Reesoge600 O Belbops a bind' vncalitation not an oul'n host, a mosters crew ara sscel speech Thensom, the
very inginiin ng uscguo O Ootooggotis de reuuuu distinatty miro qusilyy
16,0wbtgg- cimilar high-phiched
These counds
tonel nosaaill
the sertage sound eftecte lik the spout and the hosee listing sound am spice or mo--0ih
setunce paenot
Answer Panst v
cwrl.ry diveri styles
Answer Old maxe
#2
Response
Echo: Rollout #2
Response
The suai dodinet ted call cocurs al -seeg-t M. a Poseggo characheoed piihod ii Whfs sirniar Bi sounds lik owb or rcoslers night be ristaken for 8is. Te as didincl decteeps commins follwwd by "Laugherr cont ming the bir call pansfts, not ribzfa.
-a0g--.0, 4.2o0aghh cialouue leaturee high- righppiched, childiise vosoo and voice
resporaiig in diglnnc skigily outed lans, which characterose cotertte re 200--cattrr Arenican speech styee. The lingaage and vocal dellvery slign with perioo spoolle styee,
oogmont vsegudi n. tebbeee
"Laightr that "Chatte" sound
rostee
sadio hrom old movie, Ebey from the 1900s earler, when dialogue- ue- ven narialiiss and Answer: Old movie V
Answer: Farrot <
metrraiste soundsrapes wore common..
Figure 13: Audio-QA tasks typically permit multiple plausible reasoning paths.
manner. This effect is quantitatively reflected in Table 11 (lines A-B, C-D), where introducing the consistency reward consistently increases the average response length and the overall accuracy.
The segment reward, by contrast, primarily promotes greater reliance on audio segment references, as evidenced in transitions A-C, B-D. This encourages the integration of more comprehensive au- grounded analysis. The advantage of the segment reward
dio information and facilitates is also illustrated in the left example of Figure 12.
J.2 THE DECISION AGAINST DIRECT PROCESS SUPERVISION
During the initial exploration, we do not seriously consider employing direct process supervision. This is primarily because our early experiments are conducted on the AVQA dataset, which provides only QA annotations and lacks the granular labels required for process-level supervision. Moreover, using an LLMaas-a-judge mechanism to compute rewards would substantially increase training costs due to API usage, and there is insufficient evidence at the time to confirm that LALMs can reliably evaluate reasoning processes or segment re- re-listening behavior.
As our focus shifts to the dataset we construct, the LLM-synthesized CoTs offer potential reference for process supervision. We conduct small-scale experiments by separating high-quality CoTs from EAQA-SFT. The results show that, under identical RL data, reward signals based on textual semantic matching or segment loU do not produce consistent performance gains and occasionally destabilize the RL process. We hypothesize that this occurs because many audio QA tasks permit broad range of plausible reasoning paths, and imposing explicit constraints on these paths may unduly restrict the model's exploration.
As illustrated in Figure 13, the cues required to answer a question are not confined to a fixed combi- nation of segments. In the left case, although the rollouts overlap partially, rollout #1 identifies the answer based on repeated high-pitched bird calls, whereas rollout #2 relies primarily on the parrot's 's characteristic chatter and laughter. In the right case, both rollouts ri-tint to similar audio segments yet follow divergent reasoning paths: one focuses on retro sound quality, while the other attends to a speaker's s dated tone and vocal delivery, and both ultimately lead to the correct answer.
Drawing on insights from thinking wiitk-in- research, we ultimately adopt only mild constraint (the segment reward), which encourages the model to invoke segment re-listening without being overly prescriptive about the reasoning trajectory. Empirically, this formulation has proven both effective and robust: it successfully increases the model's 's propensity to reference audio segments, and notably, we do not observe the anticipated reward hacking (e.g., superficial or random segment references) in practice. This iterative process leads to the final reward composition employed in our approach.
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K
GENERALIZABILITY TO LONG AUDIO
K.1 DURATION- WISE ACCURACY
Table 12: Model accuracy comparison on MMAR for audio of different durations.
1-10s
11-20s
21-30s
31-60s
Model
173 Samples
356 Samples
468 Samples
3 Samples
Base Model
52.02
52.25
50.43
33.33
Cold-Start Model
56.07
61.80
51.28
66.67
Echo
67.63
70.22
67.31
100.00
Table 12 details the model accuracy across different audio lengths. Except for three overly lengthy cases, Echo demonstrates consistent proficiency in comprehending audio of varying durations, with a slight performance advantage in the 11-20 second range. Building upon the findings in Figure 6, these results confirm its ability to maintain accuracy when processing longer audio, confirming its generalizability.
The performance the cold-start model reveals another noteworthy insight: although the audio used during SFT is limited to 10 seconds, the model exhibits its most pronounced performance improvement on 11-20 second audio. This observation suggests that, beyond andio-interleaved reasoning, the intermediate outcomes produced by audio-grounded reasoning also play meaningful role facilitating better handling of longer audio inputs.
Table 13: Model efficiency comparison on MMAR for audio of different durations.
1-10s
11-20s
Model
Length (word)
Latency (s)
Latency/Lengtth
Length (word)
Latency (s)
Latency/Length
Base Model
65.57
1.18
0.0180
64.32
1.09
0.0169
Cold-Start Model
107.12
1.82
0.0170
112. 16
1.99
0.0177
Echo
96.39
1.99
0.0206
102.90
2.09
0.0203
21-30s Latency (s)
All Samples Latency (s)
Length (word)
Latency/Length
Length (word)
Latency/Length
Base Model Cold-Start Model
71.31
1.26
0.0177
67.95
1.18
0.0174
124.10
2.16
0.0174
117.06
2.04
0.0174
Fcho
112.99
2.20
0.0195
107.40
2.12
0.0197
Table 13 summarizes the computational efficiency of different models across varying audio dura- tions, measured by average response length, latency, and their ratio. The results show that, for all models, both response length and latency increase gradually as audio duration grows. For any fixed audio length, the cold-start model and Echo consistently produce substantially longer responses than the base model, which is consistent with the overall trends across all audio samples.
Given the conclusion from Table 3 where increased response length emerges as a natural conse- quence of SFT or RL, we further introduce the latency-to-length ratio to isolate the additional com- putational overhead specifically attributable to intii-iittereaaee reasoning. The analysis reveals that the gegment-appendiggg operation incurs only about a 13% increase in latency, and this overhead re- mains stable across different audio durations. These results indicate that the proposed reasoning strategy maintains acceptable computational efficiency, even when handling long audio.
K.2 COVERAGE OF REVISTED SEGMENTS
Table 14: Proportion of re- re-listened segments intersecting with each time interval.
Intersect with [0-10s]
Intersect with Intersect
Audio Duration
[10.1-20s]
[20.1-30s)
0-10s (173 Samples)
99.42
11-20s (356 Sumples)
97.75
24.16
21-30s (468 Sumples)
90.38
37.18
9.40
31-60k Samples)
100.00
66.67
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Table 14 reports the proportion of re-listened segments that overlap with different time intervals, il- lustrating the temporal coverage of the Echo's re- listeniin behavior across audio of varying lengths. For instance, 97.75 in line 2 represents that 97.75% of Echo's 's responses to 11-20 second audio listen segments intersecting with [0-10s]. These results provide an intuitive view of the distribution of re-listened audio segments. Although segments beyond the 10-second mark are accessed less frequently than those within the first 10 seconds, the model still retrieves excerpts longer than 10 seconds-and even beyond 20 seconds-in non-trivial fraction of cases. This fine- ane-ggained evi- dence further substantiates the model's is generalization ability in segment retrieval and helps explain its sustained accuracy on 21-30 second audio.
L LIMITATION ANALYSIS
L. INFERIOR PERFORMANCE EASY VS. HARD TASKS
Table 15:
Difficulty-wise accuracy comparison on MMAU-mini. Difficulty levels are provided as native metadata in the benchmark.
(L0.7%)
Mutical Knendedge
Model
Easy
Medium
Hard
9%.%%
Base Model
62.95
69.26
66.53
Cold-Start Model
64.29
75.37
69.49
Echo
75.00
84.07
77.12
Figure 14: A task-wise error categoriza- tion for Echo within the easy subset.
Table 15 presents a closer examination how the model's performance on tasks of varying difficulty evolves during training. A consistent trend emerges, with the model performing best on medium- difficulty tasks, followed by hard tasks, and least effectively on easy tasks. From our categorical analysis of error cases within the easy subset (Figure 14), attribute this pattern to labeling bias in the benchmark's 's difficulty annotations. Most of the model's errors occur in areas such as counting, musical knowledge, and vocal identification. Although these tasks appear straightforward human experts, they impose distinct challenges for the model:
1). Counting requires the accurate localization of all qualifying instances, often more than ten per task, which depends on reliable segment recognition. This ability is strongly influenced by the SFT process, yet our current SFT data construction does not sufficiently emphasize cases with highly frequent segment references within a single reasoning chain. 2). Musical knowledge involves instruments, rhythm, chords, and other domain-speciff concepts, and performance in these tasks largely reflects the coverage and quality of pre-training. As shown in Table I and Table 2, most LALMs underperform in music-related tasks compared to speech and sound, underscoring the need for more dedicated music-oriented pre-training data. 3). Vocal identification is further complicated by the inherent variability of acoustic features, as speakers of similar gender and age may differ sub- stantially in vocal characteristics. Addressing this challenge likely requires stronger task emphasis in both SFT and RI. data design.
L.2 FAILURE CASES
Figure illustrates three representative types of errors made by Echo. In the left case, the model appropriately re-listens the audio segments but engages only in a superficial analysis of each, which prevents from forming logically coherent CoT that leads to the correct answer. the top- right case, Echo demonstrates reasonable analysis in the early stages but subsequently confuses the number of words containing at least one stressed phoneme with the total number of stressed phonemes. This error is compounded by inconsistency between the reasoning process and the final answer. A similar issue is also observed in the bottom-right case: the model correctly infers that the minor chord is Bm in the CoT, yet outputs F#tm the answer. These errors highlight the need for further improvements in both the comprehensiveness of reasoning and the consistency between reasoning and response.
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Audio-GA
Count number of words
Audio-QA
From gleen uthernox Court the number of warde tistt canten of least one stressed phoneme?
Sexan. Nine. Ton, Fillwer.
That conta in at lest one unstressed phonome Choose snower from:
Four, Thatsenn Nims.
Thank stap- Sp-.
Think slep- Sysalep.
enswer from:
Audio-Interlenn Reasonling
Response
Echo: Audio- intorleavedd Reasoning
Response
Inood dently
sylibbic word.
syficbles
The wost
'rany/ has a strospos syfiible ot the segin ving, do heard in stage1 3., 18siogg- [[l]afedn has
In congot... 5, 1.Driseps
[+0]
word If how ane hake with erreaed nutimas ao hou no
streneed syflatii end ot uwoand sy fettew thind beck our luse dimored sylntbee
atreeeed syfiablee sylibble, tollowing
beg
owe, and wiring containe
atreaaed weste, Howeeeer dtrossed sylibles making soven ot ussed Kall les bool
In 1. 2. 2.7xieg-
Nence has one strecsed sy Backk ao has no urahnesed phonermee.
Answert X
Seeg-27, 4, 41100ep-00 M Higaark has stessed
unsressed
phone nos. "Anerkann has one stressed syluatie has no instrsssed phonomess
Audio-QA
Which of the Inllowing chords wudtoo
Făm. Bn. CG
nonge6.), Irieege II
pk slep-
'change' has atweooed pelnere, has unahnesse phonemes "thel has streaaed syllatio, soll hos uo strossed phons nos. 'ramn has one strossed syllotie, so hos no undt ussed phonoeoss.
Choose
minor chord?
typatep
In canget.11 1, 8.2kveeg- []- bf has one stresed by latre, so has unstnesee phonames pundff one stremed sy litte, soll tee unstressed phonemess 'our Ele each have one
Echo: Audio-Intereeaved Reasoning
Response
right from the baginiiggn -auge..0 D. Beblage the chond prograssion cstadéianss charda like
sylaare, they
major and A rejer and The less uas the root Lites city Howsver, when the basa with
In csegr-.B. 2, 3e0oeg [ land has one stesssdd sylaaile so has no unstessod photemess.
thidd companee choed crmstee eecee of beniion meberthhlyy thar complesante the russk metody, liting the
dertiviity mánor tooel
undt phoneesss sytall sylioke, soll stroosed phone 'Spariat/ urntwansd Mil so has one unatrusaed phoname..
stresed
one streseed
anndinaal viza deaciibe. The chand lubol the audio contimmil it's maring the only minor
Answer Nine
progresa
Answer: Film X
Figure 15: Three failure cases of Echo on MMAU-mini.
M MORE CASE VISUALIZATION
Audio-GA
How many poopll have in Imitted tmi opera? Chhoow the womwer trom:
Thenk Eystte.
Audio-QA
bottlss la lasped tvioe. Tell me which his
floe triention
Fourth has the least second the most
First has least, IM has most.
Secund has the husst fourth has the most the lesst, first the moat.
Content
Ioee lesed.
The oudio dip begins with mukis Nacce, Boyy trose young people or crild anouting bogothar in unison, "Opora cagiinge Inmed sicy ofttr they shout dhamat,, classial -tyle Exneassty
Audio Content
in highup tchhe, opendto. dnyi boriing nussiwet "Aaah" nokee.
veceliing
The aucio dip fualuros solt, dtmospheric, and calnn synthesized ruuic track playing contrrntutly in the bacagraundt
After ansel F'm swconds the reshwelos musi: cate out abrucity
sugpesting indiiduulo bying riginng part of the chillengo These sounds insludo
the remelndee of the the vocelaazzens continee
any musical accompenimen:
laodin 0.1 soo0 These sounds are crea and resorintt strong's suggosting that sonionne is
rpprosimant manierrlyy sequeence
spart.
ttin" hightsisched
thene curinnee "ding" snunde
fiakiigg ting high-plichet and someitmee diuity ralee. deedencinng atuezate The volces pocmn to be luking lumt and improvtingg clotiig enthsiesii and someohot cheokic sounds
st ng pieces of plosowaro such wno glasses The
sounce the plich the
oejoct (aka retal
pitch.
g. "Whahhahhan""
Singing shoof,
AM AN?"
Echo: Audio-Interleaved Reasoning
Response
insed sdentily the time when wich tap cosure By ladening chowely the the intiel top of recvest bertin eaage1 Tehuegs
eaegs? 7.7 7erogg and range11 Debeegs
Echo: Audio andio-Interreaved Reasoning
Response
shooiing han
Bled botllc has the eartics laps as saggi- 2.2 3.5er and saeg 3.5, S.4rgaog (ooth 0. ts). The
soless shouting togehhr: saying "upera chatl enge Laber vasged 5, 2.0eege music opera tegh plyy
The sudio contars mu iratances of opera intation Starting at sauge1 13, 2 2.00berr. muittlle
botll from botti haam the inwwel aich while sounch from the 1ith botie hawn the tigtnel pirch.
botle's laza are +aage-422 aeleeeg waogo4 4.0oagge rualy. 5.b-rog
esege 1.00legs <oeg> -eegg, 8.n.-B.3..
10.Sos fiualy cosga11.. 6, Scheep
represets
iniittionn Answer
Trandfore, con knew that the fine bottle has the loce water. and the lift botbe toue most
Counting theee segmants give. a tots of 4 dediec opern is mitiore.
Answer: First law the lesed, (ifh the moet
Reilstened Sagments:
Temporal Information
Relistnnd Segments::
Temporal Information
Secanda in Tobel
11 Seconds Total
1.3
2.3
5.1
7.2
9.3
11.6
1.6
1.7
2.6
27
322
3.3
4.2
4.3
52
53
5.9
811
10.0
12.5
1.7
1.5
2.7
2.
3.4
54
Figure 16: Case visualization on MMAR. The "audio content" is initially generated by Gemini-2.5- Pro (Comanici al., 2025) and subsequently refined manually. Echo demonstrates expertise when handling continuous and frequent segment referring.
Figure 16 and Figure 17 present several case visualizations from MMAR, where Echo demonstrates distinctive capabilities in handling complex audio. Unlike previous visualizations, we provide an extended description of the audio content (generated via Gemini-2.5-Pro (Comanici al., 2025) with human refinement) and annotate the relative positions of the referenced segments on timeline.
In Figure 16, count gunshots and assess the sharpness of tapping sounds on a water glass, Echo performs multiple consecutive segment re-listenings. Through accurate localization and coherent analysis, Echo successfully identifies nine gunshots and correctly infers the water level in the glass. In Figure 17, both audio clips are approximately 30 seconds long, significantly exceeding the 10- second segments encountered during SFT and RL. Moreover, their most informative regions are concentrated beyond the 10-second mark, presenting substantial challenges to the model. Despite this, Echo demonstrates the ability to reference segments beyond 10 seconds, even reaching the 29- second mark in the right-side case, and leverages these segments to form rational analyses leading to correct conclusions. This outcome highlights Echo's 's strong generalization in accessing and utilizing audio information across extended temporal contexts.
29

---

Published as a conference paper at ICLR 2026
Audio-QA dillonin
How many shots were fred in 10ai7 Choose the aecooe
1A 11,
Thiik stop- by-alep.
Audio-QA
Do you thin the mann drunc? Why??
womion eskoditiin hin "con you the dinc Think
Yoa Blace
Chicase the urawerr
the mon repeated "hi drunk. No.
Because gare
stop.
to repeal scarlly what worran
This audio ceptures soons of morse chaca, parks, and carfict likely flom an extinir homr mose ar ulteo game begine terreckeedy with sounds arga temifeed croun
Audio Content
moningg
The dipp begins with aounde of eostiing and movement et fat cavedd by loud, mectenior screping
then, "The other
The soone tien tomaidons jo combou. A make voice gives commandd. and "Toke thom out" This
deepair "Weir
name, the accesation on nat crunkk" The womon insturnly cha longes him: "Poss you are." The man repeors his den with
sound peeza in then angage tha starp exchange serratt acitae
by cemage numerooss, gerihooaa gurfiee
and
you are man's volo (Maro responds loudy and cmpphatioll
heard, to maie scioe reacls moddeme ming. bayy The whill the bookground offaco sorld
paing downt" Anothhe ueice echose tha, yelling "his non going stown A iuad, dastoned and not-
mar or por
scrszone
sojing, tamel" interpated by loud, cuil that lonock The mun than rapeata hox atrong denial ore best tames." facking
sobasety with lest "Gar
voluine
challlngee His moserts, "Yex" "The convereation
Echo: Audio Anterleaved Reasoning
Response
Echo: Audio-Interleaved Reasoning
Response
Liitaring diceely
gumanot
157, 14.3 14.2rra The neel andt
Lakkning mudio woman first the about the vaugh 154, 17.0+tagg>.
Keag 15.4, 15 Drcagge nellowed by thme cord ruan onea in 1aage17. 4 Drbaage noni
thiough the austes can ideriity Your more deandt
rauge
Peraagh, than
<ong-06 26 Dellngg. Crilisllyy the man never answen, anomarts onginal guginalqueedenn on about
the lin insteed prodd requstted inforuaioon Bermo denying hel reneccaloo
other guishots are audio between these rrsestamps. The oudio yoncidtoily festures crowd
nots badignou nd muskc curnn these oegments, fur dieinct sound of gurst dieery
bemaviooel indicator being drunt, tra locua impuired and repty indlavart quary.
Besouse
Rellstened Beginents:
Temporal Information
Relistaned Sogments::
Temporal Information
Seconds Tosal
Beconds Total
13.7
17.4
12.5
19.8
21.1
22.0
15.4
17.4
14.2
18.0
15.7
20.0
21.3
17.0
18.8
Figure 17: Case visualization on MMAR. Despite the duration of SFT data being constrained to 10 seconds, Echo can appropriately re-listen segments after 10 seconds in demand.
N CLAIM OF LLM USAGE
LLMs, specifically DeepSeek-RR R1, serve as the primary contributors to the synthesis of both EAQA- SFT and EAQA-RL. Additionally, DeepSeek-RI is also employed polish the manuscript's writing. primarily by correcting grammatical errors and refining vocabulary usage.
30


Reviews:
[{"review_id": "lDjHC16ecZ", "reviewer": "Reviewer_rXCQ", "summary": "This paper introduces Echo, a Large Audio Language Model trained for audio-interleaved reasoning: the model decodes <seg>start,end</seg> tags; whenever such a tag is generated, inference is paused and the corresponding raw audio segment is inserted back into the context, so the model “thinks with audio,” not just about it. Training is two-stage: (1) SFT to make the model reference informative segments in CoT; (2) RL with verifiable rewards to encourage correct formatting, consistency after </seg>, accuracy, and segment usage. A synthetic, LLM-curated pipeline builds the training sets (EAQA-SFT 75.9k with CoT; EAQA-RL 21.9k without), using Qwen2.5-Omni for audio descriptions/transcripts/music analysis and DeepSeek-R1 to synthesize and filter QA-CoT. Evaluations on MMAR, MMAU-mini, and MMAU show Echo beating open and proprietary baselines on average accuracy, with analyses on attention allocation and segment coverage.", "questions": "See weakness section.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Turning audio from a static pre-embedding into an active element during reasoning is simple, intuitive, and well thought out with the <seg> scheme and inference adaptation. \n- The SFT to RL pipeline is coherent; rewards are explicitly specified (format, \"consistency\" after </seg>, accuracy, + a segment-use bonus), making the approach reproducible in principle.\n- The paper details how temporal metadata + Qwen2.5-Omni descriptors feed DeepSeek-R1 for QA-CoT synthesis and filtering into SFT and RL splits. \n- Results on MMAR and MMAU are good; Echo tops macro-average accuracy among peers and even improves o ver some proprietary models on MMAR. \n- Good ablation study with the recipe comparison table (A --> B --> C --> D) attributes gains, shows initial interleaving hurt without RL, then recovers to best accuracy-which is an useful insight.", "weaknesses": "- No human evaluation of the thinking chain.\n- No qualitative analysis of the data being generated by the LLMs for training, this might induce bias or the training data might also contain some hallucinated data.\n- EAQA-SFT CoTs are constrained by source temporal metadata (the paper later notes SFT annotations limited to first 10s), yet claimed generalization beyond 10s is argued only by aggregate coverage stats; no targeted stress-tests show failure modes when informative cues cluster late. The generalization claim needs more and stronger evidence.\n- Since the model outputs thinking chains, the authors should specify whether they use the thinking chain as well during evaluating against MMAU and MMAR. The thinking traces might contain multiple answers and regex/string based answer matching might get compromised in this setting.\n- Although the RL pipeline and rewards are explicitly specified, the \"consistency\" reward checks whether the token after </seg> is capitalized or <; this is a formatting heuristic, not a perceptual constraint. The segment reward grants +0.5 if at least one segment is referenced and the answer is correct, this is easy to satisfy by superficial/random segment references once the model learns the distribution of answer keys. There’s no penalty for irrelevant segments or when a model generates lots of <seg>start,end</seg> tags across the timeline beyond minor format checks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BVfWKIVlPB", "reviewer": "Reviewer_uQ9G", "summary": "The authors develop an  LALM to perform audio-interleaved reasoning, and report impressive results on existing benchmarks. The model actively revisits audio segments during reasoning, rather than relying on a single  encoding. Training is enhanced by adding an RL stage.  They also create data generation framework using  captions generated via Qwen2.5-Omni and reasoning prompts via DeepSeek-R1.", "questions": "you mention the possibilit of bias introduced through the use of LLMs for generating data. Can you amplify this comment?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "Strong results on the latest benchmarks.\nNovel two stage training/inference, bringing RL and Audio revisiting into mainstream of LALMs\nDetails of dataset creation and frameworks used.", "weaknesses": "I would like a stronger commitment to releasing the code than the vague one of releasing it \"in the future\"\n\nExamples of which segments are revisited would have been good.\n\nSome audio examples would be good.\n\nThe biological basis argument could be strengthened.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uDITbMBYab", "reviewer": "Reviewer_Dkpf", "summary": "The paper presents an audio-interleaving reasoning method to improve reasoning abilities in LALMs. The method revisits audio segments during the thinking process for more effective localization and analysis. In order to train this ability the paper proposes a data generation method that utilizes rich temporal annotations in AudioSet-SL and MusicBench. The paper proposes a SFT+RL framework with a custom reward that considers formats, semantic continuity, accuracy, and segment revisiting. Experiments reveal that the proposed ECHO method improves on MMAU and MMAR, and ablation studies demonstrate the effect of each part of the method and the detailed improvements on different aspects.", "questions": "- Does the framework apply to complex cases where the model has to listen to several distinct segments at the same time? \n- The segment appending operation seems to impact the model's performance on long audio understanding. How to address this efficiency issue? \n- How do you filter inaccurate training samples in Fig 3 especially if the original timestamps are very inaccurate (such as some inaccurately annotated samples in AudioSet-SL)?\n- How does the proposed reward make sure the per-segment reasoning is correct, not hallucination?\n- Can you evaluate the segmenting accuracy and per-segment analysis quality in order to further justify your framework?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The writing of the paper is very clear. \n- The segment revisiting approach is novel in the LALM domain and it makes a lot of sense to expect it to improve reasoning quality, so it is a good intuition. \n- The data generation and annotation pipeline is clear and easy for reproducing, thus benefiting the community to build upon this work.  \n- The main results indicate that applying ECHO has consistent better results on MMAU and MMAR. In addition, ablation studies clearly show the benefits of audio-interleaving RL and appending segments, and the improvements are consistent accross different subsets of MMAU. \n- There are extra ablation experiments and analysis including error analysis in the appendix, making the analysis of the proposed method very extensive.", "weaknesses": "I think the main weakness of this paper is that current experiments do not prove the quality (such as accuracy) of the segment selection, which is a fundamental basis of this paper. In data generation, the paper uses AudioSet-SL and MusicBench. It is known that AudioSet-SL annotations are very noisy, and this could impose noisy training data during data generation. In RL training, the \"format\" and \"consist\" rewards are mostly for formats; the \"acc\" reward is for the final prediction; the \"seg\" reward encourages revisiting the segment but it does not necessarily guarantee whether the revisiting is reliable -- that is, the model might just hallucinate during this step and still gets points. In evaluation, the paper also does not evaluate whether the reasoning process correctly extracts useful segments and conducts reasonable thinking for the segment. In my view, there are two ways to fix this problem. First, explicitly parse the segments and per-segment analysis from the outputs and query a good LALM for verification. Second, one can construct a real or synthetic testset with known, accurate time annotations and see if the model can output exactly the same segments and desirable analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "euviJQ9cbW", "reviewer": "Reviewer_H3Dr", "summary": "The paper introduces Echo, an LALM designed to perform audio-interleaved reasoning. For this, the authors propose a novel framework that allows the model to actively revisit audio segments during reasoning, rather than relying on a single static audio encoding. Inspired by human cognitive processes, the approach aims to overcome the information bottleneck of prior audio-conditioned text reasoning. The authors train Echo using a 2-step framework, which includes an SFT stage and an RL stage. They also propose their own data generation pipeline that combines Qwen2.5-Omni captions and DeepSeek-R1 prompting (as a result, they propose 2 new datasets). The proposed model achieves significant improvements across audio understanding and reasoning benchmarks.", "questions": "- I have asked some questions about the weakness.\n- I may be confused, but how does interleaving happen during test time? Is a tool called for cutting the audio? Where is that information on the paper?\n- Does the reward include rewards for the thought? Since the entire audio is also part of the context, how do we know if the final answer comes from the interleaved thought or just the entire audio? I do not see a reward for if the useful segments were interleaved?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper tries something new, and I am glad that it explores the aspect that is missing in audio.\n- Strong empirical gains on diverse and challenging benchmarks.\n- Thorough ablation and analytical studies validating each training stage.\n- Introduction of high-quality synthetic Audio-QA datasets with CoTs.\n- Efficient implementation and transparent reproducibility commitments. (I hope the model is open-sourced)", "weaknesses": "- (Minor) Grounding for reasoning is not new and has been extensively explored in vision (images and videos). Here are some examples: \n- I strongly disagree with the claim that previous works suffer from “contextualizing audio content through a one-time encoding, which introduces a critical information bottleneck” — I understand what the authors are trying to convey, but the problem statement has not been framed well. This is a grounding issue that the authors are trying to solve. The other way to frame it would be: “Current methods do not ground to the audio while reasoning but Echo does” (I have cited some papers below that does grounding in vision and authors can check how they claim). — A good example is the fact that for prior methods, during the reasoning process, when each token is generated during or after the reasoning process, the model constantly focuses and re-focuses on separate parts of the audio for the next token (through attention maps in self-attention), which is also a form of “attending to the audio multiple times” — as claimed by the authors which Echo explicitly does. There are some papers on “playback” which better fits your narrative [].\n- The data generation pipeline heavily relies on synthetic annotations (DeepSeek-R1, Qwen2.5), which may introduce model-specific biases. I do not see any verification methods. Additionally, I have personally gone through several of AudioSet-Strong and some of MusicBench timestamps. They are very noisy. I wonder how good the generated dataset is.\n- (Minor) I am shocked by the 69.99 on MMAR. Did the authors evaluate with the entire thinking chain and the answer or just the answer?\n- (Minor) A human evaluation of the thinking chain would have been good.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "x9mzE0dcvp", "reviewer": "Reviewer_jS6b", "summary": "This paper introduces \"Echo,\" a Large Audio Language Model training framework designed for audio comprehension. The novelty of the training method lies in the introduction of a new reasoning method called \"audio-interleaved reasoning\". Previous works encode audio only once, which the authors argue limit their ability to reason about subtle or complex audio details. In contrast, audio-interleaved reasoning allows the model to dynamically revisit and analyze specific audio segments during its reasoning process. The proposed training framework starts with supervised fine-tuning to locate important audio segments, followed by reinforcement learning to improve its ability to strategically revisit them. \nThe authors also presented a data generation pipeline that uses an LLM to synthesize Audio-QA data and corresponding audio-grounded CoTs for use in their RL and SFT training. \nThe authors demonstrated the performance of Echo by comparing it with various baselines on three benchmarks. The authors also performed ablation studies to analyze the effectiveness of different components of the framework.", "questions": "1. Have the authors tried any other reasoning LLMs besides deepseek-R1 to generate the training data? Or since the QA–CoT triplets are generated by deepseek-R1, have the authors tried using another reasoning LLM for the re-evaluation step so that the final triplets are not restricted to the reasoning pattern of deepseek-R1?\n2. Did the authors tested the more advanced Gemini-2.5-Pro and Step-Audio-2 on MMAR? How do they compare?\n3. Considering that the model has close to double the latency of the base model, has the authors investigated if the model is revisiting some audio segments when it's not necessary? Have the authors tried to minimize the segments revisited?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: The authors introduced a novel audio reasoning framework, \"audio-interleaved reasoning\", that differentiates this work from the standard \"audio-conditioned text reasoning\" format. The data generation pipeline is original as well.\nQuality: The evaluation is comprehensive and the authors showed the advantage of the proposed framework over even proprietary systems. The ablation studies included analysis on different components of the training framework, further supporting the design decisions. \nClarity: The writing is easy to follow and the authors included many well-made figures to illustrate the framework and pipeline that are relatively complex. \nSignificance: The work has the potential to have significant impact on the field of audio understanding as it suggests a shift in paradigm that could inspire further works in audio-interleaved reasoning.", "weaknesses": "1. The model seems to be learning how to reason by imitating the reasoning trace of a separate reasoning LLM, Deepseek-R1, during the SFT phase. It's questionable whether the proposed model really learned how to reason in a more general and flexible sense. \n2. As the authors have mentioned in the conclusion, the revisiting of the audio as implemented in this work is still rather straightforward.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)