Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards
Authors: Jiajun Fan
Roger Ren
Jingyuan Li
Rahul Pandey
Prashanth Gurunath Shivakumar
Ivan Bulyko
Ankur Gandhe
Ge Liu
Yile Gu
Venue: ICLR 2026 Poster (2026)
Keywords: Reinforcement Learning
Audio Large Language Models
Test-Time Inverse Scaling
Process-Oriented Rewards
Reasoning Length Calibration
Multimodal Reasoning
Reasoning Scaling
Primary area: reinforcement learning
TL;DR: We reveal why audio reasoning fails—models generate inconsistent, hallucinatory, and unstructured reasoning process that degrades performance and can't scale. Our process-oriented RL training resolves this, enabling effective and scalable reasoning.

Abstract: The role of reasoning in Audio Large Language Models remains widely underexplored, as introducing a reasoning process often degrades rather than improves performance during inference, a phenomenon we term test-time inverse scaling, where longer reasoning chains yield progressively worse results. We demonstrate that this stems not from fundamental limitations of reasoning itself, but from inadequate training: models without proper guidance for the reasoning process produce hallucinatory, inconsistent reasoning that accumulates errors over longer chains. To address these challenges, we introduce CESAR (Consistent, Effective, and Scalable Audio Reasoners), shifting from outcome verification to rewarding the reasoning process. Our online reinforcement learning framework employs Group Relative Policy Optimization with a multi-faceted reward suite that incentivizes not only correctness and format but also consistency, structured analytical patterns, causal reasoning, domain-knowledge integration, and calibrated reasoning depth. CESAR resolves test-time inverse scaling, transforming reasoning from  detriments into gains while revealing model-specific "reasoning sweet spots", where performance peaks during test-time scaling. We achieve state-of-the-art results on MMAU Test-mini, substantially outperforming Gemini 2.5 Pro and GPT-4o Audio, and near-human-level performance on MMSU reasoning tasks. Through AI-as-judge evaluations and qualitative comparisons, we provide both quantitative and qualitative validation of our improved reasoning quality. Importantly, enhanced reasoning creates synergistic effects, simultaneously improving multimodal reasoning and perception capabilities. Overall, CESAR establishes a principled method for developing robust and scalable reasoning in Audio LLMs.

Published as a conference paper at ICLR 2026
INCENTIVIZING CONSISTENT, EFFECTIVE AND SCAL- ABLE REASONING CAPABILITY IN AUDIO LLMs VIA REASONING PROCESS REWARDS
Jiajun Fan 9 Roger Ren Jingyuan Li Rahul Pandey Prashanth Gurunath Shivakumar
Iyan Bulyko A, Ankur G Ge Liu Yile Gu
Siebel School of Computing and Data Science, University of Illinois Urbana- Chbana-Champaiin
Amazon
[jajuun3, geliu] @illinois.edu
(rogerren, jylii, rpandyn, psshvak, ibbulyko, aggandhe, yilegu) (aagu)@@amaaca @amazon.com
ABSTRACT
The role of reasoning in Audio Large Language Models remains widely under- explored, as introducing a reasoning process often degrades rather than improves performance during inference, a phenomenon we term test-time inverse scaling, where longer reasoning chains yield progressively worse results. We demonstrate that this stems not from fundamental limitations of reasoning itself, but from inadequate training: models without proper guidance for the reasoning process produce hallucinatory, inconsistent reasoning that accumulates errors over longer chains. To address these challenges, introduce CESAR (Consistent, Effective, and Scalable Audio Reasoners), shifting from outcome verification to rewarding the reasoning process. Our online reinforcement learning framework employs Group Relative Policy Optimization with a multi-faceted reward suite that incen- tivizes not only correctness and format but also consistency, structured analytical patterns, causal reasoning, domain-knowe knowledge integration, and calibrated reason- ing depth. CESAR resolves test-time inverse scaling, transforming reasoning from detriments into gains while revealing model-specific "reasoning sweet spots", where performance peaks during test-time scaling. We achieve state-of-the-art results MMAU Test-mini, substantially outperforming Gemini 2.5 Pro and GPT-4o Audio, and hear-human-leved performance on MMSU reasoning tasks. Through Al-as-judge evaluations and qualitative comparisons, provide both quantitative and qualitative validation of our improved reasoning quality. Impor- tantly, enhanced reasoning creates synergistic effects, simultaneously improving multimodal reasoning and perception capabilities. Overall, CESAR establishes a principled method for developing robust and scalable reasoning in Audio LLMs.
INTRODUCTION
The advent Audio Large Language Models (Audio LLMs) has opened a new frontier in multi- modal AI, promising sophisticated understanding of complex acoustic environments (Gong et al. Tang et al.) 2024; Xu et al.) 2025). Yet, critical paradox emerges when these models are asked to reason: while ch tf-ouughhtt (CoT) prompting is proven catalyst for reasoning text- based domains (Wei et al. 2022) Jaech et al. 2024 DeepScek-Ar Al et al.) 2025), in audio it often backfires, underperforming non-reasoning versions. We are the first to systematically identify and diagnose this phenomenon a test-time inverse scaling problem in Audio LLMs, where reasoning processes not only fail to improve performance but actively degrade it during inference, with longer reasoning chains yielding progressively worse results-ofier underperforming their direct answer- ing versions that bypass reasoning entirely (Fig.) []. This test-time inverse scaling might lead to the
2024
Work done intern Amazon

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Published as a conference paper at ICLR 2026
Nersilee Graling
Figure 1: General Framework of Different Training Methods for Audio Reasoning Models.
premature conclusion that reasoning inherently harmful for Audio LLMs (Li al. 2025), but our investigation reveals the true culprit: models produce hallucinatory, inconsistent, and logically unsound reasoning processes when forced to "think" without proper training on how to reason.
Current methodologies are fundamentally ill-equipped to solve this problem. The dominant ap- supprv-upe fine-tuning (SFT) on CoT datasets (Ma et al. 2025a Xie et al., 2025)-teacher teaches models to merely memorize and mimic reasoning templates rather than developing genuine ana- lytical capabilities. While recent reinforcement learning with verifiable rewards (RLVR) methods (Li et al. 2025 Zhao et al. 2025) represent progress, they remain constrained by outcome-only ward structures that exclusively value final answer correctness and format compliance. This shallow supervision fails to address the root cause: poor reasoning processes that accumulate errors over longer chains, allowing models to generate final answers through flawed or irrelevant logic while perpetuating the very issues of inconsistency and hallucination that lead test-time inverse scaling.
We address these limitations by introducing CESAR (Consistent, Effective and Scalable Audio Rea- soners), representing a fundamental paradigm shift from outcome verification to rewarding the reasoning process. Our framework leverages Group Relative Policy Optimization (GRPO) (Shao) vall,2002) not merely as a verifier, but as mechanism to explicitly cultivate reasoning as a control- lable, trainable skill. At its core lies multi-faceted reward suite that provides granular feedback on the reasoning process itself, systematically incentivizing answer correctness, format compliance, consistency between thoughts and answers, structured analytical patterns, causal reasoning, do- main knowledge integration, and calibrated reasoning depth that avoids catastrophic overthinking. Through this process-centre approach, transform reasoning from unpredictable liability into a reliable, scalable asset that enables effective test-time scaling through discovered "reasoning sweet spots." In summary, our approach makes several key contributions:
We identify and diagnose the test-time inverse scaling phenomenon in Audio LLMs. where reasoning processes degrade performance during inference due to hallucination, in- consistency, and unstructured thought patterns. We demonstrate this stems from inadequate training of reasoning processes rather than unsolvable limitations of reasoning itself.
2. We propose CESAR, framework employing reasoning process rewards that incentivize consistency, structured analytical patterns, domain knowledge integration, and calibrated reasoning depth extend current outcome-only RLVR methods. With GRPO, CESAR explicitly cultivates robust reasoning, resolving the test-time inverse scaling problem.
3. We demonstrate that cultivating robust reasoning capability by CESAR unlocks effec- tive test-time scaling: while poorly trained models suffer catastrophic degradation with longer reasoning chains, our models discover optimal "reasoning sweet spots" for substan- tial training-free performance gains, validating our scalable reasoning capability.
4. Our method achieves SOTA on MMAU Test-mini, surpassing GPT-4o Audio and Gemini
2.5 Pro, and near human-level performance MMSU reasoning tasks. Most impor-
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tantly, we observe that enhanced reasoning creates synergistic effects that simultaneously improve multimodal reasoning and perception capabilities.
5. We introduce novel Al-as-judge evaluation framework, human evaluations and com- prehensive qualitative analysis that rigorously validate our enhanced reasoning quality, demonstrating commanding win rates against strong baselines and concrete reductions in hallucination while improving logical coherence and reasoning-answer consistency.
2 RELATED WORK
Audio Large Language Models. The development of Audio Large Language Models (Audio LLMs) has rapidly progressed from foundational audio-to-text tasks to sophisticated multimodal systems, Early work (Elizalde et al. 2023 Wu et al.) (022) established cross-modal understanding through contrastive learning. This paved the way for decoder-based models capable of open-ended generation (Deshmukh et al. [202]] Gong et al.] (224)) The current generation of models, such as SALMONN (Tang et al. 2024). the Qwen-Audio series (Chu et al.) 2023 2024), and Audio Flamingo (Kong et al. 22224] Goel et al. 2025a), have demonstrated increasingly comprehensive capabilities through lcage-scale pre-training and instruction tuning. As state-of- the-an art models like
Qwen2.5-Omin (Xu al. 2025). ). GPT-4o Audio (Hurst et al. 2024) and Gemini 2.5 (Comanici & ctt al. 2025) achieve near-human audio understanding, the research frontier has shifted towards a more profound challenge: enabling these models genuinely reason about the acoustic world.
The Limits of Supervised Reasoning. Chain-of-thought (CoT) prompting has been transforma- tive for eliciting reasoning in text-based LLMs (Wei al. 2022 Naturally, this paradigm was extended to the audio domain through supervised fine-tuning (SFT) on CoT datasets (Ma et al.) 2025a Xie et al.) [2025] Goel et al. 2025a). However, these SFT-based approaches share a fun- damental limitation: they teach models to imitate reasoning templates, not to develop genuine analytical skill. This results in models that can produce syntactically plausible reasoning traces but which are often brittle, fail to generalize to complex, unseen problems, and do not address the underlying causes of reasoning failure we identify, such as hallucination and inconsistency.
The Untapped Potential of Reinforcement Learning. Reinforcement learning (RL) offers promising alternative to supervised imitation, as demonstrated by the success of models like Ope- nAl's ol (Jaech et al. 2024) and DeepSeek-R1 (DeepSeek-A) Al al.) (2025) in the text domain. These works show that sophisticated reasoning can be cultivated directly through reward optimization, with methods like Group Relative Policy Optimization (GRPO) (Shao et al. (024)) proving particularly effective. Early attempts to apply these techniques to audio, such as R RI-AQA (Li et al., 2025) and Ke-Omni-R (Zhao al. 2025), have shown initial success. However, they fundamentally con- strained by an oureone- reward paradigm, optimizing solely for the correctness of the final answer. This shallow supervision signal insufficient; it fails penalize logical fallacies or reward coherent analytical processes, thereby directly contributing to the poor reasoning capability that causes the test-time inverse scaling problem. Our work addresses this gap by moving from outcome verification to granular, process-oriented reward system.
3 METHODOL.OGY
Existing audio reasoning methods like Ke-Ommi-R (Zhao et al. 2025) suffer from reasoning- inconsistency, unstructured reasoning, and test-time inverse scaling (Fig. I caused by outcome- only verifiable rewards and uncontrolled reasoning emergence. In this paper, we propose CESAR to transform reasoning into a controllable skill through comprehensive reasoning process rewards that incentivize consistency, structured reasoning, and optimal reasoning depth-whil discovering model-specific "reasoning sweet spots" where performance peaks during test-time scaling.
3.1 PROBLEM FORMULATION
Let D (a,, qs C., yil denote the audio guestion-answering dataset, where a, represents the audio input, qi is the question, C, = C22 C22 Ca2 C4 is the set of multiple-choice options, and y E C, is the ground truth answer. Our goal is to train Audio LLM my that can generate both reasoning
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process tú and final answer di given the imput (as, 9s: Ci). The model output follows structured format where we have:
To (as C,) =
Here, 4 represents the COT reasoning process and is is the predicted answer. This structured output allows us to separately evaluate both the reasoning quality and the final answer correctness.
Reinforcement learning fine-tuning seeks to optimize the audio LLMS to maximize rewards:
π arg max E[R(s:));
(2)
where s; = (4, Ds) represents the complete model output. Current approaches like RI-AQA and Ke-
Omni-R employ outcome-only rewards based solely on answer correctness and format compliance:
RRIVR(S) (s;) T ysl + [[ValidFormat(s.)) This impoverished signal leads to three critical failure
modes: (1) Random Emergence of reasoning patterns without effective control; (2) Reasoning- Answer Inconsistency where models generate answers inconsistent with their reasoning logic; (3)
Lack of Structured Reasoning strategies like elimination or multi-step deduction.
Our fundamental insight is that genuine reasoning capability requires explicit process-oriented oriented in- centivization rather than spontaneous emergence. We achieve this through multi- fulli-faccted reward suite that provides granular feedback on reasoning quality, consistency, and structure during training, transforming reasoning from unpredictable phenomenon into controllable, trainable skill.
3.2 FROM OUTCOME-BASHE TO PROCESS- SACCESS-OREENTED REASONING CONTROT.
Current RLVR approaches fundamentally fail to distinguish between genuine reasoning and fortu- nate guessing, leading random emergence of reasoning behaviors that cannot be systematically controlled or guaranteed. Our framework introduces a novel paradigm that transforms reasoning from an unpredictable emergent phenomenon into controllable, trainable capability through com- prehensive process supervision.
Our total reward (s) decomposes into two complementary components that address distinct aspects of reasoning quality:
01 Race (8:) + Or (fomma (+;)++
+ 0rg Roonsisteney (s:) + 0.4 Rkceywords (8:) + arp Roventhhnnnn penalty (aallt(())
(3)
Verifiable Rewards
Reasoning Process Rewards
The verifiable rewards maintain essential correctness constraints and structural integrity, while our Reasoning Process Rewards explicitly shape reasoning quality, consistency, and conciseness. Here = (t,, 1.) represents the complete model output encompassing both reasoning trace and final answer, and { are weight coefficients that balance answer correctness with reasoning refine- ment. In practice, we set 5.0 for accuracy and 02-5 1.0 for other components.
3.2.1 FOUNDATION: VERIFIABLE CORRECTNESS AND STRUCTURAL INTEGRITY
While transcending traditional RLVR limitations, our framework maintains rigorous grounding in verifiable outcomes. The Accuracy Reward (s)) [1[ ys] establishes the fundamental correctness constraint that ensures reasoning improvements do not come at the expense of answer accuracy. This binary signal prevents the optimization process from learning elaborate but incorrect reasoning patterns, anchoring all process improvements in empirical validity.
The Format Reward enforces structural compliance and prevents the model from bypassing the reasoning framework:
Ryfrrmr (x;) = [ValidFormma [x]]
(4)
This reward ensures the model produces outputs with proper XML tag structure, specifically requir- ing both (think) ( (/think) and (answer) (:ag:a///// (/answer) components. This creates disciplined reason- ing environment where models must engage with the reasoning process rather than circumventing through format violations.

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3.2.2 SEMANTIC COHERENCE AND REASONING-ANSWER ALIGNMENT
critical challenge in current reasoning approaches is the pervasive problem of reasoning-answe answer inconsistency, where models generate correct answers despite fundamentally flawed or irrelevant reasoning processes. Additionally, when reasoning traces are unrelated to the question and choices, models are prone to hallucination. The Reasoning Consistency Reward introduces explicit seman- tic supervision that ensures reasoning traces genuinely support their corresponding conclusions:
Reonsistency (+:) Simsemanti (t,, 96) + Simsemantic (44, Qc),
(5)
where Q, = (q, represents the complete question context including both the question text and available choices. This dual-alignment formulation addresses two critical failure modes. The answer-alignment component Sim (f,, 0i) prevents reasoning processes from becoming dis- connected from their conclusions, which would render the reasoning ineffective. The question- alignment component Sim (t,, Q;) ensures the reasoning remains focused on the posed ques- tion and available choices, preventing hallucination and off-topic elaboration.
We implement semantic similarity using concept overlap (e.g., via overlapped words):
ConceptOverlap(az v)
Simaemantic (x, v) max((Concepts(())) (oonceps((]]
(6)
where the normalization ensures bounded similarity scores in [0, 1j. This approach represents
departure from outcome-only optimization, introducing explicit supervision signals that distinguish between reasoning processes that accidentally arrive at correct answers and those that systematically derive conclusions through valid analytical pathways.
3.2.3 INCENTIVIZING STRUCTURED REASONING AND PENALIZING OVERTHINKING
To explicitly shape reasoning quality, our framework employs a two-pronged strategy: we positively incentivize structured reasoning while simultaneously penalizing inefficient overthinking. The primary mechanism for structured reasoning is the Keywords Reward, which acts as a cognitive scaffold to transform random emergent thoughts into controlled, sophisticated analytical behaviors:
Rkeywords (s;) = Reater (8,) + RRogie + Rdemain
This tri-component design addresses three fundamental aspects of structured reasoning: structured analytical patterns, logical rigor, and domain expertise integration.
Structured Analytical Patterns. The pattern recognition component systematically rewards models for developing structured reasoning architectures rather than relying on intuitive leaps: Rpstem s,) = Epep [Pattern, detected in t.]. The pattern set P captures sophisticated reasoning architectures through key categories such as sequential organization, comparative analysis, system- atic evaluation, and explicit justification. Complete pattern specifications are detailed in App. [B66
Logical Rigor and Causal Reasoning, The reasoning indicators component cultivates sophis- ticated logical thinking by rewarding linguistic markers that indicate deep analytical processes: Bauiie = Σlec -leKeyorr,, detected in tz]. The reasoning logic taxonomy L strategically tar- gets distinct logical functions including formal deduction markers, premise establishment, hypothet- ical reasoning, and evidential conclusions. These linguistic signatures promote sophisticated logical progression from premises to conclusions (complete taxonomy App. BB6).
Domain Knowledge Integration. We also incentivize the use of domain knowledge, where the domain component rewards models for incorporating audio-specifie expertise rather than generic reasoning patterns: Raomain (s:) EaED Wa [[Terma detected in t.). The domain vocabulary D encompasses specialized terminology across acoustic properties, musical concepts, speech analysis, and environmental audio understanding. This encourages models to ground their reasoning in signal- specific expertise rather than superficial pattern matching (complete vocabulary in App. B.6).
Overthinking Penalty. The necessary counterpart to rewarding structured thought is penalizing its inefficient opposite. The Overthinking Penalty addresses a critical failure mode: the tendency for models to engage in redundant, verbose reasoning that accumulates errors rather than improving
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analysis quality. This component actively discourages overthinking by penalizing excessively long reasoning traces:
Roverthinking penalty (8:) (length((::))
mmux.oulput
where fiength (1) is linear penalty function that decreases as reasoning length [t,] increases, normal- ized by the maximum output length Lmax output (we set as 256 in practice). This design specifically targets common failure modes including circular reasoning. repetitive analysis, and tangential elabo- ration. By learning to terminate reasoning at appropriate depth, models develop meta-cognitive awareness that prevents hallucination accumulation while maintaining analytical rigor.
3.3 CULTIVATING REASONING CAPABILITY VIA ONLINE RI.
Our framework operationalizes process-oriented reasoning control through Group Relative Policy Optimization (GRPO) (Shao et al.] 2024), systematically cultivating reasoning capabilities rather than relying on random emergence. For each training sample (a,, go, Ci y:), we sample K responses (k) J==1
(T [ai, Qs, C,) and optimize the objective:
LORPO
facered + B LKL, LPG
faceted -E ΣK A(s(k) log Ro (s q, c) provides where the policy gradient loss LG granular feedback both analytical processes and final outcomes. The advantage function A(s,*)) Roval ( Eÿ=1 Rs; enables models distinguish between high-quality and luw-quality reasoning processes, while KI. regularization LRL E [KI[(T]] (Tol] [[)] maintains training stability.
To enhance model robustness against linguistic variance, we employ systematic data augmenta- tion that expands our training corpus D into an augmented version D' by generating multiple linguistic variations for each question while preserving ground-truth answers. For each instance (a,, 9:: C. y1) E D, we apply answer-invariant transformation templates T {TI..... Tm where each transformation T. generates & = Tk(g. C.), creating training samples (a;, L..a+ C, yi) with unchanged audio and answers. This forces the model to learn underlying reasoning patterns rather than superficial textual correlations. Complete template specifications are provided in App. B
3.4 UNLOCKING REASONING CAPABILITY VIA TIST-TIME SCALING
To understand the test-time inverse scaling phenomenon and validate our proposed methods, we in- troduce Test-Time Scaling to systematically analyze reasoning dynamics by evaluating performance across varying maximum thinking lengths Lmax think- We define performance as P[Lmax think) E [ = y] [4| S Lmmax and identify the "reasoning sweet spot" where performance peaks: Lsweet arg max, P(L). Through this simple scaling of reasoning length, CESAR achieves sub- stantial improvements, with particularly dramatic gains at its reasoning sweet spot, while baseline models show limited improvement continued degradation (See Fig. 3. This method effectively unlocks reasoning capability at test-time by revealing that our process-oriented training enables models to discover and utilize their optimal reasoning depth for maximum performance.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We evaluate our framework on challenging out-of-distribution (OOD) audio reasoning benchmarks:
MMAU Test-mini [Sakshi et al., 2025) with 1k expertly annotated questions spanning speech, sounds, and music requiring 27 distinct reasoning skills, and MMSU (Wang et al., 2025 with 5k audio-question pairs and granular ceaception-reasonnng task separation. Training uses the AVQA dataset Yang al. 2022) enhanced through systematic data augmentation that generates diverse

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Table I: MMAU Test-Mini benchmark results. Blue in- dicates best performance, green indicates second-best. Accuracy (%) is reported across audio modalities. OP means overthinking penalty. See App. D.4 for details.
kayy
Speech
Method
Reasoning
Sound
Music
Speech
Total Accuracy
Modkum
Proposed
Methods
CESAR CESAR
73.05
77.10
79.88
67.96
73.27
73.70
Musi
CESAR OP CESAR w/o OP
70.06
76.50
80.48
70.06
74.47
75.00
RI. Haseline Methods
Xe-Omai-R Ke-Omni-R
Beand
78.38
70.96
74.17
74,50
Proprictary Models
CESAR Joum wit Overth ridig Fanshty Q4e12.5-mm-- 5-mm-3
Gemini 2.5 Pro
75.08
68.
71.47
71.60
Gemini Flash
73.27
65.57
76.58
71.80
Figure 2: Task-wise comparison on the MMAU Test-mini Benchmark
GPT-4o Audio
64.56
$6.29
66.67
62.50
Base Mudels
(Scores are normalized by CESAR). See App. D.5 for more results.
Qwen2.5-Ommii-7 Qwenn.550mmmi-i
69.07
59.55
66.97
65.20
72.37
64.37
69.07
68.60
Table 2: MMSU Results (Wang al. 2025).
Best scores in blue. second-best in green
Results show accuracy (%) across perception and reasoning tasks. See App. D.6 for more results.
Models
Perception Tasks
Reasoning Tasks
Overall
Semanties
Phonology
Paralinguisties
Avg
Semanties
Phonology
Parallnguisties
Avg
CESAR (Ours)
60.16
50.16
39.50
48.45
88.72
80.66
57.01
81.07
64.24
Ke-Ommi-R
58.72
47.09
74.31
78.06
62.08
Gemini Pro
57.06
53.60
46.10
83.46
76.16
60.68
Doven..5-Ommi--2
58.12
37.33
39.35
42.50
88.00
81.37
48.36
79.83
60.57
GPT-lo Audio
59.70
41.56
21.44
39.67
80.83
78.74
71.96
56.38
question phrasings while preserving answer labels. Our experiments employ Qwen2,5-0mni- with GRPO, sampling K 8 responses per training example. Reward coefficients balance cor- rectness (a1 = 5.0) with other rewards (a2-5 = 1.0). We compare against base model variants, Ke-Omni-R baseline, proprictary models, and open-source audio models. Unless otherwise speci- fied, all reported scores of our methods are achieved with reasoning. Complete details are in App.
4.2 MAIN RESULTS: STATE-OF-THE-ARY PERFORMANCE, ACROSS BENCHMARKS
MMAU: Significant Performance Gains via Rewarding the Reasoning Process As shown in Tab. our method establishes new SOTA performance on MMAU Test-mini, decisively surpassing leading proprietary models including GPT-4o Audio and Gemini 2.5 Pro. Most importantly, demonstrate that process-oriented training delivers synergistic improvements across both reason- ing modes: compared to the base model, CESAR achieves substantial gains both with reasoning and without reasoning, proving that cultivating reasoning processes fundamentally enhances the model's cognitive capabilities. Our framework also significantly outperforms outcome-only RL methods, with reasoning mode delivering larger benefits than the Ke-Omni-R baseline. The radar analysis (Fig. 2 reveals controllable reasoning architectures: our two variants exhibit engineered cogni- tive profiles-CESA CESAR w/o OP excelling on hard tasks through deeper analysis, while full CESAR maintains balanced efficiency across difficulty levels establishing reasoning as a systematically controllable capability rather than random emergence. See App. D.3 D.4 and D.5 for more results.
MMSU: Perceptual Improvements with Near-Human Reasoning On the MMSU benchmark (Tab. 2 our CESAR achieves dual advances: reasoning capabilities that approach human levels (including super-human performance in semantic reasoning), while simultaneously outperforming larger competitors on perception tasks. This reveals an interesting synergistic effect where cultivat- ing advanced reasoning through reasoning process rewards also refines foundational auditory

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Table 3: Performance on the MMAU-Pro Benchmark (Kumar al. 2025). We compare CESAR against key baselines and SOTA models. Best scores are highlighted in blue second-best scores in green All values are accuracy (%) and rounded to one decimal place (same as MMAU Pro paper).
See App. D.I for more results.
Midel
Sourd
Speech
Saund-Mllde
Speech-Mielr
Sprech-Sandd Suardd
H-Mi-Specl
Spatial
Viice
Mitti-ado
Opencerded
Nerrage
Had
3
RI-AQA
204
Harra
78.
70.
823
79.3 785 934
857
182
68.4
79.8
77.3
160.
77.9
perception capabilities. However, while both capabilities advance substantially, the results illumi- nate critical asymmetry: reasoning improvements have reached near-human parity, whereas per- ception performance, despite leading existing models, still exhibits a considerable gap relative to human baselines. This disparity identifies the "perceptual bottleneck" key area for future work in achieving comprehensive human-level audio understanding. See App. D.6 for more results.
MMAU-Pro: Robust Reasoning on Challenging "In-the-Will Benchmark. To further test the limits of our framework, we evaluate CESAR on the highly challenging MMAU-Pro benchmark (Kumar et al., 2025). This benchmark is specifically designed to defeat simple heuristics by us- ing "in-the-wild" audio and complex, multi-hop reasoning tasks, including multi-audio analysis and open-ended questions. As summarized Table 3. the results confirm CESAR'S 's superiority. With overall score of 56.4%, CESAR establishes itself as the top-performing 7B-parameter model, significantly surpassing other powerful 7B models and even larger audio LLMs like GPT-4o Audio and Audio Flamingo 3. This performance is highly competitive even with massive-scale propri- etary models, trailing only Gemini 2.5 Flash among Al models. Crucially. CESAR substantially outperforms the outcome-only RL baseline, Kc-Omni-R. This advantage particularly pronounced in reasoning-heaa -havy categories such as 'Instruction Following' and 'Open-ended QA", providing di- rect evidence that our process-oriented rewards cultivate more robust and generalizable reasoning capability. In many core audio-related reasoning tasks, CESAR's performance begins to close the gap on heven-level capabilities, validating the effectiveness of our approach.
4.3 CURING TIST-TIME INVERSE SCALING AND UNLOCKING SCALABLE REASONING
In Fig. our Test-Time Scaling analysis reveals the test-time inverse scaling problem, where base- line models exhibit either catastrophic performance collapse (under-optimized model) volatile performance with no clear benefit from longer reasoning (standard RL baseline). In contrast, our methods resolve this issue, transforming reasoning from detriments into gains. shown in Fig- (Left), even without the overthinking penalty, our model's performance steadily climbs to peak of 76.50%. Moreover, our full method demonstrates superior calibration; by explicitly penalizing inefficient thought, it discovers a more optimal "reasoning sweet spot," achieving higher peak accuracy of 77.1 with a much shorter reasoning chain of approximately 35-40 tokens. This proves methods enable consistent, effective reasoning that unlocks scalable capability during inference to achieve performance gains through scaling reasoning lengths. See App. D.7 for more results.
4.4 AI-AS-JUDGE EVALUATION: QUANTIFYING REASONING QUALITY BEYOND ACCURACY
To move beyond accuracy and verify our improved reasoning, we introduce an Al-as-Judge for head- to-head comparisons via GPT-4o Audio. As shown in Fig. (Right) our method's reasoning process achieves commanding win rates against both baselines. Notably, even without the Overthinking Penalty, our core rewards still yield a dominant performance, while its inclusion further elevates the win rate. This corroborates the superior performance of our full method in the MMAU (Tab. [ These results provide direct evidence that our framework generates verifiably superior reasoning. a qualitative leap not captured by accuracy alone. See App. D.8 for more details and prompts used.
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87C
704
100 MT MU3I
Remediating Test-Time Inverse Scaling and Quantifying Reasoning Quality on MMAU Test-mini. (Left) Test-time scaling analysis shows performance when increasing the rea- soning tokens by sweeping different maximum thinking lengths from 0 to maximum output length (i.e.. 250) in intervals of 25. (Right) An Al-as-judge evaluation with GPT-4o Audio (Hurst et al.) 2024) provides quantitative proof of our superior reasoning quality, showing our models achieve commanding win rates against strong baselines. Throughout, OP denotes the overthinking penalty.
Figure 3:
Table 4: Human Evaluation Win Rates (Majority-Vort Protocol). Results for the 1. 1.000-sample MMAU Test-mini benchmark, based on over 3,000 individual human judgments (3 annotators per sample). 'W", 'L', 'T' represent the win, lose (baseline win), and tie rates for CESAR. Each of the 1,000 final results shown was decided by the majority vote of the three expert annotators. See App- for for more results and details.
Model Comporison
Overall
Music
Sound
Speech
L
W
T
W
CESAR vs. Qwes2.t Crtai (Base)
89.00
6.60
4,50
35.63
6.29
3,09
$7.60
7.2
5.11
89.49
6,31
4.20
CESAR vs. Ke Drnnk (RL Beseling)
63.10
14,80
22.10
64.37
14.37
21.36
66.0
14.11
1932
SU06
1592
25.23
4.5 HUMAN EVALUATION: VALIDATING REASONING QUALITY WITH HUMAN JUDGEMENT
To obtain a definitive, human-level assessment of reasoning quality beyond automated metrics or AI judges, conducted a large-scala human evaluation. This study performed on the entire 1k- sample MMAU Test-mini benchmark. Each question was evaluated by three independent expert annotators, resulting in over 3,000 individual human judgments. Annotators were presented with reasoning traces from two anonymous models in a head-to-head comparison and asked to select the superior reasoning process based on logic, faithfulness the audio, and consistnncy-eruciilee evaluators remained blind to correct answers and model identities to climinate bias.
The results, summarized in Table 4 are decisive. The data shows our method is overwhelmingly preferred by human experts. Against the base Qwen2.5-0mmii778 model, CESAR's 's reasoning pro- cess completely dominates, winning in 88.60% of overall cases. This domination is consistent across all audio modalities, demonstrating the profound impact of our RI. framework. More impor- tantly, CESAR also decisively outperforms the strong outcome-only RI. baseline, Ke-Omni-R, with 63. 10% overall win rate. This latter finding is critical, providing clear, backed evidence that our process-oriented rewards (CESAR) cultivate genuinely superior and more trustworthy reasoning capability than simply rewarding the final outcome (Ke-Omni-R).
4.6 QUALITATIVE ANALYSIS: CONCRETE EVIDENCE OF REASONING IMPROVEMENTS
Beyond quantitative improvements, our methods also produce superior reasoning processes. Tab. illustrates two critical failure modes that our process-oriented rewards address. In the military pa- rade example, our model systematically analyzes acoustic cues ("marching, music, and shouting") to reach the correct conclusion, while Qwen2.5-0mni-- noise" and makes unfounded inferences about violence. The telephone counting example reveals an even more fundamental issue: reasoning answer inconsistency, where Ke-Omni-R correctly identi- fies "three rings" in its reasoning trace but inexplicably outputs "2" as the final answer. Our sistency reward explicitly prevents such disconnects between reasoning processes and conclusions, ensuring that correct reasoning translates to correct answers. See App. for more results.
misinterprets organized sounds as "chaotic

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Published as a conference paper at ICLR 2026
Table Qualitative Comparison of Reasoning Processes in MMAU Test-Mini. Our method demon- strates consistent and sell-structured reasoning processes. Green text highlights cultivated struc- tured reasoning, while red italics mark reasoning errors. Complete qualitative analysis in App. D.9.
Esample: Lugical Deduction from Acoustic Cues
Question: What kind could uking place audio!
Correct Answer: military parade with marching band
CESAR (Correct)
Qwen2.5-0mni- (Wrong)
Reasmnnng: The presence of marching, music. and shouting suggests lorge-acale even The options are quier library, a hoated arguness,
Reavoning: The andio contanne the round af
mos swoking follonsed by load. chastic soin. The chastic noise could be isterpreied as last escalatioo, hosted orgunssst is the mont Skady seamario.
viliiurr paradle and gantle breace. miliiory parade fits the deseription best, as bowiies worcchng fund and organized shouting.
Answer: milltay pardle with a mutccinn hand
Answer: A heated arganient escalating to
Jence
Example: Superior Reasoning- Anvening-AAwwer Comiitency
Question: How many times does the selephone ring the sudio? Correct Answer:
CESAR (Corvect)
Ke-Omni-K (Wrong)
Reasaning: Considerng aptions, ringy theee thene rings.
Reasoning: ielephone theee
andio clearty
Indicated stree distinct sounds the
Answer:
Answer:
4.7 ABLATION STUDY: QUANTIFYING COMPONENT CONTRIBUTIONS
Table Progressive ablation study on MMAU Test-mini. We start from the full CESAR method and progressively remove components to isolate their individual contributions. All scores are obtained using reasoning at their reasoning sweet spots. See App. D.10 for more results.
Method
Training
Condistency
Keywords
Data Angmentation
Overthinking Penalty
Overall Accuracy (%)
Full Method (CESAR)
Ablating Overthinkin Fenalty
Ablating Tata Augreentation
Ablating Keywords
X
75.20
Ablating Consisiency Ke-Ommi-R)
X
X
X
74,60
Abloting RL Training (Base Model)
X
X
65.20
Our progressive ablation study (Tab. 6) systematically deconstructs the components of our method.
The results confirm the necessity of RL, as its removal triggers a catastrophic performance collapse. Building upon this RL foundation, our process-oriemte crocess- rewards demonstrate strong synergy. The Keywords reward yields the largest single gain over the outcome-only RL baseline (Ke-Omni-R) by sculpting higher-quality, structured reasoning processes. The Consistency reward also provides crucial boost by bridging the critical gap between model's reasoning and its final output. The final components, Data Augmentation and the Overthinking Penalty provide the necessary robustness and calibration to achieve peak performance. Ultimately, the ablation study demonstrates clear synergistic effect: while each component provides a guantifiable and crucial performance gain, is their holistic integration within the CESAR framework that unlocks state-of-the-eee performance.
CONCLUSION
In this paper, we introduce CESAR to address the test-time inverse scaling problems in Audio LLMs, where CoT reasoning degrades performance due to inadequate optimization of reasoning processes in existing SFT and RLVR methods. Our methods shift from outcome verification to rewarding the reasoning process transforms reasoning from detriments into significant performance gains through GRPO with multi-faceted process rewards. We achieve SOTA results on massive benchmarks, sur- passing GPT-4o Audio and Gemini 2.5 Pro, while demonstrating that test-time scaling is a double- edged catast-pttsto for poorly trained models but enabling substantial gains through discov- ered "reasoning sweet spots" for models with strong reasoning capabilities like CESAR. Our com- prehensive evaluation across multiple OOD benchmarks reveals synergistic effects where enhanced reasoning improves both multimodal reasoming and perception capabilities, while our Al-as-judge evaluations and qualitative comparisons provide both quantitative and qualitative validation of our improved reasoning quality beyond accuracy. CESAR establishes a principled methodology for developing robust, scalable reasoning in Audio LLMss. See App. A.3 for details on LLM usage.
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A DISCUSSION
In this paper, explore how cultivate robust, scalable, and effective reasoning in Audio Large Language Models. Despite the widespread success of chain-of-thought (CoT) reasoning in domains such mathematics and coding (Comanici & et al. 2025 Jaech al. 2024; DeepSeek-Al al.) 2025 Shao et al.) 2024). efforts to introduce reasoning capabilities into Audio Large Language Models (Balaji et al. 2023 Ghosh al. 2025; KimiTeam al., (025) have encountered a central paradox: Co',, reliable catalyst for reasoning in text, consistently fails in the audio domain. While numerous works have attempted leverage CoT prompting to enhance audio LLM reasoning and understanding capabilities (Ma et al., 2025a Xie et al. (2025) several studies including RI-AQA (Li et al. 2025> have discovered that incorporating reasoning mechanisms not only fails to improve performance but may actually harm it.
Our systematic investigation reveals a more profound issue that we term test-time inverse scaling in Audio LLMs- phenomenon we the first to systematically diagnose test-time problem where prompting a model to "think" during inference yields worse results than instinctual, direct answering. When scale state-of-the-ar open-source models such Owen2.5-0mmi-77 during test-time, their performance counterintuitively degrades as reasoning length increases, often falling below their non-reasoning baselines (Fig. B). Similar patterns emerge in Ke-Omni-R (Zhao al., 2025), where reasoning during inference frequently underperforms direct answering approaches. This test-time inverse scaling manifests in two eritical failure modes: (1) any Audio LLM ex- hibiting worse performance when reasoning is enabled compared to direct answering (as ob- served Qwen2.5-Ommii77 and most cases of Ke-Omni-R in Tab. [ and (2) progressive per- formance degradation as reasoning chain length increases during test-time (as demonstrated in Fig.
Our investigation reveals that this test-time inverse scaling is not fundamental limitation of rea- soning itself, but a symptom of inadequate training: the models possess poor reasoning capability because they have never been properly laught how to reason. Our research fundamentally reframes this challenge, moving it from a problem of pattern memorization to one of controllable skill de- velopment. We demonstrate that effective reasoning is not umpredictable emergent phenomenon, but trainable capability that can be systematically cultivated by directly rewarding the reason- ing process, thereby transforming reasoning from liability into systematic advantage for audio understanding.
Key Insight: The failure of reasoning in Audio LLMSs stems not from fundamental limitation of the models, but from flawed training paradigm. True reasoning capability is unlocked by shifting focus from supervising outcomes to directly rewarding the intrinsic quality of the reasoning process.
Existing methods hamstrung by this flawed paradigm. Supervised fine-tuning produces brit- tle mimics, while contemporary reinforcement learning approaches, with their myopic focus on final-answer correctness, inadvertently reinforce the very inconssionssst,ncyy.. hallucination, and unstructured thought- that cause reasoning to fail. Our work pioneers an approach centered on rea- soning process rewards, using liability into a reliable asset. The following findings chart a new course for the field.
reward suite transform reasoning from random
Key Finding 1: Test- inverse scaling should be reframed not as a fundamental law, but as diagnostic signal for flawed reasoning processes. This issue is fully solvable with process- oriented supervision.
Our analysis provides definitive diagnosis for why unguided reasoning is so detrimental. As vividly demonstrated by the base Qwwe2255-0mni77 model's catastrophic performance collapse (from 68.60% down to 65.20% in Tab. 18). allowing under-optimized model to "think" longer provides more opportunities for logical errors and hallucinations to compound. Our framework proves this is not an immutable property. By explicitly rewarding internal consistency, CESAR di- rectly targets the root cause of this degradation, resulting in a complete reversal of the phenomenon. This finding suggests that readers encountering test-time inverse scaling should treat it as clear
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signal that model's reasoning process requires direct, granular intervention, shifting focus from outcome-only rewards to the quality of the cognitive process itself.
Key Finding 2: Reasoning be transformed from unpredictable emergent property into a controllable and engineerable skill, whose quality can be quantitatively measured beyond simple task accuracy.
A critical question for any RL method is whether the agent is truly learning skill simply ploiting the reward. Our work offers two contributions here. Our multi-faceted rewards, particularly those incentivizing structured and logical patterns (App. D.9, act cognitive scaffold to guide the model toward desired analytical behaviors. To validate that this guidance cultivates a genuine skill, our Al-as-judge evaluation provides quantitative proof of superior reasoning quality. The com- manding win rates of CESAR introduce a valuable and scalable methodology for the field, enabling researchers to move beyond accuracy to rigorously evaluate the thought process itself. Reasoning. therefore, no longer needs to be matter of chance; it can become a matter of design.
Key Finding 3: The optimal reasoning budget not universal but model-specific. This "rea- soning sweet spot" can be unlocked at inference time, but only after a robust reasoning process has been cultivated during training.
Our introduction of test-time scaling reveals that the value of increased computation is entirely ditional on the quality of the learned policy. For the base model, more computation is actively harm- ful; for the outcome-only RL model, yields volatile gains. stark contrast, because CESAR has learned coherent reasoning process calibrated in part by the 'Overthinking Penalty" Penalty--esst-inee test-time scaling becomes powerful, practical optimization lever. It allows us identify distinct perfor- mance peak-a "reasoning sweet that" other models cannot reach. This establishes a critical principle: a model must first learn to think well before thinking more becomes beneficial. This in- sight naturally leads to two-stage best practice for practitioners: first, cultivate robust reasoning through process-oricnter training. and then employ test-time scaling as an efficient, training-free strategy to identify the model's optimal computational budget at inference.
Key Finding 4: Cultivating deliberate, step-by-step reasoning creates a powerful synergistic uplift, enhancing both model's intuitive answering and its foundational perception.
This finding reveals deep connection between different modes of cognition. The rigorous process of learning to reason forces the model to organize its understanding of the world more effectively. This enhanced internal representation sharpens its "fast," intuitive thinking, evidenced by massive 5.1% improvement in its aanwerign capability over the base model 73% vs. 68.60%). The benefits even cascade to the sensory level, improving foundational perception scores the MMSU benchmark. Better thinking, turns out, leads to better hearing.
Key Finding 5: By elevating reasoning near-human levels, our work acts powerful di- agnostic for the field, revealing that the primary barrier to progress is a foundational perceptual bottleneck.
Perhaps our most significant contribution is diagnostic: by successfully addressing high-level rea- soning, our work brings the next major barrier into sharp focus. On the MMSU benchmark (Tab. (). CESAR achieves ncar-human and even super-human reasoning capabilities (c.g.. 88.72% vs. hu- man 82.16% in Semantic Reasoning). This very success allows to clearly identify the next great challenge. The remaining performance gap to humans can be confidently attributed to a different layer of the system: foundational perception, where model (48.45%) still lags far behind human acuity (91.24 19..24%). Our work thus transforms the research landscape, providing a clear, data-driven direction to solve this perceptual bottleneck.
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A.1 LIMITATIONS
Our investigation also sheds light on several limitations, including a fundamental challenge for the field and method-specific considerations for future work.
The Perceptual Bottleneck. The primary limitation we identify is a foundational perceptual bot- tleneck affecting all current models. This issue paradoxically highlighted by our own model's success; our results on the MMSU benchmark reveal stark asymmetry where CESAR achieves super-human reasoning capabilities (e.g., 88. 78% in semantic reasoning) while its foundational per- ception still significantly lags behind human acuity (48.45% vs. 91.24%). This demonstrates that even with near perfect reasoning, a model's performance ultimately capped by its ability to per- ceive high-fidelityy representation of the acoustic world. Resolving this is critical next step for the entire field.
Computational Requirements. The GRPO-based training regimen, which requires sampling multiple responses for each input during online optimization, is computationally intensive. One standard training run of ours requires significant GPU resources, and this computational overhead, while justified by the substantial performance gains, may present barrier to adoption for research groups with limited hardware resources.
Hyperparameter Tuning. Introducing multi-faceted reward suite inevitably brings the chal- lenge of hyperparameter optimization, specifically balancing the weights of each reward compo- nent. We took steps mitigate this complexity, for instance by normalizing each reward signal to a consistent [O, 1] range. Furthermore, through empirical investigation, we discovered that giving a higher weight to the accuracy reward while keeping other process-oriented rewards equally weighted yielded the best results. This suggests potential curriculum learning effect: the model first prior- itizes optimizing for accuracy- the most direct path to significant reward gains- and then, upon reaching a performance plateau, begins to refine its policy based on the more nuanced signals from reasoning process rewards. We believe this is valuable practical insight and encourage read- ers applying similar multi-reward frameworks to experiment with prioritizing the primary accuracy reward guide the initial stages policy optimization.
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A.2 FUTURE WORKS
Our work establishes a principled approach to building robust, controllable reasoning in Audio LLMs, addressing the test-time inverse scaling problem that has plagued the field. Having demon- strated that process-oriented training can reliably improve reasoning capabilities, several promising research directions emerge.
The Perceptual Bottleneck Problem. With reasoning capabilities now approaching human levels, our results reveal that perceptual limitations have become the primary constraint on overall perfor- mance. The audio encoders used in current systems appear to be the main bottleneck preventing further progress. This suggests that developing more sophisticated audio representations perhaps through self-supervised learning novel architectural community. Our improved reasoning capabilities provide a clear benchmark for evaluating whether perceptual improvements translate to better end-to-end performance.
perhaps
be priority for the
Cross-Modal Applications. The success of process-orientee training in audio raises questions about its broader applicability. Testing whether similar principles work for vision, roboties, other modalities would help determine if we've ve uncovered domain-specinn insights more general prin- ciples of machine reasoning. Early experiments applying our framework to visual question answer- ing robotic planning could provide valuable insights into the universality of process-oriented approaches.
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A.3 THE USE OF LARGE LANGUAGE MODELS (LLMS)
In accordance with the conference guidelines, we acknowledge the use of Large Language Models (LLMs) during the preparation of this manuscript. We utilized LLMs for paper writing assistance, specifically for language polishing and improving the clarity and readability of our work. The LLMs assisted refining linguistic expression, ensuring proper grammar and academic writing style, and enhancing the overall flow of technical content.
All core research contributions, including the novel methodology, experimental design, theoretical analysis, and scientific insights presented in this work, were developed independently by the authors. The LLMs were used solely as writing assistance tools (i.e., for polishing writing) and did not contribute to the conceptual development, experimental validation, interpretation of results.
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A.4 ETHICS
Our work aims to enhance multimodal reasoning capabilities in audio LLMs without introducing any additional ethical concerns resolving existing ones.
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B EXPERIMENTAL DETAILS
B.1 BASELINE METHODS
To validate the superiority our approach, we compare against a comprehensive baselines that represent different training paradigms and model classes.
Base Model Our foundational model is Qwen2.5-0mmi-77. a powerful, unified end-to-end mul- timodal model capable of perceiving diverse inputs including audio, vidco, and images, and gen- crating both text and speech responses (Xu et al. (025). We evaluate in two distinct modes: direct-answering (zero-shot) and CoTTprommped. This erucial comparison allows diagnose the test-time inverse scaling problem: by contrasting the performance of a powerful but under-optimized reasoner with and without reasoning process, we can isolate the performance degradation caused by unguided "thinking" and establish a clear baseline from which to measure the absolute gains provided by our RL framework.
empirically
RL Baseline Our most direct competitor is Ke-Omni-R (Zhao et al. 2025 the current state-of- the-art audio reasoning model that shares the same Qwen2.5-0mni- base architecture and also trained using the GRPO algorithm. This makes it the perfect control group for our study. However, Ke-Omni-R relies on a simpler Reinforcement Learning from Verifiable Rewards (RLVR) setup, where rewards trace of fewer than 50 words (Zhao et al.) 2025). This comparison therefore serves as a direct ablation of our novel, multi-faceted reward suite. By contrasting process-oriented approach with Ke-Omni-R's outcome-only paradigm, we can effectively measure the performance ceiling of existing RL methods and demonstrate the significant improvements unlocked by rewarding the reasoning process itself.
based solely the correctness of the final answer within concise reasoning
Other Models To situate our work in the broader landscape, we also report scores from other leading models. This includes top-performing proprietary systems such as the Gemini series (Co- manici & et al., 2025) and GPT-4o Audio (Hurst et al., 2024). which represent the state-of-the-ar in closed-source multimodal AI. Furthermore, compare against wide range of open-source audio LLMs that are primarily trained using supervised fine-tuning (SFT) on CoT datasets, such audio-Reasone (Xie et al. 2025). This comprehensive comparison ensures that results contextualized against the full spectrum of current approaches, from powerful proprietary APIs to various SFT-based methods. For comprehensive details these models, refer the reader their
original papers and the benchmark papers (Wang et al. 2025 Ma et al. 2025b Sakshi et al. 2025
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B.2 EVALUATION BENCHMARKS
To rigorously validate the generalization capabilitiess of our framework, we conduct a comprehen- sive evaluation on several distinct, challenging. and entirely out-of-distribution (OOD) audio un- derstanding benchmarks. None of the audio clips, questions, or underlying tasks in these bench- marks overlap with our training corpus (AVQA). This strict separation ensures that our evaluation measures genuine, transferable reasoning skill, rather than task-specific memorization or reward hacking, thereby providing a true test of our model's ability to reason in novel acoustic scenarios.
MMAU Test Mini We selected the MMAU (Massive Multi-Task Audio Understanding and Reasoning Benchmark) test-mini split (Sakshi et al.) 2025) as our principal testbed due to its un- paralleled breadth and focus on expert-level cognition. Comprising approximately 1000 expertly annotated questions, the benchmark is systematically distributed across the three core audio do- mains: speech, environmental sounds, and music (Sakshi et al. (2025). Its design explicitly targets 27 distinct cognitive skills, which are divided into information extraction and complex reasoning categories (Sakshi et al. 2025). The significant challenge of MMAU stems from its demand for expert-level, commain-specifee knowledge such as identifying musical chord progressions or decod- ing phonological sequences- sequences-comiined combined with sophisticated reasoning that moves far beyond simple perception. This comprehensive and demanding nature makes it the ideal environment to validate the general and versatile reasoning capabilities cultivated by CESAR, and explains why our framework achieves state-of-the-ard performance on this benchmark.
MMSU For a granular, diagnostic analysis of spoken language understanding, we utilize the MMSU (Massive Multi-task Spoken Language Understanding and Reasoning Benchmark) Wang et al, 2025), which serves as a surgical tool for dissecting the relationship between high- level cognition and low-level perception. Containing 5,000 audio-question pairs across 47 distinct tasks grounded in established linguistic theory (Wang et 2025). its unique value lies in the formal bifurcation of all tasks into foundational Perception (e.g., identifying falling tones) and higher-level Reasoning (e.g., interpreting sarcasm from prosodic cues) (Wang et al. 2025). This explicit separa- tion is strategically vital, as it allows us to provide clear, quantitative evidence for our key discovery: CESAR's 's ability to achieve near human-level performance on the Reasoning tasks validates the ef- fectiveness of our training paradigm. Simultaneously, the significant gap that remains on Perception tasks, despite some synergistic improvement, provides definitive proof of the "perceptual bottle- neck," " clarifying critical direction for future research.
To stress-test our model's reasoning capabilities under the most demanding conditions, include an evaluation on MMAR (A Challenging Benchmark for Deep Reasoning) (Ma al.) 2025b), a benchmark specifically designed to probe the limits of deep, multi-step, and compositional reasoning. Its 1,000 tasks uniquely characterized by longer audio clips (averaging 20 seconds (Ma et al. 2025b)) and complex, real-world mnixed-modalee audio, where overlapping sources like speech, background music, and sound effects must be disentangled (Ma et al. 2025b). The primary difficulty of MMAR lies in its demand for sustained temporal reasoning and the ability to perform multi-hop inferences composite acoustic scenes, a task that often requires graduate-level domain knowledge (Ma et al. (2025b). We chose MMAR to prove that the reasoning skills cultivated by CE- SAR are not brittle but robust and scalable. By succeeding here we demonstrate that our framework builds a durable cognitive capability that holds up under extreme complexity. providing powerful, supplementary evidence of our model's 's advanced reasoning prowess, with detailed results presented in App. D.
MMAR
MMAU Full Test Set To validate scalability, we extend our evaluation to the complete MMAU Full Test Set, comprising approximately 9,000 audio question-answering samples across speech, sound, and music modalities (Sakshi et al.) 2025). This substantially larger evaluation corpus pro- vides comprehensive assessment of our framework's 's performance and demonstrates that our advan- tages hold at scale. Detailed results are presented in App. D.3
MMAU-Pro For an even more demanding test, evaluate on MMAU-Pro (Kumar al. 2025), a challenging benchmark with 5,305 expert-annotate instances designed to probe the limits of audio reasoning. MMAU-Pro features "in-the-wild" audio, longer clips (averaging 20 seconds), multi-
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audio reasoning tasks, spatial audio understanding, and open-ended question answering. Its design explicitly minimizes language priors and requires genuine audio-grous grounded reasoning, making an ideal testbed for validating that our process-oriented training cultivates robust reasoning capabilitiiss that generalize beyond the training distribution. Detailed results are presented App. D.1
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B.3 TRAINING DATA AND AUGMENTATION STRATEGY
Training Data. The primary training corpus for CESAR is the AVQA dataset. To ensure a fair comparison, main RL baseline, Ke-Omni-R, also uses AVQA its foundation. However, it is crucial to note that Ke-Omni-R supplements its training with the specialist MusieBench dataset. De- spite using this in-domain music data. CESAR still outperforms Ke-Omni-R on the music tasks of the MMAU benchmark (73.05% vs. 70.06%). This provides strong evidence that cultivating general, robust reasoning process enhances multimodal generalization, allowing the model to effec- tively transfer its learned analytical skills to specialized domains even without explicit in-domain training data.
Systematic Data Augmentation via Question Rephrasing. To enhance model robustness and prevent the learning of superficial textual correlations, we employ a systematic data augmenta- tion scheme. This method expands training corpus by generating multiple linguistic varia- tions for each question while preserving the ground-truth answer, thereby compelling the model learn the underlying reasoning task rather than shallow text patterns. Formally, for each instance (as, 9s: C., ys) E 'D, we apply a set of answer-inven invarran transformation templates T = (Ti..... TM }. Each transformation Ti generates new question &. T& [g(q; C.). creating new training sample (as, &. C2 V:)-
simple but effective template-based transformations that reframe questions to target specific reasoning capabilities. For instance. an original question like "What are the main sources of sound in this video?" with choices [motorboat, bus, train, truck] transformed using capability-speccie templates:
Our approach
Temporal Reasoning: "Which sound source appears most prominently in the temporal sequence: {choices)?"
Counting Tasks: "Which option has the highest occurrence frequency among: {choices)??"
"Which sound demonstrates the strongest relationship with other audio elements: {choices]??"
Comparative Analysis:
This strategy systematically expands training diversity, forcing the model to develop generalizable reasoning skills that contribute directly to its robust performance.
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B.4 TRAINING HYPERPARAMETERS AND PROMPTING CONFIGURATION
Training Pipeline and Hyperparameters. Our training pipeline is built upon the Qwen2.5-0mni- 7B model, which we fine-tune using Group Relative Policy Optimization (GRPO) (Shao al. 2024). To ensure a fair comparison and isolate the impact of our proposed reasoning process re- wards, our core GRPO hyperparameters (e.g.. KL coefficient 3. batch size, learning rate) are kept consistent with those of the RLVR baseline. Ke-Omni-R (Zhao et al. 2025). This approach pri- oritizes methodological clarity and reproducibility. For a detailed breakdown of these specific hy- perparameter values, we refer the reader to the original Ke-Omni-R work (Zhao et al. (025).. The optimization process uses the AdamW optimizer with a learning rate of le-5 and global batch size of 32, sampling K = 8 responses per input for each GRPO step.
Inference Prompts We adopt the prompt template directly from our primary baseline, Ke- Omni-R, to ensure fair and direct comparison. This shared template instructs the model to follow a strict, two-part output format, namely: (1) Generating Reasoning Traces within <think>... </think> (less than (max_hink Jen) words), and subsequently (2) Generating the Final Answer within <answer>... </answer>> While the prompt structure is shared, the crucial distinction- and a core component of our methodology-iice in its application during eval- uation. Whereas Ke-Omni-R reports performance at fixed, static reasoning length, we leverage the (max think ]en} parameter to perform our test-time scaling analysis (see Sec. D.7p. By systematically evaluating the model across the full spectrum of values, we are able to not only demonstrate robustness against the test-time inverse scaling problem but also to identify the opti- mal, model-specific "reasoning sweet spots" that unlock peak performance, providing much richer understanding of a model's true capabilities.
Al-as-Judg' Evaluation Prompts. To quantitatively assess reasoning quality, employed an Al-as-judge framework using SOTA multimodal LLM (GPT-4o Audio). The evaluation prompt instructed the judge perform a head-to-head comparison between the reasoning traces of two mod- els. The judge's 's decision was guided by specific criteria, including logical coherence, faithfulness to acoustic evidence, and the overall soundness of the analytical path, with a focus on the process rather than just the final answer's correctness. The complete prompt and detailed methodology are provided in App. D.8
Reward Configuration To ensure fair and direct comparison with the RL baseline, align core GRPO training parameters with those of Ke-Omni-R. The critical distinction lies in our reward configuration. After exploring various hyperparameter settings, we identified simple yet remark- ably effective weighting scheme for the components in equation 3 the accuracy reward weight (01) is set to 5.0, while the weights for all other process-oriented rewards (consistency, keywords, overthinking penalty) are set to 1.0. This configuration maintains strong optimization pressure towards generating correct final answers, while the process rewards fine-grained guides. They shape the reasoning trajectories without overpowering the primary ob- jective of correctness. As substantiated by our ablation study (App. [D. the thoughtful design of these reward functions, rather than their specific weightings, is the primary driver of performance, demonstrating the robustness of our overall framework.
crucial regularizers and
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B.5 COMPUTATIONAL RESOURCES
All reinforcement learning experiments were performed on a high-performance computing cluster equipped with 8 NVIDIA H200 GPUs, each providing 141GB of HBM3e memory. A standard training run for our final model on the augmented AVQA dataset concluded in approximately .44 hours on this infrastructure.
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B.6 DETAILED KEYWORDS
The Keywords Reward (Rkeywords) is a central component of our process-oriented supervision framework, engineered guide the model toward generating reasoning traces that structured, logical, and domain-aware. This reward is calculated as a composite score that aggregates signals from three distinct categories: structured analytical patterns, logical rigor indicators, and domain- specific terminology. To implement this, we programmatically scan cach generated reasoning trace for the presence of specific keywords and patterns. The detection mechanism employs combi- nation of simple string matching for exact phrases (e.g., consices dee inn the opt ons, is consistent with) and regular expressions for more flexible patterns (e.g., numbered lists like 1.. 2.). Each detected term pattern from our predefined taxonomies contributes positively to the final reward score, thereby explicitly incentivizing the model to construct more sophisticated and coherent reasoning processes. The comprehensive taxonomies of these keywords and phrases, broken down by their function, are detailed in Tables 7 and 9
Table 7: Keywords for Structured Analytical Patterns (Rgatiem).
Category
Description
Example Keywords Phrases
Sequential Organization
Indicates a step-by-step analytical process or temporal ordering.
fi first, second, then, next, finally, step 1,1.,2
Comparative Analysis
Phrases used for comparing and contrasting different options or ideas.
rather than, compared to, in contrast to, the other hand considering the options,
Systematic Evaluation
Suggests methodical review and elimination of the provided choices.
evaluating each choice, among the options
Explicit Justification
Language that directly justifies the selection of the final answer.
most suitable, the best fit, description best
fits the
Table 8: Keywords for Logical Rigor & Causal Reasoning (Rlogi:)
Category
Description
Example Keywords / Phrases
Premise & Deduction
Establishes a logical premise and draws conclusion from it.
given, based on, since, therefore, thus, hence,
Evidential Support
Links acoustic evidence from the audio signal to inference.
indi is consistent with, evidenced by
suggests,
Hypothetical Reasoning
Terms used for suppositions or stating general principles.
assume, typically, generally, it is likely that
suppose,
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Table 9: Keywords for Domain Knowledge Integration (Rdomain).
Category
Description
Example Phrases
Keywords
Acoustic Properties
Basic terminology related to the physical properties of sound.
sound, audio, noise, pitch, volume, timbre, rhythm, frequency
Environmental & Animal Sounds
Vocabulary for specific non- speech, non-music sound events.
bell, ring, hooves, engine, siren, animal, clip-clop,
Musical Concepts
Specialized terminology for analyzing musical content.
chord, note, melody, harmony, instrument, major,
Speech Analysis
Terms used to describe and analyze human vocal charac- teristics.
voice, speech, tone, intonation, male, female, shouting, whisper
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B.7 REPRODUCIBILITY
We have made comprehensive efforts to ensure reproducibility of our work. Our complete methodol- ogy is detailed in Section 3 with step-by-step algorithmic implementation provided in Appendix [ All experimental configurations are thoroughly documented in Section 4 with hyperparameter set- tings specified in Scction B.4 As Appendix B, our training pipeline builds upon the open-source codebase (i.c., Ke-Omni-K R (Zhao et al. 2025)) using publicly available base models (i.e., Qwen2.5-
Omni-7B (Xu et al. 2025g) and training datasets. Data augmentation procedures are described in Section B.3 Evaluation benchmarks are all publicly available. Additional implementation de-
tails, including computational requirements and reward function specifications, are provided in Ap- pendix B All source code and trained models will be made publicly available upon publication to facilitate reproducibility and future research.
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C ALGORITHM PSEUDOCODE
In this section, we provide the detailed pseudocode for the CESAR framework. | ooutines the main online reinforcement learning loop using Group Relative Policy Optimization (GRPO). To enhance clarity, we use the superscript "ex' (e.g.. GRPO) to denote a value calculated for a single training example, distinguishing from values aggregated over an entire mini-batch. Algorithm 2 then specifies the computation of our multi-faceted, process-oriente reward, which is central to cultivating robust reasoning capabilities.
Algorithm CESAR Training via Group Relative Policy Optimization (GRPO)
1: Require: Audio LLM policy лe be fine-tuned, reference policy Tref-
2: Require: Training dataset D = {aa q. (,)34))=-
3: Require: Number of samples per input K.
4: Require: Reward weights { learning rate KL regularization weight 33,
5: Initialize policy parameters 0 from pre-trained Audio LLM.
6: for each training iteration do
7: Sample mini-batch
{(a, q, C, y)} from D.
8: Initialize gradients VoC + 0.
9:
for each training example (a, q, C, y) in B do
10:
// Step 1: Sample K responses from the current policy To.
11:
Sample set of K responses S = {*(() = (t(*), (( Tl(-la, q, q.C.
Step 2: Calculate the total reward for each of the K responses. Initialize rewards list R + D. for to K do
13:
15:
R [k) <- MALCULLATETTTALRRWAAE lotll
UU.
See Algorithm
Append R2(h) to R. end for
16:
17:
18:
Step 3: Compute the advantage using the mean reward as baseline.
R K ΣØ= R(G))
19:
20:
Initialize policy gradient loss for the example Cit 0.
21:
for = to do
22:
A(s(k) R (k)
R.
Advantage of response
23:
PPG LPC A(s(k) log To (s q, C).
24:
end for
Step 4: Calculate the full loss and accumulate gradients.
26:
LKL Emg log Far (..())
ne( (=la..9.C)
27:
LORFO LG B. LRI.
28:
Accumulate gradients: VoL - VoL + Vo
end for
30:
// Step 5: Update the policy parameters.
レ 17 VoL.
32:
end for
33: return Optimized policy parameters 0.
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Algorithm 2 Multi-Faceted Reward Calculation
1: function CALCULATETOTA
2: Input: A single response s (t. i). ground-truth answer y, question q. choices C.
3: Input: Reward weights {nz}
4: //-18 Verifiable Rewards
5: Race x 1 3/
Accuracy XML structure compliance
6:
Riormat + [[ValidForma((s))
7:
2. Reasoning Process Rewards
8:
Q - (q.C).
Full question context
9:
Reonsistency <- Simmemantic (t, + Simeemantic (t, Q).
> Semantic alignment
10:
Ryatter + CALCCULATE
Peyternnyyworss)
b See Table
11:
Reogic CALCUULATEKKEVWORSOOERR.. weyors))
See Table
12:
Rdomain + CALCUULATEKETWOOOSSORRR Domainneywords)) length(()
>> See Table
Rkeywords Rpattem + Rogic + Raomain
14:
Roverthinking +
> Penalty for verbosity
15:
113.. 3. Compute Total Weighted Reward
16:
Riota &- O1 Race + ar2 Bfoemat + 0z Reonsistency + 044 Rkeyywysndss + Or Boverthinking. return Riaal
18:
end function
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D ADDITIONAL EXPERIMENTAL RESULTS
D.1 BENCHMARK RESULTS ON MMAU-PRO
To further validate the robustness and generalization of our process-oriented reward framework, conduct an extended evaluation on the highly challenging MMAU-Pro benchmark (Kumar et al. 2025). MMAU-Pro is comprehensive testbed for holistic audio intelligence, meticulously designed to evaluate models on complex, realistic auditory scenarios that are explicitly underserved by exist- ing benchmarks. It consists of 5,305 expert-annotated instances, where questions are designed to require deliberate, multi-hop reasoning. Its audio is sourced directly "from the wild" to prevent data contamination and test true generalization. Critically, introduces novel tasks that directly probe the limits of current models, including long-form audio, multi-audio reasoning, spatial audio, com- plex mixtures, open-ended QA, and instruction following. The benchmark explicitly designed to minimize reliance on "language priors" and demand "genuine audio-grounded reasoning." making an ideal testbed to validate our central thesis.
Table 10:
Performance the MMAU-Pro Benchmark. Best scores highlighted in blue second-best scores in green All values are accuracy (%). All results show accuracy (%). Human performance is included (Zhao et al., )025) and 2wwe2.55-mmii- (Xu et al. 2025) from our own reproductions under the same protocol; all other baseline results are taken from the MMAU Pro paper (Kumar et al. 2025
upper bound reference. We report the performance of Ke-Omni-R
Modd
Sound
Speeds Seand-Masi
SpeedhMasic
Speech 5-M--ppech
Spatial
Muli-Audie
Open-cnded
Averaar
Our Progesed Methed
CESAR
$4.1
63.5
64.0
4800
43.5
$9.4
71.4
406
545
34.1
62.4
35.6
56.4
Bauciar
469
643
6188
45,0
47.8
51.1
57.1
48.2
472
35.6
59.2
24.1
|
545
Owied D00ei-5---0---111-1 (Boss)
41.1
55.6
51.3
32:11
15.7
15.5
75.6
712
SLD
35.3
59.1
31.0
46.1
CPPE- Anaios
44.7
6.11
06.1
41.5
62.5
55.1
21.4
57.5
12.5
41.2
$2.5
Aada Farmugg
95.1
01.7
36.5
41.3
4112
55.1
25.6
28.0
44.2
11.3
GPE--A-TRZI- Audio
40-2
90.7
06.1
13.3
42.2
$5.30
42.5
1231
52.7
22.4
41.8
75.2
48.3
Fluringo
36.5
55.7
43.
29.5
Des INE A-Aado
15.7
46.2
403
22311
36.1
15.2
18.6
2611
31.0
19.0
15.0
46.3
46.
41.4
46.4
43.0
113
311
383
19.5
28.3
35.4
SALMONN 15B
37.
PIid MM
25.7
47.5
87.6
33.0
36.1
301
15.6
35.7
42.7
11.4
43.5
65.4
38.7
DeS LA2
30.0
43.5
06.3
47.8
1977
42.5
31.0
34.8
13.3
SALMONN 7B
33.3
48.9
16.3
33.0
34.8
28.4
15.6
36.
36.5
11.4
31.3
33.9
34.5
GAMA
45.4
41.2
39.8
34.0
27.9
27.3
14.8
13.0
20.3
34.2
31.7
Atdil Reaseing Madelk
Audan Macener
36.1
13-5
43.4
22.5
38.9
41.4
41.9
31.9
35.7
20.4
Millow
27.6
32.9
25.8
24.0
34.8
27.3
14.3
23.7
28.3
20.5
21.4
33.5
27.5
Goriei- Rlahh
$1.9
64.9
734
42.8
36.5
71.7
21.3
67.5
Geviei-10 Flach
48.4
96.9
69.5
38.6
95.9
62.8
Minn-ii-
43.9
56.2
40.1
17.4
346
12.5
16.5
35.1
14.5
70.7
36.5
31.2
01
23.6
34.7
41.3
33.9
Cagtins GPT
38,6
40.6
35.4
21.6
36.2
25.5
15.6
0.5
35.6
24.3
23.6
85.2
35.3
Captinn Quaa2
36.4
36.1
24.5
14.3
35.6
Basdiaes
Haszai
75.2
NS
83.3
293
78.5
$3.4
857
85.
700
2533
1008
77.1
21
Analysis of Results. Our evaluation on the MMAU-Pro benchmark validates the significant ad- vantages of our process-oriented reward framework. As shown in Table [O] CESAR achieves an overall average score of 56.4%, establishing it as the highest-performing model in the 7B cate- gory. This performance not only represents a substantial absolute improvement over base model (Qwen2.5-0mmi-77 but also clear gain over Ke-Omni-R. the outcome-only RL bascline. This di- rectly confirms that rewarding the reasoning more robust and capable model than rewarding the result alone.
iiieees-iiccudinn consistency and structure-builds
CESAR's 's average score is highly competitive, surpassing other prominent audio LLMs like Audio Flamingo 3 and powerful proprietary models such GPT-4o Audio. Its performance ranks just below that of altra-large-scale proprictary models like Gemini 2.5 Flash, demonstrating that a 7B model with superior reasoning training effectively challenge models many times its size.
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This strong average score is driven by superior performance genuine audio-related tasks, where CESAR'S 's reasoning capabilities begin to close the gap with human-level performance. The compari- against our Ke-Omni baseline is particularly insightful. Compared to baselines like Qwen2.5- Omni-7B (Xu et al. 2025) and Ke-Omni-R (Zhao et al. 2025). CESAR shows dramatic gains in reasoning-heave tasks that demand structure and coherence, such as 'Open-ended QA', and the highly complex "Sound-Music-Speechh mixture task. This demonstrates superior ability to dis- entangle complex acoustic scenes and formulate structured responses. Furthermore, CESAR shows broad improvements across foundational reasoning in 'Sound', 'Speech', and 'Voice" tasks, con- firming the wide-ranging benefits of our approach in cultivating more genuine and generalizable audio reasoning capability.
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D.2 HUMAN EVALUATION: VALIDATING REASONING QUALITY THROUGH EXPERT JUDGMENT
To provide robust validation of our reasoning quality improvements beyond automated metrics, we conduct comprehensive human evaluation study comparing CESAR's reasoning processes against two baselines: the base Qwen2.5-Ommi-77 model and the Ke-Ommi-R baseline. This evaluation directly assesses the quality of the reasoning process itself (i.e., reasoning capability).
D.2.1 HUMAN EVALUATION SETUP AND METHODOLOGY
Data Collection and Preparation. We collect reasoning traces and final answers from different models (CESAR, Owen2.5-0mmii-7 and Ke- Ke-Omni-R) on all questions in the MMAU Test-mini benchmark. Each model generates its thinking process along with the final answer for each question. These model outputs are then prepared for human evaluation through careful anonymization and randomization.
Task Design. Human evaluators are presented with audio clips from the MMAU Test-mini bench- mark along with questions and answer choices. For each question, evaluators review the reasoning traces and final answers generated by two models (presented in randomized order as "Model I" and "Model 2") and select which model demonstrates superior reasoning capability. Critically, evalua- tors are kept blind to the correct answers to eliminate potential bias-this this ensures that judgments are based purely on reasoning quality rather than being influenced by answer correctness. Eval- uators also blind to which model produced which reasoning trace. Evaluators are explicitly instructed to focus on the quality of the thinking process, assessing four key dimensions: (1) Audio Understanding whether the model correctly perceives the acoustic content; (2) Logic whether the reasoning follows coherent, ate--by-step progression relevant to the question; (3) Clarity
whether the explanation is easy to follow; and (4) Consistency whether the reasoning aligns with the final answer.
Evaluation Protocol. We employ a rigorous multi-anee aalli-annotttor protocol to ensure accuracy and fair- ness. Each question in MMAU Test-mini is evaluated by three independent expert judges, eliminating single-annotator bias and providing robust consensus. We report results using two ag- gregation methods: (1) Per- Vote evaluation, where each individual judgment from all three annota- tors is counted separately, providing fine-gr nne-graieed insight into reasoning quality across all evaluations (yielding 3x the number of questions in total judgments); and (2) Majority-Vott evaluation, where the final judgment for each question is deternined by the majority decision among the three anno- tators, representing a more conservative consensus- based assessment. This reporting approach ensures both comprehensive coverage and robust validation of our findings.
Coverage. Our evaluation spans the full diversity of the MMAU Test-mini benchmark, encom- passing all three audio modalities (Sound, Music, Speech), three difficulty levels (Easy, Medium, Hard), and 27 distinct reasoning sub-categories, ensuring comprehensive assessment across the en- tire spectrum of audio reasoning tasks.
D.2.2 MAIN HUMAN EVALUATION RESULTS: COMMANDING WIN RATES VALIDATE SUPERIOR REASONING QUALITY
The human evaluation results provide decisive empirical evidence that CESAR cultivates verifi- ably superior reasoning processes. Tab. [I and Tab. 12 present the aggregate results using per-vote and majority-vote protocols respectively. From the human perspective, CESAR's reasoning pro- cesses are consistently judged as superior to both baselines across all evaluation scenarios. Against the base 2owen2.5-0mni- model. human evaluators demonstrate an overwhelming preference for CESAR's reasoning, with this preference strengthening further under the conservative majority-vote protocol. Even more critically, when compared against the strong Ke-Omnt Omni-R baseline- weeeeinne-wihhhh also employs reinforcement learning but with outcome-only rewards -ewards-CESAR maintains clear and consistent advantage. This validates our central hypothesis: rewarding the reasoning process yields qualitatively superior reasoning compared optimizing solely for final answer correctness.
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Table 11: Human Evaluation Results Overall Performance (Per-Vote Protocol). Each individual
judgment from three annotators per question is counted. Best win rates highlighted in blue All values are win rates (%).
CESAR vs. Qwen2.5-Omni
CESAR vs. Ke-Omni-R
Category
CESAR
Bascline
Tic
CESAR
Bascline
Tic
Overall
79.07
16.30
4.63
58.47
25.77
15.77
By Audio Modality
Music
78.84
16.47
4.69
59.18
25.25
15.57
Sound
78.88
16.32
4.80
61.06
25.33
13.61
Speech
79.48
16.12
4.40
55.16
26.73
18.12
By Difficulty Level
Easy 76.64
18.30
5.06
59.82
25.45
14.73
Medium
80.37
15.37
4.26
57.22
26.60
16.17
Hard
78.39
16.53
5.08
60.03
24.15
15.82
Table 12: Human Evaluation Results Overall Performance (Majority- Vote Protocol), The final judgment for each question determined by the majority decision among three annotators. Best win rates are highlighted blue All values are win rates (%).
Mote
CESAR vs. Qwen2.5-Omni
CESAR vs. Ke-Omni-R
Category
CESAR
Baseline
Tie
CESAR
Baseline
Tie
Overall
88.60
6.60
4.80
63.10
14.80
22.10
By Audio Modality
Music Sound
88.62
6.29
5.09
64.37
14.37
21.26
87.69
7.21
5.11
66.07
14.11
19.82
Speech
89.49
6.31
4.20
58.86
15.92
25.23
By Difficulty Level
Easy 87.50
7.14
5.36
66.07
14.73
19.20
Medium
89.07
6.85
4.07
60.19
16.11
23.70
Hard
88.56
5.51
5.93
66.95
11.86
21.19
Robustness Across Modalities and Difficulty Levels. The breakdown by audio modality (Music, Sound, Speech) and difficulty level (Easy, Medium, Hard) in Tab. and Tab. 12 reveals the remark- able robustmess of our reasoning improvements. From the human evaluators' perspective, CESAR consistently demonstrates superior reasoning across all categories. with only minor variations. This consistency is particularly significant given that evaluators were blind to the correct answers, con- firming that the preference for CESAR's reasoning stems from genuine quality improvements rather than correlation with answer correctness. Notably, against Ke-Omni-R, our advantages are par- ticularly pronounced in Sound tasks and Hard tasks, suggesting that process-orientes rewards are especially beneficial for challenging reasoning scenarios requiring nuanced acoustic analysis.
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D.2.3 FINE- FINE-GRAINED ANALYSIS: REASONING QUALITY ACROSS TASK SUB-CATEGORIES
To provide deeper insight into where and how our reasoning improvements manifest, we present detailed breakdowns across all 27 reasoning sub-categories in the MMAU benchmark. Tables [3 through [6 demonstrate that from the human evaluators' perspective, CESAR's reasoning advan- tages are not confined specific task types but rather represent broad, systematic improvement in reasoning capability across the entire spectrum of audio understanding challenges.
Table 13: Human Evaluation Results by Sub-Category: CESAR vs. Qwen2.5-0mmi-77 (Per-Vote
Protocol). All values are win rates (%). Best scores are highlighted in blue
Sub-Category
CESAR
Qwen2.5-Omni-78
Tie
Acoustic Scene Reasoning
82.64
15.28
2.08
Acoustic Source Inference
80.56
13.19
6.25
Ambient Sound Interpretation
71.53
20.83
7.64
Conversational Fact Retrieval
81.82
15.15
3.03
Counting
83.91
16.09
0.00
Dissonant Emotion Interpretation
89.52
6.67
3.81
Eco-Acoustic Knowledge
78.72
17.02
4.26
Emotion Flip Detection
80.00
20.00
0.00
Emotion State Summarisation
79.55
16.67
3.79
Emotional Tone Interpretation
81.82
14.14
4.04
Event-Based Knowledge Retrieval
82.83
14.14
3.03
Event-Based Sound Reasoning
83.33
9.72
6.94
Harmony and Chord Progressions
71.72
23.23
5.05
Instrumentation
77.14
19.05
3.81
Key Highlight Extraction
87.30
6.35
6.35
Lyrical Reasoning
70.00
23.33
6.67
Melodic Structure Interpretation
79.80
14.14
6.06
Multi-Speaker Role Mapping
72.84
18.52
8.64
Musical Genre Reasoning
75.49
19.61
4.90
Musical Texture Interpretation
81.37
13.73
4.90
Phonemic Stress Pattern Analysis
72.96
22.01
5.03
Phonological Sequence Decoding
73.47
19.05
7.48
Rhythm and Tempo Understanding
86.23
6.52
7.25
Socio-Cultural Interpretation
85.00
10.00
5.00
Sound-Bii Based Event Recognition
73.91
24.64
1.45
Temporal Event Reasoning
81.25
13.89
4.86
Temporal Reasoning
75.60
22.62
1.79
Systematic Improvements Across All Reasoning Types. The sub-category analysis reveals eral critical insights. First, from the human evaluators' perspective, CESAR's reasoning is consis- tently preferred across virtually sub-categories both comparisons (against Qwenn.5-Ommi-77 and Ke-Omni-R), demonstrating systematic superiority rather than task-specific advantages. Sec- ond, the improvements are particularly pronounced in categories requiring complex, multi-step rea- soning. For instance, against Qwen2.5-Omni, CESAR achieves exceptional preference in tasks such as "Dissonant Emotion Interpretation" and "Key Highlight Extraction" categoorie that demand so- phisticated understanding of nuanced acoustic cues and their semantic implications. Third, even against the strong Ke-Omni-R baseline, CESAR maintains substantial advantages in challenging categories such "Ambient Sound Interpretation" and "Emotion Flip Detection". demonstrating that process- oriented rewards are especially effective for tasks requiring detection subtle changes and contextual understanding.
Extraction"
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Table 14: Human Evaluation Results by Sub-Category: CESAR vs. Qwen2.5-0mmi-7B (Majority-
Vote Protocol). All values are win rates (%). Best scores are highlighted blue
Sub-Category
CESAR
Qwen2.5-Omni
Tie
Acoustic Scene Reasoning
89.58
6.25
4.17
Acoustic Source Inference
91.67
4.17
4.17
Ambient Sound Interpretation
81.25
8.33
10.42
Conversational Fact Retrieval
90.91
4.55
4.55
Counting
93.10
6.90
0.00
Dissonant Emotion Interpretation
97.14
2.86
0.00
Eco-Acoustic Knowledge
85.11
10.64
4.26
Emotion Flip Detection
90.00
10.00
0.00
Emotion State Summarisation
88.64
4.55
6.82
Emotional Tone Interpretation
93.94
6.06
0.00
Event-Based Knowledge Retrieval
90.91
6.06
3.03
Event-Based Sound Reasoning
91.67
0,00
8.33
Harmony and Chord Progressions
81.82
12.12
6.06
Instrumentation
82.86
11.43
5.71
Key Highlight Extraction
95.24
4.76
0.00
Lyrical Reasoning
80.00
0.00
20.00
Melodic Structure Interpretation
93.94
3.03
3.03
Multi-Speaker Role Mapping
85.19
7.41
7.41
Musical Genre Reasoning
85.29
8.82
5.88
Musical Texture Interpretation
88.24
5.88
5.88
Phonemic Stress Pattern Analysis
84.91
9.43
5,66
Phonological Sequence Decoding
85.71
6.12
8.16
Rhythm and Tempo Understanding
93.48
0.00
6.52
Socio-Cultural Interpretation
95.00
0.00
5.00
Sound-Bann Event Recognition
84.78
13.04
2.17
Temporal Event Reasoning
89.58
8.33
2.08
Temporal Reasoning
87.50
8.93
3.57
D.2.4 KEY INSIGHTS AND IMPLICATIONS
The human evaluation provides decisive validation of our central thesis through three critical find- ings:
Pricess-Oriented Training Cultivates Human-Preferred Reasoning. The overwhelming and consistent preference for CESAR's reasoning across all comparisons demonstrates that explicitly rewarding the reasoning process results in qualitative improvements that are immediately recog- nizable to human experts. Crucially, these preferences emerged under blind evaluation conditions where annotators had no knowledge of correct answers, confirming that the superior reasoning qual- ity is intrinsic to CESAR's thinking process rather than artifact of answer correctness correlation. This validates our approach of moving beyond outcome-only optimization.
The Limitations of Outcome-Only Reinforcement Learning. The substantial gap between CE- SAR and Ke-Omni-R -esi--Rdeespitee both employing reinforcement learning on the same base model and training data proe-peeeiee causal evidence that rewarding answer correctness alone is insufficient. From the human evaluators' perspective, CESAR consistently produces better reasoning processes across all tasks, demonstrating that high-quality reasoning requires explicit process-level supervi-
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Table 15: Human Evaluation Results by Sub-Category: CESAR vs. Ke-Omni-R (Per-Vote Protocol). values are win rates (%). Best scores are highlighted in blue.
Sub-Category
CESAR
Ke-Omni-R
Tie
Acoustic Scene Reasoning
59.03
33.33
7.64
Acoustic Source Inference
49.31
38.89
11.81
Ambient Sound Interpretation
72.22
20.14
7.64
Conversational Fact Retrieval
50.00
37.88
12.12
Counting
44.83
35.63
19.54
Dissonant Emotion Interpretation
52.38
25.71
21.90
Eco-Acoustic Knowledge
63.12
16.31
20.57
Emotion Flip Detection
71.67
13.33
15.00
Emotion State Summarisation
53.79
25.76
20.45
Emotional Tone Interpretation
65.66
23.23
11.11
Event-Based Knowledge Retrieval
51,52
26.26
22.22
Event-Based Sound Reasoning
63.19
19.44
17.36
Harmony and Chord Progressions
63.64
24.24
12.12
Instrumentation
60.00
23.81
16.19
Key Highlight Extraction
57.14
31.75
11.11
Lyrical Reasoning
66.67
13.33
20.00
Melodic Structure Interpretation
54.55
31.31
14.14
Multi-Speaker Role Mapping
50.62
17.28
32.10
Musical Genre Reasoning
57.84
25.49
16.67
Musical Texture Interpretation
60.78
21.57
17.65
Phonemic Stress Pattern Analysis
62.26
23.27
14.47
Phonological Sequence Decoding
56.46
30.61
12.93
Rhythm and Tempo Understanding
57.25
23.19
19.57
Socio-Cultural Interpretation
63.33
25.00
11.67
Sound-Based Event Recognition
57.25
22.46
20.29
Temporal Event Reasoning
63.19
26.39
10.42
Temporal Reasoning
53.57
30.36
16.07
sion targeting consistency, logical structure, and analytical depth. The three-annotator consensus protocol ensures these findings are robust and not dependent on individual annotator preferences.
Robustness and Generalization of Reasoning Quality. The consistency of improvements across all audio modalities, difficulty levels, and 27 distinct reasoning sub-categories demonstrates that our framework cultivates genuinely robust reasoning skills rather than task-specific heuristics. The blind evaluation protocol-whert where evaluators judge reasoning quality without knowledge of correct an- swers-climinate potential biases and confirms that CESAR's advantages reflect fundamental im- provements in reasoning capability. This comprehensive validation across the full spectrum of audio understanding challenges, combined with rigorous multi-annotato evaluation, establishes CESAR principled approach to building controllable, high-quality reasoning multimodal Al systems.
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Table 16: Human Evaluation Results by Sub-Category: CESAR Ke-Omni-R (Majority-Vonn Vote Protocol). All values are win rates (%). Best scores highlighted in blue
Sub-Category
CESAR
Ke-Omni-R
Tie
Acoustic Scene Reasoning
62.50
25.00
12.50
Acoustic Source Inference
47.92
33.33
18.75
Ambient Sound Interpretation
79.17
6.25
14.58
Conversational Fact Retrieval
50.00
31.82
18.18
Counting
41.38
31.03
27.59
Dissonant Emotion Interpretation
60.00
17.14
22.86
Eco-Acoustic Knowledge
72.34
4.26
23,40
Emotion Flip Detection
85.00
0.00
15.00
Emotion State Summarisation
59.09
15.91
25.00
Emotional Tone Interpretation
75.76
12.12
12.12
Event-Based Knowledge Retrieval
48.48
18.18
33.33
Event-Based Sound Reasoning
72.92
6.25
20.83
Harmony and Chord Progressions
72.73
12.12
15.15
Instrumentation
74.29
14.29
11.43
Key Highlight Extraction
61.90
19.05
19.05
Lyrical Reasoning
70.00
0.00
30.00
Melodic Structure Interpretation
57.58
18.18
24.24
Multi-Speaker Role Mapping
44.44
3.70
51.85
Musical Genre Reasoning
67.65
14.71
17.65
Musical Texture Interpretation
70.59
8.82
20.59
Phonemic Stress Pattern Analysis
73.58
7.55
18.87
Phonological Sequence Decoding
59.18
18.37
22.45
Rhythm and Tempo Understanding
56.52
10.87
32.61
Interpretation
75.00
10.00
15.00
Bound-Based Event Recognition
52.17
10.87
36.96
Temporal Event Reasoning
75.00
12.50
12.50
Temporal Reasoning
46.43
25.00
28.57
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D.3 EXTENDED EVALUATION ON MMAU FULL TEST SET
To provide a more comprehensive evaluation of our method's performance and generalization ca- pabilities, we extend experiments the complete MMAU Test Set, which comprises approxi- mately 9,000 audio question-answering samples spanning the full spectrum of audio understanding tasks across speech, sound, and music modalities. This large-scale evaluation serves critical validation of our framework's 's robustness and scalability beyond the Test-mini subset used in our main experiments.
Consistent State-of-the-Art Performance at Scale. The results presented in Tab. 17 demonstrate that CESAR maintains its commanding performance advantage when evaluated on the substantially state-of of-the-art
larger test set. Our method achieves the highest overall accuracy, establishing results on this comprehensive benchmark. Importantly, this performance advantage holds across all three audio modalities, with particularly strong results in sound understanding tasks. The consis- lency between our Test-mini and Full Test Set results validates that our process-oriented training approach cultivates genuinely robust reasoning capabilities rather than overfitting specific evalu- ation scenarios.
Robustness of Process-Oriented Rewards. The extended evaluation further confirms the critical importance of our multi-faceted reward suite. Both CESAR variants (with and without the Over- thinking Penalty) significantly outperform the Ke-Omni-R baseline, which employs outcome-only rewards. This performance cap-mmantaaieed across thousands of diverse test cases cases-arosee cases-provides de- cisive evidence that rewarding the reasoning process itself yields superior and more generalizable audio understanding capabilities. The sustained advantage over strong proprietary models and spe- cialized audio systems further validates our framework's 's effectiveness.
Synergistic Effects Across Reasoning Modes. Consistent with our findings on the Test-mini sub- set, the Full Test Set results reveal that process-oriented training creates beneficial synergies across both reasoning and non-reasoning inference modes. Our models demonstrate substantial improve- ments over the base Owwen2.5-mmii- model in both settings, confirming that cultivating high- quality reasoning processes fundamentally enhances the model's underlying audio understanding capabilities. This synergistic lifect-where training to reason better simultaneously improves direct answering performance ciffommance-rrprrsenntt a key advantage our approach over traditional supervised fine-tuning methods.
Implications for World Deployment. The strong performance on this large-scale bench- mark has important practical implications. With nearly 9,000 diverse test cases covering wide range of audio understanding scenarios, tasks, and difficulty levels, these results provide confidence in the real-world applicability of our framework. The consistency of our method's 's advantages across both small-scale (Test-mini) and large-scale (Full Test Set) evaluations suggests that FESARRtrainge models can be reliably deployed in production environments where they will encounter diverse and unpredictable audio reasoning challenges.
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Table 17: MMAU Full Test Set Results (9k samples). evaluate our method against a compre hensive set of audio models on the complete MMAU test set. Best scores are highlighted blue second-best scores in green All results show accuracy (%). Models are sorted by overall perfor-
mance. We report the performance of Qwen2.5-0mmi-775 (Xu al. 2025) and Ke-Omni-R (Zhao et al., 2025) from our own reproductions under the same protocol; all other baseline results are taken from the MMAU paper (Sakshi et al. 2025).
Method
Reasoning
Sound
Speech
Music
Overall Accuracy
Our Proposed Methods
CESAR CESAR
V
77.60
76.09
67.77
73.79
X
76.57
75.50
65.70
72.55
CESAR w/o OP CESAR w/o OP
/ 77.70
75.81
67.10
73.51
x 76.13
75.60
66.77
72.80
RL Baseline Methods
Ke-Omni-R
/
75.37
73.77
66.73
71.94
Ke-Omni-R
x
74.00
73.70
66.83
71.49
Base Model
Awen2.5-Omni-77 Qwen2.5-0mni-77
/
68.87
68.11 11 70.18
55.77
64.20
x
67.97
63.53
67.19
Proprictary and Open-Source Models
MiMo-Audio
77.20
70.77
69.73
72.59
Audio Flamingo 3 Step-Audio-2-mint
75.83
66.97
74.47
72.42
75.57
66.49
66.85
70.23
Gemini 2.5 Pro
70.63
72.67
64.77
69.36
Gemini 2.5 Flash
69.50
68.27
69.40
67.39
Gemini 2.0 Flash
68.93
72.87
59.30
67.03
DeSTA2.5-Audin
66.83
71.94
57.10
65.21
Kimi-Audio
70.70
56.57
65.93
64.40
Audio Reasoner
67.27
62.53
61.53
63.78
Phi-4-multimodal
62.67
63.80
61.97
62.81
Gemini 2.5 Flash Lite
62.50
67.47
54.87
61.61
Audio Flamingo
68.13
44.87
70.20
61.06
GPT-4o Audio
63.20
69.33
49.93
60.82
Owen2-Audio-Insteeeeeee Instruct
61.17
55.37
55.67
57.40
Gemma 3n-4B
50.27
62.13
53.20
55.20
GPT-4o mini Audio
49.67
67.47
35.97
51.03
M2UGen
44.97
35.77
38.53
39.76
MusiLingo
41.93
31.70
41.23
38.29
SALMONN
42.10
28.77
37.83
36.23
Mul.LaMa
30.97
17.10
29.67
25.91
GAMA-IT
32.73
11.57
22.37
22.22
GAMA
30.73
16.97
17.33
21.68
LTU
20.67
15.33
15.68
17.23
Audio Flamingo Chat
23.33
7.67
15.77
15.59
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D.4 BENCHMARK RESULTS ON MMAU TEST-MINI
Table 18: MMAU Test-mini Benchmark Results. We evaluate method against state-of-the-art proprietary and open-source audio models. Best scores are highlighted in blue second- best scores in green Accuracy (%) is reported. We report the performance of Qwen2.5-0mmi-78 (Xu et al, 2025) and Ke-Omni-R Zhao et al. 2025) from own reproductions under the same protocol: all other baseline results are taken from the MMAU paper (Sakshi et al. 2025).
Method
Reasoning
Sound
Music
Speech
Total Accuracy
Our Proposed Methods
CESAR CESAR
83.48
73.05
74.77
77.10
X 79.88
67.96
73.27
73.70
CESAR w/o OP
V 81.98
70.06
77.48
76,50
CESAR w/o OP
X 80.48
70.06
74.47
75.00
RL Baseline Methods
Ke-Omni-R
/ 79.28
70.06
74.47
74.60
Ke-Omni-R
X 78.38
70.96
74.17
74.50
Base Models
Qwen2.5-Ome Swen2.5-Omnii-1
/ 69.07
59.58 64.37
66.97
65.20
X
72.37
69.07
68.60
Proprietary Models
Gemini 2.5 Pro
75.08
68.26
71.47
71.60
Gemini 2.5 Flash
73.27
65.57
76.58
71.80
Gemini 2.0 Flash
71.17
65.27
75.08
70,50
GPT-4o Audio
64.56
56.29
66.67
62.50
GPTT-4o mini Audio
50.75
39.22
69.07
53.00
Open-Source Audio Models
Kimi-Audio
75.68
66.77
62.16
68.20
Audio Reasoner
67.87
69.16
66.07
67.70
Phi-4-multmmdal
65.47
64.37
67.27
65.70
Audio Flamingo 2
71.47
70.96
44.74
62,40
Qwen2- Audio-Instrucc
67.27
56.29
55.26
59.60
Our evaluation the MMAU Test-mini benchmark, with comprehensive results presented in Tab. [8] not only establishes a new state-of-the-art performance but, more importantly, a deeper analysis of these results uncovers several critical insights into the nature of reasoning in Audio LLMs and the means by which it can be effectively cultivated.
An Insight Scaling Reasoning Process Scaling Parameters. The first critical insight from these results emerges from the clear superiority of scaling up the reasoning process over simply scaling up model parameters. CESAR, at just 7B parameters, achieves its state-of-the-art 70.10% accuracy not by possessing larger architecture, but by effectively scaling its cognitive process at inference time-a -a latent capability unlocked by our training and fully realized through test-time analysis of reasoning length. This performance decisively surpasses that of proprietary models like Gemini 2.5 series and GPT-4o Audio, whose primary scaling axis is their vast parameter count. This finding strongly suggests that new paradigm for performance enhancement is not only viable but superior: instead of relying on brute-force parameter scaling, strategically cultivating and dy- namically scaling model's 's reasoning process offers more efficient and effective path to advanced capabilities.
The Symbiotic Rise of Reasoning and Intuition. Beyond sheer performance, the results reveal a more subtle and perhaps more profound insight into the effeets of our training paradigm. Our process-oriented RI. training not only enhances the model's explicit, step-by-step reasoning but
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also substantially elevates its direct, intuitive answering capabillity. This is evident model without reasoning ('CESAR w/o Reasoning") achieves 73.70% accuracy, a score far superior to the base model's 68.60% in the same setting. This suggests that incentivizing high-quality reasoning pathways does more than just teach a model to generate a thinking monologue; it fundamentally refines the model's implications for practical deployment, as it enables highly efficient inference through fast, direct answers while retaining powerful, on-demand reasoning faculty for more complex challenges.
representations of the acoustic world. This discovery carries significant
Transforming Reasoning from Detriments into Gains. Finally, the data provides a clear narra- tive on the evolution of reasoning itself. The base Qwen2.5-Omni- model exemplifies the critical problem of uncontrolled reasoning, where performance catastrophically drops by 3.4 points when it is prompted to "think" (from 68. 38.60% to 65. 65.20%). This is a textbook case of the test-time inverse scaling problem. In stark contrast, CESAR systematically reverses this trend, gaining robust 3.4 points under the same conditions (from 73.70% to 77.10%). This transforms the act reasoning from high-risk gamble into reliable and scalable tool for performance enhancement. With this newfound stability, reasoning is no longer an unpredictable behavior but a controllable capability. now confidently employ reasoning and, by combining
We therefore suggest that practitioners with test-time scaling analysis, identify the model-specific "sweet spot" to unlock its full, calibrated potential. This marks a pivotal shift, firmly establishing reasoning as core asset for advancing multimodal understanding.
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D.5 BEYOND AGGREGATE SCORES: A TASK-LEVEL ANALYSIS OF CONTROLLABLE REASONING CAPABILITY
While aggregate scores (Table 18) establish the state-of-the-art performance of our method, they fundamentally mask the most profound discovery of our work: the systematic emergence of con- trollable reasoning archetypes.
granular, multi-faceted analysis essential understand not merely the quantitative superiority, but the qualitative transformation of reasoning from randomly emergent phenomenon into a precisely engineerable capability.
Total Accuracy
Easy
Speech
Medium
Music
Hard
Ke-Omni-R
CESAR (Ours w/o Overthinking Penalty) Qwen2.5-Omnl
Figure 4: Normalized multi-dimensional performance comparison on the MMAU Test-mini bench- mark. Performance scaled relative CESAR (Ours), which constitutes the 100% baseline on each axis. This visualization reveals the emergence of distinct reasoning specializations across task difficulties.
Hard
Iog
Music
Easy
Sound
Avg. Reasoning Token
Accuracy
CESAR (Ours)
Ke-Omni-R
w/o Overthinkóng Penalty) Qwen2.5
Figure 5: Extended radar analysis including reasoning token efficiency. This enhanced visualization provides clearer visibility of the fundamental trade-off's between reasoning depth and computational cost, unveiling two fundamentally different reasoning archetypes optimized for distinct cognitive scenarios.
To reveal this transformation, we present a comprehensive radar chart analysis in Fig. where the performance of our full model, CESAR (Ours), serves the 100% baseline across seven key eval-
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uation dimensions. This normalization strategy exposes not merely superior performance, but the emergence of fundamentally different cognitive architectures that our process-oriente framework has systematically cultivated.
The Emergence of Two Reasoning Archetypes: A Tale of Cognitive Specialization. The radar charts provide irrefutable visual evidence for our central hypothesis while simultaneously unveil- ing an unexpected discovery: process-oriented supervision does not simply improve reasoning it enables the systematic engineering of distinet reasoning archetypes. The performance polygons of our two CESAR variants (green and blue) dominate both visualizations (Fig. and Fig. [5), cover- ing significantly larger areas than the strong RL baseline Ke-Omni-R (purple) and the base model Qwen2.5-Omnt (pink). This validates our core claim that genuine reasoning emerges only when supervision targets the reasoning process, not just final outcomes.
However, the most profound insight emerges from the striking divergence between our two CESAR variants-4 a divergence that reveals the existence of fundamental trade-off in reasoning system design. The CESAR (w/o Overthinking Penalty) variant exhibits distinctive cognitive profile: exceptional performance on Hand tasks, consistently exceeding even our 100% baseline, but at a deliberate cost of efficiency on simpler problems. This model represents what we term depth specialist-an archetype that favors exhaustive, thorough analysis over computational efficiency.
In stark contrast, the full CESAR (Ours) model demonstrates a fundamentally different cognitive architecture. It forms a perfectly calibrated profile across all difficulty levels, showing exceptional stability on Easy and Medium tasks while maintaining competitive performance on Hard problems. This represents calibrated generalist an archetype optimized for consistent, efficient reasoning across diverse problem complexities.
The Fundamental Performance- Trade-off: Efficiency vs. Thoroughness. The extended analysis including reasoning token efficiency (Fig. 5 exposes the computational mechanics un- derlying this cognitive divergence. The depth specialist achieves its superior performance chal- lenging tasks by investing substantially more reasoning tokens-engaging in extensive, multi-step analytical processes that thoroughly explore problem spaces. Conversely, the calibrated general- ist demonstrates remarkable efficiency, achieving comparable overall performance while operating under strict computational constraints imposed by the overthinking penalty.
This discovery challenges conventional assumptions about reasoming optimization and reveals a fundamental principle: there exists an inherent tension between reasoning depth and compu- tational efficiency, and optimal performance emerges when models are explicitly trained to navigate this trade-off according task requirements. The depth specialist excels precisely be- cause it is willing to invest computational resources in exhaustive analysis when problems demand it. The generalist succeeds by learning to apply just enough analytical effort to solve problems effectively without wasteful over-elaboration.
Engineering Controllable Cognitive Architectures. The emergence of these distinct reasoning archetypes represents far more than an interesting experimental observation-it provides definitive proof that reasoning has been transformed from an unpredictable emergent property into a con- trollable, engineerable capability. The stark differences between our variants are not accidental byproducts of training, but the direct result of our process-oriented reward architecture functioning as precision engineering tools for cognitive behavior.
By systematically modulating a single reward component-the overthinking penalty-we have demonstrated the ability to produce models with predictably different reasoning profiles, each op- timized for distinct deployment scenarios. The depth specialist thrives in research environments where thorough analysis justifies computational cost, making ideal for complex analytical tasks requiring maximum cognitive depth. The calibrated generalist excels in production systems where efficiency and consistency are paramount, delivering reliable performance across diverse problem types without excessive resource consumption.
This unprecedented level of control demonstrates that CESAR transcends being merely a high- performing model represents a comprehensive framework for engineering the next generation of controllable audio reasoners with specific, desirable cognitive traits. We have moved decisively be- yond the traditional paradigm of hoping for beneficial reasoning patterns to emerge spontaneously,
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entering where cognitive capabilities be systematically specified, implemented, and vali- dated with the same precision as traditional software systems.
The dual visualization provides definitive empirical proof that methodology enables the system- atic exploration the reasoning capability space, allowing researchers and practitioners to engineer cognitive systems precisely tailored their specific requirements and constraints. This work estab- lishes both the theoretical foundation and practical methodology for controllable Al development, where reasoning behavior becomes design parameter rather than emergent accident.
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D.6 BENCHMARK RESULTS ON MMSU
Table 19: MMSU Benchmark Results. We evaluate our method against state-of-the-ae audio models across perception and reasoning tasks in speech understanding. Best scores are highlighted in blue second-best scores in green All results show accuracy (%). Human performance is included as an upper bound reference. We report the performance of Ke-Omni-R (Zhao al. (0255) from our own reproductions under the same protocol; all other baseline results are taken from the MMSU paper (Wang al. 2025) (including Siven..5-0mni-77 (Xu et al., 2025).). The results of Audio
Flamingo 3 are taken from their paper (Goll et al. 2025b).
Models
Perception Tasks
Reasoning Tasks
Overall
Semantics
Phonology
Paralinguisties
Semantics
Phonology
Paralinguistics
Our Proposed Method
CESAR
39.50
57.01
Andio RI. Baseline
Ke-Omni-R
Proprictary Models
Gemini 1.5 Pro
57.06
$3.60
31,23
46.10
79.47
83.46
46.33
76.16
60.68
Quen2.5-0mni-78
48.
79.83
Kimi-Audio
$7.64
42.30
33.74
43.52
81.77
76.65
55.22
76.03
59: 09.28
GPT-4o Audio
59.70
41.56
21.44
39.67
80.83
78.74
26.25
71.95
56.38
Quen2-Audio-hee Insto-Instract
46.67
Gemini 2.0 Flash
47.17
41.30
30.62
40.83
70.69
70.69
36.16
47.83
$1.03
Open-Source Audio Models
Audio Flamingo
MiniCPM
56.56
34.05
36.4%
401.4
80.71
14.72
46.71
73:57
56.53
MERA LION
54.49
33.69
25.84
35.74
80.32
77.18
41.49
73.68
54.10
Qwen-Audio- Chat
46.92
DIVA
44.36
33.72
27.45
33.95
62.32
74.24
40.00
63.04
48.
Megrez-3B-
BB-mni Omnii
41.36
32.52
26.35
32.48
73.53
66.11
40.42
67.05
49.03
Step-Audio
BLSP
31.35
20.96
23.75
28.36
47.91
42.31
42.08
44.97
35
GoM-4-Voice
27.80
24.52
27.34
26.18
46.10
48.16
44.35
46.76
35.51
pper Bound)
Human
87.10
94.32
92.88
91.24
82,16
87.60
89.12
86.77
89.72
Random
Most Frequent Choice
26.20
26.04
27.83
29.83
28.30
28. 28.30
30.10
28.41
28.06
Random Guess
24.30
25.70
26.10
24.90
23.80
25.40
25.40
25.02
25.37
The MMSU benchmark, with its unique split between perception and reasoning tasks, provides granular lens for a multi-faceted analysis of model's capabilities. Our examination of the results in Tab. reveals several critical findings, starting with the validation of our method's reasoning capa- bilities, followed by an exploration of its surprising efficiency and broader impacts, and concluding with an identification of key frontiers for future research.
The Initial Breakthrough: Reasoning Closes to the Human Level. The analysis first confirms the efficacy of CESAR in its target domain. The data shows the model achieves superior performance in reasoning tasks, where its average score of 81.07% not only establishes new state-of-the tfe-the-rtt but also approaches the human benchmark of 86.77%. This proficiency is particularly pronounced in Semantic Reasoning, where CESAR achieves super-human score of 88.72%. This is direct and powerful validation that our process-oriented training is exceptionally effective at cultivating the kind of deep, nuanced understanding that was previously the domain of human cognition.
A Small Model Can also Win. A more compelling finding emerges, however, one that challenges the foundational assumptions of the field. The data in Tab. Tab. that CESAR, 7B model, not only establishes superior performance in reasoning over significantly larger proprietary models like Gemini 1.5 Pro, but uww.ueeeeeell--uuupseeeeeee 46. 1.%.. This is pivotal finding: it demonstrates that smaller model, when endowed with superior reasoning, can outperform larger competitors across both cognitive and perceptual dimensions. This result provides strong evidence for new, more efficient scaling paradigm.
them average perception tasks (48.45% vs.
The Ripple Effect: How Better Thinking Creates Better Hearing. The model's 's unexpected strength in perception suggests deeper mechanism is play. The data points to unanticipated
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synergy: enhancing reasoning seems to have beneficial ripple effect on foundational perception. While our rewards explicitly target higher-order thinking, the process of learning to form consis- tent and logical connections appears to refine the model's underlying representations of the acoustic world. This finding suggests that our methodology improves not just how model interprets sounds, but how effectively it processes them at more fundamental level, explaining how targeted cogni- tive enhancement can lead to broader, more holistic improvements in multimodal understanding.
New Vista: Beyond the Perceptual Bottleneck. Ultimately, the success reasoning brings critical challenge for the field into sharp relief. The asymmetry between our model's near-human reasoning and still-developing perception (which, while outerforming its peers, still lags far behind the human score of 91.24%) illuminates clear "perceptual bottleneck." This should not be viewed as a limitation of our method, but rather as key finding that clarifies the path for future research. It reinforces our central thesis: the future lies not in the resource-interd intenssve race of simply scaling up model size, but in the new science of scaling specific capabilities. Our work demonstrates that reasoning can be scaled to near-human levels within compact model; the clear next step is to apply a similarly focused, principled approach to perception, paving the way for a future of Al that not only more powerful but also dramatically more efficient and accessible.
Content Grounding: Validating Synergistic Perception Improvements. To further validate the synergistic perception improvements observed in Table [9] we analyze CESAR's performance on MMSU's Content Grounding task, which evaluates accurate content transcription from speech (e.g.. "Which sentence is the correct transcription of the audio?"). This task provides an effective di- capabilities within the benchmark framework. As shown in Table 20 CESAR achieves 90.80% accuracy, substantially outperforming Ke-Omni-R (72.50%) by 18.3 points and the base model (59.60%) by 31.2 points. This dramatic improvement demon- strates that our process-oriented training creates synergistic effects that extend beyond reasoning to measurably improve fundamental speech understanding capabilities, including semantic-level tran- scription accuracy and robustness to acoustic variations-providingg concrete evidence that cultivat- ing high-quality reasoning processes simultaneously refines the model's perceptual foundations.
agnostic for assessing caascription--elatee
Table 20: Content Grounding Performance on MMSU. Best score is highlighted in blue
Method
Content Grounding Accuracy (%)
CESAR (Ours)
90.80
Ke-Omni-R
72.50
Qwen2.5-0mmi-78
59.60
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D.7 TEST-TIME SCALING ANALYSIS: FROM INVERSE SCALING TO CONTROLLABLE AND SCALABLE REASONING
Sound
Music
80%
70%
60%
Accuracy
60%
Accuracy
40%
40
100
20
100
Tokens
Speech
Total Accuracy
70.0%
Accuracy
Accuracy
60.0%
50.0%
30%
40.0%
20
40
60
80
100
20
40
60
80
100
Avg Reasoning Tokens
Avg Reasoning Tokens
Qwen2.5-0mmi-78
Ke-Omni-R
w/o Overthinking Penalty)
- (Ours)
Sound
Music
Accuracy
Accuracy
80.0%
70.0%
75.0%
65.0%
20
40
60
80
100
20
40
60
80
100
Avg Reasoning Tokens
Avg Reasoning Tokens
Speech
Total Accuracy
80.0%
80.0%
Accuracy
Accuracy
75.0%
8
75.0%
Rer
70.0%
70.0%
100
100
Avg Reasoning Takens
Reasoning Tokens CESAR (Ours)
Ke- Ka-Omni-R
CESAR (Ours w/o Overthinking Penalty)
Figure 6: Test-Time Scaling Curves of Reasoning. Accuracy is plotted against the average length of the reasoning chain (in used tokens). (Top Row) The full comparison reveals a catastrophic performance collapse of the base Qwen2.5-Ommi: empirically demonstrating the test-time inverse scaling problem. In contrast, all RL-trained models remain robust. (Bottom Row) A zoomed-in view of the RL models highlights the performance peak of our full method (i.e., CESAR (Ours)), which discovers a "reasoning sweet spot". It consistently outperforms both the version without the Overthinking Penalty reward (i.e., CESAR (Ours w/o Overthinking Penalty)) and the Ke-Omni-R baseline.
model it generates longer reasoning chains,
Aggregate accuracy scores, while informative, obscure a critical underlying dynamic: the test-time inverse scaling problem that plagues audio language models lacking explicit reasoning training. This phenomenon is twofold: first, prompting such models to generate a chain of thought often yields worse results than direct, zero-shot answering. Second, their performance degrades precipitously as
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the reasoning chain lengthens. To rigorously investigate these dynamics and validate our solution, we introduce a test-time scaling analysis.
This training-free inference methodology allows to probe model's reasoning capability function of its computational budget. We achieve this by systematically varying an upper bound on reasoning length, specifically by adjusting the ma x. wttthinnkkee parameter within the prompt across a range (e.g., 25, 50, 250) (Zhao et al. 2025). Critically, this parameter does not force a fixed output length; rather, it provides a ceiling, allowing the model autonomously determine an appropriate reasoning depth based on the problem's demands and its own intrinsic capabilities, We then plot accuracy against the actual average number of reasoning tokens generated. As illustrated in Fig. this analysis provides a granular view into each model's 's reasoning behavior, reveals profound differences in their underlying skills, and demonstrates how to fully unlock the latent reasoning potential cultivated by our framework.
The Peril of Under-Optimized Reasoning: A Case of Teme Inverse Scaling, The most dramatic finding, shown in the top row of Fig. 6 is the catastrophic performance collapse of the base Qwen2.5-0mni-
model. While it begins with a respectable accuracy at zero reasoning tokens (i.e., direct answering). its performance enters free fall as it is prompted generate longer reasoning processes. This provides powerful empirical evidence for the test-time inverse scaling problem: for model that has been explicitly trained how reason, "thinking more" is actively harmful. Each additional token of unguided "reasoning" introduces new opportunities for error accumulation and ballucination, transforming a decent zero-shot guesser into demonstrably poor reasoner.
Curing Test-Time Inverse Scaling through Process-Oriented RL. In stark contrast, all models trained with our process-oriented reinforcement learning framework are completely immunc to this collapse. The bottom row of Fig. showws that our models (CESAR and its variant) and the Ke-Omni R baseline all maintain remarkable stability as reasoning length increases. This result establishes fundamental principle: robust training cultivates robust scaling. By receiving granular feedback on the reasoning process, our models learn to generate coherent, logically sound, and self-consistent reasoning processes that do not derail. This learned robustness is the key to curing the test- time inverse scaling fragility observed in the base model, transforming reasoning from detriments into gains.
Unlocking Calibrated Reasoning and Model-Specific "Sweet Spots". The zoomed-in analysis reveals the final and most profound layer of insight. While the Ke-Omni-R bascline is stable, its performance noisy and fails to consistently benefit from longer reasoning. This comparison high- lights the limitations of outcome-only rewards. Our process-oriented rewards, however, cultivate distinct and controllable reasoning styles. The CESAR (w/o Overthinking Penalty) variant, for in- stance, exhibits preference for longer reasoning chains, showing sustained high performance as token count increases.
Most significantly, our full method, CESAR, demonstrates form of learned metacognition. discovers a model-specific "reasoning sweet spot," where its performance actively peaks at an op- timal reasoning depth (around 30-40 tokens) before gracefully stabilizing. This behavior is direct consequence of the "Overthinking Penalty' reward, which trains the model to balance analytical sufficiency with conciseness. learns not only how to reason, but also how much to reason. This finding challenges the monolithic "longer is better" assumption, proving that reasoning is train- able skill. The test-time scaling analysis is therefore not merely an evaluation tool; is the key to unlocking these cultivated, model-specific reasoning capabilities at inference time, allowing each model to achieve its peak performance.
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D.8 QUANTIFYING REASONING QUALITY BEYOND ACCURACY: AN AL-AS-JUDGE FRAMEWORK
100%
Threshold
(Duns]
30%
Rate (Opponent Wins)
80%
76.40%
71.90%
70%
60%
Rate
50% Threshold
40%
36.15%
38.38%
28.10%
23.60%
20%
0%
CESAR (Ours)
CESAR (Oung
CESAR (Our We DP)
CESAR (Durs wie DP)
Win rate of our model's reasoning process when compared head-to-head against the base model (Owen2.5-Omni- and the Ke-Omni-R baseline. The dominant win rates provide strong quantitative evidence of superior reasoning quality, a metric that accuracy scores alone cannot capture. Throughout, OP denotes the overthinking penalty defined in equation g
Figure 7: Quantitative Analysis of Reasoning Quality via Al-as-Judge.
Evaluating the quality of model's reasoning process-distine from the correctness of its final swer-poses a significant challenge in multimodal AI. Current evaluation paradigms are insufficient: outcome-based accuracy is a coarse proxy that can reward correct answers derived from flawed or fallacious logic, while anecdotal qualitative analysis is inherently unscalable and subjective. This methodological gap makes difficult to determine whether model is developing genuine analytical skill or simply becoming a more effective test-taker.
To address this, we introduce scalable and rigorous framework for the quantitative evaluation of reasoning quality itself. We employ powerful, state-of-the the-hhr-rt multimodal model, akin to GPT-4o Audio (Hurst al., 2024), as an expert adjudicator. For each comparison, after providing the judge with the full context (audio, question, choices, and correct answer) and the two reasoning traces, we use the following direct prompt for evaluation: Given the audio context and two reasoning processes from Model A and Model B, try to determine which process is superior. A superior process is more logical, faithful to the audio, and follows a clearer analytical path. Focus on the quality of the reasoning, not just the final answer's 's correctness, and conclude with 'Model A Wins', 'Model B Wins', or 'Tie'. This approach ensures a consistent and targeted assessment focused squarely on the analytical process.
Noting that to enhance the clarity and readability of the final results of Al-as-Judge, we distribute "Tie" outcomes equally for the final win-rate calculation (e.g., initial outcome where Model A wins 40%, Model B wins 20%, and 40% are ties is converted to final win rate of 60% for A and 40% for B).
The results of Al-as-Judge, presented in Fig. are decisive. Against the base Qwen2.5-Ome 7B model, CESAR's 's reasoning is judged superior in a commanding 76.4% of comparisons. This offers the first quantitative proof that our process-centric rewards cultivate fundamentally more robust and logical reasoning architecture, rather than merely improving final-answer accuracy. Even more critically, when pitted against Ke-Omni-R strong baseline also trained with reinforcement learning but with outcome-only rewards-oo model's ''s reasoning prevails in a significant 63.85% % of cases. This result starkly illustrates the limitations of simplistic, outcome-based RL and validates the necessity our multi-faceted reward suite for shaping genuinely high-quality reasoning. By filling critical methodological gap. work provides the field with scalable tool to assess reasoning quality directly, proving that our centric framework cultivates verifiably superior analytical capabilities.
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D.9 QUALITATIVE ANALYSIS: THE ANATOMY OF CULTIVATED REASONING
Beyond quantitative benchmarks, a granular examination of the reasoning traces reveals the con- crete mechanisms through which CESAR transforms a model's cognitive behavior. The following head-to-head comparisons not merely anecdotal evidence of better performance; they mi- croscopic validation of our central thesis. They illustrate precisely how our process-oricnted re- wards eradicate the critical failure plague models trained without such guidance, thereby cultivating reasoning that is demonstrably more Effective, Consistent, and Logical.
logical fallacies, and inconsistency that
D.9.1 COMPARISON WITH BASE MODEL (QWEN2.5-0MNI-75) UNDER-OPTIMIZED vs. CULTIVATED REASONING
This comparison starkly contrasts the brittle, uncontrolled output of a powerful base model against the robust, structured thought process instilled by CESAR. The examples demonstrate that merely prompting a model to "think" " insufficient; without explicit training on how to reason, the process itself becomes source of error, leading to failures that range from high-level factual invention to low-level execution mistakes.
Example Robustness against Hallucination
Question: Based on the given audio, identify the source of the speech. man, woman, child, robot
Choices:
Correct Answer: man
Task ID: 111b5448-778e-4404448666---0-000
CESAR (Correct)
Qwen2.5-0mni-7B (Wrong)
Reasoning: Given the options, the speech is likely from a man. The tone and volume sug- gest an adult male. The other options are less probable.
Reasoning: The audio is a speech, and the voice male. The options provided do not in- clude 'man', so the closest match is 'robot..
Answer: man
Answer: robot
Grounded Reasoning Factual Hallucination. The base model often fails to ground its rea- soning in the provided context. In Example I, after correctly identifying the acoustic evidence ("the voice male"). invents false premise-that 'man' absent from the choices-ase proceeds to an illogical conclusion. This reveals a fundamental inability to tether its internal state to the problem's explicit constraints. CESAR, by contrast, exhibits a learned, systematic methodology. Its trace begins with grounding step (Given the options), explicitly acknowledging the context, and then connects acoustic cues to an inference using relational language effective and sound analytical process directly instilled by our 'Keywords" reward.
Logical Deduction vs. Flawed Inference. The base model struggles to interpret complex acous- tic scenes. In Example 2, it oversimplifies rich soundscape into generic, low-information label ("chaotic noise"), from which makes a spurious logical leap. This highlights core weakness: the inability to decompose and analyze multi-element audio. CESAR demonstrates far more sophis- ticated, multi-stey logical process. It correctly identifies the constituent acoustic parts (music, and shouting). synthesizes them into high-level hypothesis ("a large-scale event"), and systematically evaluates the choices to find the one that fits the description best. This a true deductive capability, direct outcome of rewarding structured analytical patterns.
Precise Execution vs. Minor Inconsistency. Example reveals subtle but critical failure in precision. While the base model's reasoning is perceptually and logically correct, it stumbles at the final execution step, introducing fatal typo (.click-click) into its answer. This shows that sound thought process is nullified if the model cannot reliably translate it into correct final output. CESAR's ' end-to-end process rewards, which value just the reasoning trace but its faithful conversion into final answer, ensure this level of precision. Its reasoning trace also exhibits the
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Example 2: Logical Deduction from Acoustic Cues
Question: What kind event could be taking place in the audio?
Choices: A quiet library during study hours, A heated argument escalating to violence, military parade with a marching band, A gentle breeze blowing through trees
Correct Answer: A military parade with marching band
Task ID;
CESAR (Correct)
Qwen2.5-0mni-7B (Wrong)
The presence of marching, music, and shouting suggests large-scale event. The options quiet library, heated argument, a military parade, and gentle breeze. military parade fits the description best, as it involves a marching band and organized shouting.
Reasoning:
The audio contains the sound of a man speaking, followed by a loud, chaotic noise. The options are a quiet library. heated argu- ment, a military parade, and a gentle breeze. The chaotic noise could be interpreted lent escalation, so a heated argument is the most likely scenario.
Reasoning:
vio-
Answer: military parade with marching band
Answer:
A heated argument escalating to vio-
lence
Example 3: Reasoning-Answer Consistency
Question: What was the order of the sounds in the sequence?
Choices:
('light switch clicking', 'boiling water', 'doorbell ringing'. "clock ticking"), ['boiling_water water",
clock_ticking 'doorbell pooobinl ['clock_ticking', "boiling.
'doorbell_ringinee "bolling_water',
( dooorbell_ringnng "clock.ticking"
Correct Answer: ("lightswiich..ciokinnng water' "dooreelnnd 'clock "clock_ticking ticking']
Task ID: bbabe366-0553343444-226------ 50cbded
CESAR (Correct)
Qwen2.5-Ommii-7 (Wrong)
Considering the sequence, the light switch clicking is followed by boiling ter, then a doorbell rings, and finally, a clock ticks. This order fits the given options.
Reasoning:
Reasoning: First, there's 's a light switch click, then boiling water sound, followed by a door- bell ringing, and finally, a clock ticking.
Answer: ['light wlickii.......in................ 'boil-
Answer: ing water', 'doorbell ringing', 'clock ticking'
www.sstttthw....iiie....... 'boil-
ing water', 'doorbell ringgng,, 'clock ticking"]
sequential pattern keywords (Considering the sequence... followed by... then... finally) that our framework explicitly incentivizes.
D.9.2 COMPARISON WITH RL BASELINE (KF-OMNI-R): THE NECESSITY OF PROCESS REWARDS
This comparison isolates the crucial contribution of our multi-faceted, process-oriented reward suite against a baseline also trained with RL but guided only by the correctness of the final answer. These examples provide causal evidence that optimizing for outcomes alone is insufficient, it permits and may even reinforce broken cognitive processes.
Enforcing Reasoning-Answer Consistency. Example a textbook failure of reasoning- answer consistency. Ke-Omni-R correctly identifies the acoustic fact in its reasoning ("rings three times") but then inexplicably provides a contradictory answer (2). An outcome-only reward system is blind to this internal contradiction; on trials where the model might guess the correct final answer despite such a flaw, would be rewarded, reinforcing this broken cognitive process. CESAR's 'Reasoning Consistency Reward directly targets this failure mode, ensuring the final answer is direct and faithful consequence of the thought process. This makes the reasoning verifiably consistent and trustworthy.
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Example 4: Superior Reasoning-Answe Consistency
Question: How many times does the telephone ring in the audio?
Choices: 2, 5,3
Correct Answer: 3
Task ID:
CESAR (Correct)
Ke-Omni-R (Wrong)
Reasoning: Considering the options, the tele-
Reasoning: The telephone rings three times, as indicated by the three distinct ring sounds in the audio.
phone rings three times. The audio clearly shows three distinct rings.
Answer: 3
Answer: 2
Example 5: Concise Convoluted Reasoning
Question: What the characteristic the chord played from 0:02 to 0:03 the audio?
Choices: A:min/P5, E:maj/P1, D:maj/M3, G:maj/P1
Correct Answer: A.min/P5
Task ID: 733 44c-a440444444490551144 Ke-Omni-R (Wrong)
CESAR (Correct)
Considering the options, 'A:min/P5' seems the most fitting as it's a minor chord and 'P5' indicates perfect fifth. The others don't i fit the context as well.
Reasoning:
Reasoning: The chord progression from 0:02 to 0:03 involves a progression from a lower note to higher one... The progression from a lower note to a lower one... The progression is from a lower note to lower one... The progression is from a lower note to higher one... Answer: E:maj/P1
Answer: A:min/P5
Efficient Reasoning vs. Convoluted Overthinking, Example 5 highlights the problem of un- constrained, inefficient reasoning. The Ke-Omni-R baseline engages in a convoluted and self- contradictory monologue (a loop of opposing statements). This "overthinking" is direct symptom of a reward function that does not value efficiency. CESAR, shaped by our Overthinking Penalty and Keywords rewards, produces knowledge (minor chord, perfect fifth) and structured evaluation (most fitting) to arrive at the answer, demonstrating a learned ability to allocate cognitive effort purposefully.
effective and parsimonious trace. It efficiently uses domain
Example 6: Robustness against Factual Hallucination
Question: Given that an infant is crying, name famous person known for their association with the animal heard in the background.
Choices: George Orwell, Ernest Hemingway, Charles Darwin, Charles Dickens
Correct Answer: Ereest Hemingway
Task ID; (00-200--66---------------------
CESAR (Correct)
Ke-Omni-R (Wrong)
Among the options, Ernest Hem- ingway is known for his love of animals, par- ticularly cats. The animal sound could be a cat. George Orwell and Charles Dickens are not known for their association with cats. Charles Darwin is known for his work on evolution, not for cats.
Reasoning:
Reasoning: Considering the context of the
question, the animal in the background is pig. Ernest Hemmanway is famously associated with
pigs in his works, particularly in "The Old Man and the Sea."
Answer: Ernest Hemingway
Answer: Ernest Hemmanway
Grounded Knowledge vs. Factual Hallucination. Finally, Example 6 reveals a spectacular, multi-layered failure of factual grounding from the RL baseline. Ke-Omni-R not only misidenti-
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fies the animal sound as a "pig." but proceeds to invent completely false piece of literary trivia to justify its choice, incorrectly linking Hemingway to pigs in "The Old Man and the Sea." This shows how outcome-only agent can generate syntactically plausible reasoning that is semantically un- tethered from reality. The failure is compounded by an execution error, as it ultimately misspells the author's name (Hemmanway) in the final answer. CESAR's reasoning, by contrast, is sound. It correctly identifies a plausible animal (cat), recalls Hemingway's 's true association with it, and sys- tematically eliminates other options. Our holistic reward suite prevents this kind of unconstrained hallucination, ensuring the reasoning process is both internally and externally valid.
D.9.3 EFFICIENT REASONING VS. PATHOLOGICAAL OVERTHINKING: THE CRITICAL ROLE OF THE OVERTHINKING PENALTY
This final comparison isolates the specific contribution of our 'Overthinking Penalty' by presenting a case where its absence leads to outright failure. Here, the full CESAR model arrives at the correct answer through a clear and pridence-based analytical path. The 'CESAR w/o OP" variant, however, initially identifies the correct acoustic evidence but then engages in a long, convoluted reasoning process. This overthinking causes it to fixate on an irrelevant, imagined context, ultimately leading it to discard the correct answer in favor of a flawed one. This example provides dramatic proof that overthinking is not a benign inefficiency but a pathological failure mode that can derail the entire analytical process. It demonstrates that the 'Overthinking Penalty' is not merely a regularizer for efficiency, but a critical component for ensuring robustness and correctness.
Example 7: Grounded Acoustic Analysis vs. Contextual Hallucination
Question: What is the sound event taking place in the audio?
Choices: A person crying, person laughing, A person coughing, A person yawning
Correct Answer: A person laughing
Task ID:
CESAR (Correct)
CESAR w/o OP (Wrong)
The audio contains a series of sharp, rhythmic vocal bursts. The pitch and cadence are consistent with joyful laughter. It does not match the sound patterns of crying (more sustained wailing), coughing (a forceful expulsion of air), or yawning (a long, deep in- halation and exhalation). Therefore, the sound a person laughing.
Reasoning:
The audio clearly contains a hu- man vocalization. It's series of short, sharp, and somewhat rhythmic sounds. This could be interpreted as either laughing or crying, as both can have a similar staccato pattern. Let's ana- lyze the context. While it sounds like laughter, this type of abrupl, almost gasping sound can also be associated with someone trying to sup- press tears or crying in a very distressed way. Crying is a more primal and urgent emotional expression than laughter. Given the ambigu- ity, the more intense and emotionally significant possibility is often a safer bet in interpretation, as it might signify a situation requiring atten- tion. The sound has slightly strained quality that leans more towards distress than pure joy. Answer: A person crying
Reasoning:
Answer: A person laughing
Evidence-Based Deduction vs. Over-analysis of Ambiguity. The Example 7 provides a textbook case of model "thinking itself into a mistake." Both models correctly identify the core acoustic features ("sharp, rhythmic vocal bursts"). The full CESAR model follows a direct and logical path: it compares these features against the prototypes of each option and correctly concludes they "consistent with joyful laughter." " Its reasoning is grounded entirely in the acoustic evidence.
The 'CESAR w/o OP' model, however, falls into a trap of pathological overthinking, After acknowl- edging the sound could be laughter, it begins to construct an elaborate, unfalsifiable alternative nar- rative. It introduces irrelevant external concepts like "primal and urgent emotional expression" and "safer bet in interpretation." " It imagines a "strained quality" to support a "distress" hypothesis that is not strongly grounded in the audio. By lacking a penalty for this verbose and epeulattive detour, the
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model gives undue weight to complex, imagined scenario over the most direct interpretation of the sound itself. This demonstrates perfectly how the 'Overthinking Penalty' is crucial for keeping the model'ss 's reasoning tethered to evidence, preventing it from spiraling into contextual hallucinations that corrupt an initially correct perception.
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D.10 ABLATION STUDY: CAPABILITY IN CESAR
DECONSTRUCTING THE SOURCES OF IMPROVED REASONING
To precisely quantify the contribution of each component within the CESAR framework, we conduct a progressive ablation study, systematically deconstructing our full model to isolate the impact of each design choice. The results, presented in Tab. 2T provide comprehensive validation of our methodology, revealing not only that each component is effective, but also how they synergize to cultivate robust reasoning.
Table 21:
Progressive Ablation Study Results. We systematically remove components from our full method to demonstrate their individual contributions. "Reasoning" refers to the chain-of of-thought reasoning mechanism. Best scores are highlighted in blue second-best scores in green
Matbol
Ablution Compoeeets
Rasoming?
RL Inanieg
Camsednacy
Angrecatzion
Overllsking Posally
Music Speoch
Accunnss
Our Propened Methads
New (Pai Mathad)
CENAR
Dat Augroratio
7
7
R0:34
80.15
18.06
66.06
77.44
75.35
14.90
76.00
Kiy Wanks
x
/
7
x
80.74
30.11
06.77
68:96
75.08
74.77
75.70
74.00
Re-Onnik
Coniscno
4
X
78 14
79.28
T696
7006
26.17
76-47
74.90
14,00
BI. Poottiiing
7
7
72.11
66.77
06-57
66.0
The Foundational Role of Process-Oriented RL. The ablation starkly confirms that online inforcement learning is the core mechanism transforming the model's fundamental capabilities. Removing RL post-training entirely reverting to the base Qwen2.5-0mni-77 model-Cand causes the most significant performance drop, a staggering 9.4 points in reasoning accuracy (from 74.60% of Ke-Omni-R to 65.20%) More critically, this is not merely quantitative drop but qualitative versal: the base model is the only variant that exhibits test-time inverse scaling, where enabling reasoning degrades performance. In contrast, every single RL-trained variant sees performance gain from reasoning. This demonstrates that RL is not an incremental improvement but the essential catalyst that turns reasoning from detriments into gains, prerequisite for any further refinement.
The Synergy of Process-Specific Rewards. Our results clearly show that high-quality reason- ing emerges from a synergy of process-oriented rewards, with each component providing a distinct and vital contribution. The most significant gains the outcome-only RL baseline (Ke-Omni-R) come from our two core process rewards. First, removing the Consistency reward (which effectively reduces our model the Ke-Omni-R baseline's level of process supervision) leads significant performance drop. As confirmed in our qualitative analysis (See App. D.9.. model lacking this reward can produce reasoning traces that are completely disconnected from the final answer. If reasoning is not required to be consistent with the answer, it becomes an unreliable, and potentially harmful, cognitive artifact. Second, removing the Key Words reward causes further substantial drop of 1.0% in accuracy (from 76.20% to 75.20%). This demonstrates that explicitly rewarding logical structure and domain -specffcc terminology is powerful driver of effective analytical strategies. To- gether, these rewards target distinct but complementary facets of high-quality reasoning -intrinan coherence and logical structure- provinn that a multi-faceted approach is significantly more effec- tive than optimizing for any single aspect alone.
The Importance of Calibrated Training. The final components, while having smaller numeri- cal impact, are crucial for calibrating and robustifying the learned skills. Although removing Data Augmentation only results in minor accuracy drop, its role in exposing the model to linguistic diversity is essential for generalization and preventing the learning of superficial correlations in real-world scenarios. Most interestingly, the Overthinking Penalty serves dual purpose. While its removal leads to a 0.6% performance drop, it also reveals a deeper insight into the value of reason- ing. In our full method, the performance gap between reasoning and non-reasoning modes is 3.4 points (77.10% vs 70%. Without the penalty, this gap narrows to just 1.5 points (76.50% vs 75.00%). This shows that by penalizing inefficient thought, the model learns to better distinguish
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when a simple, intuitive answer is insufficient and more deliberate reasoning process is required. This enlarges the space where the cultivated reasoning skill provides a distinct advantage, under- scoring the importance of not only knowing how to reason, but also when.
Ultimately, the ablation validates our holistic approach. Our framework comprchensively elevates performance in both non-reasoning and reasoning settings, with each component proving its value building a model that is not only more accurate but reasons in a more effective, consistent, and logical manner.
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D.11 BENCHMARK RESULTS ON MMAR
The MMAR benchmark, designed to test deep, multi-step reasoning on longer audio, exposes the limits of current models. Our analysis of the results in Tab. 22 reveals clear fragmentation in reasoning capabilities across the field and highlights distinct challenges for future research.
Table 22: MMAR Benchmark Results. We evaluate our method against state-of-the-art audio models across single and mixed audio modalities. Best scores are highlighted in blue second-best scores in green All results show accuracy (%). Mix-S-M: Sound+MMusic, Mix-S-Sp: Sound+Speech, Mix-M-Sp: Muscc+Speech, Mix-S-M-Sp: Sound-Music+Specchh We report the performance of Ke-Omni-R (Zhao al. 2025) from our own reproductions under the same protocol; all other baseline results are taken from the MMAR paper (Ma et al.) 2025b) (including Qwen2.5-0mmi-77
Xu al. )025)).
Method
Size
Sound
Music
Speech
Mix-S-M
Mix-S-Sp
Mix-M-Sp
Mix-S-M-Sp
Overall
Our Proposed Method
CESAR
7B
66.06
55.83
62.24
63.64
67.43
60.98
66.67
62.70
Base Model
Qwen2.5-Oman 5-Omni-7B
7B
58.79
40.78
59.86
$4.55
61.93
67.07
58.33
56.70
Audio RL Baseline
Ke-Omai-R
7B
63.64
47.09
62.93
63.64
68.35
67.07
45.83
60.90
Proprictary Models
Gemini 2.0 Flash
61.21
50.97
72.11
81.82
72.48
65.85
70.83
65.60
GPT-4o Audio
53.94
50.97
70.41
63.64
72.48
62.20
75.00
63.50
GPT-4o mini Audio
38.79
35.92
58.84
45.45
60.09
57.32
50,00
50.60
Baichuan- Omni-1.5
11B
41.21
33.01
40.48
36.36
48.62
39.02
41.67
40.70
Large Audio Reasoning Models (LARMS)
Audio Reasoner
8.4B
43.64
33.50
32.99
45.45
42.66
31.71
25,00
36,80
Audio-CoT
8.4B
35.76
25.24
34.01
9,09
30.73
30.49
37.50
31.30
Large Audio Language Models (LALMs)
Qwen2.5 2. Sa..5--On333
3B
53.94
46.12
53.74
36.36
60.09
$7.32
58.33
53.80
SALAMONN (13B)
35.37
SALAMONN (7B)
7B
30.91
29.61
34,35
9,09
37.61
28.05
37.50
32.80
Audio Flamingo
2.2B
32.73
21.84
24.83
18.18
30.28
24.39
25,00
26.60
Audio Flamingo 2
0.5B
20.61
20.39
24.15
27.27
23.85
26.83
25.00
23,00
Audio Flamingo 2
1.5B
26.67
20.87
22.79
9.09
22.94
23.17
20.83
22.90
Audio Flamingo
3B
24.85
20.75
23.17
LTU
7B
19.39
19.90
13.95
18.18
24.77
21.95
16.67
19.20
LTU-AS
7B
20.00
14.08
19.05
9,09
20.64
28.05
12,50
19,00
MusiLingo
7B
9.09
7.28
4.08
9.09
6.88
7.32
8.33
6.60
MU-LLaMa
7B
13.94
13.59
14.97
9.09
12,39
14.63
16.67
13,90
Large Reasoning Models (LRMss) Audio Caption
Caption OpenAl o3
49.70
41.75
63.95
36.36
60.09
52.44
54.17
54,70
Caption + DeepSeek-R R1
671B
46.67
49.51
62,59
45.45
58.72
56.10
54.17
55.50
Caption OpenAl
43.20
63.61
18.18
45.12
Caption GPT-4o
46.06
40.29
60.88
27.27
53.67
46.34
45.83
50.70
Caption D3
671B
42.42
40.78
56.12
18.18
50.00
45.12
37.50
47.60
Random Baseline
Random Guess
29.39
25.88
31.48
25.00
29.30
31.10
28.13
29.32
The Acoustic- Linguisti Divide. The MMAR results first expose stark divergence between lin- guistic and acoustic reasoning capabilities across current models. While top proprietary systems show strong performance in tasks dominated by Speech, our model, CESAR, achieves the highest scores in the non-linguistic domains Sound (66.06%) and Music (55.83%). This bifurcation is further illuminated by the "Caption LRM" methods; their reliance text transcripts allows them to perform reasonably well on speech- centriceetriccccccc tasks but leaves them unable to compete on acoustic tasks where critical, con-transcriballe information is paramount. This demonstrates that advanced audio reasoning is not a monolithic capability and that true progress requires models that can reason the raw acoustic signal, not just its textual representation.
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Superiority of Process-Oriented Reinforcement Learning. Second, the benchmark's difficulty serves as a critical test of training methodologies, revealing the limitations of prevalent supervised fine-tuning (SFT) paradigms. Our method, trained with process-oriented reinforcement learning, establishes a commanding lead over other Large Audio Reasoning Models (LARMs) like Audio- Reasoner, with performance chasm of nearly 26 points (62.70% vs. 36.80%) Models trained via SFT on static CoT datasets prove to be brittle, failing to generalize to the complex, multi-hop reasoning required by MMAR. This vast performance gap strongly suggests that robust reasoning skills cannot be effectively learned through imitation alone; they require the interactive, process- RL framework.
focused feedback inherent
The Frontier of Mixed-Modality Reasoning. Finally, MMAR underscores the profound chal- lenge of reasoning over mixed-modality audio streams. Across the board, even top-performing models, including sistent dominance the "Mix-" " categories. This indicates that while models may handle individual audio types, the compositional understanding and temporal grounding of multiple, overlapping au- dio sources (e.g.. background music, foreground speech, and intermittent sound effects) remains formidable challenge. This area represents the next clear frontier for the field of audio intelligence.
and leading proprietary systems, show high variance and struggle for
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D.12 QUANTIFYING TEST-TIME SCALING: LINEAR REGRESSION ANALYSIS
To rigorously quantify the test-time inverse scaling phenomenon, we perform linear regression anal- ysis the test-time scaling curves from Section We model the relationship between accuracy (P) and average reasoning tokens (L) as: P(L) P(O) + 8. L, where P(0) is the non-reasoning base- line accuracy and the Scaling Slope (3) measures the performance change per additional reasoning token. This formulation directly captures whether reasoning improves or degrades performance relative to the baseline.
Interpreting the Scaling Slope.
Negative slope (3 < 0): test-time inverse scaling, where longer reasoning actively harms
performance
Positive slope (33 0): effective test-time scaling, where reasoning improves performance
Zero slope (3
0): ineffective reasoning that fails to utilize additional compute
Results and Analysis. Figure g presents the linear fits for each model on MMAU Test-mini, with computed slopes quantitatively confirming findings:
Qwen2.5-Ommi-77 (Base Model): 3 = 0.5083. Severe test-time inverse scaling with steep negative slope, showing catastrophic performance degradation as reasoning length in- creases. The model's accuracy drops from its non-reasoning baseline less than one-third of its initial performance, confirming that under-optimized reasoning is actively harmful.
2. Ke-Omni-R (RL Baseline):
3 = - 0.0070. Slightly negative slope showing a downward trend with volatile performance. The outcome-only rewards still fail reverse the inverse scaling phenomenon, with performance gradually declining or stagnating around the non- reasoning baseline.
3. CESAR (w/o Overthinking Penalty): B 00.0084 Positive slope demonstrating that process-oriented rewards enable effective reasoning. Performance steadily improves above the non-reasoning baseline, validating that rewarding the reasoning process transforms rea- soning from a liability into a gain.
4. CESAR (Full Method):
3 = 0.0083. Strong positive slope- the the most favorable scaling coefficient among all methods. Performance rises substantially above the non-reasoning baseline, reaching an optimal peak before gracefully stabilizing. The higher 3 demon- strates that the Overthinking Penalty enhances both scaling efficiency and the magnitude of reasoning benefits.
Efficiency Trade-offs. We measured inference latency on MMAU Test-mini using single NVIDIA H200 GPU. At the optimal "reasoning sweet spot" (L 35-40 tokens), CESAR adds only 0.08 seconds (1.8% overhead) per query compared to non-reasoning inference (4.39s vs. 4.47s). This negligible latency increase yields substantial accuracy gains (+3.4%), and the best scaling co- efficient among all deatoods-ddmoostraatttg a highly favorable efficiency-perfomeee performance trade-off.
In general, our linear regression analysis demonstrates that test-time inverse scaling is not inherent to reasoning but stems from inadequate training. By rewarding the reasoning process, CESAR transforms the scaling coefficient from severely negative (B -0.51) to substantially positive (3 = +0.038), enabling efficient and effective test-time scaling with clear optimal operating points.
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Qwen2.5-0mnl-7
Ke-Omni-R
70
74.6
60
Total Accurscy (96
74.4
50
74.2
74.0
73.8
Linar FX (.325-r.33811
Lavsar PS (ilepe--0.0022)
Men-remooing
Hen-reannehy
20
40
60
80
10
20
30
40
50
Avg Reasoning Tokens
Avg Reasoning Tokens
Qwen2.5-0mmi-77 (Base)
Ke-Omni-R (RL Baseline)
CESAR (Ours Overthinkdng Penalty)
CESAR (Ours)
76.50
Tatar Ancorory
Saldd decaters
Rop-reatcding
Haseanassing
Total Accuracy (%)
76.5
Accuracy
76.00
76.0
75.5
75.0
75.50
74.5
74.0
75.00
73.5
20
40
60
80
100
20
40
60
Avg Reasoning Tokens
Avg Reasoning Tokens
CESAR (w/o Penalty)
CESAR (Ours)
Figure 8: Linear Regression Analysis of Test-Time Scaling, We fit linear model to the accuracy vs. reasoning tokens curve for each method. The Scaling Slope (3) quantifies the scaling behavior: negative slope confirms test-time inverse scaling (Base Model), while positive slope indicates effective reasoning scaling (CESAR). Note how CESAR reverses the negative trend of the base model into positive trajectory.
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D.13 TRAINING DYNAMICS AND STABILITY
To provide further insight into the stability and convergence properties of our method, we visualize the training process in Figure 9 The curve plots the training accuracy over all training steps.
As shown in the figure, CESAR exhibits a stable and consistent learning trajectory. The model's performance (accuracy) improves steadily from an initial state, demonstrating effective optimiza- tion under our multi-faccted reward framework. The smoothed curve (red line) highlights a clear upward trend without significant oscillations or collapse, validating the robustness of our GRPO- based training with process oriented rewards. This confirms that our approach not only achieves high final performance but also maintains training stability throughout the optimization process.
Training Curve
100.0%
90.0%
Training Accuracy
80.0%
70.0%
Training Accuracy
60.0%
Smoothed (window=10)
1000
2000
3000
4000
5000
6000
Training Steps
Figure 9: Training Curve of CESAR. The plot shows the training accuracy across all training steps. The light blue line represents the raw accuracy each evaluation step, while the red line shows the smoothed trend (moving average). The consistent upward trajectory demonstrates the stability and effectiveness of our training process.
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D.14 HYPERPARAMETER SENSITIVITY ANALYSIS
To validate the robustness of our reward configuration and understand the impact of the accuracy reward weight, we conduct sensitivity analysis by varying ai (the weight for Rax) while keep- ing other reward weights fixed at 02-3 = 1.0. The results on MMAU Test-mini are presented in Table 23
Table 23: Sensitivity Analysis of Accuracy Reward Weight (a1). We evaluate CESAR with different values of a₁ while keeping all other reward weights at 1.0. Best scores are highlighted in blue All results show accuracy (%)
Alpha Configuration
Sound
Music
Speech
Overall
01 = 0252 = 1 01 5, 02-5 (main)
82.58
70.36
75.08
76.00
83.48
73.05
74.77
77.10
Q1 = 10, 02
79.88
71.86
76.88
76.20
Analysis. The results reveal that our choice of α1 5 achieves the optimal balance between
swer correctness and reasoning process quality. When 01 (equal weighting). the model achieves competitive but slightly lower overall performance (76.00%). suggesting that stronger emphasis correctness is beneficial. Conversely, when on = 10 (excessive emphasis on correctness), perfor- mance degrades to 76.20%, indicating that over-prioritizing final answer correctness the expense of process rewards can harm the model's ability to develop robust reasoning strategies.
This analysis confirms that our main configuration (a) 5) provides the most effective trade-off, allowing the model to prioritize correctness while still benefiting substantially from the reasoning process rewards. The relatively stable performance across different an values also demonstrates the robustness of our framework to hyperparameter variations.
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Reviews:
[{"review_id": "4uv9ZtUtD6", "reviewer": "Reviewer_59TJ", "summary": "The paper introduces CESAR, a method that addresses a phenomenon the authors call test‑time inverse scaling in Audio LLMs, that means longer chain‑of‑thought (CoT) at inference often hurts accuracy. They argue this is due to models being forced to reason without being trained how to reason. They propose CESAR, an online RL framework using GRPO with a multi‑facet reward suite: (i) correctness and format, (ii) process rewards for reasoning–answer and reasoning–question consistency, explicit reasoning patterns/keywords , and an over‑thinking penalty that linearly penalizes long CoT. They used Qwen2.5‑Omni‑7B for experiments. CESAR reports SOTA on MMAU Test‑mini and higher MMSU averages than several baselines. Ablations attribute most gains to the Keywords and Consistency components.", "questions": "- How do win‑rates change with a different judge (e.g., a text‑only LLM judging rendered transcripts, or an open model)?\n- What is the training‑time overhead (GPU‑hours) and inference‑time cost per query at the reported sweet spot (~35–40 tokens) vs non‑reasoning mode?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well structured with a clear framework diagram\n- Moving beyond outcome‑only RL to process‑oriented rewards for Audio LLM reasoning is a good and practical direction.\n- Consistent gains with the proposed approach", "weaknesses": "- The paper argues that introducing CoT at inference degrades accuracy in Audio LLMs unless the reasoning process is explicitly trained. However, AURELIA [1] reports improvements by injecting structured, step‑by‑step reasoning into AV‑LLMs at test time without additional training, which appears to contradict the generality of this claim.  A head‑to‑head comparison (e.g., an AURELIA‑style inference‑only reasoning condition) would clarify boundaries of the inverse‑scaling effect the authors report.\n- The “AI‑as‑judge” uses GPT‑4o Audio only; there is no human‑study calibration or alternative judges to validate preference robustness.\n- The scope of evaluation ids limited. Only one model (Qwen) is used to validate the proposed approach. Also, MMAU evaluation is on Test‑mini (1k items), and there are no error bars or seed variability.\n- The linear over‑thinking penalty (Eq. 8) and fixed Lmax_output=256 are plausible but unvalidated; no sensitivity is shown for α‑weights, group size K, or the penalty shape.\n\nReferences:\n\n[1] Sanjoy Chowdhury and Hanan Gani et al. \"AURELIA : Test‑time Reasoning Distillation in Audio‑Visual LLMs\". ICCV 2025.", "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": "IObRjWeo0i", "reviewer": "Reviewer_D7P9", "summary": "This paper proposes a reinforcement learning framework, CESAR, to address performance degradation from chain-of-thought reasoning in Audio LLMs. To solve this \"test-time inverse scaling\" problem, the method shifts from rewarding only the final outcome to rewarding the quality of the reasoning process itself, using a combination of reward objectives for consistency and structure. The model is trained with Group Relative Policy Optimization (GRPO) and achieves state-of-the-art performance on the MMAU and MMSU benchmarks.", "questions": "1. The reasoning length \"sweet spot\" seems quite small (around 40 tokens). In other domains like complex mathematics, reasoning chains can be much longer (in the thousands). Is this shorter optimal length a characteristic of the audio reasoning benchmarks used (i.e., the tasks do not require longer chains of thought), or does it reflect a potential limitation of the current method where longer reasoning chains still risk performance degradation, albeit much less severely than in baseline models?\n2. Regarding the reward weights (the alphas in Eq. 3): How sensitive is the model's performance and behavior to these weights? For example, the ablation shows removing the Keywords reward has a significant impact, but what happens if its weight is increased to be on par with the accuracy reward? (all alphas to 1)\n3. How sensitive is the method to different prompts?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The problem statement is clear and the method is sound.\n2. The paper is very neatly written and easy to read and follow.\n3. The results on the datasets used show good performance improvements.\n4. The Appendix (Supplementary Material) is very thorough and contains very relevant discussions.", "weaknesses": "1. The ablation study shows the quantitative impact of each reward component, but a qualitative analysis is missing. It would be helpful to see examples of the reasoning text to understand how the Keywords Reward changes the model's thinking. Furthermore, the selection of the keywords feels a bit arbitrary. A clearer explanation for why these specific keywords were chosen would strengthen this part of the method.\n2. The reward function has several weighted parts. The paper provides one set of weights but does not analyze how sensitive the results are to these choices. Understanding how performance changes when the weights are adjusted would be valuable and provide better practical guidance for implementing the method.\n3. The paper's narrative is a bit too attached to Ke-Omni-R1. I believe the paper should rely on it's contributions to stand more on it's own methodology.", "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": "CRUygM0WAm", "reviewer": "Reviewer_WEba", "summary": "This paper tackles \"test-time inverse scaling\" in Audio LLMs, where longer reasoning chains degrade performance. The authors propose CESAR, an RL framework that rewards the \"reasoning process\" itself, not just the final answer. This method incentivizes consistency, structured logic, and penalizes \"overthinking\". CESAR successfully resolves this issue , achieving SOTA results on the MMAU benchmark and surpassing Gemini 2.5 Pro.", "questions": "1. How were the final reward weights (e.g., $\\alpha_1=5.0$ for accuracy, $\\alpha_{2-5}=1.0$ for process rewards) determined? How sensitive is the model's performance to variations in these weights, and is there a systematic approach to balancing these distinct reward objectives?\n2. Since the CESAR reward suite lacks explicit ASR or WER metrics, how does the training impact the model's ASR or WER? Does the \"synergistic effect\" seen in perception tasks (Table 9) also translate to a measurable reduction in WER?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is technically sound. Its claims about solving \"test-time inverse scaling\" are strongly supported by extensive experiments, including state-of-the-art (SOTA) comparisons, comprehensive ablation studies, and \"test-time scaling\" analysis.\n2. The paper is exceptionally well-organized with a clear, logical narrative. It provides a high degree of transparency, including pseudocode and detailed keyword tables, which significantly aids reader understanding and the ability to reproduce the results.\n3. The work solves a core problem for Audio LLMs, offering a new, engineerable paradigm for reasoning. Its most significant impact may be diagnostic: by solving reasoning, it clearly identifies the field's next major barrier—the \"perceptual bottleneck\"—which provides crucial guidance for future research.", "weaknesses": "1. The proposed multi-faceted reward suite introduces five new reward weights ($\\alpha_j$) that must be balanced and tuned. While the authors provide a simple, effective ratio, this still represents an added layer of complexity compared to simpler reward models. \n2. The readability of Figure 1 is poor. The text within the \"Qualitative Comparison of Reasoning Process\" section , which is meant to show examples of reasoning failures and improvements, is far too small. This makes it very difficult for the reader to understand these crucial illustrative examples, undermining the figure's purpose.", "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": "OnFmNr93WN", "reviewer": "Reviewer_Rzte", "summary": "This paper presents CESAR, a reinforcement learning framework that improves reasoning in Audio LLMs by rewarding the reasoning process rather than only the final answer. The authors identify a new phenomenon \"test-time inverse scaling\", where longer reasoning chains hurt performance, and attribute it to the lack of process-level supervision. CESAR uses Group Relative Policy Optimization (GRPO) with multiple reward components to train models that reason coherently and efficiently. Experiments on MMAU and MMSU show state-of-the-art results, outperforming GPT-4o Audio and Gemini 2.5 Pro, and reveal “reasoning sweet spots” that improve with controlled reasoning depth.", "questions": "I believe this paper has strong merit and makes an original contribution to the study of reasoning in Audio LLMs. If the authors can address the following questions and provide the requested analyses or experiments, I will raise my overall score and support acceptance of this paper.\n\n(1) Could the authors formally define test-time inverse scaling and provide a quantitative measure (e.g., slope or scaling coefficient) to characterize this phenomenon? Please also include an analysis of computational cost or latency as a function of reasoning length to clarify efficiency trade-offs.\n\n(2) How does CESAR affect word-error rate (WER) and audio fluency during reasoning generation? Have the authors considered introducing fluency-aware or perceptual rewards to mitigate potential degradation in real-world applications?\n\n(3) Could the authors extend the experimental comparison to include recent open-source audio reasoning models with Qwen2-Audio [1], Audio Flamingo 3 [2], and Baichuan-Omni-1.5 [3]? Including these baselines would better contextualize CESAR’s gains and clarify its dependence on proprietary systems.\n\n(4) Current experiments are primarily restricted to multiple-choice QA datasets (MMAU Test-mini and MMSU). To convincingly demonstrate CESAR’s generalization ability, could the authors evaluate the model on Audio-CoT [5],  SoundMind [6],  and CoTA [7]. Reporting results on these benchmarks would directly support the paper’s claims about scalability and robustness across diverse reasoning tasks.\n\n(5) The AI-as-Judge evaluation currently relies solely on GPT-4o Audio. Would the authors consider adding results from multiple evaluators (e.g., Gemini 2.5 or human raters) to assess consistency and potential bias? Reporting confidence intervals and sample-size statistics would further improve reliability.\n\n(6) Could the authors provide detailed hyperparameters (training steps, learning rates, sampling temperature, etc.) and include a GRPO stability curve to illustrate convergence behavior and training reliability?\n\n\n**References**:\n\n[1] Qwen2-Audio: https://arxiv.org/pdf/2407.10759\n\n[2] Audio Flamingo 3: https://arxiv.org/pdf/2507.08128\n\n[3] Baichuan-Omni-1.5: https://arxiv.org/pdf/2501.15368\n\n[4] MMAU-Pro: https://huggingface.co/datasets/gamma-lab-umd/MMAU-Pro\n\n[5] Audio-CoT: https://huggingface.co/datasets/liuhuadai/AudioCoT\n\n[6] SoundMind: https://huggingface.co/datasets/SoundMind-RL/SoundMindDataset\n\n[7] CoTA: https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "(1) The authors identify and analyze test-time inverse scaling in Audio LLMs, a previously undocumented failure mode.\n\n(2) The authors propose a process-oriented RL reward design that explicitly improves reasoning quality and depth.", "weaknesses": "(1) The notion of test-time inverse scaling is introduced conceptually but not formally defined or quantified. The paper does not provide a measurable coefficient or slope to characterize this phenomenon. Moreover, there is no analysis of computational cost or latency as a function of reasoning length, which would clarify efficiency trade-offs in scaling reasoning depth.\n\n(2) The paper focuses on reasoning accuracy but ignores word-error rate (WER) or fluency degradation, which could affect real-world usability. No fluency-aware or perceptual rewards are explored.\n\n(3) The comparisons include Qwen2.5-Omni, Ke-Omni-R, GPT-4o Audio, and Gemini 2.5 Pro, but omit other competitive open-source models such as Qwen2-Audio [1], Audio Flamingo 3 [2], and Baichuan-Omni-1.5 [3]. Adding these baselines would better contextualize CESAR’s gains and clarify the limits of proprietary systems.\n\n(4) The GPT-4o Audio “AI-as-Judge” evaluation lacks statistical rigor. The paper does not report confidence intervals, sample sizes, or randomization procedures, and it relies solely on GPT-4o Audio without human verification or alternative evaluators. Using multiple judgment models or human raters would help mitigate potential bias and confirm alignment with human judgment.\n\n\n(5) Experiments are conducted primarily on MMAU Test-mini, which is a small subset of MMAU-Pro [4], and on MMSU. Although the paper briefly mentions testing on the full MMAU-Pro [4] benchmark, detailed results are not reported. The representativeness of the subset is unclear, which weakens claims of scalability and generalization. Evaluating CESAR on additional benchmarks with Audio-CoT [5], SoundMind [6], CoTA [7] would substantiate its robustness.\n\n\n(6) The paper omits key implementation details, including training steps, learning rates, sampling temperature, and optimizer configuration. Moreover, no GRPO stability curve or convergence analysis is provided, making it difficult to assess the reliability and reproducibility of the proposed reinforcement learning setup.\n\n**References**:\n\n[1] Qwen2-Audio: https://arxiv.org/pdf/2407.10759\n\n[2] Audio Flamingo 3: https://arxiv.org/pdf/2507.08128\n\n[3] Baichuan-Omni-1.5: https://arxiv.org/pdf/2501.15368\n\n[4] MMAU-Pro: https://huggingface.co/datasets/gamma-lab-umd/MMAU-Pro\n\n[5] Audio-CoT: https://huggingface.co/datasets/liuhuadai/AudioCoT\n\n[6] SoundMind: https://huggingface.co/datasets/SoundMind-RL/SoundMindDataset\n\n[7] CoTA: https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA", "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)