AudioJev: Direct Audio Decisions with Order-Calibrated Probabilities

Sihan Lv; Zhen Li; Zhiqi Cao; Jinshan Zhang; Ying Li; Meng Xi; Jianwei Yin

cs.SDOct 1, 2026

AudioJev: Direct Audio Decisions with Order-Calibrated Probabilities

Authors: Sihan Lv, Zhen Li, Zhiqi Cao, Jinshan Zhang, Ying Li, Meng Xi, Jianwei Yin

Organizations: Zhejiang University, Hangzhou, China · China Academy of Space Technology, Beijing, China · Innovation and Management Center of the School of Software (Ningbo), Zhejiang University, Ningbo, China · Binjiang Institute of Zhejiang University, Hangzhou, China · Zhejiang Key Laboratory of Digital-Intelligence Service Technology, Hangzhou, China

Abstract

Audio decisions often depend on evidence that a transcript does not preserve, while their probability estimates can depend on how answer options are ordered. AudioJev maps a waveform, question and supplied alternatives directly to a candidate distribution through one shared full-parameter model. We define order calibration as preserving an answer's probability under meaning-preserving option permutations. Random-derangement SKL training pairs each question with a reordered view in which every alternative changes position, supervises both answers, and aligns the two distributions before applying a symmetric KL penalty. Inference retains a single candidate-scoring forward, with no calibration head or order ensemble. Across three training seeds, AudioJev reaches 68.88%/55.33% mean accuracy on complete MMAU/MMAR and reduces random-order SKL by 43.8%/60.5% relative to the single-view removal ablation. The same model handles intent, environmental sound, note properties, speech activity and conversational transitions. Paired removal ablations and multi-order evaluation measure predictive accuracy and probability stability together, establishing a direct audio interface whose calibration objective acts on candidate meaning rather than presentation position. Inference code and model weights are available at https://github.com/SihanLv/AudioJev-Inference and https://huggingface.co/shlv/AudioJev.

Figures & tables

Figure 1: AudioJev overview. A waveform, question and caller-supplied alternatives define a decision for one shared audio classifier. Training (top): the original and randomly deranged answer lists receive candidate-only supervision, with identity-aligned symmetric KL encouraging consistent probabilities across presentations. Inference (bottom): one forward pass scores the supplied alternatives and returns their probability distribution, supporting different audio tasks through the same interface.

Direct audio models
Model	SLURP@8	MINDS	MMSU	MMAU	MMAR	Clotho-AQA	NSynth	Latency (ms) $\downarrow$
AudioJev	95.14 $\pm$ 0.40	88.59 $\pm$ 0.23	61.70 $\pm$ 0.68	68.88 $\pm$ 0.46	55.33 $\pm$ 0.84	90.93 $\pm$ 0.62	67.90 $\pm$ 0.92	62.4 $\pm$ 1.2
Frozen Omni 3B	85.42	84.67	60.14	68.64	53.50	90.93	45.70	-
Whisper large-v3 + text classifier
Laya English	76.56	54.91	44.90	38.70	35.60	-	-	1206.9 $\pm$ 2.5
Laya multilingual	70.31	41.22	37.73	32.71	36.40	-	-	1212.8 $\pm$ 0.6

Table 1: Classification accuracy (%) and waveform-to-probabilities latency. AudioJev reports mean

\pm

sample SD over three seeds at the fixed final step. Text pipelines use the same Whisper large-v3 transcripts; timing includes fresh ASR on the fixed five-benchmark panel. AudioJev/Laya timings are refreshed; other reader timings are same-panel references (Appendix F ). Frozen Omni 3B shares AudioJev’s inference architecture. Dashes denote unreported measurements.

Model	Event@8	Event@36	Scene	VAD F1	Orig. AUC	Fresh AUC
Frozen Omni 3B	95.25	84.50	29.50	0.174	0.584	0.678
Frozen Omni 7B	96.00	88.75	33.25	0.387	0.582	0.651
CLAP	94.75	88.50	41.75	0.395	0.365	0.494
Silero VAD	-	-	-	0.873	-	-
Smart Turn (frozen)	-	-	-	-	0.855	0.804
AudioJev	96.17 $\pm$ 0.88	91.17 $\pm$ 0.95	58.83 $\pm$ 1.26	0.935 $\pm$ 0.014	0.895 $\pm$ 0.002	0.900 $\pm$ 0.014

Table 2: Acoustic and conversational tasks. Event and scene columns are accuracy (%); VAD is macro F1 and transition columns are AUROC. AudioJev: mean

\pm

sample SD over three seeds. Fixed references use their established task definitions; Smart Turn transfers completion prediction to observed next-speaker change. Dashes indicate inapplicable tasks.

Task	Random SKL $\downarrow$	Random TV $\downarrow$	Flip (%) $\downarrow$
SLURP (all)	0.0387 $\pm$ 0.0064	0.0169 $\pm$ 0.0025	1.59 $\pm$ 0.37
MINDS	0.0623 $\pm$ 0.0113	0.0487 $\pm$ 0.0066	4.32 $\pm$ 1.69
MMSU	0.0805 $\pm$ 0.0123	0.0924 $\pm$ 0.0101	18.10 $\pm$ 2.29
Scene	0.0382 $\pm$ 0.0131	0.0602 $\pm$ 0.0086	6.33 $\pm$ 0.76
Event/activity	0.0157 $\pm$ 0.0046	0.0080 $\pm$ 0.0009	0.70 $\pm$ 0.19
Transition	0.0039 $\pm$ 0.0006	0.0114 $\pm$ 0.0055	0.42 $\pm$ 0.38

Table 3: Random-order calibration: mean

\pm

sample SD over three seeds. Distributions are compared with the original ordering after semantic alignment. SKL and TV measure distribution differences; flip is the percentage of changed semantic answers. Random permutations are drawn independently of training partners. Lower is better.

Test	Acc. (%)	Macro F1	BA	AUROC
Transition	86.00 $\pm$ 2.95	0.741 $\pm$ 0.033	0.842 $\pm$ 0.007	0.895 $\pm$ 0.002
New sessions	85.67 $\pm$ 2.90	0.731 $\pm$ 0.031	0.823 $\pm$ 0.022	0.900 $\pm$ 0.014

Table 4: Causal next-speaker prediction (mean

\pm

sample SD over three seeds). BA is balanced accuracy. Macro F1 and BA describe the native argmax decision; AUROC measures the ranking of switch probabilities.

	MMAU full			MMSU			MMAR
Configuration	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$
w/o paired training	69.47 $\pm$ 0.32	0.1239 $\pm$ 0.0312	14.10 $\pm$ 1.29	62.55 $\pm$ 0.52	0.1577 $\pm$ 0.0402	19.91 $\pm$ 1.72	55.93 $\pm$ 0.42	0.1703 $\pm$ 0.0707	18.20 $\pm$ 2.94
AudioJev (RD-SKL)	68.88 $\pm$ 0.46	0.0697 $\pm$ 0.0050	13.09 $\pm$ 1.63	61.70 $\pm$ 0.68	0.0805 $\pm$ 0.0123	18.10 $\pm$ 2.29	55.33 $\pm$ 0.84	0.0674 $\pm$ 0.0108	15.44 $\pm$ 0.56

Table 5: Paired derangement training versus its removal ablation (mean

\pm

sample SD over three seeds). Original examples, seeds, updates and final steps match. Accuracy is native-order percent; SKL and flip use independent non-cyclic reordering. The ablation removes the second view and SKL together.

	MMAU full			MMSU			MMAR
$\lambda$ ( $n$ )	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$	Acc. (%)	SKL $\downarrow$	Flip (%) $\downarrow$
0.25 (1)	68.97	0.0721	12.17	62.48	0.0871	16.87	55.50	0.0771	15.74
0.5 (3)	68.88 $\pm$ 0.46	0.0697 $\pm$ 0.0050	13.09 $\pm$ 1.63	61.70 $\pm$ 0.68	0.0805 $\pm$ 0.0123	18.10 $\pm$ 2.29	55.33 $\pm$ 0.84	0.0674 $\pm$ 0.0108	15.44 $\pm$ 0.56
0.75 (1)	68.95	0.0734	11.09	61.89	0.0911	15.47	54.70	0.0858	13.42
1 (3)	68.68 $\pm$ 1.12	0.0743 $\pm$ 0.0065	13.33 $\pm$ 0.09	61.99 $\pm$ 0.79	0.0928 $\pm$ 0.0082	17.89 $\pm$ 0.45	55.50 $\pm$ 0.66	0.0789 $\pm$ 0.0129	15.78 $\pm$ 1.75
1.5 (1)	68.04	0.0525	12.23	60.98	0.0571	17.48	54.00	0.0527	15.84

Table 6: SKL-weight sensitivity on MMAU/MMSU/MMAR. Rows with

n=3

report mean

\pm

sample SD;

n=1

rows report one seed. Accuracy uses native order; SKL and flip use independent random reordering of 9,968/3,931/991 eligible questions. All runs use the fixed final checkpoint.

Evaluation order	Model	MMAU SKL $\downarrow$	MMSU SKL $\downarrow$	MMAR SKL $\downarrow$
One-step rotation	w/o paired training	0.1417 $\pm$ 0.0375	0.1826 $\pm$ 0.0549	0.2016 $\pm$ 0.1030
	AudioJev (RD-SKL)	0.0750 $\pm$ 0.0057	0.0835 $\pm$ 0.0137	0.0742 $\pm$ 0.0107
Half-cycle rotation	w/o paired training	0.1509 $\pm$ 0.0359	0.1975 $\pm$ 0.0470	0.2013 $\pm$ 0.0738
	AudioJev (RD-SKL)	0.0927 $\pm$ 0.0088	0.1143 $\pm$ 0.0181	0.0935 $\pm$ 0.0110
Random non-cyclic	w/o paired training	0.1239 $\pm$ 0.0312	0.1577 $\pm$ 0.0402	0.1703 $\pm$ 0.0707
	AudioJev (RD-SKL)	0.0697 $\pm$ 0.0050	0.0805 $\pm$ 0.0123	0.0674 $\pm$ 0.0108

Table 7: Transfer across evaluation orders: mean

\pm

sample SD over three paired training seeds. Each reordered distribution is compared with native order after semantic alignment, using the same 9,968/3,931/991 eligible MMAU/MMSU/MMAR questions for both models. All comparisons use fixed final checkpoints. Lower SKL is better; bold marks the lower mean within each order.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Capability	Dataset	Training questions
Event	ESC-50	1,024
Scene	TUT2018	1,024
Activity	Oration	512
Transition	SpokenWOZ	1,024
Intent	SLURP	512
Sound questions	Clotho-AQA	2,048

Appendix

Table 8: Original questions in the final general-audio stage; each has two training views. Repeated questions about a recording are not independent audio sources.

Seed	General step	MMAU	MMSU	MMAR
01	2048	69.41	62.45	56.30
02	2048	68.66	61.13	54.80
03	2048	68.56	61.51	54.90

Appendix

Table 9: Final RD-SKL models: fixed final general step and native-order accuracy (%). Seed labels abbreviate 20261001–20261003.

Seed	Public (1k)	Hidden (9k)	Full (10k)
01	71.10	69.22	69.41
02	71.10	68.39	68.66
03	70.50	68.34	68.56

Appendix

Table 10: MMAU partition accuracy (%) for the final method. Full accuracy adds public and hidden correct counts before division by 10,000.

Dataset	Native	Canonical
SLURP (all)	94.62 $\pm$ 0.29	94.64 $\pm$ 0.49
MINDS	88.59 $\pm$ 0.23	88.64 $\pm$ 0.48
MMSU	61.70 $\pm$ 0.68	62.27 $\pm$ 1.04
Scene	58.83 $\pm$ 1.26	59.17 $\pm$ 3.11
Scene (permuted)	59.25 $\pm$ 2.78	59.17 $\pm$ 3.11
Event/activity	95.96 $\pm$ 0.56	95.90 $\pm$ 0.55

Appendix

Table 11: Final-model accuracy (%): mean

\pm

sample SD over three seeds. Native order is the main interface. Canonical order is a separate deterministic-wrapper diagnostic.

Seed	Clotho	NSynth
01	90.36	68.95
02	91.59	67.19
03	90.83	67.58

Appendix

Table 12: Held-out sound questions and note properties for each final RD-SKL seed: accuracy (%).

Seed01 configuration	MMAU acc.	SKL $\downarrow$	MMSU acc.	SKL $\downarrow$	MMAR acc.	SKL $\downarrow$
Ablation: no paired training	69.55	0.0879	63.14	0.1145	56.40	0.1055
Cyclic pairs, CE	69.87	0.1182	61.51	0.1534	56.10	0.1167
Cyclic pairs, CE+SKL ( $\lambda=1$ )	69.48	0.0547	61.56	0.0631	54.00	0.0509
Deranged pairs, CE+SKL ( $\lambda=1$ )	69.94	0.0667	61.82	0.0869	56.20	0.0696
AudioJev: deranged pairs ( $\lambda=0.5$ )	69.41	0.0644	62.45	0.0742	56.30	0.0548

Appendix

Table 13: Seed01 pairing controls with explicit SKL weights. Accuracy is native-order percent; SKL measures an independent non-cyclic random reordering. All two-view arms share original examples and update budgets. The single-view row is the removal ablation. The cyclic CE row is not a matched random-derangement CE control, so differences against it are recipe effects.

Pipeline	ASR	Reader	Total	p50	p95
AudioJev (RD-SKL)	0.0	62.4	62.4 $\pm$ 1.2	59.6	84.7
ASR + Laya English	1177.8	29.1	1206.9 $\pm$ 2.5	557.3	7248.0
ASR + Laya multilingual	1188.8	24.1	1212.8 $\pm$ 0.6	564.1	7304.1

Appendix

Table 14: Fresh final-model and ASR–Laya timing on the fixed panel, in milliseconds. Total means include sample SD across three repetition means.

Task/order	SKL $\downarrow$	TV $\downarrow$	Flip (%) $\downarrow$
MMAU full: one-step	0.0750 $\pm$ 0.0057	0.0890 $\pm$ 0.0059	14.04 $\pm$ 2.14
MMAU full: half-cycle	0.0927 $\pm$ 0.0088	0.1025 $\pm$ 0.0090	16.31 $\pm$ 2.99
MMAU full: random	0.0697 $\pm$ 0.0050	0.0829 $\pm$ 0.0052	13.09 $\pm$ 1.63
MMAU full: reverse	0.0960 $\pm$ 0.0101	0.1036 $\pm$ 0.0077	16.64 $\pm$ 2.88
MMSU: one-step	0.0835 $\pm$ 0.0137	0.0977 $\pm$ 0.0121	19.68 $\pm$ 2.52
MMSU: half-cycle	0.1143 $\pm$ 0.0181	0.1189 $\pm$ 0.0161	23.63 $\pm$ 3.17

Appendix

Table 15: Multi-order diagnostics for the final method: mean

\pm

sample SD over three seeds. Probability differences are computed against native order after semantic alignment; flip rates are percentages.

Explore similar work

Hearing the Order: Investigating Position Bias in Large Audio-Language Models
Oct 1, 2025Yu-Xiang Lin, Chen-An Li, Sheng-Lun Wei +3Large Audio Language Models Large Language Model Bias
National Taiwan University
Learning from Audio-Dependency Errors: Data Curation Strategies Based on Model Confusion Patterns in Audio Question Answering
Jun 20, 2026Hyeonuk NamLarge Audio Language Models Audio Understanding
Independent Researcher · Seoul, South Korea
Robustness assessment of large audio language models in multiple-choice evaluation
Oct 6, 2025Fernando López, Santosh Kesiraju, Jordi LuqueLarge Audio Language Models Text-To-Audio
Scientific Research, Telef´onica Innovaci´on Digital, Spain · Brno University of Technology, Czech Republic

Oct 1, 2025, cs.SDJ/K move · Enter open · S save

Hearing the Order: Investigating Position Bias in Large Audio-Language Models

Yu-Xiang Lin, Chen-An Li, Sheng-Lun Wei, Po-Chun Chen+2

National Taiwan University

Large Audio Language Models Large Language Model Bias Position Bias Order Permutation Answer+1

Open paper AskPDF arXiv

Large audio-language models (LALMs) are often used in tasks that involve reasoning over ordered options. An open question is whether their predictions are influenced by the order of answer choices, which would indicate a form of position bias and undermine their reliability. In this paper, we identify and analyze this problem in LALMs. We demonstrate that no model is immune to this bias through extensive experiments on six LALMs across three widely used benchmarks and their spoken counterparts. Shuffling the order of answer options can cause performance fluctuations of up to 24% and even change model rankings, raising concerns about the reliability of current evaluation practices. We also study permutation-based strategies and show that they can mitigate bias in most cases. Our work represents the first systematic investigation of this issue in LALMs, and we hope it raises awareness and motivates further research in this direction.

Abstract

Explore similar work

Hearing the Order: Investigating Position Bias in Large Audio-Language Models

Learning from Audio-Dependency Errors: Data Curation Strategies Based on Model Confusion Patterns in Audio Question Answering

Robustness assessment of large audio language models in multiple-choice evaluation