pith:GQ37DA3B
JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation
Even the best Omni-LLMs reach only 65.3 percent average accuracy on a benchmark that demands strict joint audio-visual reasoning in videos.
arxiv:2512.12772 v2 · 2025-12-14 · cs.MM · cs.CV
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Claims
even the best-performing Omni-LLM achieves an average accuracy of only 65.3%, outperforming uni-modal baselines but revealing substantial room for improvement, especially in cross-scene reasoning.
The automated pipeline using vision-LLMs, audio-LLMs, and general LLMs produces questions and answers that strictly require joint audio-visual understanding without introducing biases or answer leakage from the generation process itself.
JointAVBench is a benchmark for joint audio-visual reasoning that shows leading Omni-LLMs reach only 65.3% accuracy, with particular weakness in cross-scene tasks.
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| First computed | 2026-05-17T23:39:00.521344Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GQ37DA3BEJQ5ZGO6DKY3C3CIWC \
| jq -c '.canonical_record' \
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# expect: 3437f183612261dc99de1ab1b16c48b0a44c4604c1a8637a855d0b16143662e5
Canonical record JSON
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