pith:B67AW5CH
WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
The WorldSense benchmark shows that current multimodal models reach at most 65.1 percent accuracy on tasks requiring tight audio-visual synergy in real-world videos.
arxiv:2502.04326 v3 · 2025-02-06 · cs.CV · cs.AI
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Claims
The experimental results indicate that existing models face significant challenges in understanding real-world scenarios (65.1% best accuracy).
That the manually annotated QA pairs and the chosen 26 tasks accurately capture the requirements of real-world omnimodal understanding without introducing annotation bias or task selection that favors certain model architectures.
WorldSense provides the first benchmark requiring synergistic audio-video-text understanding on 1,662 real-world videos and 3,172 QA pairs, where the best current multimodal LLM reaches only 65.1% accuracy.
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| First computed | 2026-05-17T23:38:14.896721Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
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| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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