arXiv Computer Vision
Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model
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OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
OmniHallu is a unified framework for detecting hallucinations in multimodal large language models across both comprehension and generation tasks involving image, video, and audio modalities. It introduces OmniHallu-Bench, a 10,000-sample benchmark with claim-level human annotations for six cross-modal tasks (I2T, V2T, A2T, T2I, T2V, T2A). The system uses a multi‑agent architecture that decomposes outputs into atomic claims, verifies them with modality‑specific experts, and aggregates evidence through structured reasoning, while a preference‑optimized verifier reduces expert calls by 66% with minimal performance loss.
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Empowering Long-form Omni-modal Understanding with Robust Audio Perception
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OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning
OmniReasoning introduces a new benchmark, OmniReasoningBench, that requires both audio and visual evidence for answering 1,150 multiple-choice and open-ended questions across two tasks. The authors also develop OmniQA, a data engine that automatically generates evidence‑grounded QA pairs with time‑stamped clue chains, producing training datasets OmniReasoning‑SFT‑112K and OmniReasoning‑RL‑19K. Finally, they propose Modality‑Factored Self‑Distillation (MFSD), an on‑policy self‑distillation method that assigns token‑level credit by evaluating responses under modality‑specific clue contexts, enabling the OmniReasoning‑30B‑A3B model to achieve significant performance gains on both the new benchmark and existing video benchmarks.
Hugging Face Trending Papers
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OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities inde...
arXiv Computation and Language
Oct 23
TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
TRIAD is a new benchmark designed to test omni‑modal large language models’ ability to resolve ambiguity across text, vision, and audio. Each item consists of a text–image–audio triplet, a question, and answer options, constructed so that the full triplet yields a unique answer while any single modality removed makes the answer ambiguous. The benchmark includes 327 queries in 86 scene groups, covering 18 ambiguity categories, and shows that current models lag humans in scene‑level disambiguation, especially when answer options are removed.