arXiv AI
Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts
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arXiv AI
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C$^3$PO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models
arXiv:2608. 05381v1 Announce Type: new Abstract: Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning.
arXiv Machine Learning
Sep 30
Seeing What Should Be Heard: Diagnosing and Repairing Cross-Modal Shortcuts in Omni-Modal LLMs
arXiv:2609.36798v1 Announce Type: cross Abstract: Omni-modal large language models (LLMs) are expected to answer a question using the modality it explicitly refers to. However, existing training para...
arXiv AI
Oct 15
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs
arXiv:2610.18141v1 Announce Type: new Abstract: Omnimodal large language models (OLLMs) are susceptible to cross-modal hallucinations under audio-visual interference. We find that, for a substantial...
arXiv Computer Vision
Sep 16
Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?
Video-HolmesV2 is a new benchmark that tests multimodal large language models on their ability to reason with spatio‑temporal audio‑visual evidence in long videos. It requires models to justify answers with precise evidence, uses a multi‑model cross‑verification pipeline and a spatio‑temporal evidence‑aware metric, and introduces an audio‑text guided token compression framework to reduce long‑context noise. In evaluations, even strong proprietary models score below 60% while the proposed approach outperforms comparable open‑source omni‑models.
arXiv AI
Oct 2
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
The paper introduces IMAVB, a 500‑clip benchmark that tests whether omnimodal large language models can detect when a textual premise contradicts their visual or audio input. Experiments on eight open‑source models and Gemini 3.1 Pro reveal a Representation‑Action Gap: internal states encode mismatches, yet the models rarely reject false premises, exhibiting under‑rejection or over‑rejection. A probe‑guided logit adjustment improves rejection behavior, suggesting the main bottleneck is in translating perception to action rather than in perception itself.
arXiv AI
2d ago
Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
The paper introduces Synergistic Faithfulness (𝔽_syn), a new metric based on the Shapley Interaction Index that isolates the joint contribution of visual and textual modalities in Vision‑Language Models (VLMs). It demonstrates that existing explainers overemphasize visual salience and fail to capture true cross‑modal synergy, and provides a scalable evaluation framework for auditing VLM reasoning. The authors evaluate nine XAI methods across three VLM architectures and datasets, revealing significant shortcomings in current approaches.