aiqbench analysis
MCBench addresses the critical gap in safety evaluation by introducing a multimodal benchmark capable of assessing Omni Large Language Models across vision, audio, and text inputs simultaneously. The study reveals that current models struggle with cross-modal reasoning for subtle risks unless salient cues are present, highlighting specific architectural limitations in integrating diverse data streams for safety judgments. This research is essential for developers aiming to build robust AI systems where failure modes depend on the synthesis of multiple sensory modalities rather than isolated visual inputs.
MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
arXiv:2606.05177v2 Announce Type: replace Abstract: Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a minimally different safe counterpart to assess model sensitivity. Our evaluations of state-of-the-art models reveal significant challenges. Omni LLMs struggle with subtle or non-physical risks but perform better when salient visual or acoustic cues are present. Analysis of reasoning traces shows that, although models can extract modality-specific information, they often fail to integrate these cues effectively for safety judgments. Our findings reveal that current Omni LLMs lack robust cross-modal reasoning in safety-critical settings, underscoring the need for improved architectures and training strategies for multimodal safety.
Abstract:Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a minimally different safe counterpart to assess model sensitivity. Our evaluations of state-of-the-art models reveal significant challenges. Omni LLMs struggle with subtle or non-physical risks but perform better when salient visual or acoustic cues are present. Analysis of reasoning traces shows that, although models can extract modality-specific information, they often fail to integrate these cues effectively for safety judgments. Our findings reveal that current Omni LLMs lack robust cross-modal reasoning in safety-critical settings, underscoring the need for improved architectures and training strategies for multimodal safety.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
arXiv:2606.05177 [cs.CL]
(or arXiv:2606.05177v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05177 Focus to learn more arXiv-issued DOI via DataCite
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