Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
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Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have identified a critical failure mode in multimodal language models: the ability to detect when text contradicts sensory input. The IMAVB benchmark, spanning 500 video clips with controlled premise conflicts, reveals that eight open-source omnimodal LLMs and Gemini 3.1 Pro exhibit a representation-action gap, where internal representations capture sensory mismatches but models fail to flag them in output. This finding exposes a fundamental grounding weakness in systems marketed as perception-aware agents, suggesting that multimodal alignment remains incomplete despite joint video, audio, and text processing. The gap has immediate implications for deployment in safety-critical domains where hallucination detection is essential.
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The troubling detail buried in the findings is that the models are not simply blind to sensory mismatches: their internal representations do capture the conflict, but that signal never surfaces in output. This is a different problem than ordinary hallucination, because the information is present inside the model and still gets suppressed.
This connects directly to coverage from the same day: 'Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry' tackles a structurally similar problem, where the gap between what a model internally represents and what it outputs is the core diagnostic challenge. Both papers are converging on the same uncomfortable finding: output-level evaluation is insufficient, and probing hidden states is necessary to understand model failures. Together, they suggest a quiet but significant shift in how researchers are framing reliability, away from benchmarking outputs and toward interrogating internal geometry.
Watch whether safety-critical deployment frameworks, particularly in robotics or medical imaging, begin requiring hidden-state auditing rather than output-only confidence thresholds as a direct response to findings like this. If IMAVB gets adopted as a standard evaluation by any major model provider within the next two release cycles, that would confirm the benchmark has real traction beyond the paper.
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MentionsGemini 3.1 Pro · IMAVB · omnimodal LLMs
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