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Omnimodal LLMs struggle to detect conflicting sensory and textual information

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a "Representation-Action Gap" in omnimodal large language models, where models can encode mismatches between textual claims and their sensory input but fail to act on this information in their outputs. A new benchmark, IMAVB, was developed using movie clips to test this conflict detection capability. Across eight open-source models and Gemini 3.1 Pro, the study found that models either under-reject false claims or over-reject, impacting comprehension accuracy. The gap is more pronounced in audio than vision and is resistant to prompting, though a probe-guided logit adjustment technique showed promise in improving rejection behavior. AI

IMPACT Highlights a critical gap in LLM grounding, suggesting current models may misinterpret or ignore conflicting sensory data, impacting their reliability as agents.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and findings about omnimodal LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Omnimodal LLMs struggle to detect conflicting sensory and textual information

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The cluster contains an academic paper detailing a new benchmark and findings about omnimodal LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Trung Nguyen Quang, Yiming Gao, Fanyi Pu, Kaichen Zhang, Shuo Sun, Ziwei Liu ·

    Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs

    arXiv:2605.13737v2 Announce Type: replace Abstract: When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perception or in action? Recent omnimodal models are positioned as perception-gr…

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