AI RESEARCH
ORIC: Benchmarking Object Recognition under Contextual Incongruity in Large Vision-Language Models
arXiv CS.LG
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ArXi:2509.15695v3 Announce Type: replace-cross Large Vision-Language Models (LVLMs) excel at captioning, visual question answering, and robotics by combining vision and language, yet they often miss obvious objects or hallucinate nonexistent ones in atypical scenes. We examine these failures through the lens of uncertainty, focusing on contextual incongruity, where objects appear unexpectedly or fail to appear in expected contexts, and show that such cases increase recognition difficulty for state-of-the- art LVLMs. To study this regime, we.