Opening — Why this matters now Hallucination has become the embarrassing tic of multimodal AI — a confident assertion untethered from evidence. In image–language models, this manifests as phantom bicycles, imaginary arrows, or misplaced logic that sounds rational but isn’t real. The problem is not stupidity but unfaithfulness — models that reason beautifully yet dishonestly. ...
Vision-language models (VLMs) may describe what they see, but do they truly understand what they’re looking at — especially in social contexts? A recent paper introduces Cognitive Chain-of-Thought (CoCoT), a deceptively simple yet remarkably effective prompting strategy that helps these models reason like humans: through layered cognition, not flat logic. The Problem with Flat Reasoning Traditional Chain-of-Thought (CoT) prompting, while powerful for math and symbolic tasks, falls short when it comes to social or moral interpretation. Consider a scene where a person wears a mask indoors, and another says, “Hiding from the paparazzi, huh?” CoT may recognize the mask, but often misfires in guessing intent — is it a joke? A warning? An instruction? ...