Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models

Vision-Language Models (VLMs) frequently `hallucinate'x0015generate plausible yet factually incorrect statementsx0016posing a criticalbarrier to their trustworthy deployment. In this work, we propose a newparadigm for diagnosing hallucinations, recasting them from static out-put errors into an auditable process-level anomaly. Our framework isgrounded in a normative principle of computational rationality, allowingus to model a VLM's generation as a dynamic cognitive trajectory. Wedesign a suite of information-theoretic probes that project this trajec-tory onto an interpretable, low-dimensional Cognitive State Space.Our central discovery is a governing principle we term the geometric-information duality: a cognitive trajectory's geometric abnormalitywithin this space is fundamentally equivalent to its high information-theoretic surprisal. Hallucination detection is thus elegantly re-framedas a geometric anomaly detection problem. Evaluated across diversesettingsx0016from rigorous binary QA (POPE) and comprehensive reason-ing (MME) to unconstrained open-ended captioning (MS-COCO)x0016ourframework achieves state-of-the-art performance. Crucially, it operateswith high ex001Eciency under weak supervision and remains highly robusteven when calibration data is heavily contaminated. This approach en-ables a causal attribution of failures, mapping observable errors to dis-tinct pathological states: perceptual instability (measured by Percep-tual Entropy, HEvi ), logical-causal failure (measured by InferentialConx001Dict, SConf ), and decisional ambiguity (measured by Decision En-tropy, HAns ). Ultimately, this opens a path toward building AI systemswhose reasoning is transparent, auditable, and diagnosable by design.