👻 Hallucination Detection

🎞️ ECCV2026 · 3 paper notes

📌 Same area in other venues: 📷 CVPR2026 (33) · 🔬 ICLR2026 (40) · 💬 ACL2026 (28) · 🧪 ICML2026 (21) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (17)

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

Cognitive Anomaly Detection (CAD) summarizes a vision-language model's evidence-to-answer process with three interpretable information measures and detects departures from a Gaussian-mixture model of nominal behavior, reaching a mean detection AUC of 0.858 on adversarial POPE while distinguishing perceptual instability, evidence-dependence anomalies, and decision uncertainty.

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

CRISP is a structural diagnostic benchmark for visual spatial intelligence. Through a dual-task paradigm of Spatial QA + 3D Scene Graph combined with a cross-task consistency protocol, it reveals whether VLM spatial reasoning represents genuine 3D geometric understanding or merely semantic shortcuts relying on language priors.

No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs

This paper proposes VidPair-Halluc, a video hallucination benchmark that utilizes adversarial video pairs with "highly similar backgrounds but significantly different foreground semantics" to decouple model errors from background variations and cleanly attribute them to foreground hallucinations. The accompanying PairFlow three-stage generation pipeline leverages T2I and video generation models to automatically synthesize 1K high-quality video pairs and 11K spatiotemporal QA pairs. Evaluations indicate that mainstream large video models generally suffer from a severe degradation in performance under this controlled setting.