Attention-graph bottlenecks give models a single-pass hallucination check
A new method could flag hallucinated answers in one pass, without generating multiple responses, by checking how an LLM shares context across its attention graph. It measures Forman-Ricci curvature, a way to spot bottlenecks, then combines local and global patterns across attention heads. Tests across several LLMs and two hallucination benchmarks found consistent gains over attention-based and multi-response baselines, with hallucinations linked to self-focus, diffuse retrieval, and over-squashing in the final layer.