HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models

Excerpt

arXiv:2604.26139v3 Announce Type: replace Abstract: Diffusion large language models generate text through iterative denoising, exposing hidden trajectories that may contain reliability signals beyond the final output. We propose HIVE, which compresses trajectory hidden states, selects informative step-layer evidence, and conditions a verifier through continuous prefix embeddings to produce a hallucination score and structured diagnostics. Across two D-LLMs and three QA benchmarks, HIVE outperfor