[2601.18753] HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

Computer Science > Machine Learning arXiv:2601.18753 (cs) [Submitted on 26 Jan 2026 (v1), last revised 2 Mar 2026 (this version, v2)] Title:HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs Authors:Xinyue Zeng, Junhong Lin, Yujun Yan, Feng Guo, Liang Shi, Jun Wu, Dawei Zhou View a PDF of the paper titled HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs, by Xinyue Zeng and 6 other authors View PDF HTML (experimental) Abstract:The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data-driven hallucinations and reasoning-driven hallucinations. However, existing detection methods usually address only one source and rely on task-specific heuristics, limiting their generalization to complex scenarios. To overcome these limitations, we introduce the Hallucination Risk Bound, a unified theoretical framework that formally decomposes hallucination risk into data-driven and reasoning-driven components, linked respectively to training-time mismatches and inference-time instabilities. This provides a principled foundation for analyzing how hallucinations emerge and evolve. Building on this foundation, we introduce HalluGuard, an NTK-based score that leverages the induced geometry and captured representations of the NTK to jointly identify data-driven and reasoning-driven ...