HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

• URL: http://arxiv.org/abs/2601.18753v1
• Date: Mon, 26 Jan 2026 18:23:09 GMT
• 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,
• Abstract summary: We introduce a unified theoretical framework that decomposes hallucination risk into data-driven and reasoning-driven components.<n>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 hallucinations.
• Score: 20.59139155257836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 hallucinations. We evaluate HalluGuard on 10 diverse benchmarks, 11 competitive baselines, and 9 popular LLM backbones, consistently achieving state-of-the-art performance in detecting diverse forms of LLM hallucinations.

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