Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models
Zhiwen Luo, Siyu Jiang, Weilong Jiang Huazhong University of Science and Technology, Kun He Huazhong University of Sceince and Technology
Visual hallucination remains a major obstacle to the reliability of Large Vision-Language Models (LVLMs). We argue that this issue originates from a fundamental statistical misspecification: the conventional softmax attention implicitly assumes i.i.d. noise, yet real LVLM attention patterns exhibit structured and competitive biases (e.g., attention sinks) that violate this assumption. To address this mismatch, we introduce Latent Attention Denoising (LAD), a principled and training-free framework that recasts attention calibration as a one-step score-based denoising process. LAD employs an interpretable energy function to derive an analytic score and applies a single Langevin-inspired update to actively steer corrupted attention logits toward more faithful configurations. This intervention imposes negligible computational overhead and operates at a speed comparable to standard greedy decoding. Extensive evaluations across diverse architectures confirm that LAD achieves superior performance on both generative and discriminative tasks, effectively mitigating hallucinations while maintaining efficiency comparable to standard decoding.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表