← 研踪 PaperTrailCVF · CVPR 2026:cvf:e7d31bce89bda86fd4397785397fc1f6 · 记录 #1
KVSmooth: Mitigating Hallucination in Multi-modal Large Language Models through Key-Value Smoothing
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:57
摘要
Despite the significant progress of Multi-modal Large Language Models (MLLMs) across diverse tasks, hallucination, which corresponds to the generation of visually inconsistent objects, attributes, or relations, remains a major obstacle to their reliable deployment. Unlike pure language models, MLLMs must ground their generation process in visual inputs; However, existing models often suffer from semantic drift during decoding, causing outputs to diverge from visual facts as the sequence length increases.To address this, we propose KVSmooth, a training-free, plug-and-play method that mitigates hallucination by performing attention-entropy-guided adaptive smoothing on hidden states. Specifically, KVSmooth applies an exponential moving average (EMA) to both keys and values in the KV-Cache while dynamically quantifying the sink degree of each token through its attention distribution entropy to adaptively adjust the smoothing strength. Unlike computationally expensive retraining or contrastive decoding methods, KVSmooth operates efficiently during inference without additional training or model modification. Extensive experiments demonstrate that KVSmooth significantly reduces hallucination (\mathit CHAIR _ S from 41.8 -> 18.2) while improving overall performance (F_1 score from 77.5 -> 79.2), achieving higher precision and recall simultaneously, whereas prior methods often sacrifice one for the other, thereby validating the effectiveness and generality of our method.
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记录 #1 · 2026 · 最新观测版本
KVSmooth: Mitigating Hallucination in Multi-modal Large Language Models through Key-Value Smoothing
Despite the significant progress of Multi-modal Large Language Models (MLLMs) across diverse tasks, hallucination, which corresponds to the generation of visually inconsistent objects, attributes, or relations, remains a major obstacle to their reliable deployment. Unlike pure language models, MLLMs must ground their generation process in visual inputs; However, existing models often suffer from semantic drift during decoding, causing outputs to diverge from visual facts as the sequence length increases.To address this, we propose KVSmooth, a training-free, plug-and-play method that mitigates hallucination by performing attention-entropy-guided adaptive smoothing on hidden states. Specifically, KVSmooth applies an exponential moving average (EMA) to both keys and values in the KV-Cache while dynamically quantifying the sink degree of each token through its attention distribution entropy to adaptively adjust the smoothing strength. Unlike computationally expensive retraining or contrastive decoding methods, KVSmooth operates efficiently during inference without additional training or model modification. Extensive experiments demonstrate that KVSmooth significantly reduces hallucination (\mathit CHAIR _ S from 41.8 -> 18.2) while improving overall performance (F_1 score from 77.5 -> 79.2), achieving higher precision and recall simultaneously, whereas prior methods often sacrifice one for the other, thereby validating the effectiveness and generality of our method.
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