Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.g., because, however, and wait) are closely associated with hallucinations and tend to exhibit high-entropy states. We argue that adequate contextual reasoning information can be directly extracted from the token probability distribution. Inspired by superposed representation theory, we propose leveraging latent superposed reasoning to integrate multiple candidate semantics and maintain latent reasoning trajectories. The hypothesis is that reliance on discrete textual inputs may drive the model toward sequential explicit reasoning, underutilizing dense contextual cues during high-entropy reasoning stages. Therefore, we propose constructing rich semantic representations from the token probability distributions to enhance in-context reasoning. With this goal, we present Latent Entropy-Aware Decoding (LEAD), an efficient plug-and-play decoding strategy that leverages semantic context to achieve reliable reasoning. The heart of our method lies in entropy-aware reasoning mode switching. The model employs probability-weighted continuous embeddings under high-entropy states and transitions back to discrete token embeddings as entropy decreases. Moreover, we propose a prior-guided visual anchor injection strategy that encourages the model to focus on visual information. Extensive experiments show that LEAD effectively mitigates hallucinations across various MLRMs on multiple benchmarks.
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Zhongxing Xu
claimed_verified
2026-03-18T08:46:15.638Z
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zhongxing xu
Gtime666
user
Gtime666
69b8eb506b3f36c249fbc50e
Zhonghua Wang
claimed_verified
2026-03-17T08:19:10.952Z
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Zhonghua Wang
JerryWzh
user
JerryWzh
69b8eb506b3f36c249fbc50f
Zhe Qian
69b8eb506b3f36c249fbc510
Dachuan Shi
69b8eb506b3f36c249fbc511
Feilong Tang
69b8eb506b3f36c249fbc512
Ming Hu
69b8eb506b3f36c249fbc513
Shiyan Su
69b8eb506b3f36c249fbc514
Xiaocheng Zou
69b8eb506b3f36c249fbc515
Wei Feng
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Dwarikanath Mahapatra
admin_assigned
2026-03-18T09:59:59.049Z
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Dwarikanath Mahapatra
nutis
user
nutis
69b8eb506b3f36c249fbc517
Yifan Peng
69b8eb506b3f36c249fbc518
Mingquan Lin
69b8eb506b3f36c249fbc519
Zongyuan Ge
Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.g., because, however, and wait) are closely associated with hallucinations and tend to exhibit high-entropy states. We argue that adequate contextual reasoning information can be directly extracted from the token probability distribution. Inspired by superposed representation theory, we propose leveraging latent superposed reasoning to integrate multiple candidate semantics and maintain latent reasoning trajectories. The hypothesis is that reliance on discrete textual inputs may drive the model toward sequential explicit reasoning, underutilizing dense contextual cues during high-entropy reasoning stages. Therefore, we propose constructing rich semantic representations from the token probability distributions to enhance in-context reasoning. With this goal, we present Latent Entropy-Aware Decoding (LEAD), an efficient plug-and-play decoding strategy that leverages semantic context to achieve reliable reasoning. The heart of our method lies in entropy-aware reasoning mode switching. The model employs probability-weighted continuous embeddings under high-entropy states and transitions back to discrete token embeddings as entropy decreases. Moreover, we propose a prior-guided visual anchor injection strategy that encourages the model to focus on visual information. Extensive experiments show that LEAD effectively mitigates hallucinations across various MLRMs on multiple benchmarks.
Latent Entropy-Aware Decoding (LEAD) enhances multimodal large reasoning models by using entropy-aware reasoning mode switching and prior-guided visual anchor injection to reduce hallucinations.
multimodal large reasoning models visual question answering transition words hallucinations token probability distribution superposed representation theory latent superposed reasoning in-context reasoning latent entropy-aware decoding probability-weighted continuous embeddings discrete token embeddings prior-guided visual anchor injection