Author

Jun Bai

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#artificial intelligence Preprint Oct 2026

Mitigating Video Hallucinations via Attention-Guided Representation Perturbation

Video large language models (Video LLMs) often generate responses unsupported by the input video. Contrastive decoding (CD) can mitigate these hallucinations, but its effectiveness depends on how the negative branch weakens visual evidence. Existing methods rely on predefined perturbations or masking guided by question...

Zhi-Xi Cai, Ruo-Qi Yuan, Xin-Cheng Yang et al. · 0 citations

Jul 2026

BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning

A lightweight framework that dynamically terminates reasoning by estimating whether the generated prefix is sufficient for correct answering and derives robust training labels through repeated answer completions is presented, demonstrating an effective approach to more efficient LLM reasoning.

Keshu Fu, Ke-Qin Peng, Jun Bai et al. · arXiv.org · 0 citations