3 papers
cs.AI2026
Are Prompt Optimizers Blind? Cross-Modal Visual Feedback for Automatic Prompt Optimization
Haoyue Liu, Xiaoyu Ma, Ye Chen +2
Automatic prompt optimization (APO) has been widely adopted to adapt vision-language models (VLMs) to downstream tasks without weight updates, yielding promising results. However,…
cs.CL2026
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Lexiang Tang, Weihao Gao, Bingchen Zhao +4
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However,…
cs.CV2025
Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation
Lexiang Tang, Xianwei Zhuang, Bang Yang +5
Large vision-language models (LVLMs) have demonstrated impressive capabilities across diverse multimodal tasks, yet they remain highly susceptible to visual hallucinations (VH), of…