Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning 事件

Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning arXiv:2606.01558v1 Announce Type: new Abstract: The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often degrades performance compared to direct prompting. In this paper, we provide a systematic analysis of CoT behavior in three modern MLLM families across model scales on d