AI RESEARCH
Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
arXiv CS.CV
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ArXi:2606.01558v1 Announce Type: new 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 datasets requiring step-wise visual evidence. Our analysis identifies two recurring failure modes: premature answer commitment and limited direct visual-token access during rationale generation.