AI RESEARCH
HopChain: Multi-Hop Data Synthesis for Generalizable Vision-Language Reasoning
arXiv CS.CV
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ArXi:2603.17024v1 Announce Type: new VLMs show strong multimodal capabilities, but they still struggle with fine-grained vision-language reasoning. We find that long CoT reasoning exposes diverse failure modes, including perception, reasoning, knowledge, and hallucination errors, which can compound across intermediate steps. However, most existing vision-language data used for RLVR does not involve complex reasoning chains that rely on visual evidence throughout, leaving these weaknesses largely unexposed.