SOURCE-LINKED INTELLIGENCE
Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models
HopChain has shown on still images that multi-hop data synthesis improves vision-language reasoning, because long chain-of-thought reasoning exposes errors that compound across steps, while most data used for reinforcement learning with verifiable rewards (RLVR) rarely demands a chain of visual evidence, so these weaknesses are likely to stay unexposed. We observe the same problem in video, where this framework has not yet been explored. We therefore build Video-HopChain, a dataset of 22,550 multi-hop video questions over 13,378 videos, together with a held-out benchmark of 1,000 questions. Ea
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T07:08:13.000Z
First collected: 2026-09-23T04:21:13.910Z. This is not the publication date.