Question-Aware Evidence Ledgers for Video Relational Reasoning
The VRR-QA challenge evaluates visual relational reasoning in videos, where answers often depend on implicit spatial relations, event boundaries, target identity, and dialogue context rather than a single salient frame. We present a test-time reasoning pipeline built around a strong GPT-5.5 video QA solver and a set of question-aware evidence ledgers. The initial solver answers each question from a uniform video representation, while routed ledgers are prompted to make the required targets, count units, reference frames, and temporal or spatial scope explicit for counting, spatial, endpoint, viewpoint, and dialogue reasoning. External tools such as open-vocabulary detection, depth cues, p...
| Authors | Yilin Ou, Mengshi Qi, Huadong Ma |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 14 June 2026 |
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