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arXiv · 2606.24726

SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards

Sheng Xia·Zhengqin Lai·Tianxiang Jiang·Kanghui Tian·Shoujun Zhou·Bin Li·Yi Wang
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Abstract

Video MLLMs often struggle with fine-grained spatio-temporal reasoning, sometimes generating correct answers based on irrelevant frames or objects. Although outputting spatio-temporal evidence during reasoning is a promising direction, existing RL frameworks typically rely on geometry-only (IoU) rewards, which can be sensitive to boundary perturbations and overlook semantic alignment. To address this, we propose Semantic Evidence Reward (SER), which reformulates spatio-temporal evidence grounding as a constrained verification task. Instead of computing pixel-level overlap, SER uses a referee VLM as a local checker to evaluate model-generated evidence claims across two dimensions: relevance and localization quality, combined with a temporal penalty. This design reduces the reliance on dense box annotations and enables training directly on standard video QA data. On the V-STAR benchmark, SER achieves 49.6% mLGM, improving by 3.0 points over the strong evidence-grounded baseline Open-o3-Video, demonstrating its potential in enhancing both answer accuracy and evidence grounding.

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Sheng Xia, Zhengqin Lai, Tianxiang Jiang, Kanghui Tian, Shoujun Zhou, Bin Li, Yi Wang. 2026-06-23. SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards. https://arxiv.org/abs/2606.24726

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