Long-to-Short Video Evidence Reasoning for Grounded Question Answering

• URL: http://arxiv.org/abs/2609.15224v1
• Date: Mon, 14 Sep 2026 08:43:27 GMT
• Title: Long-to-Short Video Evidence Reasoning for Grounded Question Answering
• Abstract summary: LOVER highlights three innovations over existing reinforcement-learning based video reasoning models.<n>Lover built on Time-R1 achieves new state-of-the-art results among open-source models on popular GQA benchmarks: NExT-GQA and ReXTime.
• Score: 22.18808808973094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present LOVER, a \underline{L}ong to sh\underline{O}rt \underline{V}ideo \underline{E}vidence \underline{R}einforced model for grounded question answering (GQA). LOVER highlights three innovations over existing reinforcement-learning (RL) based video reasoning models: (1) \textbf{Long-to-short Video Evidence Curriculum Learning}, which organizes RL training according to evidence duration and progressively adapts the model from long-range grounding to short-term reasoning; (2) \textbf{GQA Rewards}, which underscore the benefit of IoP reward over IoU for evidence spotting rather than strict temporal span overlap; (3) \textbf{Adaptive Timestamp Rendering}, which adaptively renders timestamps onto video frames using background-aware position and color selection to enhance temporal observability. The three designs are model-agnostic and reciprocal. They effectively improve QA, grounding, and grounded QA performance over different backbones. Notably, LOVER built on Time-R1 achieves new state-of-the-art (SOTA) results among open-source models on popular GQA benchmarks: NExT-GQA and ReXTime. Comprehensive ablation studies further validate the effectiveness of our three innovative components.

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