COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models
Score
73首次系统提出显式运动分支与方向敏感优化结合的完整时间建模框架，对视频MLLM时序瓶颈提供了新思路。
方法设计严谨，实验覆盖多个基准和两个模型家族，对比充分，但缺乏更细粒度消融细节公开。
框架轻量且能直接应用于现有视频MLLM，提升时序推理能力的同时不损害静态感知，具备较强落地潜力。
视频大模型时间理解是当前热点，但论文编号显示为2026年，实际时效性需谨慎判断。
来源为arXiv预印本，未经同行评审，但作者团队与实验严谨性较高，可作为可信参考。
Base 81.5 × T2 Source weight ×0.9 = 73
Curation line 70Selected
Why selected
The paper features strong novelty, complete experimental design, and significant gains that generalize across models, making it a high-quality research contribution.
AI commentary
「This paper is worth attention because it tackles the long-standing weakness of video LLMs in temporal reasoning from both modeling and optimization angles. For those working on video understanding, agent temporal decision-making, or on-device video models, COMET's framework and experimental analysis are highly relevant, especially its ability to avoid static perception degradation, making it practical for deployment.」
Quick overview
Video multimodal large language models still suffer from fragile fine-grained motion-temporal understanding, and the core bottleneck is not just sparse frame sampling but the lack of a complete temporal modeling pipeline. To address this, the authors propose COMET, a time-grounded framework that systematically strengthens video MLLMs. COMET introduces a temporal motion branch built on Taylor frame differences and injects motion evidence into the appearance stream via temporal attention bias-enhanced cross-attention. It combines temporal prior distillation with a forward-reverse TC-GRPO stage, turning temporal order into a direct learning signal. On Qwen3-VL-8B, action-centric tasks (STAR, SSv2) improve by 4.9% on average, temporal reasoning tasks by 2.1%, while static perception remains on par. The same gain pattern transfers to InternVL2.5-8B, indicating cross-family generalization.
- Proposes COMET, a framework that explicitly models inter-frame motion to complete the temporal modeling pipeline for video MLLMs.
- Builds a temporal motion branch using Taylor frame differences and injects motion evidence via attention-bias-enhanced cross-attention.
- Introduces forward-reverse TC-GRPO optimization that treats temporal order as a direct learning signal, combined with temporal prior distillation.
- Achieves consistent gains on multiple video benchmarks while keeping static perception intact, demonstrating cross-model generalization.
Full text
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