AI 中文总结
研究针对Video LLMs视觉令牌多致效率受限问题，提出GeoTrace框架，通过CFPA和TCRC分别处理骨架与残差令牌，经实验评估证明该框架在多模型架构和场景下有效，能在大幅减少计算量时保留高比例性能。
AI 中文摘要
尽管视频大语言模型（Video LLMs）在视频理解方面表现出色，但其效率仍受大量视觉令牌限制。现有视频令牌压缩方法存在局限性。为此，我们提出GeoTrace，一个无需训练的时空令牌压缩框架，将视频证据分解为精确的骨架令牌和可追踪的残差事件令牌。具体而言，上下文最远点锚定（CFPA）保留显著、上下文一致且高覆盖率的骨架令牌，轨迹约束残差压缩（TCRC）通过一对一的时间轨迹和约束近流形压缩来压缩残差令牌，产生模糊性降低的可追踪事件令牌。我们在四个视频理解基准上对四个Video LLMs评估GeoTrace，结果证明了其有效性和泛化性。在LLaVA-OneVision上，仅保留10%视觉令牌时，GeoTrace实现了12.99倍的TFLOPs减少，同时保留了99.1%的原始性能。总体而言，GeoTrace为高效且强大的Video LLM推理提供了紧凑且可追踪的令牌表示。
英文摘要
Although Video Large Language Models (Video LLMs) have shown strong performance in video understanding, their efficiency is still limited by the large number of visual tokens. Existing video token compression methods typically rely on frame-wise saliency or heuristic token merging, which can over-focus on locally salient regions and produce ambiguous fused features. To address these issues, we propose GeoTrace, a training-free spatiotemporal token compression framework that decomposes video evidence into exact skeleton tokens and traceable residual event tokens. Specifically, Contextual Farthest-Point Anchoring (CFPA) preserves salient, context-consistent, and high-coverage skeleton tokens, while Trajectory-Constrained Residual Condensation (TCRC) compresses residual tokens through one-to-one temporal trajectories and constrained near-manifold condensation, producing traceable event tokens with reduced ambiguity. We evaluate GeoTrace on four Video LLMs across four video understanding benchmarks, and the results demonstrate its effectiveness and generalization across different model architectures and scenarios. On LLaVA-OneVision, with only 10\% visual tokens retained, GeoTrace achieves a \(12.99\times\) TFLOPs reduction while preserving 99.1\% of the vanilla performance. Overall, GeoTrace offers a compact and traceable token representation for efficient and robust Video LLM inference. Code is available at \href{https://github.com/guohuan-xie/GeoTrace.git}{\texttt{Code}}.
CommentsWithdrawn by the authors due to an incomplete internal approval process
