Vision-language-action (VLA) models increasingly condition robot policies on history, depth, or 4D features to resolve ambiguity in long-horizon manipulation. However, more spatiotemporal evidence is not necessarily better: when the injected evidence is not motion-consistent, it can introduce geometric drift, fragmented temporal cues, and unstable action generation. This raises a simple question: should a VLA remember past frames, or remember the motion that connects them? We introduce MotionVLA, a motion-history interface that converts a short past-only video window into compact, time-continuous trajectory-field tokens. Instead of treating history as a sparse set of ndependently lifted frames, MotionVLA represents recent observations as physically coherent motion evidence. Current visual tokens query this history to retrieve task-relevant motion information, which is then recoupled into the VLA stream under trajectory-grounded supervision. Experiments across simulation benchmarks and preliminary real-robot rollouts show that MotionVLA improves long-horizon manipulation while producing smoother and more direct executions. These results suggest that effective VLA memory is not just about providing more 4D context, but about exposing motion-consistent evidence that is usable for control.

翻译：视觉-语言-动作（VLA）模型越来越多地基于历史、深度或四维特征对机器人策略进行条件约束，以解决长时域操控中的模糊性问题。然而，更多的时空证据未必更优：当注入的证据与运动不一致时，可能引入几何漂移、碎片化时序线索和不稳定的动作生成。这引出一个简单问题：VLA应该记忆过去的帧，还是记忆连接这些帧的运动？我们提出MotionVLA——一种运动-历史接口，能将仅包含过去信息的短视频窗口转化为紧凑且时间连续的轨迹场令牌。 MotionVLA不将历史视为一组彼此独立提升的稀疏帧，而是将近期观测表征为物理上一致的运动证据。当前视觉令牌会查询该历史以获取任务相关的运动信息，随后在轨迹监督下将其重新耦合至VLA流中。在仿真基准和初步实体机器人部署上的实验表明，MotionVLA在提升长时域操控性能的同时，生成了更平滑、更直接的执行动作。这些结果揭示，有效的VLA记忆不仅在于提供更多四维上下文，更在于暴露对控制具有可用性的运动一致证据。