Vision-Language-Action (VLA) policies have achieved remarkable single-step manipulation, yet they remain brittle precisely where each stage depends on what was just completed. The core issue is structural: short-window VLAs lack an explicit channel for rouxting information across sub-task boundaries, and existing memory-augmented variants either write at every frame, retrieve from demonstration-time stages, or fire at sub-goal events without performing an explicit sub-task-to-sub-task hand-off into the action expert. We identify the sub-goal completion event as the natural temporal unit for cross-subtask memory hand-off, and present WeaveLA (Weave Latent memory for Vision-Language-Action policies), a cross-subtask memory interface that, on top of a frozen VLA backbone, compresses each completed segment into latent tokens via query-driven attention pooling and routes them directly into the action-generation path of the next sub-task. This event-triggered, action-side design preserves the base policy's short-window interface while adding a lightweight cross-subtask channel. Through stratified evaluation on RoboMME with a $π_{0.5}$ backbone, WeaveLA's gains land exactly where the channel is needed: on the hardest repetition slice (SwingXtimes, $N{=}3$), success rises from $0\%$ to $47.8\%$, while single-execution episodes remain unchanged. Per-episode paired analysis confirms the gains are confined to tasks whose causal structure requires cross-subtask information.

翻译：视觉-语言-动作（VLA）策略已在单步操作中取得显著成果，但在每个阶段高度依赖前序完成状态时仍显脆弱。核心问题源于结构性缺陷：短窗口VLA缺乏跨子任务边界传递信息的显式通道，而现有记忆增强变体要么在每帧写入，要么从演示阶段检索，或是在子目标事件触发时未执行显式的子任务间交接便直接激活动作专家。我们提出子目标完成事件作为跨子任务记忆交接的自然时间单元，并由此构建WeaveLA（面向视觉-语言-动作策略的潜记忆编织框架）。该跨子任务记忆接口在冻结的VLA主干网络上，通过查询驱动的注意力池化将每个已完成片段压缩为潜记忆令牌，并直接路由至下一子任务的动作生成路径。这种事件触发、动作侧嵌入的设计保留了基础策略的短窗口接口，同时新增轻量级跨子任务通道。基于RoboMME平台以π_{0.5}为主干的层级评估表明，WeaveLA的增益精准作用于所需通道的场景：在最高难度重复操作子集（SwingXtimes, N=3）中，成功率从0%跃升至47.8%，而单次执行回合性能保持不变。成对回合分析进一步证实，增益仅出现在因果结构依赖跨子任务信息的任务中。