做机器人VLA策略可解释性的研究者终于有了一个行为锚定的分析框架——事件锚定SAE直接关联动作与行为事件，比纯文本分析更贴近闭环控制，建议做机器人学习或可解释AI的团队点开看看。
本文提出一种事件锚定的可解释性方法，将稀疏自编码器（SAE）的特征分析与机器人行为事件（如末端执行器关键帧）对齐，而非依赖文本上下文。该方法通过视觉、状态和时间线索聚类任务内的关键帧，将SAE特征与行为事件关联，并可选地通过VLM注释提供语义背景。实验在两种仿真架构和真实机器人上验证，事件锚定排序对OpenVLA产生最强因果效应，并迁移到π₀.5的连续动作块。研究同时指出SAE作为干预基础存在稀疏性和不完美性，干预效果因架构和干预位置而异，激进干预会暴露安全性和可解释性限制。代码已开源。
Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies
Vision-Language-Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape closed-loop behavior. However, mechanistic interpretability tools from language and vision-language models do not transfer cleanly to VLAs: outputs are robot actions rather than human-readable tokens, and interventions can only be tested via expensive closed-loop rollouts. We propose an event-grounded interpretability pipeline that anchors SAE feature analysis to behavioral events rather than text contexts. End-effector keyframes are clustered within each task using visual, state, and temporal cues, linking SAE features to behaviorally salient events and, via optional VLM annotations, to semantic context. To our knowledge, our pipeline is among the first to ground SAE-based VLA analysis in closed-loop behavioral events. Across two simulation architectures and a real-robot study, event-grounded ranking yields the strongest causal effects on OpenVLA and transfers to the continuous action chunks of $π_{0.5}$. SAE is a sparse but imperfect intervention basis: usability varies with architecture and intervention site, and aggressive intervention reveals safety and interpretability limits. Overall, event-grounded SAE analysis emerges as a practical starting point for behavior-anchored VLA interpretability, motivating future work on SAE features beyond action-aligned coordinates, finer-grained closed-loop evaluation, and safe interventions for high-stakes VLA deployments. Code is available at \url{https://github.com/xc-j/Event-SAE}.