做视频生成或世界模型研究的团队，这篇论文帮你拆解了记忆机制中容量、压缩、读取和循环四个关键维度，看完能直接指导你的模型设计。
Echo-Memory 是一项针对动作条件世界模型中记忆机制的受控研究。这类模型根据首帧、文本提示和相机动作序列生成多段视频，但其主要失败点往往是记忆而非局部图像合成：当相机离开再返回时，场景或关键物体可能悄然改变。现有记忆设计难以比较，因为增益与骨干网络、训练、检索和评估差异纠缠不清。Echo-Memory 固定了动作到视频的接口，仅改变历史信息的存储和读取方式，在共享的视频扩散骨干、优化器、相机动作表示、采样器和评估流程下，比较了原始上下文、基于压缩的记忆、不同读取路径的空间摘要以及状态空间循环。研究通过三分支协议（回放质量、域内循环重访和开放域返回探测）评估记忆，发现回放保真度不足以作为记住世界的代理指标。主要结论包括：原始上下文是强大的容量基线，能显著提升开放域返回性能；紧凑性不能替代容量；块状状态空间循环是最强的开放域返回机制。
Echo-Memory: A Controlled Study of Memory in Action World Models
We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: \emph{capacity}, \emph{compression}, \emph{read-out}, and \emph{recurrence}. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the salient evidence needed for return. Finally, block-wise state-space recurrence is the strongest open-domain return mechanism in our matrix, showing that the structure of implicit memory matters as much as the decision to use it. These results provide a compact protocol for studying memory in action world models beyond isolated replay metrics.