Echo-Memory: A Controlled Study of Memory in Action World Models
2026-06-08 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionGraphicsMachine Learning
AI summaryⓘ
The authors study how different memory methods impact video generation models that create videos based on camera movements and text. They designed a fair test called Echo-Memory to compare how these models remember past scenes, keeping all other conditions the same. They found that simply storing all past information works well for remembering, but squeezing memory too much loses important details. Also, a memory type called state-space recurrence helped most with returning to previous scenes. Their work shows that how memory is organized is just as important as having memory at all.
action-conditioned world modelsvideo diffusion backbonememory mechanismsstate-space recurrencecompression-based memoryreplay qualitycamera-action sequenceopen-domain returnspatial summariesvideo generation
Authors
Wayne King, Zeyue Xue, Yuxuan Bian, Jie Huang, Haoran Li, Yaowei Li, Yaofeng Su, Yuming Li, Haoyu Wang, Shiyi Zhang, Songchun Zhang, Yuwei Niu, Sihan Xu, Junhao Zhuang, Haoyang Huang, Nan Duan
Abstract
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.