Memorizon: Training World Models Beyond Their Context Window
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: control wants a short horizon, memory wants an unbounde...
arXiv:2608.23565v1 Announce Type: new Abstract: An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: contro...
arXiv:2609.40222v1 Announce Type: new Abstract: When a camera revisits a previously observed region, a video world model should reproduce what was there before. This requires both remembering past ob...
Memory Forcing is a new few‑step streaming method for autoregressive video diffusion that keeps a diverse mid‑horizon history without enlarging the KV cache. It partitions the cache into Archive, Working, and sink banks, and uses Bank‑aware RoPE to keep each bank distinguishable even as absolute temporal indices drift. Experiments show that at 1.3B parameters it outperforms other methods on longer clips, maintains subjects and scenes when they leave and re‑enter the window, and scales to a 5B model that produces more realistic, dynamic videos.
arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily...
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.