# Memory Forcing: Attendable Mid-Horizon History for Streaming Video Generation

arXiv: https://arxiv.org/abs/2610.11756 · HTML: https://mlsanity.com/paper/2610.11756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)
- Efficient LLM inference and serving / [Fast autoregressive visual generation](https://mlsanity.com/inference/fast-autoregressive-visual-generation/)

## Builds on (most cited first, 6 of 6)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration](https://mlsanity.com/paper/2605.14278/) (2026)
- [RoboStereo: Dual-Tower 4D Embodied World Models for Unified Policy Optimization](https://mlsanity.com/paper/2603.12639/) (2026)
- [MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment](https://mlsanity.com/paper/2512.06628/) (2025)
- [3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation](https://mlsanity.com/paper/2602.03796/) (2026)
- [SubjectAnchor: Subject-Aware Memory-to-Video for Multi-Shot Storytelling](https://mlsanity.com/paper/2609.34502/) (2026)

## Abstract (arXiv)

Autoregressive video diffusion enables causal video streaming without a bidirectional pass over the full clip, but existing few-step systems usually retain only the opening and most recent frames in a fixed-size KV cache. Once an event leaves this window, later frames can no longer attend to it, a failure we term mid-horizon forgetting. We present Memory Forcing, a few-step streaming method that preserves this missing history without increasing the cache size. Its Archive & Working Banks partition the cache into sink, archive, and working regions, retaining diverse intermediate events alongside recent motion under fixed memory. Because absolute temporal indices drift outside the training range, Bank-aware RoPE reassigns indices at attention time so each bank remains distinguishable. At 1.3B, Memory Forcing leads on longer clips, shows the smallest drop from 5s to 60s among methods reporting all four lengths, and preserves subjects and scenes through leave-and-return. The same design scales to Wan2.2 5B, producing more physically plausible, realistic, and dynamic videos and, to our knowledge, the first public 5B model on this forcing line.