# TinyHistory: Lightweight Video History Embeddings via Two-Stage Context Learning

arXiv: https://arxiv.org/abs/2512.23851 · HTML: https://mlsanity.com/paper/2512.23851/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2025.

## Built on by (most cited first, 15 of 15)

- [Context Forcing: Consistent Autoregressive Video Generation with Long Context](https://mlsanity.com/paper/2602.06028/) (2026)
- [Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory](https://mlsanity.com/paper/2602.02393/) (2026)
- [LoL: Longer than Longer, Scaling Video Generation to Hour](https://mlsanity.com/paper/2601.16914/) (2026)
- [Mode Seeking meets Mean Seeking for Fast Long Video Generation](https://mlsanity.com/paper/2602.24289/) (2026)
- [Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation](https://mlsanity.com/paper/2606.04527/) (2026)
- [Efficient Video Diffusion Models: Advancements and Challenges](https://mlsanity.com/paper/2604.15911/) (2026)
- [RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO](https://mlsanity.com/paper/2605.15190/) (2026)
- [Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion](https://mlsanity.com/paper/2608.26794/) (2026)
- [From Pixels to States: Rethinking Interactive World Models as Game Engines](https://mlsanity.com/paper/2607.14076/) (2026)
- [AnchorWorld: Embodied Egocentric World Simulation with View-based Evolution Customization](https://mlsanity.com/paper/2606.07326/) (2026)
- [Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation](https://mlsanity.com/paper/2608.10439/) (2026)
- [WorldPlay2: Extending Real-Time Interactive World Models in Control and Horizon](https://mlsanity.com/paper/2609.35560/) (2026)
- [DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency](https://mlsanity.com/paper/2609.39096/) (2026)
- [Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory](https://mlsanity.com/paper/2610.02521/) (2026)
- [FrameFold: Response-Guided KV Cache Consolidation for Autoregressive Long Video Generation](https://mlsanity.com/paper/2610.14568/) (2026)

## Abstract (arXiv)

History context is central to autoregressive video generation, driving consistency and storytelling for both commercial models and personal use cases. For example, personal users, offline workflows, and individual-scale finetuning need to encode longer video histories under tight compute and memory budgets. We observe that content and identity consistency is an essential requirement, and that complete, uninterrupted history coverage together with content query and interpretation capabilities is broadly desired. We present TinyHistory, a lightweight history embedding learned through two-stage context learning. In the first stage, we pretrain the encoder on large-scale video data with a randomized frame query objective; in the second stage, we repurpose the pretrained encoder within an autoregressive video diffusion model to learn content-level consistency. As a result, we show that the learned lightweight embeddings achieve consistency comparable (by VLM, VBench, ELO, etc) to heavier alternatives, while reducing training overhead and extending the encodable history length within a given memory budget. We conduct ablation studies to analyze the influence and trade-offs of each component.