# MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives

arXiv: https://arxiv.org/abs/2512.14699 · HTML: https://mlsanity.com/paper/2512.14699/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 46 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://mlsanity.com/paper/2407.01392/) (2024)
- [An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://mlsanity.com/paper/2403.06764/) (2024)
- [Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://mlsanity.com/paper/2506.08009/) (2025)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [From Slow Bidirectional to Fast Autoregressive Video Diffusion Models](https://mlsanity.com/paper/2412.07772/) (2024)
- [Pyramidal Flow Matching for Efficient Video Generative Modeling](https://mlsanity.com/paper/2410.05954/) (2024)
- [MAGI-1: Autoregressive Video Generation at Scale](https://mlsanity.com/paper/2505.13211/) (2025)
- [StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text](https://mlsanity.com/paper/2403.14773/) (2024)
- [StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation](https://mlsanity.com/paper/2405.01434/) (2024)
- [MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding](https://mlsanity.com/paper/2404.05726/) (2024)
- [SkyReels-V2: Infinite-length Film Generative Model](https://mlsanity.com/paper/2504.13074/) (2025)
- [Autoregressive Video Generation without Vector Quantization](https://mlsanity.com/paper/2412.14169/) (2024)
- [WorldMem: Long-term Consistent World Simulation with Memory](https://mlsanity.com/paper/2504.12369/) (2025)
- [Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval](https://mlsanity.com/paper/2506.03141/) (2025)
- [In-Context LoRA for Diffusion Transformers](https://mlsanity.com/paper/2410.23775/) (2024)
- [Test-Time Training Done Right](https://mlsanity.com/paper/2505.23884/) (2025)
- [Long-Context Autoregressive Video Modeling with Next-Frame Prediction](https://mlsanity.com/paper/2503.19325/) (2025)
- [VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory](https://mlsanity.com/paper/2506.18903/) (2025)
- [One-Minute Video Generation with Test-Time Training](https://mlsanity.com/paper/2504.05298/) (2025)
- [Mixture of Contexts for Long Video Generation](https://mlsanity.com/paper/2508.21058/) (2025)
- [MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequence](https://mlsanity.com/paper/2407.16655/) (2024)
- [Vlogger: Make Your Dream A Vlog](https://mlsanity.com/paper/2401.09414/) (2024)

## Built on by (most cited first, 30 of 45)

- [Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation](https://mlsanity.com/paper/2603.21366/) (2026)
- [MBench: A Comprehensive Benchmark on Memory Capability for Video World Models](https://mlsanity.com/paper/2606.00793/) (2026)
- [Anchor Forcing: Anchor Memory and Tri-Region RoPE for Interactive Streaming Video Diffusion](https://mlsanity.com/paper/2603.13405/) (2026)
- [Pathwise Test-Time Correction for Autoregressive Long Video Generation](https://mlsanity.com/paper/2602.05871/) (2026)
- [WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG](https://mlsanity.com/paper/2603.23497/) (2026)
- [Mode Seeking meets Mean Seeking for Fast Long Video Generation](https://mlsanity.com/paper/2602.24289/) (2026)
- [KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration](https://mlsanity.com/paper/2605.14278/) (2026)
- [CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives](https://mlsanity.com/paper/2605.12496/) (2026)
- [Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation](https://mlsanity.com/paper/2606.04527/) (2026)
- [Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation](https://mlsanity.com/paper/2604.10103/) (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)
- [Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory](https://mlsanity.com/paper/2605.18733/) (2026)
- [FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion](https://mlsanity.com/paper/2606.10671/) (2026)
- [DySink: Dynamic Frame Sinks for Autoregressive Long Video Generation](https://mlsanity.com/paper/2605.21028/) (2026)
- [SlotMemory: Object-Centric KV Memory for Streaming Long-Video Generation](https://mlsanity.com/paper/2605.31033/) (2026)
- [CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation](https://mlsanity.com/paper/2606.09639/) (2026)
- [Echo-Memory: A Controlled Study of Memory in Action World Models](https://mlsanity.com/paper/2606.09803/) (2026)
- [Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms](https://mlsanity.com/paper/2603.28489/) (2026)
- [Head Forcing: Long Autoregressive Video Generation via Head Heterogeneity](https://mlsanity.com/paper/2605.14487/) (2026)
- [DIM-WAM: World-Action Modeling with Diverse Historical Event Memory](https://mlsanity.com/paper/2606.27677/) (2026)
- [StreamAV-Bench: A Comprehensive Benchmark for Streaming Audio-Video Generation](https://mlsanity.com/paper/2608.26336/) (2026)
- [Adapting VACE for Real-Time Autoregressive Video Diffusion](https://mlsanity.com/paper/2602.14381/) (2026)
- [OmniMem: Scalable and Adaptive Memory Retrieval for Long Video Generation](https://mlsanity.com/paper/2605.30519/) (2026)
- [TempAct: Advancing Temporal Plausibility in Autoregressive Video Generation via Planner-Executor RL](https://mlsanity.com/paper/2606.28016/) (2026)
- [LayerRecall: A State-Conditioned Memory Router for Long-Horizon Consistency in Video Generation](https://mlsanity.com/paper/2608.28460/) (2026)
- [CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation](https://mlsanity.com/paper/2601.11096/) (2026)
- [Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks](https://mlsanity.com/paper/2602.01630/) (2026)
- [SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation](https://mlsanity.com/paper/2605.09442/) (2026)
- [Surprise Forcing: What to Remember, When to Skip in Long Video Generation](https://mlsanity.com/paper/2607.18436/) (2026)

## Abstract (arXiv)

The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solutions maintain the memory by compressing historical frames with predefined strategies. However, different to-generate video chunks should refer to different historical cues, which is hard to satisfy with fixed strategies. In this work, we propose MemFlow to address this problem. Specifically, before generating the coming chunk, we dynamically update the memory bank by retrieving the most relevant historical frames with the text prompt of this chunk. This design enables narrative coherence even if new event happens or scenario switches in future frames. In addition, during generation, we only activate the most relevant tokens in the memory bank for each query in the attention layers, which effectively guarantees the generation efficiency. In this way, MemFlow achieves outstanding long-context consistency with negligible computation burden (7.9% speed reduction compared with the memory-free baseline) and keeps the compatibility with any streaming video generation model with KV cache.