mlsanityData to 14 Oct 2026

Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation

arXiv 2610.20571 ↗First posted 14 Oct 2026cs.CVcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally consistent across nonconsecutive requests. Passing prompts or recent frames between shots does not provide a shared spatial frame for revisiting a scene or a time-indexed record for replaying an earlier event. We present Any Time, Any Space (ATAS), which compiles a screenplay into shot requests with explicit location, camera, and event-time references. Before generation, it retrieves relevant evidence from a scene memory of registered static geometry and an event memory of dynamic observations indexed by scene, event, and time. After generation, ATAS reconstructs and registers the clip. It updates memory only when review and state checks accept the observation, while film delivery remains independent of memory admission. Across four video generators, ATAS improves all reported cross-shot consistency measures and achieves the highest aggregate benchmark score for each backbone. It also outperforms the compared agents and 3D-guided methods on target-image spatial revisit, leads three event-recapture criteria, and receives the highest human ratings on all six criteria. The video results and code are available on our project page: https://lhmd.top/atas/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 57 of its 59 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

57 of 59 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Automated Movie Generation via Multi-Agent CoT Planning10 Mar
  2. 2024DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework21 Aug

Other references · 55

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  9. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
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  11. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 670
  12. 2024VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Modelscited by 579
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  14. 2025Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modelingcited by 96
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  17. 2024VideoStudio: Generating Consistent-Content and Multi-Scene Videoscited by 64
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  19. 2023DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generationcited by 49
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  22. 2025Spatia: Video Generation with Updatable Spatial Memorycited by 40
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  28. 2025ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGScited by 32
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  37. 2026CoV: Chain-of-View Prompting for Spatial Reasoningcited by 9
  38. 2026Feed-Forward 3D Scene Modeling: A Problem-Driven Perspectivecited by 6

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