scene-memory

S-Agent 2.0 通过为通用编码智能体集成命令行感知工具包、持久场景记忆与自然语言解读，显著提升了空间推理能力，在 MMSI-Bench 和 ReVSI 上超越了专用系统 SpatialClaw。

提出 Any Time, Any Space (ATAS) 框架，将剧本编译为带显式位置、相机和事件时间引用的镜头请求，通过场景记忆和事件记忆实现长时程 agentic 视频生成的跨镜头时空一致性，在四种视频生成器上均提升一致性指标并取得最佳综合评分。

The paper introduces Honeycomb, a video world model that uses HexMemory, a low-rank fixed-size scene memory represented as six spatial and spatiotemporal planes, enabling constant storage during long-horizon video generation with strong generation quality and revisit consistency on WorldScore and RealEstate10K benchmarks.

Echo-Forcing introduces a scene memory framework for interactive long video generation, using hierarchical temporal memory, scene recall frames, and difference-aware memory decay to handle prompt switching and long-term recall. The method is training-free and achieves strong performance on VBench-Long.

AnyRecon proposes a scalable framework for 3D reconstruction from arbitrary sparse inputs using a video diffusion model with persistent scene memory and geometry-aware conditioning.