Evoke長程世界模型發佈

Papers arxiv:2608.

13546 Copy markdown Alaya-EVOKE: From Linear-Scaling Supervision to Endless World Published on Aug 13 · Submitted by taesiri on Aug 14 #1 Paper of the day Upvote 82 +74 Authors: Yuanyang Yin ,Gongxuan Wang ,Yifan Zhan ,Chuanhao Li ,Kaipeng Zhang ,Feng Zhao Abstract Evoke is an interactive world model that uses external persistent memory and a redesigned long-horizon teacher to enable responsive, open-ended video generation with bounded context and low latency.

Generated by thinkingmachines/Inkling-Small Interactive world models must support persistent memory, responsive interaction, and long-horizon generation, yet these requirements place conflicting demands on the model.

Maintaining history in the denoiser context or key-value cache incurs growing cost, forcing a trade-off between session length and retained memory, while low-latency interaction relies on few-step generation whose capabilities are bounded by its teacher.

Evoke addresses both limitations by externalizing persistent world state and redesigning the teacher for long-horizon interactive generation.

Scene geometry is maintained in an external, camera-indexed world state bank, from which only view-relevant information is retrieved, keeping the denoiser context bounded as the session grows.

Rather than treating the teacher as a fixed generator, we design it for long-horizon supervision: its sparse attention combines chunk-wise grouping, retrieval of selected distant frames, and a linear-attention global state, yielding linear growth in memory and compute while enabling supervision over long horizons.

Such supervision exposes content drift that stays locally plausible within short windows, while per-chunk conditioning enables prompt changes and event control throughout the sequence.

A 30-second distribution-matching objective, applied under self-forced rollouts, transfers both capabilities to a three-step student that uses no classifier-free guidance, improving resistance to long-term drift while preserving responsive conditioning.

With bounded context and recurrent external memory, Evoke supports open-ended, continuously evolving generation; on a single H200 at 384times 640, each 1.5,s chunk is generated in 2.11,s.

As a three-step world model, Evoke achieves state-of-the-art performance on WBench while remaining competitive on VBench-Long and VBench-2.0.View arXiv page View PDF Project page Add to collection Community SII-YuanyangYin about 23 hours ago • edited about 21 hours ago Code: https://github.

com/SII-YuanyangYin/EvokePage: https://evoke-world.github.io/Evoke/YouTube Demo: https://youtu.be/QX7PBBaBGdc Reply librarian-bot about 1 hour ago This is an automated message from the Librarian Bot.I found the following papers similar to this paper.

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