Alaya-EVOKE: From Linear-Scaling Supervision to Endless World

2026-08-13

AI Summary

This work addresses the challenge of balancing persistent memory, low-latency response, and high-quality long-horizon generation in interactive world models. The authors propose a novel architecture that stores scene geometry in an external world state bank and retrieves relevant views via camera indexing to maintain bounded context for the denoiser. A teacher model employs sparse attention—incorporating block-wise grouping, distant-frame retrieval, and linear global state representation—to enable linearly scalable long-horizon supervision. A student model is then trained through a three-stage distillation process for efficient inference. This approach uniquely integrates an external persistent world state with scalable supervision, enabling open-ended continuous generation, full-trajectory event control, and effective mitigation of content drift. Evaluated on a single H200 GPU, the method generates 1.5-second (384×640) videos in 2.11 seconds, achieving state-of-the-art performance on WBench and strong results on VBench-Long and VBench-2.0.

Abstract

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 $384\times 640$, each $1.5\,\mathrm{s}$ chunk is generated in $2.11\,\mathrm{s}$. 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.

Problem

• interactive world models
• persistent memory
• long-horizon generation
• low-latency interaction
• content drift

Innovation

• interactive world models
• persistent memory
• long-horizon generation
• external memory
• linear-scaling supervision