Alaya-EVOKE: From Linear-Scaling Supervision to Endless World
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 i
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%Exploring continual learning without replay buffers: Our findings using dynamic task-similarity routing [P] →
- FuzzySimilar title/name (fuzzy) · 84%roboflow/supervision →
“Fuzzy title match (0.92): “Alaya-EVOKE: From Linear-Scaling Supervision to Endless Worl” ≈ “roboflow/supervision””
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- LinkedLinked via arxiv author · 85%Yuanyang Yin →
“Alaya-EVOKE: From Linear-Scaling Supervision to Endless World”
- LinkedLinked via arxiv author · 85%Gongxuan Wang →
“Alaya-EVOKE: From Linear-Scaling Supervision to Endless World”
- LinkedLinked via arxiv author · 85%Yifan Zhang →
“Alaya-EVOKE: From Linear-Scaling Supervision to Endless World”