Multics: Generative Multi-Player World Model with Hierarchical Communication

Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging.

By Huang, Yu, Huang +6Oct 12, 2026

Score█████░░░░░5.2

Caveats

Preprint; not yet peer reviewed.

VerdictCompetent work. Briefs at most.

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Abstract

Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging. Existing approaches that rely on fully-connected communication (e.g., Solaris) incur quadratic attention costs as the number of players grows, while centralized approaches (e.g., $γ$-World) suffer from interference between unrelated events in a shared global representation. To address these challenges, we present Multics, a multi-player video generation framework with hierarchical communication that structures information exchange across player, group, and global levels. This hierarchy introduces group tokens into self-attention as intermediate group-level representations that aggregate locally relevant player information. These tokens connect to global tokens for cross-group communication, while the hierarchical topology limits direct mixing between unrelated local events. Group-token parameters are shared across group slots, allowing the same communication module to accommodate varying numbers of players without introducing new group-specific parameters. We further propose budget-constrained dynamic grouping based on recent camera trajectories, which adaptively groups relevant players while limiting unnecessary communication. Experiments demonstrate that Multics achieves state-of-the-art performance in 4- and 6-player settings, with improved cross-player consistency, player-specific action and camera controllability, and interference robustness. Moreover, Multics generalizes from 4-player training to 6-, 8-, and 10-player inference without retraining, while reducing self-attention computation compared with fully-connected communication.

Kaiyi Huang, Wangbo Yu, Yukun Huang, Jiwen Yu, Kunhao Liu, Donglin Yang, Wenbo Hu, Ying Shan, Xihui Liu

The editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	██░░░ 2	14%	Limited: single setting, weak baselines, or an observational association presented as causal.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (state of the art); scale (scalable); stakes (global scale).

How the score was computed

rank-2026-10-07

Score█████░░░░░5.2

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Merit: 4.0 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.0 / 10; Shrunk toward the desk prior by editor confidence (38%).
Attention: 79%; Citations, upvotes, points, mentions.
Freshness: 60%; Half-life decay since publication.

Hugging Face upvotes50 (reference 25, via hf-daily, Oct 14, 2026, 13:49 UTC)
GitHub stars19 (reference 250, via hf-daily, Oct 14, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v6 on Oct 14, 2026, 13:49 UTC. Paper type: method.
Categories: cs.CV
BRIEF, No.2 in the Artificial Intelligence edition of October 14, 2026.