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ReactiveGWM: Steering NPC in Reactive Game World Models

Summary

ReactiveGWM is a reactive game world model that synthesizes dynamic interactions between player and NPC by decoupling player controls from NPC behaviors. It injects player actions via a lightweight additive bias into the diffusion backbone and grounds high-level NPC responses through cross-attention modules. These modules learn a game-agnostic representation of interactive logic, enabling zero-shot strategy transfer to off-the-shelf world models of different games without retraining. Evaluated on two Street Fighter games, it maintains fine-grain player controllability while achieving robust NPC strategy adherence.

SourcearXiv Computer VisionAuthor: Zeqing Wang, Danze Chen, Zhaohu Xing, Zizhao Tong, Yinhan Zhang, Xingyi Yang, Yeying Jin
ReactiveGWM: Steering NPC in Reactive Game World Models
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[2605.15256] ReactiveGWM: Steering NPC in Reactive Game World Models

[Submitted on 14 May 2026]

Title:ReactiveGWM: Steering NPC in Reactive Game World Models

View a PDF of the paper titled ReactiveGWM: Steering NPC in Reactive Game World Models, by Zeqing Wang and 6 other authors

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Abstract:Current game world models simulate environments from a subjective, player-centric perspective. However, by treating the Non-Player Character (NPC) merely as background pixels, these models cannot capture interactions between the player and NPC. In that sense, they act as passive video renderers rather than real simulation engines, lacking the physical understanding needed to model action-induced NPC reactivities. We introduce ReactiveGWM, a reactive game world model that synthesizes dynamic interactions between the player and NPC. Instead of entangling all interaction dynamics, ReactiveGWM explicitly decouples player controls from NPC behaviors. Player actions are injected into the diffusion backbone via a lightweight additive bias, while high-level NPC responses (e.g., Offense, Control, Defense) are grounded through cross-attention modules. Crucially, these modules learn a game-agnostic representation of interactive logic. This enables zero-shot strategy transfer: our learned modules can be plugged directly into off-the-shelf, unannotated world models of different games. This instantly unlocks steerable NPC interactions without any domain-specific retraining. Evaluated on two Street Fighter games, ReactiveGWM maintains fine-grain player controllability while achieving robust, prompt-aligned NPC strategy adherence, paving the way for scalable, strategy-rich interaction with the NPC.

Comments: The code is available at this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.15256 [cs.CV]

(or arXiv:2605.15256v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.15256

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zeqing Wang [view email] [v1] Thu, 14 May 2026 17:52:03 UTC (4,117 KB)

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Key points and analysis

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Key points

  • ReactiveGWM decouples player controls from NPC behaviors using additive bias and cross-attention modules.
  • The cross-attention modules learn game-agnostic interactive logic for zero-shot strategy transfer.
  • Evaluated on Street Fighter games, it preserves player controllability while enabling steerable NPC interactions.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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