CounterStrike-1K: A Multi-Perspective Dataset of Professional Gameplay for World Modeling

Large-scale human demonstrations have been a key ingredient in training interactive agents, from robot policies to computer-use agents and game world models. But for multi-agent world modeling, the open data landscape is missing a crucial combination: expert humans acting together in the same evolving world, observed from every participant’s viewpoint, with precise controls and state. Internet-scale video captures human behavior, but its controls are inferred and its viewpoints are not synchronized; simulator and policy rollouts provide exact labels and all-player views, but not strategic, collaborative human play. We present CounterStrike-1K, a public benchmark dataset built from professional Counter-Strike 2 match replay files. It contains 1,490 rendered player-view hours across seven maps, with every released round synchronized across all ten active player viewpoints. Each view is paired with audio, replay-derived game controls, player state, world position, and sparse game event annotations. Because each round records expert human teams coordinating under partial observability, CounterStrike-1K tests whether models can simulate not just game physics or single-view visual plausibility, but skilled decisions and their consequences across all players’ views. We provide fixed splits and benchmarks for action-conditioned prediction, cross-POV retrieval, multi-POV action coverage, and shared-state recovery. By releasing round-complete multi-perspective demonstrations at this scale, CounterStrike-1K provides a grounded training and evaluation testbed for multi-agent world-model research.