arXiv Computer Vision
GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay
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GameXpert-Bench: How Far Are Coding Agents from Expert Game Development?
arXiv:2608.21833v1 Announce Type: new Abstract: Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especial...
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Sep 16
AI for Games in the Foundation Model Era
arXiv:2609.16679v1 Announce Type: new Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mo...
arXiv AI
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RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models
arXiv:2606. 18950v1 Announce Type: new Abstract: Modern Vision-Language Models (VLMs) often struggle with strategic reasoning, i.
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GameReplica: A Benchmark for Black-Box Visual Game Replication by Vision-Language Agents
arXiv:2609.22308v1 Announce Type: new Abstract: Coding-agent benchmarks usually evaluate implementation after the target behavior has been specified in text, code, or demonstrations. Existing researc...
arXiv AI
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AI Native Games: A Survey and Roadmap
arXiv:2607. 00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime.
arXiv Computer Vision
Aug 25
WorldMind: Decoupled Game World Model for State-Aware NPC Behavior
WorldMind is a decoupled framework for state-aware NPC behavior in game world models, separating interactive world modeling into four layers: Understanding, Decision, Control, and Generation. It constructs a compact state from generated frames, reasons over it to plan NPC actions, translates actions into temporally aligned conditions, and synthesizes visual outcomes. Experiments on the newly introduced BOSS-140K dataset show that WorldMind achieves more tactically appropriate and coherent NPC behavior than baseline models in about 70% of pairwise comparisons.