GameVerse: Can Vision-Language Models Learn from Video-based Reflection?
Human gameplay is a visually grounded interaction loop in which players act, reflect on failures, and watch tutorials to refine strategies. Can Vision-Language Models (VLMs) also learn from video-based reflection? We present GameVerse, a comprehensive video game benchmark that enables a reflective visual interaction loop. Moving beyond traditional fire-and-forget evaluations, it uses a novel reflect-and-retry paradigm to assess how VLMs internalize visual experience and improve policies. To facilitate systematic and scalable evaluation, we also introduce a cognitive hierarchical taxonomy spanning 15 globally popular games, dual action space for both semantic and GUI control, and milestone...
| Authors | Kuan Zhang, Dongchen Liu, Qiyue Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 March 2026 |
| Last updated at source | 10 March 2026 |
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