Mastering PokeGym: Graph-Guided Multimodal Evolution at Test Time
While artificial intelligence has mastered structured games like chess and Go, vision-language agents still struggle in visually-driven 3D games without access to game states. Existing game environments typically evaluate a fixed agent configuration, rather than an agent's ability to improve its configuration across consecutive episodes of the same task---a paradigm known as test-time learning (TTL). Furthermore, current TTL methods typically optimize single modalities---such as text prompts or actions---in isolation, ignoring the synergy between perception, reasoning, and control. To bridge these gaps, we first introduce \textbf{PokeGym}, a long-horizon benchmark built upon the 3D open-w...
| Authors | Ruizhi Zhang, Ye Huang, Yuangang Pan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 3 August 2026 |
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