Spatial Reasoning in LLM Game Agents: Impact of Causal Context and Multi-Step Planning
LLM-based game agents often perform poorly on more complex tasks. This work examines whether these failures are linked to limited spatial reasoning and evaluates whether causal prompt augmentation and multi-step planning can improve win-rates while managing response latency. Using the open-source Qwen3 model family, we conduct experiments across varying model scales, reasoning modes, and planning horizons. We further introduce a focused GVGAI benchmark consisting of three custom games with five difficulty levels to isolate spatial navigation. The evaluation follows two paradigms: an initial ``positioning experiment'' to test an agent's ability to find its exact coordinates, and a study of...
| Authors | Mohit Jiwatode, Ronja Fuchs, Robin Schmöcker et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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