LLMs for Text-Based Exploration and Navigation Under Partial Observability
Researchers evaluated whether large language models can function as text-only controllers for navigation and exploration in unknown environments under partial observability. Testing nine contemporary LLMs on ASCII gridworld tasks, they found reasoning-tuned models reliably complete navigation goals but remain inefficient compared to optimal paths, with few-shot prompting reducing invalid moves and improving path efficiency.