AGENTIC-SYSTEMSApril 14, 20261 min read10 views

LLMs for Text-Based Exploration and Navigation Under Partial Observability

A new study evaluates the ability of Large Language Models (LLMs) to function as text-only controllers for exploration and navigation in unknown environments. The findings reveal that reasoning-tuned models outperform traditional instruction-tuned ones, suggesting a path toward hybrid autonomous systems.

Computer Science > Artificial Intelligence

Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language models (LLMs) can function as text-only controllers under partial observability -- without code execution, tools, or program synthesis. We introduce a reproducible benchmark with oracle localisation in fixed ASCII gridworlds: each step reveals only a local 5x5 window around the agent and the model must select one of UP/RIGHT/DOWN/LEFT. Nine contemporary LLMs ranging from open/proprietary, dense / Mixture of Experts and instruction- vs. reasoning-tuned are evaluated on two tasks across three layouts of increasing difficulty: Exploration (maximising revealed cells) and Navigation (reach the goal on the shortest path). The experimental results are evaluated on quantitative metrics including success rate, efficiency such as normalised coverage and path length vs. oracle as well as qualitative analysis. Reasoning-tuned models reliably complete navigation across all layouts, yet remain less efficient than oracle paths. Few-shot demonstrations in the prompt chiefly help these Reasoning-tuned models by reducing invalid moves and shortening paths, while classic dense instruction models remain inconsistent. We observe characteristic action priors (UP/RIGHT) that can induce looping under partial observability. Overall, training regimen and test-time deliberation predict control ability better than raw parameter count. These findings suggest lightweight hybridisation with classical online planners as a practical route to deployable partial map systems.

Source: arXiv cs.AI Recent

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