SpatialAgent: Working Memory for Long-Horizon Spatial Reasoning

Long-horizon interactive tasks require large language model (LLM) agents to retain and use information gathered across many steps, particularly in partially observed environments where spatial knowledge remains relevant throughout an episode. However, existing working-memory approaches are not generally organized around the spatial structure of the environment itself. We investigate whether explicitly maintaining a structured spatial representation in memory can improve both agent performance and inference efficiency. We introduce \it SpatialAgent, an agent framework that constructs a persistent map from textual observations and provides it to an underlying LLM. The map is adapted to the structure of each environment and updated incrementally throughout the episode as new information is observed. We evaluate SpatialAgent across multiple environments from the BALROG benchmark, comparing it with existing agent baselines while also studying model scale, alternative map-construction methods, and inference efficiency. Results show that SpatialAgent outperforms existing agent baselines across most evaluated models and environments. We further find that SpatialAgent generally improves over an otherwise equivalent no-map variant, indicating that the explicit spatial representation itself contributes to performance; SpatialAgent can reduce inference cost by maintaining strong performance with shorter context windows and less expensive models. These results suggest that spatially structured working memory can be a useful design choice for LLM agents operating in long-horizon environments while reducing reliance on extensive trajectory context.