Researchers Introduce Interactive Framework to Test Foundation Model Spatial Reasoning

Global: Interactive Evaluation of Foundation Model Spatial Reasoning

A team of researchers released a new study in December 2025 that proposes an interactive evaluation framework for assessing how foundation model (FM) agents explore, remember, and reason within symbolic map environments. The work aims to address gaps in existing spatial ability tests, which often rely on static maps or text queries, by focusing on the dynamic, experience‑driven nature of map‑based reasoning.

Framework Overview

The framework places FM agents in partially observable, grid‑based maps composed of roads, intersections, and points of interest (POIs). At each step, agents receive only local observations, mirroring real‑world navigation constraints. Spatial understanding is measured through six distinct tasks that probe navigation, landmark identification, path planning, and other map‑related competencies.

Exploration Strategies

Experiments systematically vary exploration strategies, revealing that while exploration influences the amount of spatial experience gathered, it has a limited effect on the agents’ final reasoning accuracy. In other words, the choice of how agents traverse the map primarily determines data acquisition rather than ultimate task performance.

Memory Representations

Memory architecture emerges as a central factor. Structured memories—particularly sequential and graph‑based representations—significantly boost performance on structure‑intensive tasks such as path planning. These findings suggest that how spatial information is consolidated, rather than how it is collected, drives reasoning success.

Reasoning Schemes and Prompting

The study also examines reasoning schemes, noting that advanced prompting techniques enable more effective multi‑step inference. By tailoring prompts to guide the use of stored spatial knowledge, agents can better apply their memories to solve complex tasks.

Scaling Limits and Future Directions

Across multiple foundation model versions, spatial reasoning performance appears to plateau beyond a certain capability threshold. The authors conclude that further improvements will likely require dedicated mechanisms for spatial representation and reasoning, rather than relying solely on model scaling.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.