The MindTopo benchmark released by Microsoft Research aims to evaluate the topological reasoning capabilities of multimodal large language models. Based on Piagetian cognitive theory, the benchmark divides tasks into five categories: continuity, separation, sequence, closure, and knotting, and tests them at both reasoning and planning levels. All scenarios are generated by a controlled simulator to ensure accurate truth values. The study found that current models perform reasonably well in static image recognition, but they struggle to maintain an understanding of topological relationships in planning tasks involving sequential actions. This is manifested in losing structural relationships due to changes in scenarios or proposing actions that violate physical constraints. This benchmark reveals the key gaps in AI’s ability to make reliable decisions in robotic and interactive environments.
MindTopo has released a new benchmark to evaluate the capabilities of multimodal large language models in topological reasoning (connectivity, closure, sequence, separation, and knotting). The study found that current models perform well in static image recognition, but they struggle to maintain an understanding of topological relationships in tasks involving sequential actions, often losing structural relationships due to changes in the scenario or proposing actions that violate physical constraints. This benchmark is based on Piagetian cognitive theory, categorizing tasks into five types: continuity, separation, sequence, closure, and knotting, and testing them at both cognitive levels of reasoning and planning. All scenarios are generated by a controlled simulator to ensure precise truth values, aiming to reveal the key gaps in AI’s ability to make reliable decisions in robotic and interactive environments.