Microsoft Research has presented MindTopo, a benchmark for examining whether vision-language and other foundation models can reason about topological relationships including continuity, enclosure, knots, ordering, and separation.
Microsoft Research has presented MindTopo, a benchmark designed to assess how AI models understand topological relationships in visual and multimodal tasks.
Topological reasoning concerns relationships that remain meaningful when shapes are bent, stretched, or otherwise deformed without being cut apart or joined together. It differs from conventional geometric reasoning, which commonly focuses on measurements such as angle, length, or distance.
Microsoft Research introduces MindTopo with examples including “a path, a fence, a knot,” describing it as a benchmark for testing how AI understands topological relationships. The organization says the work highlights opportunities to strengthen spatial reasoning and planning.
Questions in this area can involve whether a route remains connected, whether a boundary encloses a region, whether paths are separated, or whether structures are entangled. A model may be able to recognize individual objects in an image while failing to determine these higher-level relationships between them.
The MindTopo dataset release on Hugging Face, published by MLL Lab, identifies the resource as a benchmark for topology, multimodal reasoning, vision-language-model evaluation, and spatial reasoning. Its tasks cover five categories:
Continuity tasks can test whether a model identifies an uninterrupted path or connected structure. Enclosure focuses on whether boundaries surround an area or object. Knot tasks concern entanglement and linked forms, while ordering and separation address relative arrangement and whether structures are disconnected.
These categories are relevant to models that must interpret diagrams, maps, scenes, and other visual material in response to natural-language prompts. They also provide a way to evaluate spatial capabilities that may not be captured by image classification, captioning, or object-recognition benchmarks.
The CVPR 2026 ReLearn Workshop describes MindTopo in an invited-talk listing by coauthor Manling Li as testing topological reasoning over spatial invariants that persist under deformation. This framing places the benchmark’s emphasis on structural properties rather than a scene’s precise visual appearance.
For example, a curved path can retain its connectivity after being reshaped, and an enclosing boundary can remain enclosing despite changes to its outline. Such relationships can matter in tasks involving navigation, visual planning, diagram interpretation, and embodied reasoning.
A publication page maintained by MindTopo coauthor Anbang Liu lists the work under the title “MindTopo: Can Foundation Models Reason in Topological Space?” and identifies it as submitted to NeurIPS. The title reflects the central question behind the benchmark: whether general-purpose foundation models can reliably reason about topological structure.
Microsoft Research positions MindTopo as an evaluation resource rather than a claim that topological reasoning has been solved. By organizing tests around continuity, enclosure, knots, ordering, and separation, the benchmark offers researchers a more structured way to identify where vision-language models succeed or struggle with visual spatial relations.
Microsoft Research has presented MindTopo , a benchmark designed to assess how AI models understand topological relationships in visual and multimodal tasks.
Testing spatial relationships beyond geometry Topological reasoning concerns relationships that remain meaningful when shapes are bent, stretched, or otherwise deformed without being cut apart or joined together.
It differs from conventional geometric reasoning, which commonly focuses on measurements such as angle, length, or distance.
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