MapReason-OSM supplies 6000 graph-verifiable instances across 12 mobility tasks on rendered OSM maps from 10 U.S. downtowns and shows that seven VLMs succeed at simple routing but perform near chance on cost-based facility placement and cross-zoom consistency.
and Ma, Wei-Chiu and Krishna, Ranjay , title =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
Empirical study of five LVR variants finds cosine alignment negatively correlates with accuracy (r=-0.94), supervised latents are bypassed under corruption (max 4-point shift), and answers are decodable downstream but not at the latent.
citing papers explorer
-
MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?
MapReason-OSM supplies 6000 graph-verifiable instances across 12 mobility tasks on rendered OSM maps from 10 U.S. downtowns and shows that seven VLMs succeed at simple routing but perform near chance on cost-based facility placement and cross-zoom consistency.
-
Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents
Empirical study of five LVR variants finds cosine alignment negatively correlates with accuracy (r=-0.94), supervised latents are bypassed under corruption (max 4-point shift), and answers are decodable downstream but not at the latent.