MJev-Spatial is a System 1 multimodal decision model that addresses three types of spatial understanding tasks within a unified classification framework. It scores candidates using the backbone's pretrained query and key projections without adding parameters, enabling faster processing.
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The model efficiently handles spatial understanding tasks, providing high performance and accuracy.
The paper introduces S-Space, a linear subspace in multimodal language models where object-token representations encode continuous spatial coordinates. It demonstrates that S-Space serves as an internal workspace for spatial reasoning, enabling accurate spatial judgments through perception, internal reasoning, and selective verbal access, while highlighting limitations in spatial transformations across viewpoints.
6 days agoDaily PapersUrbex is an agentic vision-language framework that reformulates grounding as active spatial search. It represents a city-scale 3D scene as an interactive multi-view environment, enabling a VLM agent to query landmarks, zoom into local regions, and commit grounded bounding boxes. Experiments show Urbex achieves the best Hit@1 and IoU on CityRefer.
3 days agoDaily PapersWaypoint-OPD uses an intermediate teacher checkpoint to compare early and late shifts in action preferences during policy transfer. It evaluates the consistency between early and late shifts and suppresses the endpoint shift when they conflict.
yesterdayDaily PapersThis paper introduces RuleTrek, a multi-domain benchmark for evaluating agents' ability to reason about rules in complex scenarios. The benchmark uses structured rule graphs and generates sub-scenarios to assess capabilities in rule identification, deduction, and end-to-end reasoning.
2 days agoDaily PapersContext can enhance the performance of large language models, but these gains may be lost after context removal. Researchers introduced Cortex, which generates prospective queries to anticipate context usage and distills context-conditioned outputs into mergeable low-rank or structural network updates. Cortex shows effectiveness in consolidation and retention of performance.
2 days agoDaily PapersDetermining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. We introduce EviLens, an expert-reviewed benchmark of 10,174 supported and refuted images covering twelve factual error types.
6 days ago