DriveSpatial benchmark shows the strongest of 15 VLMs trails humans by 28.4 points on spatiotemporal tasks, with cognitive scene construction as the primary weakness.
Spatialvlm: Endowing vision-language models with spatial reasoning capabilities
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DepthVLM converts a standard VLM into a dense metric depth predictor by attaching a lightweight head and training under unified vision-text supervision, outperforming prior VLMs and some pure vision models on a new indoor-outdoor benchmark.
AssemLM fuses SO(3)-equivariant point-cloud features into a VLM to predict discrete 6D assembly poses, reaching ~89% success on AssemBench and improved real-robot multi-step assembly.
citing papers explorer
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DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
DriveSpatial benchmark shows the strongest of 15 VLMs trails humans by 28.4 points on spatiotemporal tasks, with cognitive scene construction as the primary weakness.
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Unlocking Dense Metric Depth Estimation in VLMs
DepthVLM converts a standard VLM into a dense metric depth predictor by attaching a lightweight head and training under unified vision-text supervision, outperforming prior VLMs and some pure vision models on a new indoor-outdoor benchmark.
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AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly
AssemLM fuses SO(3)-equivariant point-cloud features into a VLM to predict discrete 6D assembly poses, reaching ~89% success on AssemBench and improved real-robot multi-step assembly.