arXiv Machine Learning
GAGR-Lab: Evaluating Joint Spatial-Geometric and Analytic Function Reasoning
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arXiv AI
Oct 31
SpaceAct: Benchmarking 3D Spatial and Temporal Reasoning via Closed-Loop Action and Active Perception
SpaceAct is a new benchmark that tests multimodal large language models on 3D spatial and temporal reasoning through closed‑loop physical action and active perception. It uses interlocking bricks and mechanical puzzles to create 108 verified instances across 36 task families, requiring models to reconstruct 3D geometry, anticipate dynamics, and execute precise 6‑DoF poses while avoiding collisions. The accompanying SAPA framework provides diagnostic modes and active‑perception capabilities, revealing that even with a valid plan, current models struggle with 3D visual grounding and action refinement.
arXiv AI
Sep 15
Beyond Generation and Accuracy: Diagnosing and Enhancing Visual Chain-of-Thought for Geometry Problem Solving
arXiv:2609.12606v2 Announce Type: replace Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as construct...
arXiv Computation and Language
Oct 21
SpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?
SpatialScout is a benchmark that tests vision‑language model agents on active, object‑level 3D mapping. It includes 80 indoor scenes with 3,140 target objects, where agents must control a camera, gather RGB observations, and estimate object centers in a shared coordinate frame. Performance is measured by localization accuracy and target coverage using object‑scale overlap scores, revealing that current VLMs, including Claude Opus 5.5, struggle to build precise metric maps.
arXiv Computer Vision
Sep 18
VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control
VABench is a benchmark that tests general‑purpose multimodal large language models (MLLMs) on embodied spatial intelligence by requiring them to observe, reason, act, and revise based on visual demonstrations and active perception. The benchmark includes 14 task families, a fixed model‑agnostic controller, and evaluates models on target localization, spatial relations, and long‑horizon composition tracks without providing privileged object poses or learned action heads. Results show that while the best model achieves perfect target localization, overall task success remains modest, and active camera control and geometric transfer significantly influence performance.
arXiv Computer Vision
Oct 22
VoxProg: 3D Spatial Reasoning through Voxel Programming
VoxProg introduces voxel programming for 3D spatial reasoning, enabling language models to translate task interpretations into executable programs that operate on local voxel views of scene surfaces. By indexing surface geometry on regular grids, the approach isolates task-relevant regions and boundaries, allowing downstream pose programs to compute target SE(3) poses. The method is evaluated on the 71-task VoxProg-Bench benchmark, showing consistent superiority over relation-based, keypoint-based, and mesh-based baselines.
arXiv AI
Jun 17
Reinforcing Dual-Path Reasoning in Spatial Vision Language Models
arXiv:2606. 17539v1 Announce Type: cross Abstract: Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging.