arXiv AI
Jun 11
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
arXiv:2606. 11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs).
Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
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arXiv AI
Jun 12
SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning
arXiv:2606. 13673v1 Announce Type: cross Abstract: Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs).
Hugging Face Trending Papers
Aug 3
SpatialQuery: Benchmarking Geometry-Grounded Multi-Instance Spatial Reasoning in Vision-Language Models
Vision-language models (VLMs) achieve strong semantic understanding but remain unreliable in metric spatial reasoning, particularly when queries require comparing multiple instances of the same object category. We study this problem through the Closest-Instance Distance Query (CIDQ), where a model must identify the nearest visible candidate to a unique reference object and estimate their gravity-aligned floor-plane distance.
arXiv Computer Vision
Sep 14
AnchorVLN: Geometry-Anchored Vision-Language Grounding Reasoning for Open-Vocabulary Navigation
AnchorVLN is an open‑vocabulary vision‑language navigation system that separates semantic proposals from geometric metrics. It uses a VLM to generate semantics while a geometry module supplies reliable metric quantities such as range and bearing, all within a Model Context Protocol server. The system achieves 64.4% on instruction following and improves object‑reference accuracy, reducing median center error from 3.37 m to 2.48 m.
arXiv AI
Jul 7
SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards
arXiv:2511. 07403v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but continue to struggle with spatial reasoning.
arXiv AI
Sep 16
SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
arXiv:2609.16233v1 Announce Type: cross Abstract: Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current ben...
Hugging Face Trending Papers
Sep 14
SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benchmarks. First, 3D datasets often rely on point clo...
arXiv AI
Jun 12
Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
arXiv:2606. 12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction.
arXiv AI
Sep 25
GeoRefer-Bench: A Benchmark from Referring Pixels to Verifiable Geospatial Reasoning
GeoRefer-Bench is a new benchmark for verifiable geospatial referring segmentation that evaluates whether models correctly resolve spatial relations in overhead imagery. Each query is expressed as an executable logical form over a metric scene graph, and predictions are scored with Exact Query Success (EQS), requiring an exact match to the query’s referent set. The dataset contains 700 UAV scenes, 26,217 instances, 142,796 spatial relations, 20,916 executable queries across five reasoning levels, and additional paraphrases, unanswerable queries, counterfactual pairs, and leakage‑controlled splits.
arXiv AI
Jul 2
MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos
arXiv:2607. 00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input.
arXiv AI
Jun 17
m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
arXiv Computer Vision
1d ago
From Reasoning Failures to Composable Video Spatial Intelligence
arXiv:2610.01999v1 Announce Type: new Abstract: Spatial reasoning benchmarks evaluate vision-language models across diverse tasks, but task-level scores do not reveal which underlying capabilities ac...