arXiv AI
Jun 26
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.
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arXiv AI
Sep 10
MV-STRIDE: Enabling MLLMs to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling
MV-STRIDE is a Multi‑View hierarchical Spatial Reasoning dataset that models dependencies among perception, scene understanding, and contextual reasoning to support 3D spatial cognition. It introduces a QA generation pipeline that enforces cross‑view constraints, producing multi‑level reasoning tasks with chain‑of‑thought supervision. Experiments show that training on MV‑STRIDE yields state‑of‑the‑art performance on multi‑view spatial benchmarks, enabling MLLMs to reason robustly across diverse viewpoints.
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 4
GraFT: A Training-Free Framework for Spatial Reasoning in Multimodal Large Language Models via 3D Scene Graphs
GraFT is a training‑free framework that enhances spatial reasoning in multimodal large language models by integrating a compact 3D scene graph (3DSG). It offers deterministic geometry via symbolic tools, allocentric layout through bird’s‑eye‑view rendering, and visual‑attribute grounding using egocentric frames. Experiments on ScanQA and VSI‑Bench show significant performance gains, with CIDEr increasing by 27% and improvements up to 65% over baseline models.
arXiv AI
Jul 16
GeoAnchor: Collaborative Reasoning via Latent Decomposition for 3D Spatial Understanding
arXiv:2607. 13454v1 Announce Type: cross Abstract: Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge.
arXiv Machine Learning
Jul 2
VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
Hugging Face Trending Papers
Sep 3
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
The paper introduces FactoSR, a factorized reinforcement learning framework designed to improve spatial reasoning in Vision‑Language Models (VLMs). By decomposing the problem into planar correspondence (XY), depth consistency (Z), and temporal reversibility (T), FactoSR addresses the dimensional mismatch between 2D visual inputs and 3D physical reasoning. Experiments on multi‑view and video benchmarks show significant performance gains, with a 5.9% improvement on VSI‑Bench and 4.5% on All‑Angles‑Bench.
Hugging Face Trending Papers
Jul 15
GeoAnchor: Collaborative Reasoning via Latent Decomposition for 3D Spatial Understanding
Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge. Existing methods primarily rely on symbolic text tokens, which inherently lack the fidelity to represent continuous geometric information.
arXiv Computation and Language
3d ago
Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering
arXiv:2609.38177v1 Announce Type: cross Abstract: Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs...
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
Sep 4
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
The paper introduces FactoSR, a factorized reinforcement learning framework designed to improve spatial reasoning in Vision‑Language Models by addressing a dimensional mismatch between 2D visual inputs and the 3D+temporal nature of the physical world. FactoSR decomposes the reasoning task into three orthogonal geometric sub‑objectives—planar correspondence (XY), depth consistency (Z), and temporal reversibility (T)—and optimizes these constraints within a unified policy learning mechanism. Experiments on multi‑view and video benchmarks show that this decomposition yields significant performance gains, achieving a 5.9% improvement on VSI‑Bench and 4.5% on All‑Angles‑Bench.
arXiv Computation and Language
Sep 1
Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs
arXiv:2604.07518v2 Announce Type: replace Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add...