arXiv Computer Vision
H-GRPO: Permutation-Invariant Reinforcement Learning for Improving Spatial Grounded Reasoning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jun 16
Thinking with Visual Grounding
arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.
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
Oct 1
SpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models
arXiv:2609.38716v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have made remarkable progress across visual perception tasks, yet spatial reasoning remains a persistent weaknes...
arXiv AI
Sep 7
MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model
MultihopSpatial is a new benchmark for Vision‑Language Models that focuses on multi‑hop, compositional spatial reasoning with queries ranging from 1 to 3 hops across varied spatial perspectives. It introduces the Acc@50IoU metric, which jointly evaluates answer selection and precise bounding‑box prediction, and provides a large‑scale training corpus, MultihopSpatial‑Train, to improve spatial intelligence. Evaluation of 37 state‑of‑the‑art VLMs shows that compositional spatial reasoning remains a significant challenge, and reinforcement learning fine‑tuning on the corpus boosts both intrinsic spatial reasoning and downstream embodied manipulation performance.
arXiv AI
Jun 11
SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning
arXiv:2606. 11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.
arXiv AI
Jun 11
The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning
arXiv:2606. 11918v1 Announce Type: new Abstract: Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.