The paper introduces TTL‑SR, a geometry‑aware Test‑Time Learning framework designed to improve quantitative spatial reasoning in visual‑language models. By augmenting queries with geometrically coupled auxiliary prompts, filtering unreliable predictions, and updating models with a geometry‑aware multi‑objective loss on unlabeled test data, TTL‑SR adapts models to new domains without additional 3D supervision. Experiments show substantial accuracy gains on the Q‑Spatial‑ScanNet dataset for two state‑of‑the‑art VLMs.
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.
arXiv:2606. 04381v1 Announce Type: cross Abstract: Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space.
arXiv:2606. 11918v1 Announce Type: new Abstract: Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.
Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space. Because LLMs operate on discrete tokens, they lack native support for continuous spatial representations, explicit geometric computation, and structured spatial operators.
arXiv:2606. 11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs).
Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning. Recent reinforcement learning (RL) methods aim to close this gap with verifiable outcomes, yet they suffer from poor credit assignment across intermediate reasoning steps.
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.
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:2608. 12220v1 Announce Type: cross Abstract: Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning.
GaussVLA is a Vision‑Language‑Action model that enhances spatial reasoning by converting flat 2D visual tokens into compact 3D Gaussian tokens using a Gaussian Spatial Tokenizer. It further employs a Depth‑Aware Chain‑of‑Thought module to perform structured, non‑autoregressive geometric reasoning conditioned on language and flow‑time. In both simulated and real‑world tests, GaussVLA achieves high spatial‑manipulation success rates—93.5% on LIBERO and 100% on the Spatial suite—while using only 200 M parameters, outperforming SpatialVLA by 19.7% relative success.
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:2608. 13226v1 Announce Type: cross Abstract: While 3D Vision-Language Models (3D VLMs) have demonstrated remarkable spatial reasoning capabilities, they suffer from massive visual token counts that create severe computational bottlenecks during inference.