The paper introduces PEPS, a training‑free framework that improves spatial reasoning in Vision‑Language Models by parsing queries into a compact set of executable requirements before execution. PEPS uses a typed FESM schema (Frame, Entity, State, Metric) to commit queries, then selectively grounds primitives via fixed geometric APIs and verifies sufficiency, allowing early termination or targeted repair. Experiments on spatial reasoning benchmarks show that PEPS boosts accuracy while reducing refinement rounds, API cost, and latency compared to existing tool‑integrated baselines.
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).
arXiv:2608. 07955v1 Announce Type: new Abstract: Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial perception and fine-grained geometric computation beyond end-to-end generation.
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:2604.12102v3 Announce Type: replace Abstract: We describe compute-grounded reasoning (CGR), a design pattern in which code computes selected sub-problems from explicit intermediate representati...
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...
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.
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. 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:2610.16679v2 Announce Type: replace Abstract: Spatial reasoning agents can inspect tool outputs and repair errors, but reusing these lessons requires preserving both the computation and the con...
arXiv:2510.13394v4 Announce Type: replace Abstract: Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, au...
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.
SpatialBrain is a framework that enhances spatial reasoning by retaining experience about when to observe, reconstruct, measure, or change reference frames while keeping its vision-language backbone unchanged. It fuses current observations into queryable spatial relations, offers reusable tool procedures through a Spatial Skill Library, and associates task conditions with experience via Spatial Cognition. The system abstracts completed trajectories into process records, consolidating knowledge across task blocks to inform future decisions without transferring scene coordinates or reference answers, and demonstrates improved performance on four out of five spatial-reasoning benchmarks.