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: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.
The paper introduces DeSpatial, a framework that separates spatial understanding into perception and reasoning. It uses a shared 3D perception tool with joint segmentation–depth co-refinement and cross-view instance fusion to produce a unified, metric, gravity-aligned 3D scene. DeSpatial achieves state‑of‑the‑art results on VSI‑Bench, MindCube‑Tiny, ViewSpatial, and VSI‑SUPER, demonstrating that the main bottleneck in spatial reasoning is precise 3D perception.
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...
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.
Code4Scene is a benchmark that evaluates coding agents on constructing and editing 3D scenes in Unreal Engine. It tests agents on two tasks: construction, where they must build a scene from open‑ended language, and editing, where they must recover a target scene from reference images while preserving everything else. The benchmark measures task fulfillment, artifact integrity, and physical validity, revealing that construction and editing performance are correlated but not interchangeable, with agents struggling most with spatial composition and precise edits.
arXiv:2608. 12743v1 Announce Type: new Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants.
arXiv:2608.12743v3 Announce Type: replace Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ab...
arXiv:2607. 27703v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning.
Think3D introduces a framework that endows Vision‑Language Models with interactive 3D chain‑of‑thought reasoning by integrating 3D manipulation tools for active spatial exploration. The approach improves performance on benchmarks such as BLINK Multi‑view, MindCube‑1K, and VSI‑Bench‑Tiny for proprietary models like GPT‑4.1 and Gemini 2.5 Pro, and a reinforcement‑learning variant, Think3D‑RL, enables open‑weight models such as Qwen3‑VL‑4B to autonomously learn effective 3D exploration strategies, yielding tool‑use patterns comparable to stronger models and turning a performance drop on MindCube‑1K into a substantial improvement.
SpaceAct is a new benchmark that tests multimodal large language models on 3D spatial and temporal reasoning through closed‑loop physical action and active perception. It uses interlocking bricks and mechanical puzzles to create 108 verified instances across 36 task families, requiring models to reconstruct 3D geometry, anticipate dynamics, and execute precise 6‑DoF poses while avoiding collisions. The accompanying SAPA framework provides diagnostic modes and active‑perception capabilities, revealing that even with a valid plan, current models struggle with 3D visual grounding and action refinement.
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...