arXiv AI By Rui Gong, Ruoyu Wang, Yalun Dai, Hao Li, Sidi Yang, Qihao Zhao, Hongyu Li, Zeyang Guan, Fangzhou Hong, Zhaoxi Chen, Kim-Hui Yap, Ziwei Liu

S-Agent 2.0: Spatial Reasoning with a General Coding Agent

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Jun 12

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning

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).

By Seokju Cho, Ryo Hachiuma, Abhishek Badki, Hang Su, Byung-Kwan Lee, Chan Hee Song, Sifei Liu, Subhashree Radhakrishnan, Seungryong Kim, Yu-Chiang Frank Wang, Min-Hung Chen
arXiv Computer Vision
Oct 16

DeSpatial: The devil of Agentic Spatial Reasoning Lies in 3D Itself

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.

By Yifei Liu, Xiaolong Zhou, Ziyang Gong, Yifan Zhou, Yuanyuan Gao, Yuning Gong, Yuchen Yang, Hongjie Zhang, Xue Yang, Xiao Sun, Zhihang Zhong
arXiv Computer Vision
4d ago

Parse Before You Execute: Efficient Spatial Reasoning with Executable Primitives

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.

By Xiwei Liu, Yulong Li, Xinling Zhuang, Xuhui Li, Yichen Li, Haolin Yang, Zhixiang Lu, Yutong Xie
arXiv AI
Sep 30

Code4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenes

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.

By Xiaokang Ye, Siddhant Hitesh Mantri, Zimeng Chen, Edward Zhang, Zhaoxu Zheng, Yuanheng Li, Yizhao Chen, Tianyang Huang, Lianhui Qin