arXiv AI

Learning an Interior Layout Policy in a Domain Specific Language Action Space

arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.

arXiv AI

Jul 3

Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments

arXiv:2607. 02407v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments.

By Xianhui Meng, Zirui Song, Yuchen Zhang, Li Zhang, Yongxuan Lv, Xiuying Chen, Kun Wang, Yan Luo, Kai Chen, Hangjun Ye, Long Chen, Jun Liu, Xiaoshuai Hao

llms benchmarks

arXiv AI

Jun 10

Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans

arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.

By Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka

llms fine-tuning multimodal safety

Hugging Face Trending Papers

Jul 15

ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning

While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based generators predominantly adopt a one-shot paradigm where the full layout is planned at once. This one-shot approach often requires global re-optimization or complete reconstruction during interactive editing (e.

llms reinforcement-learning fine-tuning multimodal safety

arXiv Machine Learning

Jun 5

Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation

arXiv:2606. 05268v1 Announce Type: cross Abstract: We present a pipeline for building and aggregating task-specific, LLM-generated weak (imperfect) verifiers into a strong verifier for spatial layout domains.

By Sharon Zhang, R. Kenny Jones, Jiajun Wu, Maneesh Agrawala

llms

arXiv AI

Jul 28

GFLAN: Generative Functional Layouts

arXiv:2512. 16275v2 Announce Type: replace-cross Abstract: Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design requirements -- a confluence that has historically resisted a unified computational treatment.

By Mohamed Abouagour, Eleftherios Garyfallidis

llms

arXiv AI

Jun 17

Reinforcing Dual-Path Reasoning in Spatial Vision Language Models

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.

By Yatai Ji, An-Chieh Cheng, Yang Fu, Yukang Chen, Han Zhang, Zhaojing Yang, Wei Huang, Ka Chun Cheung, Song Han, Vidya Nariyambut Murali, Pavlo Molchanov, Jan Kautz, Simon See, Hongxu Yin, Ping Luo, Sifei Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Aug 7

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

arXiv:2608. 06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints.

By Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel

llms agents computer-vision reinforcement-learning robotics fine-tuning

arXiv AI

Jun 4

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models

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.

By Chen Chu, Bita Azarijoo, Li Xiong, Khurram Shafique, Cyrus Shahabi

llms multimodal benchmarks

arXiv AI

Jun 9

Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning

arXiv:2606. 07602v1 Announce Type: cross Abstract: LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility.

By Yuhuan Yuan, Zhouliang Yu, Minghao Liu, Weiyang Liu, Ge Lin Kan

llms reinforcement-learning safety

arXiv AI

Jun 6

HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes

arXiv:2606. 06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design.

By Wenbo Li, Xiaoliang Ju, Zipeng Qin, Rongyao Fang, Hongsheng Li

llms diffusion robotics multimodal

arXiv Machine Learning

Aug 7

Disentangling 3D Modeling from Spatial Reasoning

arXiv:2608. 05242v1 Announce Type: new Abstract: In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly acquiring implicit 3D perception and reasoning through large-scale training.

By Haoze Sun, Jiequan Cui, Qingshan Xu, Richang Hong

llms fine-tuning benchmarks safety

Hugging Face Trending Papers

Aug 12

SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward

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.

llms reinforcement-learning multimodal benchmarks

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