# Text-to-Scene with Large Reasoning Models

arXiv: https://arxiv.org/abs/2509.26091 · HTML: https://mlsanity.com/paper/2509.26091/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 0 of its 40 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Built on by (most cited first, 7 of 7)

- [SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation](https://mlsanity.com/paper/2603.12238/) (2026)
- [SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis](https://mlsanity.com/paper/2601.07218/) (2026)
- [Function2Scene: 3D Indoor Scene Layout from Functional Specifications](https://mlsanity.com/paper/2605.30819/) (2026)
- [SDesc3D: Towards Layout-Aware 3D Indoor Scene Generation from Short Descriptions](https://mlsanity.com/paper/2604.01972/) (2026)
- [ScenePilot: Grow-and-Repair Policy for Text-Driven 3D Indoor Scene Generation](https://mlsanity.com/paper/2608.30307/) (2026)
- [Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation](https://mlsanity.com/paper/2610.00447/) (2026)
- [WorldBench: Evaluating LLMs on Three.js Voxel World Generation](https://mlsanity.com/paper/2610.10622/) (2026)

## Abstract (arXiv)

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to complex instructions. We address these limitations by introducing Reason-3D, a text-to-scene model powered by large reasoning models (LRMs). Reason-3D integrates object retrieval using captions covering physical, functional, and contextual attributes. Reason-3D then places the selected objects based on implicit and explicit layout constraints, and refines their positions with collision-aware spatial reasoning. Evaluated on instructions ranging from simple to complex indoor configurations, Reason-3D significantly outperforms previous methods in human-rated visual fidelity, adherence to constraints, and asset retrieval quality. Beyond its contribution to the field of text-to-scene generation, our work showcases the advanced spatial reasoning abilities of modern LRMs. Additionally, we release the codebase to further the research in object retrieval and placement with LRMs.