Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models

📰 ArXiv cs.AI

Scalable object relation encoding improves 3D spatial reasoning in large language models

advanced Published 27 Mar 2026

Action Steps

Encode 3D scene representations into the input space of LLMs
Leverage pre-trained LLMs to learn spatial relations
Fine-tune LLMs on 3D scene-language paired data for improved reasoning ability
Evaluate models on spatial reasoning tasks to measure performance

Who Needs to Know This

AI researchers and engineers working on embodied agents and spatial reasoning tasks can benefit from this approach to enhance their models' ability to understand 3D scenes

Key Insight

💡 Scalable object relation encoding can enhance the ability of LLMs to reason about 3D scenes

Key Takeaways

Scalable object relation encoding improves 3D spatial reasoning in large language models

Full Article

Title: Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models

Abstract:
arXiv:2603.24721v1 Announce Type: cross Abstract: Spatial reasoning focuses on locating target objects based on spatial relations in 3D scenes, which plays a crucial role in developing intelligent embodied agents. Due to the limited availability of 3D scene-language paired data, it is challenging to train models with strong reasoning ability from scratch. Previous approaches have attempted to inject 3D scene representations into the input space of Large Language Models (LLMs) and leverage the pr

Read full paper → ← Back to Reads