# FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks

arXiv: https://arxiv.org/abs/2502.17775 · HTML: https://mlsanity.com/paper/2502.17775/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Benchmarking Spatial Relationships in Text-to-Image Generation](https://mlsanity.com/paper/2212.10015/) (2022)
- [Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models](https://mlsanity.com/paper/2502.08636/) (2025)

## Built on by (most cited first, 9 of 9)

- [Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks](https://mlsanity.com/paper/2509.02175/) (2025)
- [FoR-SALE: Frame of Reference-guided Spatial Adjustment in LLM-based Diffusion Editing](https://mlsanity.com/paper/2509.23452/) (2025)
- [PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning](https://mlsanity.com/paper/2605.19382/) (2026)
- [Before We Trust Them: Decision-Making Failures in Navigation of Foundation Models](https://mlsanity.com/paper/2601.05529/) (2026)
- [Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning](https://mlsanity.com/paper/2605.28277/) (2026)
- [Can Text-to-Image Models Draw from the Right Frame of Reference?](https://mlsanity.com/paper/2608.03357/) (2026)
- [Human-Centric Image Captioning with Subject-Centered Spatial Understanding](https://mlsanity.com/paper/2609.08300/) (2026)
- [More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training](https://mlsanity.com/paper/2610.19443/) (2026)
- [Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning](https://mlsanity.com/paper/2610.19564/) (2026)

## Abstract (arXiv)

Spatial reasoning is a fundamental aspect of human intelligence. One key concept in spatial cognition is the Frame of Reference, which identifies the perspective of spatial expressions. Despite its significance, FoR has received limited attention in AI models that need spatial intelligence. There is a lack of dedicated benchmarks and in-depth evaluation of large language models (LLMs) in this area. To address this issue, we introduce the Frame of Reference Evaluation in Spatial Reasoning Tasks (FoREST) benchmark, designed to assess FoR comprehension in LLMs. We evaluate LLMs on answering questions that require FoR comprehension and layout generation in text-to-image models using FoREST. Our results reveal a notable performance gap across different FoR classes in various LLMs, affecting their ability to generate accurate layouts for text-to-image generation. This highlights critical shortcomings in FoR comprehension. To improve FoR understanding, we propose Spatial-Guided prompting, which improves LLMs ability to extract essential spatial concepts. Our proposed method improves overall performance across spatial reasoning tasks.