# Reasoning Over Space: Enabling Geographic Reasoning for LLM-Based Generative Next POI Recommendation

arXiv: https://arxiv.org/abs/2601.04562 · HTML: https://mlsanity.com/paper/2601.04562/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling](https://mlsanity.com/paper/2409.12740/) (2024)
- [OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service](https://mlsanity.com/paper/2508.14646/) (2025)
- [LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation](https://mlsanity.com/paper/2511.14221/) (2025)
- [Geography-Aware Large Language Models for Next POI Recommendation](https://mlsanity.com/paper/2505.13526/) (2025)
- [Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendation](https://mlsanity.com/paper/2508.16126/) (2025)
- [Cognitive-Aligned Spatio-Temporal Large Language Models For Next Point-of-Interest Prediction](https://mlsanity.com/paper/2510.14702/) (2025)

## Built on by (most cited first, 2 of 2)

- [Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction](https://mlsanity.com/paper/2606.08122/) (2026)
- [Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation](https://mlsanity.com/paper/2607.27944/) (2026)

## Abstract (arXiv)

Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain limited in leveraging geographic signals that are crucial in mobility and local-services scenarios. Here, we present Reasoning Over Space (ROS), a framework that utilizes geography as a vital decision variable within the reasoning process. ROS introduces a Hierarchical Spatial Semantic ID (SID) that discretizes coarse-to-fine locality and POI semantics into compositional tokens, and endows LLM with a three-stage Mobility Chain-of-Thought (CoT) paradigm that models user personality, constructs an intent-aligned candidate space, and performs locality informed pruning. We further align the model with real world geography via spatial-guided Reinforcement Learning (RL). Experiments on three widely used location-based social network (LBSN) datasets show that ROS achieves over 10% relative gains in hit rate over strongest LLM-based baselines and improves cross-city transfer, despite using a smaller backbone model.