LocationReasoner: Evaluating LLMs on Real-World Site Selection Reasoning
Summary: arXiv:2506.13841v3 Announce Type: replace
Abstract
Recent advances in large language models (LLMs), particularly those enhanced through reinforced post-training, have demonstrated impressive reasoning capabilities, as exemplified by models such as OpenAI o1 and DeepSeek-R1. However, these capabilities are predominantly benchmarked on domains like mathematical problem solving and code generation, leaving open the question of whether such reasoning skills generalize to complex real-world scenarios.
In this paper, we introduce LocationReasoner, a benchmark designed to evaluate LLMs’ reasoning abilities in the context of real-world site selection, where models must identify feasible locations by reasoning over diverse and complicated spatial, environmental, and logistic constraints.
Overview of LocationReasoner
The benchmark covers carefully crafted queries of varying difficulty levels and is supported by a sandbox environment with in-house tools for constraint-based location search. Automated verification further guarantees the scalability of the benchmark, enabling the addition of an arbitrary number of queries.
Key Findings
Extensive evaluations on real-world site selection data from Boston, New York, and Tampa reveal that state-of-the-art reasoning models offer limited improvement over their non-reasoning predecessors in real-world contexts. Some of the key findings include:
- The latest OpenAI o4 model fails on 30% of site selection tasks.
- Agentic strategies such as ReAct and Reflexion often suffer from over-reasoning.
- Over-reasoning can lead to worse outcomes than direct prompting.
Implications for Future Research
With key limitations of LLMs in holistic and non-linear reasoning highlighted, we release LocationReasoner to foster the development of LLMs and agents capable of robust, grounded reasoning in real-world decision-making tasks. The benchmark aims to encourage researchers to focus on improving the reasoning capabilities of LLMs in more practical and complex environments.
Access to Resources
Codes and data for our benchmark are available at https://github.com/miho-koda/LocationReasoner.
Conclusion
As the field of artificial intelligence continues to evolve, it is crucial to ensure that the advancements in LLMs translate into practical applications that can address real-world challenges. LocationReasoner serves as a vital tool for assessing LLM capabilities in site selection reasoning and encourages further research into enhancing these models for better performance in diverse scenarios.