A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs

2025-06-24

AI Summary

Existing spatiotemporal data mining models suffer from poor task generalization, limited complex reasoning capabilities, and insufficient interpretability—hindering multi-level decision support. To address these limitations, we propose STReason, the first framework that seamlessly integrates large language models (LLMs) with domain-specific spatiotemporal models via in-context learning, enabling automatic decomposition and execution of natural language queries into modular programs—without fine-tuning—thus supporting multi-task, long-context spatiotemporal reasoning. Our key contributions include: (1) a human-interpretable, modular reasoning pipeline; and (2) the first benchmark and evaluation framework specifically designed for long-text spatiotemporal reasoning. Experiments demonstrate that STReason significantly outperforms state-of-the-art LLM-based baselines on the new benchmark, especially on complex reasoning tasks. Human evaluation further confirms its high credibility and practical utility, substantially reducing expert effort.

Technology Category

Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal ReasoningData Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal DataMachine Learning: Large Multimodal Models (LMMs)

Application Category

Search and Retrieval-Augmented AI: Large language models for searchSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restricted to narrow tasks, lacking the capacity for multi-task inference and complex long-form reasoning that require generation of in-depth, explanatory outputs. These limitations restrict their applicability to real-world, multi-faceted decision scenarios. In this work, we introduce STReason, a novel framework that integrates the reasoning strengths of large language models (LLMs) with the analytical capabilities of spatio-temporal models for multi-task inference and execution. Without requiring task-specific finetuning, STReason leverages in-context learning to decompose complex natural language queries into modular, interpretable programs, which are then systematically executed to generate both solutions and detailed rationales. To facilitate rigorous evaluation, we construct a new benchmark dataset and propose a unified evaluation framework with metrics specifically designed for long-form spatio-temporal reasoning. Experimental results show that STReason significantly outperforms advanced LLM baselines across all metrics, particularly excelling in complex, reasoning-intensive spatio-temporal scenarios. Human evaluations further validate STReason's credibility and practical utility, demonstrating its potential to reduce expert workload and broaden the applicability to real-world spatio-temporal tasks. We believe STReason provides a promising direction for developing more capable and generalizable spatio-temporal reasoning systems.

Problem

• Integrates LLMs and spatio-temporal models for multi-task inference
• Enables complex natural language queries decomposition without task-specific finetuning
• Improves performance in reasoning-intensive spatio-temporal scenarios

Innovation

• Integrates LLMs with spatio-temporal models
• Uses in-context learning for modular programs
• Benchmark with long-form reasoning metrics