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

• URL: http://arxiv.org/abs/2506.20073v1
• Date: Wed, 25 Jun 2025 00:55:34 GMT
• Title: A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
• Authors: Kethmi Hirushini Hettige, Jiahao Ji, Cheng Long, Shili Xiang, Gao Cong, Jingyuan Wang,
• Abstract summary: We introduce STReason, a framework that integrates large language models with analytical capabilities for multi-task inference and execution.<n>We show that STReason significantly outperforms LLM baselines across all metrics, particularly in excelling in complex, reasoningintensive-temporal scenarios.<n>Human evaluations validate STReason's credibility and practical utility, demonstrating potential to reduce expert workload and broaden the applicability to real-world, multi-faceted decision scenarios.
• Score: 38.304628241767055
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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