STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

• URL: http://arxiv.org/abs/2601.03248v1
• Date: Tue, 06 Jan 2026 18:46:12 GMT
• Title: STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
• Authors: Juntong Ni, Shiyu Wang, Ming Jin, Qi He, Wei Jin,
• Abstract summary: patio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context.<n>This capability is vital for high-stakes decision-making in systems such as traffic networks, power grids, and disease propagation.<n>To address the gap, we introduce ST-Bench, a benchmark consisting of four core tasks, including etiological reasoning, entity identification, correlation reasoning, and in-context forecasting.<n>We then propose STReasoner, which empowers LLM to integrate time series, graph structure, and text for explicit reasoning.
• Score: 16.11676643415448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capability is vital for high-stakes decision-making in systems such as traffic networks, power grids, and disease propagation. However, the field remains underdeveloped because most existing works prioritize predictive accuracy over reasoning. To address the gap, we introduce ST-Bench, a benchmark consisting of four core tasks, including etiological reasoning, entity identification, correlation reasoning, and in-context forecasting, developed via a network SDE-based multi-agent data synthesis pipeline. We then propose STReasoner, which empowers LLM to integrate time series, graph structure, and text for explicit reasoning. To promote spatially grounded logic, we introduce S-GRPO, a reinforcement learning algorithm that rewards performance gains specifically attributable to spatial information. Experiments show that STReasoner achieves average accuracy gains between 17% and 135% at only 0.004X the cost of proprietary models and generalizes robustly to real-world data.

Related papers

• Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
  Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
  arXiv  Detail & Related papers  (2026-02-10T10:16:27Z)
• From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs [65.04549036809557]
  We introduce a benchmark built from pedestrian-perspective videos captured with synchronized stereo cameras, LiDAR, and IMU/GPS sensors.<n>This dataset provides metrically precise 3D information, enabling the automatic generation of spatial reasoning questions.<n> Evaluations reveal that the performance gains observed in structured indoor benchmarks vanish in open-world settings.
  arXiv  Detail & Related papers  (2025-12-22T18:58:12Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Priors in Time: Missing Inductive Biases for Language Model Interpretability [58.07412640266836]
  We show that Sparse Autoencoders impose priors that assume independence of concepts across time, implying stationarity.<n>We introduce a new interpretability objective -- Temporal Feature Analysis -- which possesses a temporal inductive bias to decompose representations at a given time into two parts.<n>Our results underscore the need for inductive biases that match the data in designing robust interpretability tools.
  arXiv  Detail & Related papers  (2025-11-03T18:43:48Z)
• A Deep Learning Approach for Spatio-Temporal Forecasting of InSAR Ground Deformation in Eastern Ireland [2.840858735842673]
  Monitoring ground displacement is crucial for urban infrastructure and mitigating geological hazards.<n>This paper introduces a novel deep learning framework that transforms sparse point measurements into a dense-temporal tensor.<n>Results demonstrate that the proposed architecture provides more accurate and spatially coherent forecasts.
  arXiv  Detail & Related papers  (2025-09-17T17:10:18Z)
• ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting [7.853736939635847]
  We introduce ST-LINK, a novel framework that enhances the capability of Large Language Models to capture sequential-temporal dependencies.<n>Its key components are spatially-Enhanced Attention (SE-Attention) and the Memory Retrieval Feed-Forward Network (MRFFN)
  arXiv  Detail & Related papers  (2025-09-17T07:11:45Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization [29.10084723132903]
  We propose an innovative Spatio-Temporal Retrieval-Augmented Pattern Learning framework, STRAP.<n>During inference, STRAP retrieves relevant patterns from this library based on similarity to the current input and injects them into the model via a plug-and-play prompting mechanism.<n>Experiments across multiple real-world streaming graph datasets show that STRAP consistently outperforms state-of-the-art STGNN baselines on STOOD tasks.
  arXiv  Detail & Related papers  (2025-05-26T06:11:05Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction [84.26340606752763]
  In this paper, we introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework.<n>The network is designed to conform to the general symmetry conservation law via symmetry where conservative and non-conservative information passes over a multiscale space by a latent temporal marching strategy.<n>Results demonstrate that CiGNN exhibits remarkable baseline accuracy and generalizability, and is readily applicable to learning for prediction of varioustemporal dynamics.
  arXiv  Detail & Related papers  (2024-12-30T13:55:59Z)
• RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming [24.9561009415531]
  We aim to harness the reasoning and generalization abilities of Pre-trained Language Models (PLMs) for intricate-temporal forecasting.<n>We propose RePST, a semantic-oriented PLM reprogramming framework tailored fortemporal forecasting.
  arXiv  Detail & Related papers  (2024-08-24T07:59:36Z)
• GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation [19.371155159744934]
  In real-world settings, such data often contain missing elements due to issues like sensor malfunctions and data transmission errors. The objective oftemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed time series. Traditionally, intricatetemporal imputation has relied on specific architectures, which suffer from limited applicability and high computational complexity. In contrast our approach integrates pre-trained large language models (LLMs) into intricatetemporal imputation, introducing a groundbreaking framework, GATGPT.
  arXiv  Detail & Related papers  (2023-11-24T08:15:11Z)
• ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting [47.74479442786052]
  Current research on Spatio-Temporal Graph Neural Networks (STGNNs) often prioritizes complex designs, leading to computational burdens with only minor enhancements in accuracy. We propose ST-MLP, a concise cascaded temporal-temporal model solely based on Multi-Layer Perceptron (MLP) modules and linear layers. Empirical results demonstrate that ST-MLP outperforms state-of-the-art STGNNs and other models in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2023-08-14T23:34:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.