A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs | Paper Citation Record

Observation b3b812fb-fb1f-4682-b591-b6878c591095 · outbound

This paper cites AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

Reference 1

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Observation 3b7ce511-5c99-4306-b366-f8a0c5382fae · outbound

This paper cites Airformer: Predicting nationwide air quality in china with transformers,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Airformer: Predicting nationwide air quality in china with transformers,

Reference 2

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Observation 1fe9333c-332c-4360-b436-6abcd7bb63f3 · outbound

This paper cites Urbangpt: Spatio-temporal large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Urbangpt: Spatio-temporal large language models,

Reference 3

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Observation 578ccdc1-b546-46b4-be6e-ecde7012ccfb · outbound

This paper cites Stden: Towards physics-guided neural networks for traffic flow prediction,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Stden: Towards physics-guided neural networks for traffic flow prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.725620Z

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Observation 8dba7ea5-12c1-4ad5-96fd-96f15b99d387 · outbound

This paper cites Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.705729Z

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Observation e0527bd6-f2cd-4489-91c3-84e3e54f3ce8 · outbound

This paper cites Urban flow prediction from spatiotemporal data using machine learning: A survey,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Urban flow prediction from spatiotemporal data using machine learning: A survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.689409Z

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Observation 5f195cd9-2833-4a75-b422-c79913b7bc09 · outbound

This paper cites Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.672421Z

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Observation 1fe48ea6-37be-4977-b84c-3f9680b1a060 · outbound

This paper cites Deep learning for spatio-temporal data mining: A survey,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Deep learning for spatio-temporal data mining: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.655987Z

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Observation 5f2cff8d-712e-4364-8b68-7ecc2b406e17 · outbound

This paper cites A Survey of Generative Techniques for Spatial-Temporal Data Mining.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs A Survey of Generative Techniques for Spatial-Temporal Data Mining

Reference 9

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Observation 60f95b61-9465-46d0-a915-c2de23f2e205 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Foundation models for time series analysis: A tutorial and survey,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.640852Z

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Observation 6be8a144-ebf7-424b-bab4-a262aef07469 · outbound

This paper cites Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.577356Z

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Observation 721f8dad-d404-45ea-91ca-5f50081a1803 · outbound

This paper cites TEMPO: Prompt-based generative pre-trained transformer for time series forecasting,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs TEMPO: Prompt-based generative pre-trained transformer for time series forecasting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.625039Z

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Observation 7cc66a85-a6cf-4d3e-a49f-de7e85d9f969 · outbound

This paper cites One Fits All: Power general time series analysis by pretrained lm,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs One Fits All: Power general time series analysis by pretrained lm,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.608408Z

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Observation bf560d79-f596-4a32-bb6a-aae4152bfec4 · outbound

This paper cites Can large language models be anomaly detectors for time series?,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Can large language models be anomaly detectors for time series?,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.590158Z

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Observation 6347046c-c905-4822-9386-46190a297bad · outbound

This paper cites GATGPT: A pre-trained large language model with graph attention network for spatiotemporal imputation,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs GATGPT: A pre-trained large language model with graph attention network for spatiotemporal imputation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.570750Z

Source-reported events for the cited work

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Observation 8692700b-8d94-400b-95e1-b46a5855ed58 · outbound

This paper cites Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.553286Z

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Observation 83b1b8e9-65a4-491e-88b6-ee1b0906cb79 · outbound

This paper cites Unist: A prompt-empowered universal model for urban spatio-temporal prediction,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Unist: A prompt-empowered universal model for urban spatio-temporal prediction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.537549Z

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Observation 530b595d-5599-4452-aa33-c4fffa00bb77 · outbound

This paper cites Are language models actually useful for time series forecasting?,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Are language models actually useful for time series forecasting?,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.520186Z

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Observation a6f28582-24db-4a96-867e-4ebb0151455d · outbound

This paper cites Position: Llms can’t plan, but can help planning in llm-modulo frameworks,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Position: Llms can’t plan, but can help planning in llm-modulo frameworks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.502505Z

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Observation ffec43a5-cd2c-4321-aa8d-26e703e32bf6 · outbound

This paper cites Spatial-temporal large language model for traffic prediction,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Spatial-temporal large language model for traffic prediction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.484494Z

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Observation 770dc12b-e8a6-498d-9dfd-2f025793526b · outbound

This paper cites TimeCMA: Towards llm-empowered multivariate time series forecasting via cross-modality alignment,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs TimeCMA: Towards llm-empowered multivariate time series forecasting via cross-modality alignment,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.466498Z

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Observation 4608e87a-5a73-4b24-bb55-90ac6be1d09a · outbound

This paper cites Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.448416Z

Source-reported events for the cited work

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Observation 4a84e75f-562e-4845-89f3-127be25b6cd6 · outbound

This paper cites Genfollower: Enhancing car-following prediction with large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Genfollower: Enhancing car-following prediction with large language models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.429137Z

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Observation d46eec2e-ebed-4263-91f0-d232d90bfeb5 · outbound

This paper cites Towards explainable traffic flow prediction with large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Towards explainable traffic flow prediction with large language models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.413181Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bdab236e-f711-4916-b66f-185082898928 · outbound

This paper cites UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models

Reference 25

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Observation 1e429b58-bfca-41eb-a0d2-5f78b346f4ee · outbound

This paper cites TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.648950Z

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Observation 6c91d945-18f5-43f6-9a41-3c1f652f282c · outbound

This paper cites ELI5: Long form question answering,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs ELI5: Long form question answering,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.395426Z

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Observation 9aa4a006-b000-47c0-ae7d-b45aaaba1985 · outbound

This paper cites GeoLLM: Extracting geospatial knowledge from large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs GeoLLM: Extracting geospatial knowledge from large language models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.378740Z

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Observation 3a53f122-16bc-457b-95be-7e3f41f7f578 · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.361385Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b622cdb-299d-431b-8e49-73495ec5bb4f · outbound

This paper cites Visual programming: Compositional visual reasoning without train- ing,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Visual programming: Compositional visual reasoning without train- ing,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.344683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b968c0e-ba51-4d82-93de-41e9d5a11494 · outbound

This paper cites Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Reference 31

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Observation 196cfb5d-fcb4-4e2a-a122-fbd069f07145 · outbound

This paper cites Unitime: A language- empowered unified model for cross-domain time series forecasting,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Unitime: A language- empowered unified model for cross-domain time series forecasting,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.327520Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0bd0b46-9bcc-4489-b996-a72b3e25fba3 · outbound

This paper cites Large language models are zero-shot time series forecasters,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Large language models are zero-shot time series forecasters,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.310201Z

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Observation 1c4f74d8-e91e-4779-8cf7-b680882f344d · outbound

This paper cites Where Would I Go Next? Large Language Models as Human Mobility Predictors.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Where Would I Go Next? Large Language Models as Human Mobility Predictors

Reference 34

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Observation 42c6bc60-a614-4722-b492-3b2e8c246f39 · outbound

This paper cites GPT-4o System Card.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs GPT-4o System Card

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.693129Z

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Observation 4b23e09c-87cd-4991-991c-e155e6242b85 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 36

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Observation f6de8e3c-ba47-44a2-a890-86746a1dc6b0 · outbound

This paper cites Language models are few-shot learners,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Language models are few-shot learners,

Reference 37

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Observation 6e5e2c60-6245-43e3-a804-859af4a63860 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Tree of thoughts: Deliberate problem solving with large language models,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.706944Z

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Observation 67d4947a-e321-4a93-ae94-ae1b1d53baf4 · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Self-consistency improves chain of thought reasoning in language models,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.269798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e7a1f23-e620-4a4b-8acf-43e13ae95047 · outbound

This paper cites Towards revealing the mystery behind chain of thought: a theoretical perspective,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Towards revealing the mystery behind chain of thought: a theoretical perspective,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.253251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 577d5e2c-560c-4866-a444-3a60c16f1283 · outbound

This paper cites Chain-of-table: Evolving tables in the reasoning chain for table understanding,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Chain-of-table: Evolving tables in the reasoning chain for table understanding,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.235534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95649d76-a71e-4434-b0fc-10ffd2a0598b · outbound

This paper cites STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

Reference 42

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Observation e2835b45-21e1-4f68-99ea-42e2e6884614 · outbound

This paper cites Situatedgen: Incorporating geographical and temporal contexts into generative commonsense reasoning,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Situatedgen: Incorporating geographical and temporal contexts into generative commonsense reasoning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.216656Z

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Observation 3e4c8e85-c33e-4017-968d-0608bf41304d · outbound

This paper cites AutoGPT.

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

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.199053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2f811a6-0b23-41c7-abf1-3ec5b75c3333 · outbound

This paper cites GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.739761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71591e0f-e411-4de5-aa07-7320fbe859a7 · outbound

This paper cites Large language models as urban residents: An llm agent framework for personal mobility generation,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Large language models as urban residents: An llm agent framework for personal mobility generation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.180642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66d13565-347d-478b-8246-bc378ef7f8c0 · outbound

This paper cites Large language models empowered agent-based modeling and simulation: A survey and perspectives,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Large language models empowered agent-based modeling and simulation: A survey and perspectives,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.161915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcfd69d6-7c0c-4350-bbae-8b37aa160ed5 · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.754035Z

Source-reported events for the cited work

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Observation 3704ba2b-470f-43e9-9698-6d07446414fc · outbound

This paper cites A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

Reference 49

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Observation 279da6df-5bbf-4af8-9df4-1938a05f8ab3 · outbound

This paper cites GPT-4 Technical Report.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs GPT-4 Technical Report

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.763933Z

Source-reported events for the cited work

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Observation ed85fb28-3215-492b-b3ae-37f95eb7ef7f · outbound

This paper cites DeepSeek-V3 Technical Report.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs DeepSeek-V3 Technical Report

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:34.768706Z

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Observation 7e4c7147-af7f-463d-bc20-d57b461385a7 · outbound

This paper cites A survey of reasoning with foundation models: Concepts, methodologies, and outlook,.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs A survey of reasoning with foundation models: Concepts, methodologies, and outlook,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.145541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 820c980a-199e-4f1d-bf7c-ec4a608cf766 · outbound

This paper cites • General understanding of spatio-temporal tasks such as analysis, anomaly detection, and forecasting.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs • General understanding of spatio-temporal tasks such as analysis, anomaly detection, and forecasting

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.128235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38700d6b-9135-4b98-a2a4-cd437d929705 · outbound

This paper cites Each query was paired with two answers, one from STReason and one from a randomly selected baseline ensuring each baseline appeared an equal number of times.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Each query was paired with two answers, one from STReason and one from a randomly selected baseline ensuring each baseline appeared an equal number of times

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.111187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 029fcae5-48e8-4f29-aed0-d5d4e24f2489 · outbound

This paper cites They were also encouraged to provide open-ended feedback explaining their choices.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs They were also encouraged to provide open-ended feedback explaining their choices

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:01:35.092757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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