Paper Citation Record · LEDGER

How Can Large Language Models Understand Spatial-Temporal Data?

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2401.14192.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.14192 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:55:26.846998Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T19:13:53.134860Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 21294a6c-ed16-49aa-b663-db0fd701899f · inbound

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions cites this paper.

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions How Can Large Language Models Understand Spatial-Temporal Data?

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-10T21:37:04.669721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f09eafc5-cd29-4698-82e5-13089f220ded · inbound

Adapting Network Information into Semantics for Generalizable and Plug-and-Play Multi-Scenario Network Diagnosis cites this paper.

Adapting Network Information into Semantics for Generalizable and Plug-and-Play Multi-Scenario Network Diagnosis How Can Large Language Models Understand Spatial-Temporal Data?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T10:17:53.769007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d43094b9-c251-498a-890a-bd62346f1c3a · inbound

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics cites this paper.

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics How Can Large Language Models Understand Spatial-Temporal Data?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T21:55:52.551448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf3bd781-e7e3-4347-ab0b-ca9f9648c434 · inbound

UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models cites this paper.

UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models How Can Large Language Models Understand Spatial-Temporal Data?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:26.846998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b443de5f-7c3f-4df9-9174-8dd40188450c · inbound

CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language cites this paper.

CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language How Can Large Language Models Understand Spatial-Temporal Data?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:40.287108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36579540-f1a2-4478-95d7-604c05d93d3a · inbound

Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review cites this paper.

Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review How Can Large Language Models Understand Spatial-Temporal Data?

Reference 185

Resolution: unresolved; no resolver link, observed 2026-08-07T11:49:47.279860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 402c46df-6872-42d4-813c-6df6afcdd072 · inbound

Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting cites this paper.

Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting How Can Large Language Models Understand Spatial-Temporal Data?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:43.264701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8d8bca9-3cc2-4cee-b44b-f372b20b67e4 · inbound

T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval cites this paper.

T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval How Can Large Language Models Understand Spatial-Temporal Data?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T05:30:59.442860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2256f8a5-7a1a-450e-b339-ab5ba29a74e1 · inbound

Text Reinforcement for Multimodal Time Series Forecasting cites this paper.

Text Reinforcement for Multimodal Time Series Forecasting How Can Large Language Models Understand Spatial-Temporal Data?

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:55.819956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64bf589-c99d-4a82-9406-b9f0cd2d68db · inbound

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey cites this paper.

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey How Can Large Language Models Understand Spatial-Temporal Data?

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-04T19:19:25.504379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c05d613-ff12-4451-a6ba-f8f4346c8343 · inbound

Vision-LLMs for Spatiotemporal Traffic Forecasting cites this paper.

Vision-LLMs for Spatiotemporal Traffic Forecasting How Can Large Language Models Understand Spatial-Temporal Data?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T07:26:03.209175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa1bfa5d-a3e4-47cc-be61-b131a828164a · inbound

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

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning How Can Large Language Models Understand Spatial-Temporal Data?

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:43:06.693246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b264de68-f70f-4714-84cd-5bb20af52f70 · inbound

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts cites this paper.

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts How Can Large Language Models Understand Spatial-Temporal Data?

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:53.136373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.