Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Can Large Language Models Understand Spatial-Temporal Data?
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2401.14192.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:40.287108Z
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 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.