Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CityGPT: Empowering Urban Spatial Cognition of Large Language Models
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.13948.
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 2026-08-07T14:47:12.923658Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T09:32:15.971097Z
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 90ac15c2-34fb-41be-9f7e-e7ba2f5f5eb2 · inbound
USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents cites this paper.
USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:47:12.923658Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e9076cc4-4451-4e07-8300-c6317c582b72 · inbound
Can LLMs Learn to Map the World from Local Descriptions? cites this paper.
Can LLMs Learn to Map the World from Local Descriptions? CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:52:56.982140Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03cfce81-0071-4574-809e-af0d5988bd7e · inbound
From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.
From Time Series Analysis to Question Answering: A Survey in the LLM Era CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T09:32:15.974554Z
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 9c6de3b8-5a34-440a-96da-d12ba365bd04 · inbound
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:07:06.340205Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b9864230-f6c5-4a8f-aab5-199e1b666f02 · inbound
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning cites this paper.
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:20:37.640195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c15d24d4-3224-4f9b-b0d4-11ff57390a71 · inbound
IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling cites this paper.
IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:05:56.865835Z
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 dc353b8b-db2a-46f1-a6fd-752466a04fff · inbound
Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit cites this paper.
Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:44:53.717220Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.