Paper Citation Record · LEDGER

CityGPT: Empowering Urban Spatial Cognition of Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.13948.

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

pith.paper-citation-record.v1
2406.13948 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:20:26.341526Z

measured 0 of 1 external citation measurements

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.

Observation ff9cbfe3-f777-4bea-8047-b9effaf7b64a · inbound

UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks cites this paper.

UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks CityGPT: Empowering Urban Spatial Cognition of Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T04:20:26.341526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.