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Paper Citation Record · LEDGER

GPT4GEO: How a Language Model Sees the World's Geography

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2306.00020.

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

pith.paper-citation-record.v1
2306.00020 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:56:32.440932Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

19
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1429f4a7-9ab4-43ef-9cf2-9fceee3b0c7e · inbound

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions cites this paper.

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions GPT4GEO: How a Language Model Sees the World's Geography

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-09T14:03:40.151838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:03:40.151838Z digest=sha256:60a60fca5148d87d2da17b5ecba161bb8db33f8a5ea91901d2fe4a91056f4e0a

Observation 8ae12ef7-6b1b-4342-ab4b-91cd8e0c99a8 · inbound

Geospatial Mechanistic Interpretability of Large Language Models cites this paper.

Geospatial Mechanistic Interpretability of Large Language Models GPT4GEO: How a Language Model Sees the World's Geography

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-15T23:56:32.440932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:56:32.440932Z digest=sha256:eaa92028ad04e4b61f579f82ee27152dd8bbaa59491467a95db1e863a8f4c321

Observation b36c70c9-bd21-443c-869f-2655c1254483 · inbound

Geography-Aware Large Language Models for Next POI Recommendation cites this paper.

Geography-Aware Large Language Models for Next POI Recommendation GPT4GEO: How a Language Model Sees the World's Geography

Reference 2010

Resolution
unresolved
no resolver link, observed 2026-08-15T20:41:06.559023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:41:06.559023Z digest=sha256:10d674cb050e915e3a7bd1b1908bc599fb34c755f9b40831de1564cd3c77a5dc

Observation c6a2d7eb-9e23-4459-a43e-dbe0e17b9b6d · inbound

MapStory: Prototyping Editable Map Animations with LLM Agents cites this paper.

MapStory: Prototyping Editable Map Animations with LLM Agents GPT4GEO: How a Language Model Sees the World's Geography

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T13:22:05.604088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:22:05.604088Z digest=sha256:8a74028bd71a7517e0a639d2382856739f73a7961aed258b32ebd436045c4c95

Observation 4c13b53a-c6de-4673-ada4-0ca20ef72cdf · inbound

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features cites this paper.

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features GPT4GEO: How a Language Model Sees the World's Geography

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T12:14:03.329611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:03.329611Z digest=sha256:74a053553b6e1038e11ddc4a46c5bad172b160432eb96b95b46ac52e64b5c7c0

Observation 726f224f-bf03-4f2a-b1e1-a8824bac73a8 · inbound

CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation cites this paper.

CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation GPT4GEO: How a Language Model Sees the World's Geography

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-15T20:03:11.464454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:03:11.464454Z digest=sha256:5b48413395ca94b6343a876407f4c654373bea41f567c568017c319a2e231422

Observation 2339044d-154f-4438-9cbf-5f79049236e5 · 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 GPT4GEO: How a Language Model Sees the World's Geography

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T14:20:39.519135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:20:39.519135Z digest=sha256:327d9e18a45a3f0c396e4d733579bcd935da55007bbc6ac5696f2d9f602ad94a

Observation e1302248-e23a-4e88-8478-c9134c68c00f · inbound

Towards Interpretable Geo-localization: a Concept-Aware Global Image-GPS Alignment Framework cites this paper.

Towards Interpretable Geo-localization: a Concept-Aware Global Image-GPS Alignment Framework GPT4GEO: How a Language Model Sees the World's Geography

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T12:07:54.535949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:07:54.535949Z digest=sha256:29083705dd34d415489fdd31f7814706f2ce9be51555f7ca9fe54c772d38bb46

Observation c7564679-6481-4561-8749-9e9c58928861 · inbound

Much of Geospatial Web Search Is Beyond Traditional GIS cites this paper.

Much of Geospatial Web Search Is Beyond Traditional GIS GPT4GEO: How a Language Model Sees the World's Geography

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:22:01.885888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T01:19:26.399524Z digest=sha256:9115ce383da90839e0307f9cc770f5e49505fa06317ee7e48c14d7620948cbcb

Observation 36f9552c-b4fd-4547-b7dd-c1e13b5b8c94 · inbound

Much of Geospatial Web Search Is Beyond Traditional GIS cites this paper.

Much of Geospatial Web Search Is Beyond Traditional GIS GPT4GEO: How a Language Model Sees the World's Geography

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:05:05.942095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T21:58:45.277252Z digest=sha256:4d7fe4dc0c9ceb7584ab470410e00f940a2b9c7d9ffd381c5f8fab7ea1eee694

Observation d2b913d1-2d93-4f39-a9d3-eb615bf1e826 · inbound

Consistency Training while Mitigating Obfuscation via Rate Matching cites this paper.

Consistency Training while Mitigating Obfuscation via Rate Matching GPT4GEO: How a Language Model Sees the World's Geography

Reference 128

Resolution
verified exact
arxiv_id, observed 2026-06-28T14:32:18.186246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-28T14:25:43.147442Z digest=sha256:f3aa35d84e83c1d1153d7835622bc01de065d8c78580cccb70b462ac0a160b93

Observation fdfb185c-bfcc-449f-b4d1-d74918dd9540 · inbound

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models cites this paper.

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models GPT4GEO: How a Language Model Sees the World's Geography

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:26:45.789113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T06:58:39.117228Z digest=sha256:39d196ff704e3a230e05b221d731f92f83d3e2771096b4bbc7971be7c7d4e99f

Observation 41735930-4308-4b52-b5db-c6d2c8c174f9 · inbound

Assessing the Geographic Diversity of AI's Platial Representations in Image Generation cites this paper.

Assessing the Geographic Diversity of AI's Platial Representations in Image Generation GPT4GEO: How a Language Model Sees the World's Geography

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-07-01T08:45:34.711220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-07-01T08:41:49.962789Z digest=sha256:8a7bc96817dddb67fdd0f52bdcf894b9c9a376bc32eee5500e1cd370e58ddc63

Observation 44e9c93c-9068-4466-a310-4cc500d0d437 · inbound

MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ? cites this paper.

MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ? GPT4GEO: How a Language Model Sees the World's Geography

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:39:42.507184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T11:07:50.534593Z digest=sha256:05f3849e801cf602a113a6eb513327a17914947fdd104a9984aa1dc3dade9ea2