{"as_of":"2026-08-12T21:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f017259f68ed709f6bc0cab2626c7c22cc639b26a8cf1048dca0f1af9ab8766d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:03:40.151838Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":19,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-09T14:03:40.151838Z","title":"Gpt4geo: How a language model sees the world’s geography","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.18470","last_updated":"2025-06-11T04:41:29Z","snapshot_observed_at":"2026-08-11T00:49:54.359461Z","submitted_at":"2025-02-04T01:30:06Z","title":"Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions","version":5},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T14:03:40.151838Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2502.18470"},"observation_digest":"sha256:b06a26cf4fc88cd50cbab683141bc774a1171661cf918969c5f23b52acfe17db","observation_id":"1429f4a7-9ab4-43ef-9cf2-9fceee3b0c7e","resolution":{"observed_at":"2026-08-09T14:03:40.151838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-07T13:22:05.604088Z","title":"arXiv preprint arXiv:2306.00020 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21966","last_updated":"2025-08-13T08:00:47Z","snapshot_observed_at":"2026-08-09T16:37:51.027370Z","submitted_at":"2025-05-28T04:36:08Z","title":"MapStory: Prototyping Editable Map Animations with LLM Agents","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T13:22:05.604088Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2505.21966"},"observation_digest":"sha256:eeacf2572afea120e55bb0c77cf997243ef74d84933ad5da873d37fc31849577","observation_id":"c6a2d7eb-9e23-4459-a43e-dbe0e17b9b6d","resolution":{"observed_at":"2026-08-07T13:22:05.604088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-07T12:14:03.329611Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00203","last_updated":"2025-05-30T20:14:17Z","snapshot_observed_at":"2026-08-10T03:30:41.710185Z","submitted_at":"2025-05-30T20:14:17Z","title":"The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:14:03.329611Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2506.00203"},"observation_digest":"sha256:81052511f3a953433c6ca1c8c5a7c8afdd372fd0c7deb0394f1fced03f87dd7d","observation_id":"4c13b53a-c6de-4673-ada4-0ca20ef72cdf","resolution":{"observed_at":"2026-08-07T12:14:03.329611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-06T14:20:39.519135Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19586","last_updated":"2025-07-25T18:00:21Z","snapshot_observed_at":"2026-08-07T21:42:42.944892Z","submitted_at":"2025-07-25T18:00:21Z","title":"Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T14:20:39.519135Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2507.19586"},"observation_digest":"sha256:6bf9f898bda2f9a0247fb0c3c2d1a3b154d6cd58f765ea65c21b6156ab8bd249","observation_id":"2339044d-154f-4438-9cbf-5f79049236e5","resolution":{"observed_at":"2026-08-06T14:20:39.519135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T12:07:54.535949Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01910","last_updated":"2025-09-05T10:42:33Z","snapshot_observed_at":"2026-08-10T07:55:55.365216Z","submitted_at":"2025-09-02T03:07:26Z","title":"Towards Interpretable Geo-localization: a Concept-Aware Global Image-GPS Alignment Framework","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T12:07:54.535949Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2509.01910"},"observation_digest":"sha256:a8f51fe607814648b1aa205cfc164fbeb1a41b7d09f2cab0d51f143441748b7b","observation_id":"e1302248-e23a-4e88-8478-c9134c68c00f","resolution":{"observed_at":"2026-08-05T12:07:54.535949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2605.11336","last_updated":"2026-05-29T12:50:13Z","snapshot_observed_at":"2026-08-01T14:17:14.067496Z","submitted_at":"2026-05-11T23:42:00Z","title":"Much of Geospatial Web Search Is Beyond Traditional GIS","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-13T01:19:26.399524Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2605.11336"},"observation_digest":"sha256:24b7801bef707d76b75dc14586e1b9761ab72c7762563701ff4b8e3ba8253013","observation_id":"c7564679-6481-4561-8749-9e9c58928861","resolution":{"observed_at":"2026-05-13T01:22:01.885888Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2605.11336","last_updated":"2026-05-29T12:50:13Z","snapshot_observed_at":"2026-08-01T14:17:14.067496Z","submitted_at":"2026-05-11T23:42:00Z","title":"Much of Geospatial Web Search Is Beyond Traditional GIS","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T21:58:45.277252Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2605.11336"},"observation_digest":"sha256:9ba4a2f8e63c47dd4d8390b94a2c30955e8882ca47b18a530b3e9657c820ec69","observation_id":"36f9552c-b4fd-4547-b7dd-c1e13b5b8c94","resolution":{"observed_at":"2026-06-30T22:05:05.942095Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2606.02211","last_updated":"2026-06-01T13:10:49Z","snapshot_observed_at":"2026-08-02T18:41:16.377453Z","submitted_at":"2026-06-01T13:10:49Z","title":"Consistency Training while Mitigating Obfuscation via Rate Matching","version":1},"reference_index":128,"source":"arxiv_source","source_observed_at":"2026-06-28T14:25:43.147442Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2606.02211"},"observation_digest":"sha256:03f955142630fc985d29ed78a2c69e384acec5b1980739b77f121dda0023fa9a","observation_id":"d2b913d1-2d93-4f39-a9d3-eb615bf1e826","resolution":{"observed_at":"2026-06-28T14:32:18.186246Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2606.04381","last_updated":"2026-06-03T02:54:59Z","snapshot_observed_at":"2026-07-06T23:44:28.265478Z","submitted_at":"2026-06-03T02:54:59Z","title":"From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T06:58:39.117228Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2606.04381"},"observation_digest":"sha256:56f7378cd32c258f0a877c8cc5fb1b08ec5350da0e90db66389af60942f5b191","observation_id":"fdfb185c-bfcc-449f-b4d1-d74918dd9540","resolution":{"observed_at":"2026-07-02T07:26:45.789113Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2606.05188","last_updated":"2026-04-28T11:57:42Z","snapshot_observed_at":"2026-08-08T09:55:06.143475Z","submitted_at":"2026-04-28T11:57:42Z","title":"Assessing the Geographic Diversity of AI's Platial Representations in Image Generation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-07-01T08:41:49.962789Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2606.05188"},"observation_digest":"sha256:d1532552a18cd53343499ba66be1ae67fc0872a10a1fcff8186ae2fcc29ed22d","observation_id":"41735930-4308-4b52-b5db-c6d2c8c174f9","resolution":{"observed_at":"2026-07-01T08:45:34.711220Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography","version":1},"cited_work":{"arxiv_id":"2306.00020","doi":"10.48550/arxiv.2306.00020","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.00020","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL: http://arxiv.org/abs/2306.00020","venue":"arXiv (Cornell University)","work_id":"aa57cf8d-3308-4ac6-a150-9f8b28904851","year":2023},"citing_paper":{"arxiv_id":"2606.22597","last_updated":"2026-06-23T04:17:28Z","snapshot_observed_at":"2026-07-06T23:57:28.172988Z","submitted_at":"2026-06-21T17:13:35Z","title":"MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T11:07:50.534593Z"},"links":{"cited_paper":"/paper/2306.00020","citing_paper":"/paper/2606.22597"},"observation_digest":"sha256:72513b297bde04f1a567e09acf8b3f7c14d041ffd2ae1a77951761ee4a247321","observation_id":"44e9c93c-9068-4466-a310-4cc500d0d437","resolution":{"observed_at":"2026-07-04T08:39:42.507184Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.00020/citation-record","integrity":"/paper/2306.00020/integrity","json":"/paper/2306.00020/citation-record.json","paper":"/paper/2306.00020"},"outbound":[],"paper":{"arxiv_id":"2306.00020","last_updated":"2023-05-30T18:28:04Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T15:36:07.713593Z","submitted_at":"2023-05-30T18:28:04Z","title":"GPT4GEO: How a Language Model Sees the World's Geography"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2306.00020."}