{"as_of":"2026-08-06T12:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c1ef0e4107bf39137e3ee37c459b8c8445ee3e035975768c5d4e332c0df66031","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:36:36.359574Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T19:08:49.784031Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-08-05T14:36:36.359574Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06975","last_updated":"2025-08-28T19:13:10Z","snapshot_observed_at":"2026-08-05T14:36:35.202312Z","submitted_at":"2025-08-28T19:13:10Z","title":"GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:36:36.359574Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2509.06975"},"observation_digest":"sha256:25b5697a1e388ea7b3a65142c84fc82bc3c81a14f3f632c0a593e9a2acab2def","observation_id":"ee8788a0-728d-429b-9275-a0c8b2c97cd2","resolution":{"observed_at":"2026-08-05T14:36:36.359574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":"2406.10727","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-07-03T19:08:49.784031Z","title":"arXiv preprint arXiv:2406.10727 , year=","venue":null,"work_id":"30241e6f-e72b-47e2-b95b-6804a9b7de0c","year":2024},"citing_paper":{"arxiv_id":"2510.08952","last_updated":"2026-05-05T05:35:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-10T02:59:19Z","title":"When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T08:45:51.992431Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2510.08952"},"observation_digest":"sha256:f5842ec69e660cfecbaf2d3f056684b9309ad37d6716f6843ca4c4930bcd83ef","observation_id":"a443f57e-d255-4537-be11-d1f882361ed7","resolution":{"observed_at":"2026-05-18T08:46:07.681687Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":"2406.10727","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-07-03T19:08:49.784031Z","title":"arXiv preprint arXiv:2406.10727 , year=","venue":null,"work_id":"30241e6f-e72b-47e2-b95b-6804a9b7de0c","year":2024},"citing_paper":{"arxiv_id":"2605.06576","last_updated":"2026-05-07T17:06:19Z","snapshot_observed_at":"2026-07-06T23:19:00.794334Z","submitted_at":"2026-05-07T17:06:19Z","title":"On the Safety of Graph Representation Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T12:17:28.087347Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2605.06576"},"observation_digest":"sha256:bac56b3f32394bda20adf9328d2e7ab45d54880f51437ff214a4a9dd5a1f80f2","observation_id":"6ce1fd7c-ccc4-43b0-b797-a67b4c366089","resolution":{"observed_at":"2026-05-11T19:21:07.082969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":"2406.10727","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-07-03T19:08:49.784031Z","title":"arXiv preprint arXiv:2406.10727 , year=","venue":null,"work_id":"30241e6f-e72b-47e2-b95b-6804a9b7de0c","year":2024},"citing_paper":{"arxiv_id":"2605.12061","last_updated":"2026-05-12T12:47:43Z","snapshot_observed_at":"2026-08-06T07:22:02.201477Z","submitted_at":"2026-05-12T12:47:43Z","title":"SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-13T04:45:34.957298Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2605.12061"},"observation_digest":"sha256:23e39412fc3a93d454de38d1b3ff0e91bd9fd378f697a891f0b349887bb937b9","observation_id":"0b6554e8-88c9-475b-b2b3-b674444804dd","resolution":{"observed_at":"2026-05-13T04:52:17.364570Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":"2406.10727","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-07-03T19:08:49.784031Z","title":"arXiv preprint arXiv:2406.10727 , year=","venue":null,"work_id":"30241e6f-e72b-47e2-b95b-6804a9b7de0c","year":2024},"citing_paper":{"arxiv_id":"2606.17579","last_updated":"2026-06-16T06:30:30Z","snapshot_observed_at":"2026-08-04T11:44:26.565956Z","submitted_at":"2026-06-16T06:30:30Z","title":"LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T02:12:05.069667Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2606.17579"},"observation_digest":"sha256:1813d45c1862171df26a2e79826a892672727596d994d535696f5755a1db34ad","observation_id":"2618cfd1-2946-4006-96dd-e44613348cae","resolution":{"observed_at":"2026-07-03T19:08:49.785774Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-08-01T15:05:39.623297Z","title":"Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18567","last_updated":"2026-07-20T23:03:26Z","snapshot_observed_at":"2026-08-05T01:29:27.360272Z","submitted_at":"2026-07-20T23:03:26Z","title":"Attacking Graph Foundation Models Through Their Shared Representation","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T15:05:39.623297Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2607.18567"},"observation_digest":"sha256:1e7e8109b1b6755ba4c45c49b2a66aa3be9115218473b3d48245310e5c0e6785","observation_id":"9f8df8a5-2d19-424e-af26-e6bca8de2761","resolution":{"observed_at":"2026-08-01T15:05:39.623297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-08-01T13:29:51.975602Z","title":"arXiv preprint arXiv:2406.10727 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19108","last_updated":"2026-07-21T13:50:24Z","snapshot_observed_at":"2026-08-03T02:15:00.058583Z","submitted_at":"2026-07-21T13:50:24Z","title":"OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T13:29:51.975602Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2607.19108"},"observation_digest":"sha256:0d248394b74f3beb51dc8a60b9bd2c393a4adf4c5850f2c04571f213681badcd","observation_id":"33a04f7a-11bb-4c0b-b5b3-e99ba88cd43a","resolution":{"observed_at":"2026-08-01T13:29:51.975602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10727","snapshot_observed_at":"2026-08-04T22:55:33.826338Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01684","last_updated":"2026-08-03T04:18:31Z","snapshot_observed_at":"2026-08-06T12:18:00.427029Z","submitted_at":"2026-08-03T04:18:31Z","title":"GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T22:55:33.826338Z"},"links":{"cited_paper":"/paper/2406.10727","citing_paper":"/paper/2608.01684"},"observation_digest":"sha256:28e494406604fd8ce2e54cfa3f5ab8b7e8eaf50551413c3a9f339c6cac3c28b6","observation_id":"b3bb4989-ac69-4ff3-b76e-bdbd23e661e8","resolution":{"observed_at":"2026-08-04T22:55:33.826338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.10727/citation-record","integrity":"/paper/2406.10727/integrity","json":"/paper/2406.10727/citation-record.json","paper":"/paper/2406.10727"},"outbound":[],"paper":{"arxiv_id":"2406.10727","last_updated":"2024-06-15T19:56:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:31:36.504766Z","submitted_at":"2024-06-15T19:56:21Z","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.10727."}