{"as_of":"2026-08-08T00:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:63b6f6e0e8b988126e8cc038f84010fb0d9f39ba054dc88168d3a7156f1f2792","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:25:45.762179Z","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":"2026-05-25T07:50:29.434886Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12449","snapshot_observed_at":"2026-08-07T15:25:45.762179Z","title":"Sgl-pt: A strong graph learner with graph prompt tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15116","last_updated":"2025-05-21T05:08:00Z","snapshot_observed_at":"2026-08-07T15:21:20.984814Z","submitted_at":"2025-05-21T05:08:00Z","title":"Graph Foundation Models: A Comprehensive Survey","version":1},"reference_index":179,"source":"pdf_text","source_observed_at":"2026-08-07T15:25:45.762179Z"},"links":{"cited_paper":"/paper/2302.12449","citing_paper":"/paper/2505.15116"},"observation_digest":"sha256:9dd38f93a4e71638d4787a8222499279bbee4cc2f81b95b174052a9405976a24","observation_id":"3ed7f7ab-79ea-4188-b12c-8ef5b56f8794","resolution":{"observed_at":"2026-08-07T15:25:45.762179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12449","snapshot_observed_at":"2026-08-07T05:17:17.430030Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08326","last_updated":"2025-06-10T01:27:19Z","snapshot_observed_at":"2026-08-07T05:11:09.756792Z","submitted_at":"2025-06-10T01:27:19Z","title":"Graph Prompting for Graph Learning Models: Recent Advances and Future Directions","version":1},"reference_index":159,"source":"pdf_text","source_observed_at":"2026-08-07T05:17:17.430030Z"},"links":{"cited_paper":"/paper/2302.12449","citing_paper":"/paper/2506.08326"},"observation_digest":"sha256:e04e8a4d640f8308a2f6e78e25005e1a3273cd017add77aa9edffe37085889a1","observation_id":"0c3918ce-59c5-44c6-8fb5-b7460088ac01","resolution":{"observed_at":"2026-08-07T05:17:17.430030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","version":2},"cited_work":{"arxiv_id":"2302.12449","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.12449","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2302.12449 , year=","venue":null,"work_id":"62e1e974-cd6e-4d0a-9394-3b5bdb89305f","year":2023},"citing_paper":{"arxiv_id":"2507.05311","last_updated":"2026-05-22T09:31:03Z","snapshot_observed_at":"2026-08-03T00:47:40.108614Z","submitted_at":"2025-07-07T09:48:09Z","title":"PLACE: Prompt Learning for Attributed Community Search in Large Graphs","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-25T07:47:47.529586Z"},"links":{"cited_paper":"/paper/2302.12449","citing_paper":"/paper/2507.05311"},"observation_digest":"sha256:1344737c003e002cf7e64fe08f542440ca46075a61409fd25c7796bf637f3adf","observation_id":"0f0fae6d-7fa1-41da-a90b-ab36d45160d0","resolution":{"observed_at":"2026-05-25T07:50:29.437163Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12449","snapshot_observed_at":"2026-08-02T23:38:57.436114Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.13075","last_updated":"2026-05-27T04:21:31Z","snapshot_observed_at":"2026-08-02T23:38:48.433563Z","submitted_at":"2026-02-13T16:34:55Z","title":"Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-02T23:38:57.436114Z"},"links":{"cited_paper":"/paper/2302.12449","citing_paper":"/paper/2602.13075"},"observation_digest":"sha256:5965cb4637c0a640b5a233d255820f2382557998dc689e8747d6ed6fc903565c","observation_id":"e6d74d8b-0edd-417d-8049-1ad46f2adfed","resolution":{"observed_at":"2026-08-02T23:38:57.436114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","version":2},"cited_work":{"arxiv_id":"2302.12449","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.12449","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2302.12449 , year=","venue":null,"work_id":"62e1e974-cd6e-4d0a-9394-3b5bdb89305f","year":2023},"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":72,"source":"arxiv_source","source_observed_at":"2026-05-13T04:45:34.957298Z"},"links":{"cited_paper":"/paper/2302.12449","citing_paper":"/paper/2605.12061"},"observation_digest":"sha256:a3907c420de3a82b756ffe0bc40d6cf321d70298339a8f6abcff2f8ec6f3b897","observation_id":"0d022376-c348-4b86-9d13-31f32de3b9c5","resolution":{"observed_at":"2026-05-13T04:57:17.756601Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2302.12449/citation-record","integrity":"/paper/2302.12449/integrity","json":"/paper/2302.12449/citation-record.json","paper":"/paper/2302.12449"},"outbound":[],"paper":{"arxiv_id":"2302.12449","last_updated":"2023-08-15T08:11:16Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T04:31:18Z","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.12449."}