{"as_of":"2026-08-17T19:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d751a144d4cd096f7740bb6d09fb60e26cbbe66706a38d760cda5b565943f33d","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:34:22.289852Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T18:10:46.264896Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.10951","last_updated":"2022-07-22T09:01:21Z","snapshot_observed_at":"2026-08-17T12:56:58.932569Z","submitted_at":"2022-07-22T09:01:21Z","title":"Hyper-Representations for Pre-Training and Transfer Learning","version":1},"cited_work":{"arxiv_id":"2207.10951","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.10951","snapshot_observed_at":"2026-08-10T18:10:46.264896Z","title":"Hyper-Representations for Pre-Training and Transfer Learning","venue":"cs.LG","work_id":"02c2d2a9-35e4-4479-8bcc-dd284446ef5a","year":2022},"citing_paper":{"arxiv_id":"2501.11587","last_updated":"2025-02-11T03:29:30Z","snapshot_observed_at":"2026-08-16T12:14:15.896014Z","submitted_at":"2025-01-20T16:46:26Z","title":"Recurrent Diffusion for Large-Scale Parameter Generation","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T18:10:45.968818Z"},"links":{"cited_paper":"/paper/2207.10951","citing_paper":"/paper/2501.11587"},"observation_digest":"sha256:c626a6bb948fe06817d5471d42ce599ed8c2d5a733a3cbf3d928281d5c85c106","observation_id":"db9be8d3-493d-4ef3-bf82-a6f6042c3d7a","resolution":{"observed_at":"2026-08-10T18:10:46.272044Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10951","last_updated":"2022-07-22T09:01:21Z","snapshot_observed_at":"2026-08-17T12:56:58.932569Z","submitted_at":"2022-07-22T09:01:21Z","title":"Hyper-Representations for Pre-Training and Transfer Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.10951","snapshot_observed_at":"2026-08-15T19:34:22.289852Z","title":"Hyper- representations for pre-training and transfer learning.arXiv preprint arXiv:2207.10951, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16406","last_updated":"2025-06-19T15:38:21Z","snapshot_observed_at":"2026-08-16T04:39:21.535380Z","submitted_at":"2025-06-19T15:38:21Z","title":"Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T19:34:22.289852Z"},"links":{"cited_paper":"/paper/2207.10951","citing_paper":"/paper/2506.16406"},"observation_digest":"sha256:48eadc8138aa8ba0fd97dbad17a691b45099f13789b404191142533c88328b89","observation_id":"54f35f69-9a9e-4e3a-84fb-7fca8e1b4907","resolution":{"observed_at":"2026-08-15T19:34:22.289852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2207.10951/citation-record","integrity":"/paper/2207.10951/integrity","json":"/paper/2207.10951/citation-record.json","paper":"/paper/2207.10951"},"outbound":[],"paper":{"arxiv_id":"2207.10951","last_updated":"2022-07-22T09:01:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T12:56:58.932569Z","submitted_at":"2022-07-22T09:01:21Z","title":"Hyper-Representations for Pre-Training and Transfer Learning"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.10951."}