{"as_of":"2026-08-10T13:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:15904f9ee13699e880c3b1b5aa0fade7bf6596591774e03df2fb09add5710a39","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":"2026-08-10T06:31:04.303077+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":"2026-08-08T20:54:10.593617Z","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":"2026-08-06T11:37:43.550516Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15207","snapshot_observed_at":"2026-08-08T20:54:10.593617Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04958","last_updated":"2025-02-07T14:22:35Z","snapshot_observed_at":"2026-08-09T00:26:10.962856Z","submitted_at":"2025-02-07T14:22:35Z","title":"SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T20:54:10.593617Z"},"links":{"cited_paper":"/paper/2401.15207","citing_paper":"/paper/2502.04958"},"observation_digest":"sha256:ac5e003683808be55609452584ddcaf05e1172275133c9cbdb62e1cd6b1d91cb","observation_id":"1cea7b6d-03f0-4d79-b005-f3be4fb59c66","resolution":{"observed_at":"2026-08-08T20:54:10.593617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15207","snapshot_observed_at":"2026-08-07T13:44:34.329800Z","title":"Hift: A hierarchical full parameter fine-tuning strategy.arXiv preprint arXiv:2401.15207, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21226","last_updated":"2025-06-03T14:43:50Z","snapshot_observed_at":"2026-08-09T03:32:40.483446Z","submitted_at":"2025-05-27T14:10:46Z","title":"Why Do More Experts Fail? A Theoretical Analysis of Model Merging","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:34.329800Z"},"links":{"cited_paper":"/paper/2401.15207","citing_paper":"/paper/2505.21226"},"observation_digest":"sha256:73a85cfc369cc5bbfe4c88e00f77d08749712a47bb73c6ce76475d23a7cfaf67","observation_id":"1c215081-916c-4983-9e2e-78cf062b1f82","resolution":{"observed_at":"2026-08-07T13:44:34.329800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15207","snapshot_observed_at":"2026-08-07T13:15:41.039555Z","title":"Hift: A hierarchical full parameter fine-tuning strategy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22355","last_updated":"2025-05-28T13:35:12Z","snapshot_observed_at":"2026-08-09T09:37:33.386478Z","submitted_at":"2025-05-28T13:35:12Z","title":"Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:15:41.039555Z"},"links":{"cited_paper":"/paper/2401.15207","citing_paper":"/paper/2505.22355"},"observation_digest":"sha256:847e946b05878f94f985c5ee6bfaf5eedca886f6a04cfa55e8fa53e45dc28ffc","observation_id":"2e3b0e7d-0f44-43e3-a0a7-358d6297b950","resolution":{"observed_at":"2026-08-07T13:15:41.039555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15207","snapshot_observed_at":"2026-08-07T13:27:08.601156Z","title":"Hift: A hier- archical full parameter fine-tuning strategy ,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23818","last_updated":"2025-05-27T22:17:27Z","snapshot_observed_at":"2026-08-10T00:55:11.763100Z","submitted_at":"2025-05-27T22:17:27Z","title":"Ratas framework: A comprehensive genai-based approach to rubric-based marking of real-world textual exams","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T13:27:08.601156Z"},"links":{"cited_paper":"/paper/2401.15207","citing_paper":"/paper/2505.23818"},"observation_digest":"sha256:396fcf268591fa300b54f16daf368a19c4622e10ad7af7c898ea23abf2febbb7","observation_id":"59340d76-f891-42ef-8770-0f4004a2d841","resolution":{"observed_at":"2026-08-07T13:27:08.601156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","version":3},"cited_work":{"arxiv_id":"2401.15207","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.15207","snapshot_observed_at":"2026-08-06T11:37:43.550516Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy","venue":"cs.LG","work_id":"1cf7f47e-2831-4b1b-87c0-ad645dff2864","year":2024},"citing_paper":{"arxiv_id":"2507.22633","last_updated":"2025-07-31T01:43:24Z","snapshot_observed_at":"2026-08-09T06:54:22.341085Z","submitted_at":"2025-07-30T12:53:18Z","title":"H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:37:42.148152Z"},"links":{"cited_paper":"/paper/2401.15207","citing_paper":"/paper/2507.22633"},"observation_digest":"sha256:99e0da11f8c6482c4f9885487a5cddf41eba914359b9c1a1593bddb8dfa910d2","observation_id":"d2e1c1e1-11cc-4295-956a-9b25fe928a0e","resolution":{"observed_at":"2026-08-06T11:37:43.640647Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.15207/citation-record","integrity":"/paper/2401.15207/integrity","json":"/paper/2401.15207/citation-record.json","paper":"/paper/2401.15207"},"outbound":[],"paper":{"arxiv_id":"2401.15207","last_updated":"2024-06-17T10:35:06Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:21:10.236126Z","submitted_at":"2024-01-26T21:14:32Z","title":"HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.15207."}