{"as_of":"2026-08-08T15:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:96b9b000876f927dab58a838d8a32c8d30c70b8d7e92d9e54bf0ca079a85e88a","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":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T11:51:10.356322Z","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":"2026-07-04T08:19:44.421796Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-08T11:51:10.356322Z","title":"Lora-ga: Low-rank adaptation with gradient approxima- tion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07739","last_updated":"2025-05-26T08:53:14Z","snapshot_observed_at":"2026-08-08T11:40:44.827344Z","submitted_at":"2025-02-11T17:59:35Z","title":"HRP: High-Rank Preheating for Superior LoRA Initialization","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T11:51:10.356322Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2502.07739"},"observation_digest":"sha256:2f59bf746392f9ece6a6bba668189112ba3f807f11d15468c3222f8a1b6e1fde","observation_id":"16c7bea6-2db8-42e7-8c08-7b110304cfd2","resolution":{"observed_at":"2026-08-08T11:51:10.356322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-08T10:26:45.517130Z","title":"Lora-ga: Low-rank adaptation with gradient approxi- mation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08141","last_updated":"2025-02-12T05:48:26Z","snapshot_observed_at":"2026-08-08T10:15:21.466304Z","submitted_at":"2025-02-12T05:48:26Z","title":"LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T10:26:45.517130Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2502.08141"},"observation_digest":"sha256:e1c03f835f610a4e34083f440d8fa43dabd4c417416de079511da3449356916e","observation_id":"fd9232b5-9df6-49a1-ae56-1b4d57f1c518","resolution":{"observed_at":"2026-08-08T10:26:45.517130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-07T15:21:55.449383Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15471","last_updated":"2025-05-21T12:46:42Z","snapshot_observed_at":"2026-08-08T09:01:42.423991Z","submitted_at":"2025-05-21T12:46:42Z","title":"CoLA: Collaborative Low-Rank Adaptation","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T15:21:55.449383Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2505.15471"},"observation_digest":"sha256:8d6db8e16c00e1c62366e5e69cc3607231b219b92bc5bf5904c3cc07c4308bb5","observation_id":"0c63dcc5-bc46-41aa-977e-89b6d0396b97","resolution":{"observed_at":"2026-08-07T15:21:55.449383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-07T14:31:47.518653Z","title":"Beyond accuracy: Evaluating the reasoning behavior of large language models -- a survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21537","last_updated":"2025-05-24T09:07:13Z","snapshot_observed_at":"2026-08-08T02:37:32.325481Z","submitted_at":"2025-05-24T09:07:13Z","title":"OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models","version":1},"reference_index":146,"source":"arxiv_source","source_observed_at":"2026-08-07T14:31:47.518653Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2505.21537"},"observation_digest":"sha256:823b37975e2b1b1a5fcedc986c2551d9650e47da5ade0ab24866e45773daeed4","observation_id":"6739a88d-d2e1-4775-8820-a5b11a4f267a","resolution":{"observed_at":"2026-08-07T14:31:47.518653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-06T18:48:54.774002Z","title":"Lora-ga: Low- rank adaptation with gradient approximation.arXiv preprint arXiv:2407.05000, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08044","last_updated":"2025-07-09T23:52:31Z","snapshot_observed_at":"2026-08-08T12:59:27.580998Z","submitted_at":"2025-07-09T23:52:31Z","title":"ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T18:48:54.774002Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2507.08044"},"observation_digest":"sha256:a5a42899c4d42884eb6123075b87e0a240a11d436b9151b2cc02021b16bde8d9","observation_id":"ce71ade5-4c56-4a0e-9682-45c5f43e62a0","resolution":{"observed_at":"2026-08-06T18:48:54.774002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":"2407.05000","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-07-04T08:19:44.421796Z","title":"Alex Warstadt, Amanpreet Singh, and Samuel R","venue":null,"work_id":"9650afce-8dba-4f1b-ba1f-5fb2041799ae","year":2024},"citing_paper":{"arxiv_id":"2509.18629","last_updated":"2026-04-22T19:36:04Z","snapshot_observed_at":"2026-07-06T22:30:31.933240Z","submitted_at":"2025-09-23T04:29:26Z","title":"HyperAdapt: Simple High-Rank Adaptation","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-18T13:44:09.263459Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2509.18629"},"observation_digest":"sha256:6e1deb2982e98f07d8a3bf936dd6b19e024c12ceee0c105c3d461e143675f07e","observation_id":"330be462-9aa2-47d5-84d0-1df3afdad9f8","resolution":{"observed_at":"2026-05-18T13:46:25.971700Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-03T11:47:19.001908Z","title":"arXiv:2407.05000","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.16991","last_updated":"2026-07-20T07:18:25Z","snapshot_observed_at":"2026-08-06T23:33:16.449906Z","submitted_at":"2026-01-08T20:34:12Z","title":"Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T11:47:19.001908Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2601.16991"},"observation_digest":"sha256:728563a7ea006718ad9889ddccbb46465c8a01cd38e407025ed00ce61cf8a5f8","observation_id":"e55e3f5e-b0aa-4b84-88d9-5b2677592d1c","resolution":{"observed_at":"2026-08-03T11:47:19.001908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":"2407.05000","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-07-04T08:19:44.421796Z","title":"Alex Warstadt, Amanpreet Singh, and Samuel R","venue":null,"work_id":"9650afce-8dba-4f1b-ba1f-5fb2041799ae","year":2024},"citing_paper":{"arxiv_id":"2604.13368","last_updated":"2026-04-15T00:30:57Z","snapshot_observed_at":"2026-08-02T07:39:46.201720Z","submitted_at":"2026-04-15T00:30:57Z","title":"TLoRA+: A Low-Rank Parameter-Efficient Fine-Tuning Method for Large Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T14:18:22.017187Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2604.13368"},"observation_digest":"sha256:e6c3889670a8df9f5c6fd66b82c2ba218dfe05b5c00c23f3732b9191134597df","observation_id":"af660c30-3218-4d44-b3ba-d44a57b3be09","resolution":{"observed_at":"2026-05-10T14:20:30.230973Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":"2407.05000","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-07-04T08:19:44.421796Z","title":"Alex Warstadt, Amanpreet Singh, and Samuel R","venue":null,"work_id":"9650afce-8dba-4f1b-ba1f-5fb2041799ae","year":2024},"citing_paper":{"arxiv_id":"2605.29460","last_updated":"2026-05-28T06:53:46Z","snapshot_observed_at":"2026-08-01T03:27:15.590666Z","submitted_at":"2026-05-28T06:53:46Z","title":"FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T08:08:47.402298Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2605.29460"},"observation_digest":"sha256:dc68cf243f43f539c45a47db787b2af17ebe1b5cbb0dfee3c529f360d3ee8349","observation_id":"340d5d58-483b-4410-8c5f-10937c98d28e","resolution":{"observed_at":"2026-06-29T08:13:15.245982Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":"2407.05000","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-07-04T08:19:44.421796Z","title":"Alex Warstadt, Amanpreet Singh, and Samuel R","venue":null,"work_id":"9650afce-8dba-4f1b-ba1f-5fb2041799ae","year":2024},"citing_paper":{"arxiv_id":"2606.02079","last_updated":"2026-06-01T11:08:31Z","snapshot_observed_at":"2026-08-02T15:24:15.565847Z","submitted_at":"2026-06-01T11:08:31Z","title":"FACT: A Simple and Efficient Framework for Active Finetuning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T15:28:21.303268Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2606.02079"},"observation_digest":"sha256:e196c2ea64196ce76252d7f719601b986a4129366e2da49be816c448aacb9486","observation_id":"423cf796-4eea-400a-9c21-d5e6ad618682","resolution":{"observed_at":"2026-07-01T22:16:17.174886Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":"2407.05000","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-07-04T08:19:44.421796Z","title":"Alex Warstadt, Amanpreet Singh, and Samuel R","venue":null,"work_id":"9650afce-8dba-4f1b-ba1f-5fb2041799ae","year":2024},"citing_paper":{"arxiv_id":"2606.22019","last_updated":"2026-06-20T12:48:31Z","snapshot_observed_at":"2026-08-06T19:21:18.734073Z","submitted_at":"2026-06-20T12:48:31Z","title":"Channel Location Constrains the Auditability of Subliminal Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T11:52:03.948568Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2606.22019"},"observation_digest":"sha256:57674ba7ffbd81fae25c10b0972ff4ce78ec31facd2e3c5b44990a97fa1459ec","observation_id":"e6056490-1f13-4844-b04f-3a0bc593837c","resolution":{"observed_at":"2026-07-04T08:19:44.424048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.05000","snapshot_observed_at":"2026-08-02T09:51:03.838707Z","title":"arXiv preprint arXiv:2407.05000 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16252","last_updated":"2026-06-27T03:25:36Z","snapshot_observed_at":"2026-08-08T02:31:05.095460Z","submitted_at":"2026-06-27T03:25:36Z","title":"SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling","version":1},"reference_index":244,"source":"arxiv_source","source_observed_at":"2026-08-02T09:51:03.838707Z"},"links":{"cited_paper":"/paper/2407.05000","citing_paper":"/paper/2607.16252"},"observation_digest":"sha256:9109931e9171d9dbbb390efc2cd77ebb7020f2449786025c44327d3a1a4ba2db","observation_id":"98c5a593-30cc-440a-9745-9d0a63ddb207","resolution":{"observed_at":"2026-08-02T09:51:03.838707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.05000/citation-record","integrity":"/paper/2407.05000/integrity","json":"/paper/2407.05000/citation-record.json","paper":"/paper/2407.05000"},"outbound":[],"paper":{"arxiv_id":"2407.05000","last_updated":"2024-07-16T07:32:23Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2407.05000."}