{"as_of":"2026-08-16T00:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:444fc34302820a097d40f3b65d437ec2c46c359f8ecf27be6b0330b49b532d19","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:46:32.651677Z","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":"2026-06-29T07:53:13.386209Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-08-07T23:22:20.522370Z","title":"Lora-drop: Efficient lora parameter pruning based on output evaluation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08905","last_updated":"2025-06-04T07:14:31Z","snapshot_observed_at":"2026-08-14T22:25:49.336576Z","submitted_at":"2025-02-13T02:41:34Z","title":"DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T23:22:20.522370Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2502.08905"},"observation_digest":"sha256:91156887ab1eff537549df0f7930abaa8f19615d06b6430b6fbca5a69e761c1c","observation_id":"6e110f34-5562-4b07-bc7c-60e1f2167ae3","resolution":{"observed_at":"2026-08-07T23:22:20.522370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-08-16T00:46:32.651677Z","title":"Lora-drop: Efficient LoRA Parameter Pruning Based on Output Evaluation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.02795","last_updated":"2025-05-05T17:09:19Z","snapshot_observed_at":"2026-08-16T00:38:15.941090Z","submitted_at":"2025-05-05T17:09:19Z","title":"HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-16T00:46:32.651677Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2505.02795"},"observation_digest":"sha256:d9cb872085aca8a2551ff059abe593b711e2975cf2576c81db097fd9344507eb","observation_id":"b8147d97-f60b-4829-af7d-89e5252d49fc","resolution":{"observed_at":"2026-08-16T00:46:32.651677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":"2402.07721","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-06-29T07:53:13.386209Z","title":"Lora-drop: Efficient lora parameter pruning based on output evaluation","venue":null,"work_id":"04193fe3-b762-4cfc-8c61-b2972a38c082","year":2015},"citing_paper":{"arxiv_id":"2507.00029","last_updated":"2026-05-13T05:28:39Z","snapshot_observed_at":"2026-08-11T06:15:13.326412Z","submitted_at":"2025-06-17T14:58:54Z","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-19T09:05:25.236355Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2507.00029"},"observation_digest":"sha256:6af1a489ac408ad5e5abb319c952a1fb23f3692e956da1366f5f44606adfd259","observation_id":"c9e5d8e5-698d-48a4-ae76-31f190735220","resolution":{"observed_at":"2026-05-19T09:07:14.682698Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-08-06T19:38:17.491829Z","title":"Lora-drop: Efficient lora parameter pruning based on output evaluation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05566","last_updated":"2025-07-08T01:11:30Z","snapshot_observed_at":"2026-08-12T19:14:38.615873Z","submitted_at":"2025-07-08T01:11:30Z","title":"SingLoRA: Low Rank Adaptation Using a Single Matrix","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:38:17.491829Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2507.05566"},"observation_digest":"sha256:7cdd7857150f468dfe6a2bb120747d580e816f7efd08fb4d01e4fbafccb86ee2","observation_id":"1e311720-137b-4784-b3f1-52d732647a88","resolution":{"observed_at":"2026-08-06T19:38:17.491829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":"2402.07721","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-06-29T07:53:13.386209Z","title":"Lora-drop: Efficient lora parameter pruning based on output evaluation","venue":null,"work_id":"04193fe3-b762-4cfc-8c61-b2972a38c082","year":2015},"citing_paper":{"arxiv_id":"2511.11051","last_updated":"2026-05-22T01:46:51Z","snapshot_observed_at":"2026-08-14T06:24:52.262441Z","submitted_at":"2025-11-14T08:06:01Z","title":"NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T07:58:25.211030Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2511.11051"},"observation_digest":"sha256:a31b917ee9e27718f71f40dc97b87415beedae67a5be758966de0fc73a1a3dde","observation_id":"9ca471bf-4565-4079-9d42-41ccaec6031c","resolution":{"observed_at":"2026-05-25T08:00:29.755868Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","version":2},"cited_work":{"arxiv_id":"2402.07721","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.07721","snapshot_observed_at":"2026-06-29T07:53:13.386209Z","title":"Lora-drop: Efficient lora parameter pruning based on output evaluation","venue":null,"work_id":"04193fe3-b762-4cfc-8c61-b2972a38c082","year":2015},"citing_paper":{"arxiv_id":"2605.29317","last_updated":"2026-05-29T03:38:01Z","snapshot_observed_at":"2026-07-06T23:38:46.361360Z","submitted_at":"2026-05-28T03:47:00Z","title":"FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T07:50:48.558830Z"},"links":{"cited_paper":"/paper/2402.07721","citing_paper":"/paper/2605.29317"},"observation_digest":"sha256:b3a27b42dca28c65834769c59a73be9f4b76b8e34e0211a5c5a65f627d2e1794","observation_id":"e1c81429-7cb8-4de5-9812-fd261bc342d3","resolution":{"observed_at":"2026-06-29T07:53:13.387550Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.07721/citation-record","integrity":"/paper/2402.07721/integrity","json":"/paper/2402.07721/citation-record.json","paper":"/paper/2402.07721"},"outbound":[],"paper":{"arxiv_id":"2402.07721","last_updated":"2024-06-18T15:13:12Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T04:53:47.234837Z","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.07721."}