{"as_of":"2026-08-09T21:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4233f20f93bab3307a1e8f14dbed646c85411512c61374bff530fc4c00b3b240","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:18:24.399979Z","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":"2026-07-03T14:08:21.932896Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":"2404.09610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-07-03T14:08:21.932896Z","title":"Lora dropout as a spar- sity regularizer for overfitting control","venue":null,"work_id":"76d5b541-d812-4382-8135-8edd1f0b2063","year":2024},"citing_paper":{"arxiv_id":"2403.14608","last_updated":"2024-09-16T02:54:50Z","snapshot_observed_at":"2026-08-04T09:07:42.158421Z","submitted_at":"2024-03-21T17:55:50Z","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","version":7},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-13T11:32:36.738536Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2403.14608"},"observation_digest":"sha256:6bb9425de7f1e9ee67006c8687b44bb7c5e0df7fa0a0e6492a051e1638d68c58","observation_id":"bbe6b508-73c1-4b3d-bea1-d6cd1d211362","resolution":{"observed_at":"2026-05-13T11:32:36.999296Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-07T10:18:24.399979Z","title":"LoRA dropout as a sparsity regularizer for over- fitting control","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05713","last_updated":"2025-07-27T08:06:57Z","snapshot_observed_at":"2026-08-08T06:35:08.618663Z","submitted_at":"2025-06-06T03:33:06Z","title":"Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:24.399979Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2506.05713"},"observation_digest":"sha256:18a7db7d63b8c60236b96d003021b2627190d6ae2faa43a991700c6311763082","observation_id":"0a02ca4b-0986-4c10-a5b1-f3a3bbadb8d8","resolution":{"observed_at":"2026-08-07T10:18:24.399979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-06T21:36:38.028151Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23762","last_updated":"2025-06-30T12:09:29Z","snapshot_observed_at":"2026-08-06T21:29:49.550137Z","submitted_at":"2025-06-30T12:09:29Z","title":"Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead","version":1},"reference_index":200,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:38.028151Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2506.23762"},"observation_digest":"sha256:9f52237249aa76e04b0148577bb46640c79f38efb87d81fe766d757731028485","observation_id":"a3cbc716-6122-4f52-9c7d-2bdf49e20e66","resolution":{"observed_at":"2026-08-06T21:36:38.028151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-06T17:34:05.918085Z","title":"Lora dropout as a sparsity regularizer for overfitting control,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10749","last_updated":"2025-07-14T19:16:13Z","snapshot_observed_at":"2026-08-09T09:40:07.554885Z","submitted_at":"2025-07-14T19:16:13Z","title":"RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:34:05.918085Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2507.10749"},"observation_digest":"sha256:29c6e8b5c007f6ecfecddee0b2e7e14b0a0bb2b2e7a2551ade00c48713ae8434","observation_id":"d3bd287b-246f-4302-82ab-37d7c445573d","resolution":{"observed_at":"2026-08-06T17:34:05.918085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-05T20:53:19.543142Z","title":"Lora dropout as a sparsity regularizer for overfitting control,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.09790","last_updated":"2025-09-09T13:53:50Z","snapshot_observed_at":"2026-08-08T02:48:07.090558Z","submitted_at":"2025-08-13T13:20:53Z","title":"BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T20:53:19.543142Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2508.09790"},"observation_digest":"sha256:ca8524629c40c2c358a17405c920ce34622afb71ffca75f83bab9a332be865fe","observation_id":"733922c7-e768-42c4-8661-de32d0abee3d","resolution":{"observed_at":"2026-08-05T20:53:19.543142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":"2404.09610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-07-03T14:08:21.932896Z","title":"Lora dropout as a spar- sity regularizer for overfitting control","venue":null,"work_id":"76d5b541-d812-4382-8135-8edd1f0b2063","year":2024},"citing_paper":{"arxiv_id":"2509.19602","last_updated":"2026-04-25T01:29:24Z","snapshot_observed_at":"2026-07-06T22:30:36.671861Z","submitted_at":"2025-09-23T21:51:04Z","title":"Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-18T13:41:05.981163Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2509.19602"},"observation_digest":"sha256:45a2cafeb6c4cd4e10c5737af103a4a9c19777dba0eba75921010150a3b5cfa8","observation_id":"30750dc6-100e-4404-9dd6-3bb19de6cbfa","resolution":{"observed_at":"2026-05-18T13:41:25.353792Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-03T09:45:42.953279Z","title":"Lora dropout as a sparsity regularizer for overfitting control.arXiv preprint arXiv:2404.09610,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.11171","last_updated":"2026-05-28T09:44:38Z","snapshot_observed_at":"2026-08-03T09:45:40.462025Z","submitted_at":"2026-01-19T08:48:03Z","title":"A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T09:45:42.953279Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2602.11171"},"observation_digest":"sha256:b2130fc8922530495d3e3f47ee9d05d515f3888f0282e187d5f9d7c6bd6be1e3","observation_id":"af254bd9-57ca-49e1-b0f7-5253de3d2a74","resolution":{"observed_at":"2026-08-03T09:45:42.953279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":"2404.09610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-07-03T14:08:21.932896Z","title":"Lora dropout as a spar- sity regularizer for overfitting control","venue":null,"work_id":"76d5b541-d812-4382-8135-8edd1f0b2063","year":2024},"citing_paper":{"arxiv_id":"2603.07561","last_updated":"2026-05-19T08:17:21Z","snapshot_observed_at":"2026-08-02T22:32:41.335454Z","submitted_at":"2026-03-08T09:50:20Z","title":"PureCC: Pure Learning for Text-to-Image Concept Customization","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-21T11:09:15.512162Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2603.07561"},"observation_digest":"sha256:902d8ca40a51e72243f01b4f521df85a6db9cc9b666fa79097e8cf040fcfd7b9","observation_id":"69efa364-4044-47c3-aac2-25f379122cc5","resolution":{"observed_at":"2026-05-21T11:10:02.195293Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":"2404.09610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-07-03T14:08:21.932896Z","title":"Lora dropout as a spar- sity regularizer for overfitting control","venue":null,"work_id":"76d5b541-d812-4382-8135-8edd1f0b2063","year":2024},"citing_paper":{"arxiv_id":"2606.12883","last_updated":"2026-06-11T04:19:32Z","snapshot_observed_at":"2026-08-02T22:02:12.932569Z","submitted_at":"2026-06-11T04:19:32Z","title":"The Hidden Power of Scaling Factor in LoRA Optimization","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-06-27T07:14:08.479610Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2606.12883"},"observation_digest":"sha256:e8b36143c494e4da3ec82474ae6ed7180ddbf69e982f0672095a912e45d0df2d","observation_id":"5c02c39e-0076-45e3-a55d-c0eb59e54b68","resolution":{"observed_at":"2026-07-03T14:08:21.934172Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09610","snapshot_observed_at":"2026-08-04T18:33:56.909201Z","title":"arXiv preprint arXiv:2404.09610 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01906","last_updated":"2026-08-03T08:39:59Z","snapshot_observed_at":"2026-08-06T23:33:49.347908Z","submitted_at":"2026-08-03T08:39:59Z","title":"Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T18:33:56.909201Z"},"links":{"cited_paper":"/paper/2404.09610","citing_paper":"/paper/2608.01906"},"observation_digest":"sha256:73759648d8379a73e0ecdd090fe6910ed64ae17110964b9c1f45c726830f1ca7","observation_id":"316086ef-5861-4be6-8b90-bccb62e2e1dd","resolution":{"observed_at":"2026-08-04T18:33:56.909201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.09610/citation-record","integrity":"/paper/2404.09610/integrity","json":"/paper/2404.09610/citation-record.json","paper":"/paper/2404.09610"},"outbound":[],"paper":{"arxiv_id":"2404.09610","last_updated":"2024-04-15T09:32:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:00:12.130869Z","submitted_at":"2024-04-15T09:32:12Z","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.09610."}