{"as_of":"2026-08-08T01:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ad72fc245a94ffad94ee3291311862e2c73c1de2e6ad900e4271d490336a1e9","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:21:55.200079Z","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":"2026-05-15T20:10:18.168382Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-07T15:21:55.200079Z","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":"2026-08-07T15:14:58.924199Z","submitted_at":"2025-05-21T12:46:42Z","title":"CoLA: Collaborative Low-Rank Adaptation","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T15:21:55.200079Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2505.15471"},"observation_digest":"sha256:9c5759a19a0557935231f9375f757de377d4abebfb3af53e3f2aa1254773baf1","observation_id":"61fb2662-7254-4005-aea0-2ba17972cf58","resolution":{"observed_at":"2026-08-07T15:21:55.200079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-07T12:57:45.092521Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning.arXiv preprint arXiv:2404.19245, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23184","last_updated":"2025-05-29T07:22:43Z","snapshot_observed_at":"2026-08-07T12:49:02.532800Z","submitted_at":"2025-05-29T07:22:43Z","title":"Two Is Better Than One: Rotations Scale LoRAs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:57:45.092521Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2505.23184"},"observation_digest":"sha256:77623252196cb778b9db73491bf98a287f811c4ebbfdc92e307c6dd08a155e6f","observation_id":"f639c738-0256-4aa2-9b98-bf9a7a1ee11e","resolution":{"observed_at":"2026-08-07T12:57:45.092521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-07T12:09:15.315090Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00495","last_updated":"2025-05-31T10:27:08Z","snapshot_observed_at":"2026-08-08T01:16:03.657287Z","submitted_at":"2025-05-31T10:27:08Z","title":"FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:09:15.315090Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2506.00495"},"observation_digest":"sha256:d052985cf54f2c25850802c8e8555e14538d841a5bfa2b5531e7ceac9adae431","observation_id":"482b0006-63b8-4908-af19-71a8acc16119","resolution":{"observed_at":"2026-08-07T12:09:15.315090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-06T22:50:12.357927Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-07T19:34:04.990906Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.357927Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:1e6cce44236373a3d5cd8b4e2f5323690833ccc5af41c40537614a11a3219162","observation_id":"ee0ed6d0-85d7-49d5-b489-a2954f848beb","resolution":{"observed_at":"2026-08-06T22:50:12.357927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-06T13:26:24.994164Z","title":"HydraLoRA: An Asym- metric LoRA Architecture for Efficient Fine-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21199","last_updated":"2025-07-28T09:33:12Z","snapshot_observed_at":"2026-08-06T13:26:18.905871Z","submitted_at":"2025-07-28T09:33:12Z","title":"Advancing Compositional LLM Reasoning with Structured Task Relations in Interactive Multimodal Communications","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T13:26:24.994164Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2507.21199"},"observation_digest":"sha256:1b2f9e26c760114f76e508ca7e1fa3284d66b435d1d0f5a97c1908931296593b","observation_id":"9e52637e-ea97-4b5f-bca6-53d412355b8a","resolution":{"observed_at":"2026-08-06T13:26:24.994164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":"2404.19245","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning.arXiv preprint arXiv:2404.19245","venue":null,"work_id":"a0ce5753-e06a-446b-b918-e9a7e16f72a5","year":null},"citing_paper":{"arxiv_id":"2602.19926","last_updated":"2026-02-23T15:05:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-23T15:05:28Z","title":"Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T20:07:38.811639Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2602.19926"},"observation_digest":"sha256:8d932e3d2bbdc92653566ceed50ed556bf63c61afec9259f5f95c7feb2e8d349","observation_id":"873973ef-fcbf-4e2a-88d9-c227a67bcd7d","resolution":{"observed_at":"2026-05-15T20:10:18.171853Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-02T19:55:06.837098Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.00573","last_updated":"2026-06-04T02:39:00Z","snapshot_observed_at":"2026-08-04T08:08:12.263429Z","submitted_at":"2026-02-28T09:40:11Z","title":"CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-02T19:55:06.837098Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2603.00573"},"observation_digest":"sha256:5295d8bbfe8d1cccd989bbfb824a3f4c1154ba3a161e9ffd28e398d3ae7d80b3","observation_id":"3fc7a3b5-9cc6-42ff-a02b-f2737f39a2f9","resolution":{"observed_at":"2026-08-02T19:55:06.837098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-02T20:06:13.976778Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.13282","last_updated":"2026-05-26T00:25:54Z","snapshot_observed_at":"2026-08-06T10:01:07.632998Z","submitted_at":"2026-02-27T16:02:16Z","title":"FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T20:06:13.976778Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2603.13282"},"observation_digest":"sha256:1a918b47cd3c7549866e9523c2fd9d18718f1ad71a6f4e42efcb7d61b18c19d7","observation_id":"f84e373a-d451-430d-8d36-b014f02b4953","resolution":{"observed_at":"2026-08-02T20:06:13.976778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":"2404.19245","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning.arXiv preprint arXiv:2404.19245","venue":null,"work_id":"a0ce5753-e06a-446b-b918-e9a7e16f72a5","year":null},"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":10,"source":"pdf_text","source_observed_at":"2026-05-10T14:18:22.017187Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2604.13368"},"observation_digest":"sha256:267543f53b2ee76f687c8c2cc72177f7e77c5798203e175633a2a35067c1ac1c","observation_id":"9327e433-e2da-4a5c-8752-d9e1609231d4","resolution":{"observed_at":"2026-05-10T14:20:30.243129Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":"2404.19245","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning.arXiv preprint arXiv:2404.19245","venue":null,"work_id":"a0ce5753-e06a-446b-b918-e9a7e16f72a5","year":null},"citing_paper":{"arxiv_id":"2605.06175","last_updated":"2026-05-08T06:32:29Z","snapshot_observed_at":"2026-08-06T12:57:32.760363Z","submitted_at":"2026-05-07T12:56:58Z","title":"VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-08T09:11:21.715023Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2605.06175"},"observation_digest":"sha256:3b5118861f9be265dd80a5cb55392c643d55e4f1962ad70d7a3803ff43988adc","observation_id":"80ad3374-7ad6-426d-9b43-ca659b33a939","resolution":{"observed_at":"2026-05-11T20:26:10.156829Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-02T09:51:03.789153Z","title":"arXiv preprint arXiv:2404.19245 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16252","last_updated":"2026-06-27T03:25:36Z","snapshot_observed_at":"2026-08-03T16:48:30.105983Z","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":223,"source":"arxiv_source","source_observed_at":"2026-08-02T09:51:03.789153Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2607.16252"},"observation_digest":"sha256:50a5003dc7e4e506c38bb1e9999fb4a56c487397559ea5cc9a0137a270431064","observation_id":"94f6d11a-e185-41c2-b527-31d436f5ea65","resolution":{"observed_at":"2026-08-02T09:51:03.789153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.19245/citation-record","integrity":"/paper/2404.19245/integrity","json":"/paper/2404.19245/citation-record.json","paper":"/paper/2404.19245"},"outbound":[],"paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2404.19245."}