{"as_of":"2026-08-09T22:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:93967be03f43161792282ec3090ca50319d3a21d6397c74ef9e93a7c8bae1821","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":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T20:54:10.522584Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"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":84,"source":"pdf_text","source_observed_at":"2026-05-13T11:32:36.738536Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2403.14608"},"observation_digest":"sha256:73bb54eed7f1890acb3a9e1b4df44be04b8eb44a1dfaeac2a4f4755b01746a18","observation_id":"d6b8a0bd-39bc-432a-b91e-bbc7ac8f6e38","resolution":{"observed_at":"2026-05-13T11:32:36.988252Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-08T20:54:10.522584Z","title":null,"venue":null,"work_id":null,"year":2023},"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":8,"source":"arxiv_source","source_observed_at":"2026-08-08T20:54:10.522584Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2502.04958"},"observation_digest":"sha256:289efc8e13e086bf6f1a0dbd24cbc2cecaa70ecccf01c5aaa2611dd057bfb23a","observation_id":"6b31efd2-9fd9-492e-81dd-255382b70c8b","resolution":{"observed_at":"2026-08-08T20:54:10.522584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-07T14:45:59.093074Z","title":"Sparse low-rank adaptation of pre-trained language models.arXiv preprint arXiv:2311.11696, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17872","last_updated":"2025-05-27T07:23:28Z","snapshot_observed_at":"2026-08-08T06:53:10.432858Z","submitted_at":"2025-05-23T13:24:39Z","title":"Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:45:59.093074Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2505.17872"},"observation_digest":"sha256:9370244c3fcc91cce928d1d4daf0ab52d9f9e1b8b47d02f92be8bdf6165dade5","observation_id":"bda4a17e-8b4b-4046-9686-83a024d6b536","resolution":{"observed_at":"2026-08-07T14:45:59.093074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-07T12:09:12.204135Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00495","last_updated":"2025-05-31T10:27:08Z","snapshot_observed_at":"2026-08-09T08:51:32.281143Z","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":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:09:12.204135Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2506.00495"},"observation_digest":"sha256:b19d61c0705855d300a5bf12eef28aef08acb84bc7f8305e9332598c434a1498","observation_id":"ea3c9c56-9ebb-4ecf-9443-872ff52aae0f","resolution":{"observed_at":"2026-08-07T12:09:12.204135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"citing_paper":{"arxiv_id":"2506.21035","last_updated":"2026-05-29T11:08:12Z","snapshot_observed_at":"2026-08-06T22:32:52.062894Z","submitted_at":"2025-06-26T06:19:05Z","title":"Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts","version":5},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-22T13:06:03.463520Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2506.21035"},"observation_digest":"sha256:380850e9318ee0a93e830d03983c714fbee3485eaca1d1c21d57707744e04dc3","observation_id":"c057b73e-c910-411f-abdb-66eae6efc873","resolution":{"observed_at":"2026-05-22T13:06:34.534990Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T17:33:13.657154Z","title":"Sparse low-rank adaptation of pre-trained language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.16191","last_updated":"2025-08-22T08:12:06Z","snapshot_observed_at":"2026-08-09T16:09:26.472798Z","submitted_at":"2025-08-22T08:12:06Z","title":"GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T17:33:13.657154Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2508.16191"},"observation_digest":"sha256:86c2fdd4e9e8e781f378dd4c7ab307125ec9d8e8e981f350c29bdd06ab8403e2","observation_id":"dd989852-0977-45ed-b658-fe7f01c8d078","resolution":{"observed_at":"2026-08-05T17:33:13.657154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"citing_paper":{"arxiv_id":"2605.01048","last_updated":"2026-05-01T19:23:33Z","snapshot_observed_at":"2026-07-06T23:14:20.123289Z","submitted_at":"2026-05-01T19:23:33Z","title":"Compared to What? Baselines and Metrics for Counterfactual Prompting","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-09T19:02:46.991897Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2605.01048"},"observation_digest":"sha256:0ab465189fcd44392c2ccf93ac3f08d52b5c0bf9747b4b3ac9172556e3b17617","observation_id":"d01c779f-28ea-4c6c-a015-d90d5681e30b","resolution":{"observed_at":"2026-05-09T19:05:10.367387Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"citing_paper":{"arxiv_id":"2605.07850","last_updated":"2026-05-08T15:13:01Z","snapshot_observed_at":"2026-07-06T23:20:11.042952Z","submitted_at":"2026-05-08T15:13:01Z","title":"MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-11T02:38:14.029660Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2605.07850"},"observation_digest":"sha256:f2db2217c5804491629f007b201875adbe665a5de05a90a271274c3b047022b6","observation_id":"55b06219-b1c8-45a0-91ce-8411aa6283d7","resolution":{"observed_at":"2026-05-11T03:10:53.334510Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"citing_paper":{"arxiv_id":"2606.29184","last_updated":"2026-06-28T04:08:09Z","snapshot_observed_at":"2026-08-04T15:21:29.245858Z","submitted_at":"2026-06-28T04:08:09Z","title":"BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T07:55:13.502149Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2606.29184"},"observation_digest":"sha256:da471dc6f8f5e9a417dea871690f2e9a4d186997771eb6c88c189a894b974974","observation_id":"160e22cc-41a2-4171-a2bd-e7f742963661","resolution":{"observed_at":"2026-06-30T08:04:28.480536Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":"2311.11696","doi":"10.48550/arxiv.2311.11696","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sparse low-rank adaptation of pre-trained language models","venue":"arXiv (Cornell University)","work_id":"83f3d6fd-59ec-437d-9018-ab06f1b4249b","year":2023},"citing_paper":{"arxiv_id":"2607.00162","last_updated":"2026-06-30T20:39:55Z","snapshot_observed_at":"2026-08-02T11:16:45.427866Z","submitted_at":"2026-06-30T20:39:55Z","title":"FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-07-02T19:48:16.399195Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2607.00162"},"observation_digest":"sha256:164e5e2b3b9125ba446f1f887906985a183df316597fa792c273c7c93a7e3d79","observation_id":"60363ae8-cc7d-46f9-9ce0-14fd7e7cf24d","resolution":{"observed_at":"2026-07-02T19:57:19.209421Z","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":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-07-12T01:54:44.256835Z","title":"arXiv preprint arXiv:2311.11696 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03522","last_updated":"2026-07-03T17:50:03Z","snapshot_observed_at":"2026-08-09T04:46:41.573529Z","submitted_at":"2026-07-03T17:50:03Z","title":"Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-07-12T01:54:44.256835Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2607.03522"},"observation_digest":"sha256:2ca203ec27790f5cb54b902cfaabc4ef615b921f38c0de39c18028ab4ef6f3bc","observation_id":"d183daae-99d6-4240-a294-88337b1a913c","resolution":{"observed_at":"2026-07-12T01:54:44.256835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-02T09:01:48.832025Z","title":"arXiv preprint arXiv:2311.11696 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19391","last_updated":"2026-07-02T19:01:59Z","snapshot_observed_at":"2026-08-06T10:12:22.643827Z","submitted_at":"2026-07-02T19:01:59Z","title":"LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-02T09:01:48.832025Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2607.19391"},"observation_digest":"sha256:84db3b82b7137b1a2e934612f516b1b272bc79a81ec5524a47f493319d26fd66","observation_id":"106ff14d-e86e-4835-837e-aa3bb7c0508f","resolution":{"observed_at":"2026-08-02T09:01:48.832025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11696","snapshot_observed_at":"2026-08-01T03:00:02.055379Z","title":"Sparse low-rank adaptation of pre-trained language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25299","last_updated":"2026-07-28T05:22:39Z","snapshot_observed_at":"2026-08-08T09:41:51.757306Z","submitted_at":"2026-07-28T05:22:39Z","title":"Retraction-Free Optimization over the Stiefel Manifold for the LoRA Fine-Tuning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T03:00:02.055379Z"},"links":{"cited_paper":"/paper/2311.11696","citing_paper":"/paper/2607.25299"},"observation_digest":"sha256:d06127f62846df2fdf268c67d5f01c482d24c98a681c1c64f47b22cc037ffaaf","observation_id":"2089cc76-a68d-4c1c-9dd4-ec733556e7d0","resolution":{"observed_at":"2026-08-01T03:00:02.055379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2311.11696/citation-record","integrity":"/paper/2311.11696/integrity","json":"/paper/2311.11696/citation-record.json","paper":"/paper/2311.11696"},"outbound":[],"paper":{"arxiv_id":"2311.11696","last_updated":"2023-11-20T11:56:25Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T08:07:45.481643Z","submitted_at":"2023-11-20T11:56:25Z","title":"Sparse Low-rank Adaptation of Pre-trained Language Models"},"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 13 inbound Pith citation observations for arXiv:2311.11696."}