{"as_of":"2026-08-19T05:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:92e16b7bb894a4e00e3e0617b546b01147bbfcb6c3b7e2fda069ad3a35ff6951","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":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":27,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:59:41.615381Z","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":"2026-07-03T04:07:36.843758Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2304.15010","last_updated":"2023-04-28T17:59:25Z","snapshot_observed_at":"2026-08-12T23:40:42.885633Z","submitted_at":"2023-04-28T17:59:25Z","title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T08:41:04.743886Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2304.15010"},"observation_digest":"sha256:d8ff6414a5148c144309cbad2bd9e2a74553320ee12f095d6ce1f85f2011bed8","observation_id":"3d2d8b35-1dff-40bc-8d22-718cad24271a","resolution":{"observed_at":"2026-05-15T08:41:04.794104Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2403.03507","last_updated":"2024-06-02T21:24:12Z","snapshot_observed_at":"2026-08-17T03:45:26.402267Z","submitted_at":"2024-03-06T07:29:57Z","title":"GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-16T23:51:50.163520Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2403.03507"},"observation_digest":"sha256:c5924e44416a651cc6427de011267612ef5f428683cb12a1a03994eafc3cd9b5","observation_id":"9c58cbcd-becb-45ba-9000-8b79233b7f35","resolution":{"observed_at":"2026-05-16T23:51:50.295166Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2404.13501","last_updated":"2024-04-21T01:49:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-04-21T01:49:46Z","title":"A Survey on the Memory Mechanism of Large Language Model based Agents","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-15T07:21:39.440092Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2404.13501"},"observation_digest":"sha256:0fecdff0f176b2bf408290cf43f72f1afc851d6ddc96989172adfac1dec8a9b8","observation_id":"3d4d83ba-78bb-467c-8921-669f0845d63e","resolution":{"observed_at":"2026-05-15T07:21:39.949513Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2406.00515","last_updated":"2024-11-10T22:02:27Z","snapshot_observed_at":"2026-08-14T16:40:35.729198Z","submitted_at":"2024-06-01T17:48:15Z","title":"A Survey on Large Language Models for Code Generation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-13T20:18:06.304134Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2406.00515"},"observation_digest":"sha256:d5b275b3cd558fccaa7acc55b6ede54c4833a32957cddb69710bd071384a6cb4","observation_id":"33ad3137-b31c-4fa1-898f-fbada4110ecf","resolution":{"observed_at":"2026-05-13T20:18:06.720369Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2410.02713","last_updated":"2025-08-01T16:40:14Z","snapshot_observed_at":"2026-08-02T12:24:31.329178Z","submitted_at":"2024-10-03T17:36:49Z","title":"LLaVA-Video: Video Instruction Tuning With Synthetic Data","version":3},"reference_index":133,"source":"arxiv_source","source_observed_at":"2026-05-10T23:20:32.330351Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2410.02713"},"observation_digest":"sha256:ec3c9925aa3450c3133880f7f544c0b67a32deb0046eb56fe1a559ecd5955348","observation_id":"dffb9ab2-0397-4a0a-a3f2-46e14e130260","resolution":{"observed_at":"2026-05-10T23:20:32.777912Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-12T14:59:24.081593Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14790","last_updated":"2025-05-15T13:02:21Z","snapshot_observed_at":"2026-08-16T11:31:39.632461Z","submitted_at":"2024-11-22T08:21:03Z","title":"KBAlign: Efficient Self Adaptation on Specific Knowledge Bases","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T14:59:24.081593Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2411.14790"},"observation_digest":"sha256:3c269fbf4e8e3c5f6a9bf909dc662512d261fc754728fae84cfe23367ace857f","observation_id":"fb5490ed-9f6c-4e42-9580-738bc5d063d4","resolution":{"observed_at":"2026-08-12T14:59:24.081593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-12T13:07:04.168998Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16525","last_updated":"2025-06-05T23:04:39Z","snapshot_observed_at":"2026-08-13T12:09:17.275284Z","submitted_at":"2024-11-25T16:12:17Z","title":"Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T13:07:04.168998Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2411.16525"},"observation_digest":"sha256:525dd2acf6795a0be3af539df31a1e9ee86c5dbf979e34270abde4b6ac9ee589","observation_id":"e721d176-9bc2-4b5e-b41d-f7747bd6b2be","resolution":{"observed_at":"2026-08-12T13:07:04.168998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-11T20:08:27.915161Z","title":"S parse A dapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06071","last_updated":"2025-02-28T05:46:45Z","snapshot_observed_at":"2026-08-18T09:43:41.463846Z","submitted_at":"2024-12-08T21:26:22Z","title":"KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-11T20:08:27.915161Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2412.06071"},"observation_digest":"sha256:d39315c15a4a381dace8f6d92cacd1c7044bb065079add4a7285fb4234f1e2d9","observation_id":"fce0980d-f86b-42a7-9cf3-6ef7a5ba5a3f","resolution":{"observed_at":"2026-08-11T20:08:27.915161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-10T22:49:57.603533Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.00684","last_updated":"2025-01-01T00:01:27Z","snapshot_observed_at":"2026-08-18T19:58:49.567235Z","submitted_at":"2025-01-01T00:01:27Z","title":"IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:57.603533Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2501.00684"},"observation_digest":"sha256:5805c76d68c505bbd9c7dfbe435fea182e416e6e4b208495eedaa9fd2e3f31e0","observation_id":"fbfbe76d-d5e2-499e-8d6c-6e3bcf9faf50","resolution":{"observed_at":"2026-08-10T22:49:57.603533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-09T16:56:09.381937Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01033","last_updated":"2025-02-03T04:06:03Z","snapshot_observed_at":"2026-08-17T02:37:20.054525Z","submitted_at":"2025-02-03T04:06:03Z","title":"PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T16:56:09.381937Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2502.01033"},"observation_digest":"sha256:3a00ea86427e2e3b3f92d3f1a9e5f9c703b18fa2cda75af5839c333ed2aa1754","observation_id":"7b6c4ea3-6c0f-41f7-83b5-76ff94731a89","resolution":{"observed_at":"2026-08-09T16:56:09.381937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-08T13:57:16.436527Z","title":"In International Conference on Learning Representations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.07072","last_updated":"2025-03-11T17:08:05Z","snapshot_observed_at":"2026-08-18T02:43:09.050335Z","submitted_at":"2025-02-10T22:07:02Z","title":"IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models","version":3},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-08T13:57:16.436527Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2502.07072"},"observation_digest":"sha256:968c9b4ca9b3544cd7363864a15e6f5410138215c39d885043a49723f6899b8a","observation_id":"d13b770b-0a2d-47ca-9ada-77c6ee0c4afe","resolution":{"observed_at":"2026-08-08T13:57:16.436527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-15T21:59:41.615381Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08463","last_updated":"2025-05-29T05:01:48Z","snapshot_observed_at":"2026-08-17T01:20:51.823122Z","submitted_at":"2025-05-13T11:47:00Z","title":"RepCali: High Efficient Fine-tuning Via Representation Calibration in Latent Space for Pre-trained Language Models","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T21:59:41.615381Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2505.08463"},"observation_digest":"sha256:7ccc1d9c7d787e62bc13920acdc68ec31ed2e19bd090c024f038161337c2b9b5","observation_id":"e99468f7-e6a1-4d65-9731-cc93f17ad700","resolution":{"observed_at":"2026-08-15T21:59:41.615381Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-07T13:47:16.628672Z","title":"Delta tuning: A comprehen- sive study of parameter efficient methods for pre-trained lan- guage models.arXiv preprint arXiv:2203.06904, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20941","last_updated":"2025-05-27T09:27:16Z","snapshot_observed_at":"2026-08-16T03:44:32.725795Z","submitted_at":"2025-05-27T09:27:16Z","title":"PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:47:16.628672Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2505.20941"},"observation_digest":"sha256:4d99df6a5898d9f6f39a60fa8b0fbc655d7ffac9a16c10e0e7ffdce4c61ce0e4","observation_id":"2cd2324b-9b75-41ba-868f-85bd6457a73c","resolution":{"observed_at":"2026-08-07T13:47:16.628672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-07T01:03:38.175326Z","title":"Delta tuning: A comprehensive study of parameter efficient meth- ods for pre-trained language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12213","last_updated":"2025-06-13T20:31:17Z","snapshot_observed_at":"2026-08-16T14:53:24.699190Z","submitted_at":"2025-06-13T20:31:17Z","title":"Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T01:03:38.175326Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2506.12213"},"observation_digest":"sha256:cf1cfa58f8f932ead9859650df37d0ff58bcf730485b4eb8ebd82683520f208e","observation_id":"1f2f099d-e45e-498f-978d-0f5dd9861fbe","resolution":{"observed_at":"2026-08-07T01:03:38.175326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-07T00:28:39.344281Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.14530","last_updated":"2025-06-17T13:55:13Z","snapshot_observed_at":"2026-08-17T10:43:26.320960Z","submitted_at":"2025-06-17T13:55:13Z","title":"Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T00:28:39.344281Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2506.14530"},"observation_digest":"sha256:5aac238bb4fa9ef9acc1764d7d82f3bf52ed8870646e11edd888046a7028f394","observation_id":"abeddf7b-7e53-4746-9c12-1baa38dae98f","resolution":{"observed_at":"2026-08-07T00:28:39.344281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-06T20:53:28.445295Z","title":"doi: 10.18653/v1/2020.emnlp-demos.7","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01806","last_updated":"2025-07-02T15:24:47Z","snapshot_observed_at":"2026-08-17T11:59:19.929516Z","submitted_at":"2025-07-02T15:24:47Z","title":"LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T20:53:28.445295Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2507.01806"},"observation_digest":"sha256:c2ccef2be55e28c81591752ca92f67c6d3e638ea9a337844633bf686a8c75d9a","observation_id":"67e36b4f-4bfb-4796-94a9-3423ad939ee1","resolution":{"observed_at":"2026-08-06T20:53:28.445295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-06T16:49:56.785510Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12566","last_updated":"2025-07-16T18:31:23Z","snapshot_observed_at":"2026-08-16T04:56:37.330824Z","submitted_at":"2025-07-16T18:31:23Z","title":"Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:49:56.785510Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2507.12566"},"observation_digest":"sha256:99718ffea72bdf7ca062dd6ffc224bbe374f872b54ef64cce61820b1d6208fcc","observation_id":"2cffd4b2-6780-4103-aa7f-85eca601f8e6","resolution":{"observed_at":"2026-08-06T16:49:56.785510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-15T18:22:34.608560Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17706","last_updated":"2025-07-23T17:12:19Z","snapshot_observed_at":"2026-08-15T18:16:06.562729Z","submitted_at":"2025-07-23T17:12:19Z","title":"HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T18:22:34.608560Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2507.17706"},"observation_digest":"sha256:b6478e0e20ea623f6cb5c7f7f96d64ea0a690e773378b40b5bd5cd52b61e4593","observation_id":"2a7c29da-8892-4103-afb3-afefb434dbef","resolution":{"observed_at":"2026-08-15T18:22:34.608560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-15T17:24:59.507458Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.12622","last_updated":"2025-08-18T04:35:26Z","snapshot_observed_at":"2026-08-17T22:04:16.627871Z","submitted_at":"2025-08-18T04:35:26Z","title":"Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes","version":1},"reference_index":124,"source":"pdf_text","source_observed_at":"2026-08-15T17:24:59.507458Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2508.12622"},"observation_digest":"sha256:a333df1da69a6f3033898bcf4ea485751cd2a501e74c0352f23630f0fe5f2555","observation_id":"3044a8f2-eb0e-4ce3-8c28-46d32b9645e0","resolution":{"observed_at":"2026-08-15T17:24:59.507458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-04T09:47:23.181611Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.13537","last_updated":"2026-06-30T22:36:40Z","snapshot_observed_at":"2026-08-09T22:17:19.142515Z","submitted_at":"2025-10-15T13:32:25Z","title":"K-Merge: Online Continual Merging of Adapters for On-device Large Language Models","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T09:47:23.181611Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2510.13537"},"observation_digest":"sha256:15b48b12c826251da542ded3536c9b8616a6918d7bc454eb1df3aea62464911b","observation_id":"36b48c1e-5e44-40a9-99ee-1f69131f7d3e","resolution":{"observed_at":"2026-08-04T09:47:23.181611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2604.06440","last_updated":"2026-04-07T20:28:24Z","snapshot_observed_at":"2026-08-11T11:36:01.821988Z","submitted_at":"2026-04-07T20:28:24Z","title":"Visual prompting reimagined: The power of the Activation Prompts","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-10T18:52:10.770345Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2604.06440"},"observation_digest":"sha256:0191b94a79b0b34c7e56cce88b33885a5c6f13aa8d596966ca83d00e96ce5fcd","observation_id":"94e8d8f0-c119-4162-8047-6c05e020b722","resolution":{"observed_at":"2026-05-10T23:45:53.688717Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2604.18124","last_updated":"2026-04-20T11:43:55Z","snapshot_observed_at":"2026-08-11T15:43:26.823470Z","submitted_at":"2026-04-20T11:43:55Z","title":"TLoRA: Task-aware Low Rank Adaptation of Large Language Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-10T05:07:10.885133Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2604.18124"},"observation_digest":"sha256:3b01af5774fa9811e88abd4dee6235c0ccc86840f92e344a078b265c817851b4","observation_id":"9fa8380c-cf61-44ce-a8c9-915998e5724b","resolution":{"observed_at":"2026-05-10T09:48:48.118759Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2605.07111","last_updated":"2026-05-18T05:08:10Z","snapshot_observed_at":"2026-08-13T15:27:13.737836Z","submitted_at":"2026-05-08T01:38:58Z","title":"Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-11T01:10:16.768269Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2605.07111"},"observation_digest":"sha256:9e0797603a48f951245fd19048f553872273942875b6c8f321b4d7a8f5d41082","observation_id":"3e53810a-03a4-46cd-8ceb-505435c7e206","resolution":{"observed_at":"2026-05-11T04:40:58.663635Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2605.07111","last_updated":"2026-05-18T05:08:10Z","snapshot_observed_at":"2026-08-13T15:27:13.737836Z","submitted_at":"2026-05-08T01:38:58Z","title":"Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-20T23:49:29.023187Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2605.07111"},"observation_digest":"sha256:4886bd0e35d700dd7c2795738777967d7ba7ffd7755c2a43ea0949e557266737","observation_id":"cdb7376a-4ea3-4307-9d07-fdce6a8a9b3a","resolution":{"observed_at":"2026-05-20T23:53:51.976530Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2605.27747","last_updated":"2026-05-26T22:44:58Z","snapshot_observed_at":"2026-08-14T15:16:04.313421Z","submitted_at":"2026-05-26T22:44:58Z","title":"Soft Specialists: $\\alpha$-R\\'enyi Ensembles for Uncertainty-Aware LLM Post-Training","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T15:14:28.128331Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2605.27747"},"observation_digest":"sha256:34f00bd32e4bad5ce75f8873139694f8dfd193a1dcb61abbebd53c1c0f1a9aa7","observation_id":"212b0dda-f5c0-45a6-9b4b-2be0b3596628","resolution":{"observed_at":"2026-06-29T15:23:32.966360Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2203.06904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-07-03T04:07:36.843758Z","title":"Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models","venue":null,"work_id":"400697bd-9533-4483-b9c4-68ce3f4ebfd0","year":2022},"citing_paper":{"arxiv_id":"2606.10706","last_updated":"2026-06-09T11:09:58Z","snapshot_observed_at":"2026-08-14T17:32:15.798375Z","submitted_at":"2026-06-09T11:09:58Z","title":"Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-27T14:12:14.785572Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2606.10706"},"observation_digest":"sha256:1511cfc6cea82d0d1063a899683dea6a4e050efffed4812b1d1c701cba5c69d1","observation_id":"ee65e143-de76-4677-877d-1d7f50175b41","resolution":{"observed_at":"2026-07-03T04:07:36.845177Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06904","snapshot_observed_at":"2026-08-04T10:14:32.903421Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.02271","last_updated":"2026-08-03T14:10:54Z","snapshot_observed_at":"2026-08-18T17:12:57.026667Z","submitted_at":"2026-08-03T14:10:54Z","title":"Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T10:14:32.903421Z"},"links":{"cited_paper":"/paper/2203.06904","citing_paper":"/paper/2608.02271"},"observation_digest":"sha256:d5472512bd2fdef27dd89f93f71dc8b44eda073a8d93ddba7959909d1062da49","observation_id":"26adb54c-cd6b-459f-9d31-3e65b6c3fd3a","resolution":{"observed_at":"2026-08-04T10:14:32.903421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2203.06904/citation-record","integrity":"/paper/2203.06904/integrity","json":"/paper/2203.06904/citation-record.json","paper":"/paper/2203.06904"},"outbound":[],"paper":{"arxiv_id":"2203.06904","last_updated":"2022-03-15T01:22:04Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T17:14:53.206525Z","submitted_at":"2022-03-14T07:56:32Z","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for 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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2203.06904."}