{"as_of":"2026-08-04T03:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5c7b9ddf10756e1ed428f874191c5f4cd837f249e6b79f437bb48303763491ac","coverage":[{"denominator":87,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":87,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-17T02:38:11.118057Z","state":"measured"},{"denominator":87,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":87,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-03T06:30:56.289259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2512.02764/citation-record","integrity":"/paper/2512.02764/integrity","json":"/paper/2512.02764/citation-record.json","paper":"/paper/2512.02764"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T10:56:11.938856Z","title":"online\" 'onlinestring :=","venue":null,"work_id":"38c07273-5071-4bbb-b2af-067af11becc7","year":null},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:d43f818790c487b4daa17dabc5ebf604769c52067e392e502a93d4f8bd9e193a","observation_id":"5d54b2b9-12ef-4bb9-88ca-7c5b5f88cdee","resolution":{"observed_at":"2026-05-17T02:38:54.397297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T10:56:11.963345Z","title":"write newline","venue":null,"work_id":"b6ace3ad-bd44-4a95-86ee-c7b73bd4cda2","year":null},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:a995bd801bc8ab5c7b2d447424dc66abd1351e8973541d29bbff4feffa3536aa","observation_id":"b7e35ec7-6b1c-4407-9072-819627d98530","resolution":{"observed_at":"2026-05-17T02:38:54.385977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02569","last_updated":"2019-06-06T13:18:47Z","snapshot_observed_at":"2026-07-06T07:58:27.988243Z","submitted_at":"2019-06-06T13:18:47Z","title":"Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild","version":1},"cited_work":{"arxiv_id":"1906.02569","doi":"10.48550/arxiv.1906.02569","metadata_source":"pith","pith_arxiv_id":"1906.02569","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild","venue":"cs.LG","work_id":"4acd1abb-5322-43c7-b701-6718c3d52b72","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/1906.02569","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:4b9bd2ecc3cabb3369b2e096572e94a95fce9bcbcd3f4a15277ce102c1a42bef","observation_id":"57ff3c45-97f2-437f-9ce8-2f3e25b14f8b","resolution":{"observed_at":"2026-05-17T02:38:53.811856Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"59d15a2d-c9cf-41ed-9fc6-8042e67ed75f","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:9d0e6eea836bf6bfb83070e813fe7aafc8c729de7c0fcd65e2ce40d5425b38d6","observation_id":"685e1d07-6e23-406c-8f67-83ebdae84c42","resolution":{"observed_at":"2026-05-17T02:38:54.389117Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/n19-1245","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T04:46:45.646050Z","title":"MathQA: Towards interpretable math word problem solving with operation-based formalisms","venue":null,"work_id":"b73d762e-5f33-41a0-a44d-b2e613bffd36","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:aeaed384859c5fc71090ac256093221b03f24c65dede9c822540dfec8442a5d7","observation_id":"390904aa-eabc-4c80-a72d-f4b028e8751e","resolution":{"observed_at":"2026-05-17T02:38:53.280299Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-13T22:22:22.182129+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T22:22:22.182129+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"61101d83-c3d2-4244-a0c0-a888f700ea69","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:c2630cde9c96422c8dbc90e7f3b4c6c29044f296538fa8ff82a81c78ba872dd8","observation_id":"12f4f549-db27-48f2-8db8-c98ad9feca2b","resolution":{"observed_at":"2026-05-17T02:38:54.406821Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.emnlp-main.446","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"975310f8-a8ce-4272-95b4-5eb92ddaa3f7","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:3a6c155a73241b00bcf4cc0afd8b423b21e66e3ef45421413471ed0928fcfaa5","observation_id":"fea894ef-525a-4929-ac53-e56ca5b5b9d7","resolution":{"observed_at":"2026-05-17T02:38:53.283695Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d862a7c3-4bf5-4ce0-82d6-cce8b95f81ce","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:591634c8943bbb1dc166d1be8b9d980c290c3838337ee8785082cd387838a576","observation_id":"224984ba-5d67-476c-b74a-b12c5044e762","resolution":{"observed_at":"2026-05-17T02:38:54.410323Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ccf1c013-d219-4af5-8c01-f0c92a9202ed","year":2006},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:b72be462e86b6ea3b2344147d95caf6106dfc5eaa2d72f01f9e68c0c817f2f12","observation_id":"5dd32b7b-358c-4d09-817c-4a9ea3587565","resolution":{"observed_at":"2026-05-17T02:38:54.382011Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.21285","last_updated":"2026-05-12T18:52:26Z","snapshot_observed_at":"2026-07-06T22:37:03.716474Z","submitted_at":"2025-11-26T11:18:06Z","title":"PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark","version":3},"cited_work":{"arxiv_id":"2511.21285","doi":null,"metadata_source":"pith","pith_arxiv_id":"2511.21285","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark","venue":"cs.CL","work_id":"4df094b0-ca4c-43c8-b293-3bfe1841f4c2","year":2025},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2511.21285","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:dba999c6bfeb6db50174d336046e17cdfc1a96dea6429c6d3b07f98c9f60aedd","observation_id":"f5dda5de-cb3b-4e76-abe3-56e892e3af2d","resolution":{"observed_at":"2026-05-17T02:38:53.816367Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.acl-short.1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T11:49:25.978476Z","title":"B it F it: Simple parameter-efficient fine-tuning for transformer-based masked language-models","venue":null,"work_id":"377562cd-e5ee-4074-b449-3282a6a345c6","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:31c60adcdc98cb07349cf35fef76ece2ee390290a8778ab136ecbe246943cd9c","observation_id":"eb5f6ffa-b94a-4ba3-9ba8-69933966b7b1","resolution":{"observed_at":"2026-05-17T02:38:53.287505Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T23:38:06.825036+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T23:38:06.825036+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T23:38:06.825036+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"53ae3d9b-cd0a-42c8-9b72-30f5109721dc","year":2009},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:1879de410a876f376be1d3b8efddf4387eea8daceea1996bc328d836e8448aca","observation_id":"4451d516-207c-4b5a-abf8-297450e2cbe9","resolution":{"observed_at":"2026-05-17T02:38:54.401394Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"98528fc5-168f-4501-b26e-8f18228f07e2","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:e6af9cd6dfc6b06f975c3f5d52523d206bc97d25bda45365993c2db08b6595ec","observation_id":"7d8b4dab-1334-47be-94cd-a16a4ea1112c","resolution":{"observed_at":"2026-05-17T02:38:54.413054Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4f09804b-ce47-4656-b098-82065effbcc7","year":null},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:631a4c0405a5b7136c9914d40beea49079c12b1c3e13d8b0c1f93e213ee0d927","observation_id":"97f4ee3a-f543-478a-ba27-d1a7bf473f82","resolution":{"observed_at":"2026-05-17T02:38:54.415668Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/s17-2001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T19:17:17.444550Z","title":"S em E val-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation","venue":null,"work_id":"50d431c2-4213-4e92-ad7f-140f4bb03fd6","year":2017},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:e91a2300ba26ae37cef4293ae758a5aa703f58b2904a42b702222ce994dbb378","observation_id":"aa572a8c-f0f1-4bac-8f4d-88eddab2487a","resolution":{"observed_at":"2026-05-17T02:38:53.291617Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d78ed77c-c018-4b8c-901b-c431ee10c487","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:89838941b60a28c126870b9ee3524fabf13048b78da405370b30298ebc235e68","observation_id":"934cfd0f-2430-4218-bb89-4c1aa0da5071","resolution":{"observed_at":"2026-05-17T02:38:54.392332Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/n19-1300","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:07:03.404326Z","title":"B ool Q : Exploring the Surprising Difficulty of Natural Yes/No Questions","venue":null,"work_id":"b0eff16f-bbcd-4d66-a41d-d89ff07a80e5","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:eb8e118f66c091f7a7ddea10ab34c33129169f7e01f8eabe098c9d6607e8ea47","observation_id":"f987738f-3f79-4093-9036-1edaebbee176","resolution":{"observed_at":"2026-05-17T02:38:53.295250Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-12T15:19:26.94061+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T15:19:26.94061+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:54675f8322ca3e2c58fa0d32f9232390c57c94ecc156e89994e6e4690fb2f889","observation_id":"3964eac9-480a-43d6-863d-591754fbccb0","resolution":{"observed_at":"2026-05-17T02:38:53.863546Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/11736790_9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T01:08:47.502855Z","title":"The pascal recognising textual entailment challenge","venue":null,"work_id":"06379ed9-486e-48e9-84ec-ad24c85cd052","year":2006},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:37e8224acd28f7fe4a7dde2517dc48948f7fa4bfd12cd5e74ffb037f8abe5b89","observation_id":"0f381ebd-ab1f-424c-806c-455ae4776526","resolution":{"observed_at":"2026-05-17T02:38:53.336649Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a8458247-ecdb-49e0-98b0-bc8e2ab55bdc","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:305cfc950acaefbbd9a8e07cd909e17582e18b489dec3c7f9d0df014dd5bf6ab","observation_id":"0d223ce4-65b4-461f-816f-883177e1f627","resolution":{"observed_at":"2026-05-17T02:38:54.520123Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"98911caf-97f0-4bef-8908-dd620bb6193c","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:cb39bf4c7f13dffededeb37ccb09e7400231c37bf46a6133835ef196df1407eb","observation_id":"3b0285c0-e16d-4803-bb21-7dbcea189ffd","resolution":{"observed_at":"2026-05-17T02:38:54.517229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12420","last_updated":"2024-05-05T13:13:02Z","snapshot_observed_at":"2026-07-06T15:45:13.905037Z","submitted_at":"2023-06-21T17:58:25Z","title":"LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models","version":2},"cited_work":{"arxiv_id":"2306.12420","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.12420","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lmflow: An extensible toolkit for finetuning and inference of large foundation models","venue":null,"work_id":"a1130a23-fe86-47b0-b557-7359588ceaa2","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2306.12420","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:442cca857095675f746cff6d61024c010f575da54a9791cff770fc7dea79e02c","observation_id":"de70d4bc-8d86-47df-af47-c7217b5aa2ed","resolution":{"observed_at":"2026-05-17T02:38:53.881392Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"88262f6b-fee7-40cd-8196-8654288d2580","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:cfaf6c3c0a5e96cc3d4132c15ac39b2fabf4dc118ef9c8bdfb78cea688521565","observation_id":"2fe042f0-b11e-4a7d-99cf-1e5b3c8699c5","resolution":{"observed_at":"2026-05-17T02:38:54.513852Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6e251335-682a-4817-a601-b42b9154c192","year":2005},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:a010c96317d4389852dc7aebb3d271c05581395090aebf2dac6e2afa2f7faea7","observation_id":"07ae6e2b-9e46-4840-aacc-e6b8817c3936","resolution":{"observed_at":"2026-05-17T02:38:54.506458Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":"2407.21783","doi":"10.1016/s0749-0720(15","metadata_source":"pith","pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"The Llama 3 Herd of Models","venue":"cs.AI","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:97aeb9582510bcbffd42d3e34e4148b40730652a3a1f655316e9f8c223dd9f15","observation_id":"b642c7a9-d031-47dd-9b64-bc73ff228d74","resolution":{"observed_at":"2026-05-17T02:38:53.877289Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.15010","last_updated":"2023-04-28T17:59:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-28T17:59:25Z","title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","version":1},"cited_work":{"arxiv_id":"2304.15010","doi":"10.48550/arxiv.2304.15010","metadata_source":"pith","pith_arxiv_id":"2304.15010","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","venue":"cs.CV","work_id":"0fe2cfd8-d442-4ceb-b1a9-a465704f39b2","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2304.15010","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:c592b2937048b2d44bf035dd759ada7900604acad869a6f1c48d185bb619d68d","observation_id":"7e4a94b9-ab18-47a0-8c7e-8f0ffd60f93d","resolution":{"observed_at":"2026-05-17T02:38:53.836535Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b62bc89e-6d29-43ff-8b8e-5cb0861b233c","year":2007},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:a1eaffceac4923c6cfc72f3cabc7edccff1177568319f96bf8af320307334c24","observation_id":"7c687285-30ce-4ab7-8bf3-74eab7e782cf","resolution":{"observed_at":"2026-05-17T02:38:54.502671Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2501.12948","doi":"10.1016/j.artmed.2024.103001","metadata_source":"pith","pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","venue":"cs.CL","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","year":2025},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:af588a131ffc5661d3c1d2a0b6b06bec94b092f3f426decabff32217e5ca1a02","observation_id":"f557cdc1-75ed-419d-901f-d4e70423d2ae","resolution":{"observed_at":"2026-05-17T02:38:53.821795Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b0d0f757-83df-4ef6-a573-f37591ea0432","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:603568e166030eefe1cf173949ec24df26efd2dcb35bc2154a9c54c11d203f09","observation_id":"841250be-ccf3-4ce1-8acc-0bbc3a27b784","resolution":{"observed_at":"2026-05-17T02:38:54.499899Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f684f802-c14e-4a9b-bf8a-84c3c0aaeee1","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:7efe5efd4ac537345605589d5f2f4333ac248481d78de9f09b66ee6a04c46569","observation_id":"443ded67-947e-4106-8e1d-53e8fce9466f","resolution":{"observed_at":"2026-05-17T02:38:54.497543Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f412994c-9b58-49de-946f-7604b3434c36","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:fe26505a2b896741a70223c61638b7abe3a9835346426bb349b8761ee00c541f","observation_id":"b54625d7-40f2-4972-96df-e888a3755a5e","resolution":{"observed_at":"2026-05-17T02:38:54.495219Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ec74bcfe-a5ca-4980-b611-ba13cc915b22","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:2e519083057419217fc4a1998b53b2dea5579f2d5329a1978fc830dcb700ea34","observation_id":"79ac4f85-a9b4-49d9-bbad-22daffd7aa27","resolution":{"observed_at":"2026-05-17T02:38:54.492741Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"42bb638e-6abe-441d-9e26-aac655d155b9","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:29b8537bafc1b9784fef9d5e53e6c8f4aba6524d053aed64b69f26a1f31934e5","observation_id":"52d1ac17-1993-4242-a853-510ab41582d4","resolution":{"observed_at":"2026-05-17T02:38:54.490427Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T19:04:03.453954Z","title":null,"venue":null,"work_id":"9fa0a464-7e1c-4a20-8347-ec49e2cd5df2","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:a2fd190681e6f88968abfed92e1e2df52996fdd273a26b239b973921b696741f","observation_id":"58527861-a7ef-4c99-9658-1b81fb997c5b","resolution":{"observed_at":"2026-05-17T02:38:54.487744Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":"2001.08361","doi":"10.1145/3616855.3635845","metadata_source":"pith","pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Scaling Laws for Neural Language Models","venue":"cs.LG","work_id":"b7dd8749-9c45-4977-ab9b-64478dce1ae8","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:39471d83bfa48f03987db2583f61bf8d02dfb770fc0a1b69fd2f910b11246d2d","observation_id":"472efeed-ba91-4176-9b33-b09856667265","resolution":{"observed_at":"2026-05-17T02:38:53.825376Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"47ed8540-64c4-4591-988b-4c713f9808f1","year":2018},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:92a3cddf92170ae9d065e266235ad4d310911e14da969c4077f9d94e9a3ddde5","observation_id":"923a59d1-0b6d-4534-9f92-54c845df0670","resolution":{"observed_at":"2026-05-17T02:38:54.485124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/d19-1281","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"33a9e148-8763-402a-b969-024837b2e716","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:5015b68f046f02acf66ea11d5d3817338241077a29e1e6914f52b6ee5217169d","observation_id":"80cd7fa4-899d-40b6-a427-a378e3a22e4c","resolution":{"observed_at":"2026-05-17T02:38:53.302845Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.emnlp-main.243","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T01:15:11.886945Z","title":"URL https://aclanthology.org/2021","venue":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","work_id":"f7865b08-1db6-4a3c-ae44-d1fc2aac179e","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:2255133f92b09b926bf7a2e07ed4051733fb84dccb54805652148ff0cfe4f4f1","observation_id":"da86134c-5279-4823-af1e-aaa4ac9e8217","resolution":{"observed_at":"2026-05-17T02:38:53.317674Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-02T00:08:08.623507+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T00:08:08.623507+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6bbbb2bd-05b8-4c3f-ac04-6bc75dadb932","year":2011},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:50320e280e25d870f8ee993cbf9bfbad632ec371d04f395a9b10a3c2bd095ca4","observation_id":"525f9283-66a2-47f7-9fb6-29bca50b8a05","resolution":{"observed_at":"2026-05-17T02:38:54.482839Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5573.360561","doi":"10.1145/3605573.3605613","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":null,"venue":null,"work_id":"ef86a16c-15c9-41ee-85d5-0b74bfd068ae","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:142cc1a033da8f7730726287bf586d8346489f287db5f0e4a305736802f531aa","observation_id":"fcb260da-eb37-4b0c-9469-232d480fab85","resolution":{"observed_at":"2026-05-17T02:38:53.328465Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.acl-long.353","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T23:16:36.659534Z","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","work_id":"efa3056f-9b61-4cd3-a53c-a2ae3d4fb078","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:315d827d29e1dbce0cb1a6a2a642d8b19747bead498fa0ed98cdfb15da8e12ba","observation_id":"663fb893-c5a7-4e66-89e3-078c922c03c9","resolution":{"observed_at":"2026-05-17T02:38:53.306527Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-01T13:38:12.787474+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T13:38:12.787474+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15647","last_updated":"2024-11-22T05:02:26Z","snapshot_observed_at":"2026-07-06T15:08:45.214225Z","submitted_at":"2023-03-28T00:06:38Z","title":"Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":"2303.15647","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.15647","snapshot_observed_at":"2026-07-01T13:55:46.477835Z","title":"Scaling down to scale up: A guide to parameter-efficient fine-tuning.arXiv preprint arXiv:2303.15647","venue":null,"work_id":"57ac9386-0e04-45d3-8cb8-7bf370819655","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2303.15647","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:af07732d3d3dd2f8ece151e5960c58ecdfc6f0c6eb6141c2d704bfa9461de9f2","observation_id":"4941e733-774d-4ce5-a655-9ce0ee01fe04","resolution":{"observed_at":"2026-05-17T02:38:53.832766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T07:00:48.922090Z","title":null,"venue":null,"work_id":"aeee2424-0856-471e-9cda-fde21d774916","year":2004},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:8e86f4e108b6a9cbc43f8bbfa281f9ec49d5b7171e62ec8615a708cf6b5099b6","observation_id":"ac8ddf13-bbc8-4fc0-89d1-39189b3a691e","resolution":{"observed_at":"2026-05-17T02:38:54.480607Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2159bf24-8270-4be7-a329-9191f4a5e00c","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:11656d8cb4cb5fbc846c453ae595d2129b3586d21b8006e2f11100bacc484b23","observation_id":"c780309b-1c2f-469b-8a6c-dca592ad2f63","resolution":{"observed_at":"2026-05-17T02:38:54.478270Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f9115c91-3d36-4235-9631-a92d7a346f90","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:d2c4c754b926bb3aad100f2baa85028cf55b31a9765d12f17542a2c16c269aef","observation_id":"95c73136-c8b1-4809-8628-729dc8c7503b","resolution":{"observed_at":"2026-05-17T02:38:54.476052Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9032bcfa-3129-49e5-bc8a-0877904d9ae7","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:06c5e0415491526d4838ef819db6f431e9db76bd0ef6740ed25c41c2fc18ffcc","observation_id":"75f1dd90-4588-48c0-8295-a6fc1f934d6b","resolution":{"observed_at":"2026-05-17T02:38:54.473796Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.acl-short.8","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T06:55:27.653601Z","title":"P -Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks","venue":null,"work_id":"b488378f-3373-4997-ae65-5e0486b9492c","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:5e38857f7b7641194af7739c25af286c7235ed4cde3911f72004e9ace58024a2","observation_id":"9b9a10a1-f6c4-4497-80bf-b64317904070","resolution":{"observed_at":"2026-05-17T02:38:53.344140Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-05-22T10:55:30.054775+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T10:55:30.054775+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"fb01abaf-eb4f-4c10-9884-649ab8c37bcf","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:26da899ef6a1a854e16e25642c7a83d5b39bd7eea03be898d98931f6601c5593","observation_id":"c72c1d8d-7d45-49da-a4a4-095b4d1c961d","resolution":{"observed_at":"2026-05-17T02:38:54.471495Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7222bf36-a58c-4d22-946f-61090170417b","year":2022},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:8d234ffcf10b24d2a4d4d5c4d27786496743a0054a827131b2f6281077f99ed5","observation_id":"26c6ac6e-5993-4fe4-906c-55561dc60dc6","resolution":{"observed_at":"2026-05-17T02:38:54.469046Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"434026c0-c732-43e4-bb84-b660deeac159","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:9232fc1d1ee2f8e96a9495053f93b747439cdf28bd7d975379215d33e74f37c1","observation_id":"6390aa01-24bb-43fd-9721-582c54ba2021","resolution":{"observed_at":"2026-05-17T02:38:54.466540Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06196","last_updated":"2025-03-23T14:51:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-09T05:37:09Z","title":"Large Language Models: A Survey","version":3},"cited_work":{"arxiv_id":"2402.06196","doi":"10.48550/arxiv.2402.06196","metadata_source":"pith","pith_arxiv_id":"2402.06196","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Large Language Models: A Survey","venue":"cs.CL","work_id":"54e385fe-1786-48c3-8aa0-d727210eb50e","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2402.06196","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:b32b05b8e079b08b3fe95c6131739898227bd75f5b9bf22713839e5732175cf5","observation_id":"0ee4ea28-2db8-4ddf-afd3-67b24824c76e","resolution":{"observed_at":"2026-05-17T02:38:53.867421Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3083.107313","doi":"10.3115/1073083.1073133","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"doi:10.3115/1073083.1073135 , editor =","venue":null,"work_id":"7a0e7f56-7c92-470e-b7bf-2e9974bf9a93","year":2002},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:f8f1db0aa00755347622f42ad6f0ca9c361be27aa99eec9374921106205dd773","observation_id":"07274a26-d8c9-4c64-8e6d-b1ba92d453b0","resolution":{"observed_at":"2026-05-17T02:38:53.352711Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:19:47.730132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:19:47.730132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3733cfc9-c1e4-44e3-93da-4bf833045770","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:2fa2e64811376253a8583066fa12f5117d23cc5821dfa4e9de2ebf37f22461da","observation_id":"1d064ef4-3ca2-491f-bec8-3ebb0e4b40f6","resolution":{"observed_at":"2026-05-17T02:38:54.464253Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07191","last_updated":"2021-04-15T06:11:12Z","snapshot_observed_at":"2026-07-06T10:49:19.314215Z","submitted_at":"2021-03-12T10:23:47Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","version":2},"cited_work":{"arxiv_id":"2103.07191","doi":"10.18653/v1/2021.naacl-main.168","metadata_source":"doi_reference","pith_arxiv_id":"2103.07191","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","venue":"cs.CL","work_id":"01c9a44a-5ebf-4209-a628-5f1fd19d2103","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2103.07191","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:85e6fac5b331673595bb562b7af6c167f1ff5b6292fb09bd4afec451f826176f","observation_id":"3822e27f-b4d5-46f2-97a7-e935349bf920","resolution":{"observed_at":"2026-05-17T02:38:53.299455Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2020.emnlp-main.617","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T06:41:42.886902Z","title":"In: Proceedings of the 2020 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP)","venue":null,"work_id":"b4e99eda-57f2-48c9-ba76-41b435c72be5","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:8c2b83091edb55cd436c1a41bd193d5508164ecca4312d0aad4bcc44cbaf5f20","observation_id":"0a4592bc-2e02-48fd-bd7d-ac75c7e3d2c1","resolution":{"observed_at":"2026-05-17T02:38:53.320729Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/n19-1128","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"doi:10.18653/v1/N19-1128 , pages =","venue":null,"work_id":"bb238cf8-b4e9-415a-8394-57632be01ee0","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:ec620565d126539d0c588a31311c06452dd8c65640cd6f3bc07a8cbf9b25521a","observation_id":"fa7b81fa-f402-4aaa-86a2-ea055d18a603","resolution":{"observed_at":"2026-05-17T02:38:53.347438Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:21:02.553441+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T06:21:02.553441+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"992a17a9-c867-4827-9d5f-9d32a6311d84","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:e180be50349126f4fb5ff843e48a6459eed5d5384c2eabddd6d9d7c01bbbf207","observation_id":"9bf827c6-ca22-48b9-ae26-d14c1d018482","resolution":{"observed_at":"2026-05-17T02:38:54.462044Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4f76ff18-43c5-4b04-beee-64a855e61c61","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:64905b0245b1825f3310908c53151b6a139abe0b1ca3bb40c869c4b2bbc417cb","observation_id":"766feb01-768f-4783-b9bb-8165a26d60ef","resolution":{"observed_at":"2026-05-17T02:38:54.459744Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"89a29ce6-1def-4492-a5c2-f3108b8510d9","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:be8afff6a0c2140073f52a090f3d33492b7cec42f7dd1f3f6ae8870ebcbcbda8","observation_id":"c975b082-5faa-48a1-8c3c-1940e3f5f291","resolution":{"observed_at":"2026-05-17T02:38:54.457429Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3779a458-bab8-4bec-a812-bbc2cdf115bf","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:fb4b286b91350437d1caef9b69a36c414523cf167a22561982be642ef12fbd18","observation_id":"d8287313-530b-4af5-acdc-4aec70da7de8","resolution":{"observed_at":"2026-05-17T02:38:54.455190Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"eb129412-21cd-46cc-9ca4-de26951e6039","year":2016},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:10b43c720f07987f1be09203c0ff93df5a5759ad7de4ea656be5e1099715838b","observation_id":"d91fe758-a4f7-4652-b638-27b53f733438","resolution":{"observed_at":"2026-05-17T02:38:54.453006Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"036104c6-bde1-4166-8e81-305335e30ed0","year":2011},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:e8cf74618799a3263f758c53ef23f97f73cb5a805f81c297f252aeddc0589b23","observation_id":"52f23974-d604-44bc-8c94-d45b9bbc70c2","resolution":{"observed_at":"2026-05-17T02:38:54.450560Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ccc0e4fd-5e8e-4cca-9497-413371244cfa","year":2021},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:f7a733d2d993073c81c29beb20c184d1ce67f46867a498bca968e4edf786c895","observation_id":"d3c53267-ae12-442e-9d35-8f46e0ef83af","resolution":{"observed_at":"2026-05-17T02:38:54.418229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/d19-1454","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T13:06:58.245746Z","title":"Social IQ a: Commonsense reasoning about social interactions","venue":null,"work_id":"67b76bf2-8e96-4f03-93f2-2b52627b4baf","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:56afcacbe36b422a6747c26b67a74a7dd5a704e969c9124e3faa03b0d3839855","observation_id":"8e849f5c-c5ff-41f5-9773-6ae06bda67b5","resolution":{"observed_at":"2026-05-17T02:38:53.324038Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T03:08:11.915484+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T03:08:11.915484+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5c41ae50-b444-426c-900d-58f95c265587","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:ecbb23da223649b5484d761b009c2c342e062be4644f8e478e4b8fc7855b8397","observation_id":"2b34d80b-1753-454e-a36f-ef0527c03065","resolution":{"observed_at":"2026-05-17T02:38:54.447979Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"26239427-f2c3-4721-8e3d-b79b1ce82746","year":2013},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:fd1b92d01d073bcf2b0520a8f55b0cbd259104f35d14a93e773712d99e55b917","observation_id":"f7f7c72c-5f70-4fca-99f5-b8c2d154cf4b","resolution":{"observed_at":"2026-05-17T02:38:54.445238Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"53e64b53-b64a-41d9-a3f8-f4f05e546d45","year":2025},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:7783e2aa9716b9a9f5781a445e611bdde4712ecf02988bc03c4fe027ca983003","observation_id":"54a6b688-8a7e-46d7-a915-d1063ae55623","resolution":{"observed_at":"2026-05-17T02:38:54.442726Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"84827e00-830c-4923-8b91-1b905363bc20","year":2017},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:68be09668da9af1adfc2b889539d389b6fd1e4039f82c608c34a4ad91e5952cd","observation_id":"af9b6e2c-e20f-41ba-b249-974fdc1f5809","resolution":{"observed_at":"2026-05-17T02:38:54.440244Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"052309a2-3641-483a-8fa6-d1de8f5eea8c","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:6829e88d1907b6422822d785f29da5ecae230da22e9563a4744ecd26bd09afd4","observation_id":"e57e6515-07f0-4466-88c3-a8ca993b08fc","resolution":{"observed_at":"2026-05-17T02:38:54.437718Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e491c9d0-9366-412a-a2f3-33535ceaf138","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:39f1b3154926536364d227ac12f7f328b967933dfa8edd29c094e4f11d1c24b9","observation_id":"05d53dcc-3cfd-46a6-b994-0c79094176b4","resolution":{"observed_at":"2026-05-17T02:38:54.435176Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.07461","last_updated":"2019-02-22T23:53:34Z","snapshot_observed_at":"2026-07-06T06:34:26.609892Z","submitted_at":"2018-04-20T06:35:04Z","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","version":3},"cited_work":{"arxiv_id":"1804.07461","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.07461","snapshot_observed_at":"2026-07-04T09:19:44.134394Z","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","venue":"cs.CL","work_id":"1bb6fb0c-482d-43cf-94a8-ed18f72a5563","year":2018},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/1804.07461","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:53238a63e817e3b4da13023aadfe890c084f25915b668e0a96579db30fa79fff","observation_id":"8666028c-0cc4-41f0-a690-db932eee3617","resolution":{"observed_at":"2026-05-17T02:38:53.840386Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04751","last_updated":"2023-10-30T20:36:20Z","snapshot_observed_at":"2026-07-06T15:39:56.892976Z","submitted_at":"2023-06-07T19:59:23Z","title":"How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources","version":2},"cited_work":{"arxiv_id":"2306.04751","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.04751","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2306.04751 , year=","venue":null,"work_id":"be09ac68-3260-4c2f-bfcb-1ae1b5717468","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2306.04751","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:709757cdabe914960e827c1d8e84d7fd102336e141d75e1c122a9704fd67c2a1","observation_id":"780f6246-777e-46dc-bc03-d4afdfa69291","resolution":{"observed_at":"2026-05-17T02:38:53.855497Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9941d5e0-a7c9-45a8-be37-d259b63fe54a","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:6f3f464b5b48bfce906e66e9263ba50791b32d2f59cc221ec89fd416726b14df","observation_id":"9062819a-229b-467a-833d-202bee5b0c8f","resolution":{"observed_at":"2026-05-17T02:38:54.432265Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1162/tacl_a_00290","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T05:08:59.599345Z","title":"Transactions of the ACL 7, 625–641 (2019)","venue":null,"work_id":"d3e135be-3a1b-4d24-8dc8-a8bb8fb565a6","year":2018},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:6163bbaa24a078c8f9d021d13dc2ccc3870b27a57a149287a80af6a7054ed25d","observation_id":"886807f1-6065-4adc-8b60-cff9b200a529","resolution":{"observed_at":"2026-05-17T02:38:53.314028Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.05426","last_updated":"2018-02-19T19:19:51Z","snapshot_observed_at":"2026-07-06T05:38:16.162925Z","submitted_at":"2017-04-18T17:10:13Z","title":"A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference","version":4},"cited_work":{"arxiv_id":"1704.05426","doi":"10.18653/v1/n18-1101","metadata_source":"pith","pith_arxiv_id":"1704.05426","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference","venue":"cs.CL","work_id":"9737cdf0-fd48-4485-ab04-c8ec9386e782","year":2017},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/1704.05426","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:51180ac6de142b8cd4b284db45b48aa024bfe1d9be926575dc6dd58730d73559","observation_id":"3fec9fff-9ffc-494f-8665-11c332d9e901","resolution":{"observed_at":"2026-05-17T02:38:53.332835Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:49:40.633782+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:49:40.633782+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a11d3c33-2bb2-4a7c-8e14-b0d7bd4c2433","year":2020},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:5a43bdcf6959206326c48f3a62b837dc8cf3aa8ee8f6148943a189e19941e5f7","observation_id":"d997b100-a477-4818-b00b-9975019173ce","resolution":{"observed_at":"2026-05-17T02:38:54.429574Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12148","last_updated":"2023-12-19T13:31:24Z","snapshot_observed_at":"2026-07-06T17:05:19.007669Z","submitted_at":"2023-12-19T13:31:24Z","title":"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment","version":1},"cited_work":{"arxiv_id":"2312.12148","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.12148","snapshot_observed_at":"2026-07-03T03:47:35.347672Z","title":"arXiv preprint arXiv:2312.12148 (2023)","venue":null,"work_id":"91718c82-1e85-40c4-83f6-749749f89012","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2312.12148","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:47b54bbfb2042c1fcfee7b162380f6d7c6b64add0d6af7424c2f37ad2efe9cce","observation_id":"683ff4eb-0f1b-4c42-9b2c-6d1ee48d1535","resolution":{"observed_at":"2026-05-17T02:38:53.872835Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":"2412.15115","doi":"10.1145/3581783.3612503","metadata_source":"pith","pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen2.5 Technical Report","venue":"cs.CL","work_id":"d8432992-4980-4a81-85c7-9fa2c2b87f85","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:2cc3852feba47bad2a0e9cc1b29e27f6e80b00606f8a54237254f036b592400c","observation_id":"ca1921c0-cd48-4411-83ec-c5dde0a324de","resolution":{"observed_at":"2026-05-17T02:38:53.829077Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a7e9873a-df96-4f74-b2dd-9f349b06ae1f","year":2018},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:d2eaaffcb018eedeb94dda73a2f9ef884bebf1137131d2563e28974df62d0586","observation_id":"522012d0-8b83-4919-8afa-bbf9a933f2cb","resolution":{"observed_at":"2026-05-17T02:38:54.426324Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/p19-1472","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:07:03.406813Z","title":"URL https:// doi.org/10.18653/v1/p19-1472","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","work_id":"11bfc949-547c-40f3-a86d-953eb9b2154c","year":2019},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:3afe9bfce19a59effd33b14112b63fcc656eb6dbb17b67c8995c23b3e1d1d966","observation_id":"861c133c-3858-44e7-877f-3f52fbc941c9","resolution":{"observed_at":"2026-05-17T02:38:53.310679Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-01T09:08:05.023254+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T09:08:05.023254+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16199","last_updated":"2024-09-18T23:54:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-28T17:59:12Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","version":3},"cited_work":{"arxiv_id":"2303.16199","doi":"10.48550/arxiv.2303.16199","metadata_source":"pith","pith_arxiv_id":"2303.16199","snapshot_observed_at":"2026-07-10T08:47:01.973242Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","venue":"cs.CV","work_id":"5c44e6f5-82ca-4461-9fc1-d630f3bfa3e1","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2303.16199","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:5ea43cb202bd5fc244e8ee4d745961c8cdcfe318da3056211ec65f6527ae44e6","observation_id":"92d6f802-6620-4519-9d81-1dc136cb2a81","resolution":{"observed_at":"2026-05-17T02:38:53.848169Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12885","last_updated":"2018-10-30T17:32:16Z","snapshot_observed_at":"2026-07-06T07:11:32.319931Z","submitted_at":"2018-10-30T17:32:16Z","title":"ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension","version":1},"cited_work":{"arxiv_id":"1810.12885","doi":"10.48550/arxiv.1810.12885","metadata_source":"pith","pith_arxiv_id":"1810.12885","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension","venue":"cs.CL","work_id":"0490655f-8d5b-467e-83a2-568b957068c9","year":2018},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/1810.12885","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:24490419b15517ff8a90e13b0af2ebbe8f05abf3aeab31e6a68e7c545cd4b706","observation_id":"1269ef5f-32a3-4413-bb40-dedde8b2832b","resolution":{"observed_at":"2026-05-17T02:38:53.844004Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8c8c4d09-1398-495f-879e-da0aa0f1e5cf","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:f874c8de59de4a1671bc6e89f6b8caf720167bfa5b800679cb9de98c47463b31","observation_id":"c3a821f2-af53-4d80-a6a2-0570b7f69e43","resolution":{"observed_at":"2026-05-17T02:38:54.423465Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b0a136d3-26fb-4127-99be-1137840ebaa3","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:5673e6646456f35c2aa72aa17d66066659e70e07ca967a5c96155f53dce5452e","observation_id":"6778271e-f097-44d2-b4a5-54a7b8d0f9b3","resolution":{"observed_at":"2026-05-17T02:38:54.420658Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05685","last_updated":"2023-12-24T02:01:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-09T05:55:52Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","version":4},"cited_work":{"arxiv_id":"2306.05685","doi":"10.1109/4235.797969","metadata_source":"pith","pith_arxiv_id":"2306.05685","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","venue":"cs.CL","work_id":"d0c30cd7-81e1-4159-a87f-f6adca77ff08","year":2023},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2306.05685","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:e4e1ce3c2dd7a08672f4d558b70c9b3f081e24dbe77d06aadfb53662ce891b49","observation_id":"c00e22b8-cfd9-4563-be13-59d5f11344b9","resolution":{"observed_at":"2026-05-17T02:38:53.851741Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.acl-demos.38","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T00:15:08.633239Z","title":"L lama F actory: Unified Efficient Fine-Tuning of 100+ Language Models","venue":null,"work_id":"dd3a4c83-69ef-49f2-8a58-aef3457f53c1","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:03adb893ba270fda1bd6c4ca8432d54a628883120509290dd88cf8ff26bd471b","observation_id":"140014b9-b16b-4855-8cd3-f99358c9c543","resolution":{"observed_at":"2026-05-17T02:38:53.340585Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-05-22T10:55:30.401234+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T10:55:30.401234+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13372","last_updated":"2024-06-27T22:44:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-20T08:08:54Z","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","version":4},"cited_work":{"arxiv_id":"2403.13372","doi":"10.48550/arxiv.2403.13372","metadata_source":"pith","pith_arxiv_id":"2403.13372","snapshot_observed_at":"2026-07-10T14:07:06.654437Z","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","venue":"cs.CL","work_id":"462b3287-e058-48e3-b5e3-82a5f2a8dc06","year":2024},"citing_paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","version":3},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-05-17T02:38:11.118057Z"},"links":{"cited_paper":"/paper/2403.13372","citing_paper":"/paper/2512.02764"},"observation_digest":"sha256:30c93ed96d3100f68c004e130cc0113743951f1e810e0b67e1a9ac5d098627fc","observation_id":"588b7b46-e298-413f-b25d-a897a7b8b840","resolution":{"observed_at":"2026-05-17T02:38:53.859804Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2512.02764","last_updated":"2026-05-12T18:50:51Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-30T07:30:39.481921Z","submitted_at":"2025-12-02T13:44:41Z","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models"},"reference_resolution":{"displayed":87,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":47,"verified_exact":35,"verified_fuzzy":2},"total_outbound_references":87},"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":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2512.02764."}