{"as_of":"2026-08-11T18:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e018792c7b0e8ab239f317237a22239dc98f69fc6ad1368470a393e52f04617","coverage":[{"denominator":70,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":70,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:17:56.885258Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:58:26.292793Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T04:58:28.692022Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"cited_work":{"arxiv_id":"2412.14426","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.14426","snapshot_observed_at":"2026-08-07T04:58:28.692022Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","venue":"cs.CL","work_id":"15276877-f20c-474b-b774-fdd25a77a881","year":2024},"citing_paper":{"arxiv_id":"2506.09451","last_updated":"2025-06-11T06:56:08Z","snapshot_observed_at":"2026-08-09T07:50:05.107029Z","submitted_at":"2025-06-11T06:56:08Z","title":"Safe Screening Rules for Group SLOPE","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:58:26.292793Z"},"links":{"cited_paper":"/paper/2412.14426","citing_paper":"/paper/2506.09451"},"observation_digest":"sha256:e9076cfb0917d251f85be0f703bf175d4b0f664cd673d7c739160a32df8ad0a2","observation_id":"a68d6938-c538-4530-88c0-4d95d9b0da3a","resolution":{"observed_at":"2026-08-07T04:58:28.769663Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.14426/citation-record","integrity":"/paper/2412.14426/integrity","json":"/paper/2412.14426/citation-record.json","paper":"/paper/2412.14426"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.611533Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.611533Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:b069efe9e8149c96f563ee1775158a5eec7f374a8b354ad0d3af6ffb7be4bb6f","observation_id":"c9289a3b-e935-408b-ba9e-bad28ee5d87a","resolution":{"observed_at":"2026-08-11T12:17:56.611533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.616062Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.616062Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:459dd02d989ad0152543bb782d64ad64c82e9adefb0aa333ac77dec8e115c835","observation_id":"63cd8f86-3ab5-4867-b766-acdd172bc0bf","resolution":{"observed_at":"2026-08-11T12:17:56.616062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.619728Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.619728Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:d6e42724a1d58ab564a08688fa90c999e5e449b1e91b1b2d66289f83e9694aba","observation_id":"303d73e3-5754-46bf-8752-eeef6c53dbb2","resolution":{"observed_at":"2026-08-11T12:17:56.619728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-11T12:17:56.623947Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.623947Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:caf6a5578aa07b96cd7c36f539c74d97c3d9c410fac50394063d44d436e00962","observation_id":"8adcb9cd-4612-4848-a386-ee899882e757","resolution":{"observed_at":"2026-08-11T12:17:56.623947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.675547Z","title":null,"venue":null,"work_id":"349d768c-018e-4e95-859c-8470d18f3ff4","year":2020},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.628188Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:5ce9d5f023482aa896eea5fcee81cfb20752a1551f4372f573327ae0321580d0","observation_id":"a5aed046-c239-484c-a7bc-34f800f0a2b7","resolution":{"observed_at":"2026-08-11T12:17:57.679308Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.663401Z","title":null,"venue":null,"work_id":"2da1f041-760f-4318-bc4e-d9bb2ace97a4","year":2022},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.631928Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:ad4d5f51d8c11a9c8f858ecffff2c5f7e80e247f0e3b0fbda88d162ce01611ed","observation_id":"b8833fdd-0169-40ec-954c-00baab62ef24","resolution":{"observed_at":"2026-08-11T12:17:57.667682Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08459","last_updated":"2024-07-17T20:56:45Z","snapshot_observed_at":"2026-08-10T10:47:30.375111Z","submitted_at":"2023-10-12T16:19:58Z","title":"A Recent Survey of Heterogeneous Transfer Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08459","snapshot_observed_at":"2026-08-11T12:17:56.635918Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.635918Z"},"links":{"cited_paper":"/paper/2310.08459","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:191c453641c195c796628c7b9df251d63935668b6aebdd74efa4b10eeac129a9","observation_id":"a2ae5180-97d8-4fe7-a1b8-f961657358f8","resolution":{"observed_at":"2026-08-11T12:17:56.635918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.652637Z","title":null,"venue":null,"work_id":"f2c58ef4-ec0d-4bb5-b5e4-b05cc46a26d2","year":2022},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.639859Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:16f434ba65df7028972c536252613d8528f07566e38f7f98d6c6d028dfc5142e","observation_id":"9af885bf-a9b0-4227-a291-f0d216e3742a","resolution":{"observed_at":"2026-08-11T12:17:57.656175Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-11T05:54:56.124984Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-11T12:17:56.644139Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.644139Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:0880d5113648024339315319f50f6495431a1748954ded78c964f125bea1cda8","observation_id":"a2fb8e0e-3045-4f6e-8d21-ac34c84b94e3","resolution":{"observed_at":"2026-08-11T12:17:56.644139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.641849Z","title":"Hudson, Ehsan Adeli, and Russ Altman","venue":null,"work_id":"c3bf4123-56ba-4426-ba1b-6f2a475d4d5a","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.648381Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:289098b17ed090ce5f7d8762d98a6bde8533b3090ab822fb4a370950b28216f7","observation_id":"549926a7-4b52-495f-95a8-5848d8ad7df1","resolution":{"observed_at":"2026-08-11T12:17:57.645622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14779","last_updated":"2024-04-23T06:36:21Z","snapshot_observed_at":"2026-08-09T07:41:35.460122Z","submitted_at":"2024-04-23T06:36:21Z","title":"Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14779","snapshot_observed_at":"2026-08-11T12:17:56.651892Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.651892Z"},"links":{"cited_paper":"/paper/2404.14779","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:6e9370756deea7b9962bfddb521f2d670e5775f2fc99825af18f788188e91500","observation_id":"a0eac82e-871e-4623-a15f-b5a8e0c36b6c","resolution":{"observed_at":"2026-08-11T12:17:56.651892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T12:17:56.655871Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.655871Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:8df3963be9b31da9678cfc236da5933c8b9e4a2723bb3b1902722c3901b94103","observation_id":"1a95f531-cea9-4155-a95d-4abf0ff81c17","resolution":{"observed_at":"2026-08-11T12:17:56.655871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.659701Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.659701Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:0d2db137c7fb2db9be2696719c8aa0f0b38477bec7fedf535a0fc34eda785420","observation_id":"8aa98df6-2906-4d90-aa23-8fd900d0a087","resolution":{"observed_at":"2026-08-11T12:17:56.659701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11988","last_updated":"2024-11-04T02:28:13Z","snapshot_observed_at":"2026-08-05T12:52:55.692166Z","submitted_at":"2024-10-15T18:51:18Z","title":"DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models","version":2},"cited_work":{"arxiv_id":"2410.11988","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.11988","snapshot_observed_at":"2026-08-11T12:17:57.297249Z","title":"DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models","venue":"cs.CL","work_id":"9edbe6a4-f287-498d-87b4-2e8698b3e3bb","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.663320Z"},"links":{"cited_paper":"/paper/2410.11988","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:a118b879c00e6a27b11220879360e4a16a50df59678b1218bb3375b103a361d2","observation_id":"cf6e3f4d-a2c4-484e-b4dd-b74911565472","resolution":{"observed_at":"2026-08-11T12:17:57.301545Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05015","last_updated":"2023-10-11T01:46:35Z","snapshot_observed_at":"2026-08-10T09:36:29.224880Z","submitted_at":"2023-10-08T05:16:28Z","title":"Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05015","snapshot_observed_at":"2026-08-11T12:17:56.668390Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.668390Z"},"links":{"cited_paper":"/paper/2310.05015","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:a605fa970b218babaa61528bdf3e595d0fe13f999e42f0235c783a870f60b98f","observation_id":"a98869ee-5890-4ffb-8efa-8a4c842cadab","resolution":{"observed_at":"2026-08-11T12:17:56.668390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04366","last_updated":"2022-02-02T16:39:23Z","snapshot_observed_at":"2026-08-09T04:45:04.547249Z","submitted_at":"2021-10-08T20:22:26Z","title":"Towards a Unified View of Parameter-Efficient Transfer Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04366","snapshot_observed_at":"2026-08-11T12:17:56.672478Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.672478Z"},"links":{"cited_paper":"/paper/2110.04366","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:72ea81b8a5d03e7edb9f140f2a15e886ea94bae8a4082ba057319fb83525e09f","observation_id":"91a47d0d-71be-461e-886c-459e0791450b","resolution":{"observed_at":"2026-08-11T12:17:56.672478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.676338Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.676338Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:6effca58338b880fe0a856a4dd990e0b3d0ac2b40068980b880a5dc899edb052","observation_id":"6ab4995c-def5-47aa-a956-185212d6e4b4","resolution":{"observed_at":"2026-08-11T12:17:56.676338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T12:17:56.683114Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.683114Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:b05f627be55090a8858e47b52494b3548236686bbaa768269e95815b6e46d66a","observation_id":"b37ec56e-6a91-4050-bb92-2c4f98328b54","resolution":{"observed_at":"2026-08-11T12:17:56.683114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-01T18:34:23.156273Z","submitted_at":"2016-11-03T19:48:08Z","title":"Categorical Reparameterization with Gumbel-Softmax","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01144","snapshot_observed_at":"2026-08-11T12:17:56.687043Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.687043Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:325be31764fd4a5d5e0fc97921d20c5fff10e86a9f376e8be99e7edc479e1ca7","observation_id":"683b9187-37ba-4485-9b2e-1c8203e9237a","resolution":{"observed_at":"2026-08-11T12:17:56.687043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02981","last_updated":"2024-01-24T18:16:34Z","snapshot_observed_at":"2026-07-06T17:12:06.715778Z","submitted_at":"2024-01-01T06:22:04Z","title":"Fine-tuning and Utilization Methods of Domain-specific LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02981","snapshot_observed_at":"2026-08-11T12:17:56.690667Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.690667Z"},"links":{"cited_paper":"/paper/2401.02981","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:0583b554be773a74a2c54808829a3c35440e6e6fc64c7485a3658050b57ae1df","observation_id":"c118b74d-40f4-45a1-bf3f-18eafd6e1160","resolution":{"observed_at":"2026-08-11T12:17:56.690667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.694257Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.694257Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:c6f50f3b73088581200217801e4460bc9eb20fa868f82f39b5ffe027a3c6938a","observation_id":"49db6295-5c76-4c38-8295-a295f36e804e","resolution":{"observed_at":"2026-08-11T12:17:56.694257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00523","last_updated":"2019-12-04T02:50:00Z","snapshot_observed_at":"2026-08-11T17:08:01.942941Z","submitted_at":"2019-10-01T16:25:12Z","title":"BillSum: A Corpus for Automatic Summarization of US Legislation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00523","snapshot_observed_at":"2026-08-11T12:17:56.697713Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.697713Z"},"links":{"cited_paper":"/paper/1910.00523","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:aa678fa5fdeabe6328b9e3f560f0ea055bdfe3baae30b40e30480f5769600da5","observation_id":"93c48780-4ff4-4c3a-8182-93d08f90c17b","resolution":{"observed_at":"2026-08-11T12:17:56.697713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.610452Z","title":null,"venue":null,"work_id":"b8e52170-4456-4b43-a54f-c66c81d39b50","year":2012},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.701608Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:997db7ad6e4c002c8f5f4d5ac596ae228e39106cf46713e6a0ec43e2926e61b7","observation_id":"b7e4a9d6-c77f-4422-8853-593d3482ec46","resolution":{"observed_at":"2026-08-11T12:17:57.614152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-06T15:24:34.790850Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-11T12:17:56.704995Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.704995Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:289048c3391b5a5a3d50db86e321670563142ffe358b35a935767c6bb2879a74","observation_id":"c881e8ae-cab6-4eac-bed4-5226ecc54c8d","resolution":{"observed_at":"2026-08-11T12:17:56.704995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00190","last_updated":"2021-01-01T08:00:36Z","snapshot_observed_at":"2026-07-06T10:29:18.734092Z","submitted_at":"2021-01-01T08:00:36Z","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00190","snapshot_observed_at":"2026-08-11T12:17:56.708764Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.708764Z"},"links":{"cited_paper":"/paper/2101.00190","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:476251f09a29a004e24ab0534c07ff2a555d465fa69aa783830e5262bab8f17a","observation_id":"27d1d1e6-44e0-4e63-827c-c8028b711794","resolution":{"observed_at":"2026-08-11T12:17:56.708764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09632","last_updated":"2025-05-02T15:34:42Z","snapshot_observed_at":"2026-08-10T19:35:16.830592Z","submitted_at":"2024-08-19T01:30:14Z","title":"MoDeGPT: Modular Decomposition for Large Language Model Compression","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09632","snapshot_observed_at":"2026-08-11T12:17:56.713172Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.713172Z"},"links":{"cited_paper":"/paper/2408.09632","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:9207d02cb4bdbbe8d73af5d9f4581a69958705f93f474956ec3b0c5ae8eb5180","observation_id":"280e1223-3438-459c-ada2-a5486a6916d4","resolution":{"observed_at":"2026-08-11T12:17:56.713172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.716980Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.716980Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:e8cc951bae76754f8f8d98213f48bfca877be197193073a599ca4f1d5258e749","observation_id":"da0e8439-8ab8-443b-815e-dbe792ecced0","resolution":{"observed_at":"2026-08-11T12:17:56.716980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.591833Z","title":null,"venue":null,"work_id":"410af09b-f63d-4cd4-998a-90555d8bc362","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.720546Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:f44556e6efa3925387166fc6a685fe1f6fe87b9fece92f956fdc75e754db41fb","observation_id":"93f7da0d-e75d-4dc6-94d6-209082789501","resolution":{"observed_at":"2026-08-11T12:17:57.595791Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18703","last_updated":"2024-03-29T14:05:07Z","snapshot_observed_at":"2026-07-06T15:35:11.938380Z","submitted_at":"2023-05-30T03:00:30Z","title":"Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18703","snapshot_observed_at":"2026-08-11T12:17:56.724468Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.724468Z"},"links":{"cited_paper":"/paper/2305.18703","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:d89bcbc9b8d318f3efac4f8bfa3aa76e148c28d8025c3393fa6bb015b4b8e65a","observation_id":"4cc9bc4b-72a8-4f25-aaa2-9d2145491a4f","resolution":{"observed_at":"2026-08-11T12:17:56.724468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09353","last_updated":"2024-07-09T05:59:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-14T17:59:34Z","title":"DoRA: Weight-Decomposed Low-Rank Adaptation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09353","snapshot_observed_at":"2026-08-11T12:17:56.728340Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.728340Z"},"links":{"cited_paper":"/paper/2402.09353","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:aab717aa081f2ac1a6ec08cef343fd63d710dada1bf37cd2c41a5188b178fd96","observation_id":"c2c8fd07-c239-4cbb-a92d-8498832db9be","resolution":{"observed_at":"2026-08-11T12:17:56.728340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.07602","last_updated":"2022-03-20T15:13:08Z","snapshot_observed_at":"2026-08-10T14:24:20.137867Z","submitted_at":"2021-10-14T17:58:47Z","title":"P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.07602","snapshot_observed_at":"2026-08-11T12:17:56.732578Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.732578Z"},"links":{"cited_paper":"/paper/2110.07602","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:92bdd950271e7985dbbf3302412b308d5f31e4f9f6d8c7adb3244a1a7cb73b7b","observation_id":"d40a94bb-efce-433d-8b19-ed7f2215c323","resolution":{"observed_at":"2026-08-11T12:17:56.732578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-08-10T10:28:47.393054Z","submitted_at":"2023-06-16T11:37:15Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-08-11T12:17:56.736630Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.736630Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:8d1ddbf2caa5f7a542f6866a836d52e2d05ad990488da3b0a012a9c72b3fbf6d","observation_id":"ec1fc60c-ff1c-44d7-a95a-aca181f4ea26","resolution":{"observed_at":"2026-08-11T12:17:56.736630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.740383Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.740383Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:ac7567cacaef65feb608acc7a99274f56c0a61abe004e6a9ab92bdf6a46f6f10","observation_id":"b7f973e8-24b5-413e-a151-b34d66e7bf39","resolution":{"observed_at":"2026-08-11T12:17:56.740383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02069","last_updated":"2024-05-19T12:40:36Z","snapshot_observed_at":"2026-07-06T15:37:31.493443Z","submitted_at":"2023-06-03T10:10:38Z","title":"MultiLegalPile: A 689GB Multilingual Legal Corpus","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02069","snapshot_observed_at":"2026-08-11T12:17:56.744222Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.744222Z"},"links":{"cited_paper":"/paper/2306.02069","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:7def690cd1f335042b2c4a974bdc5d207ea01c6bfde2f6b45b779e514d15c263","observation_id":"87f712e0-d56e-4446-9c21-bb60fcb349a8","resolution":{"observed_at":"2026-08-11T12:17:56.744222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.748772Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.748772Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:0a2bd985069b3160d029f5ee3794d13501d55e99add60256c915917f363c5969","observation_id":"38d5e26f-2792-455d-bed0-3fc30e773878","resolution":{"observed_at":"2026-08-11T12:17:56.748772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06752","last_updated":"2018-08-27T16:06:44Z","snapshot_observed_at":"2026-07-06T06:56:34.657007Z","submitted_at":"2018-08-21T04:00:23Z","title":"Lessons from Natural Language Inference in the Clinical Domain","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.06752","snapshot_observed_at":"2026-08-11T12:17:56.752344Z","title":"https://arxiv.org/abs/1808.06752 Lessons from natural language inference in the clinical domain","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.752344Z"},"links":{"cited_paper":"/paper/1808.06752","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:8f6ab427bdcbc10131b5f8237906ec9079fdbcb33588d14aea950c2529211185","observation_id":"470119a2-c773-4483-8f6f-b1ee3165b1d6","resolution":{"observed_at":"2026-08-11T12:17:56.752344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14394","last_updated":"2024-10-02T20:36:11Z","snapshot_observed_at":"2026-07-06T18:18:30.066741Z","submitted_at":"2024-05-23T10:12:03Z","title":"Instruction Tuning With Loss Over Instructions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14394","snapshot_observed_at":"2026-08-11T12:17:56.756532Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.756532Z"},"links":{"cited_paper":"/paper/2405.14394","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:74f336cfe168c8933bb23ca133f46f4e0624b9ef99e1403fa55e6e3fee5aae08","observation_id":"6d01ff88-e681-47bf-afa1-1c5929237b8c","resolution":{"observed_at":"2026-08-11T12:17:56.756532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20306","last_updated":"2024-03-29T17:22:48Z","snapshot_observed_at":"2026-08-10T13:32:41.965262Z","submitted_at":"2024-03-29T17:22:48Z","title":"Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20306","snapshot_observed_at":"2026-08-11T12:17:56.760236Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.760236Z"},"links":{"cited_paper":"/paper/2403.20306","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:6645e63c3b5ce817e2d159675a808a68f75b8c1f58facd6bdbec734f822b54af","observation_id":"427419b7-83bd-4d53-b60c-3292869875d5","resolution":{"observed_at":"2026-08-11T12:17:56.760236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11695","last_updated":"2024-05-06T17:47:01Z","snapshot_observed_at":"2026-07-06T15:44:42.776459Z","submitted_at":"2023-06-20T17:18:20Z","title":"A Simple and Effective Pruning Approach for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11695","snapshot_observed_at":"2026-08-11T12:17:56.764087Z","title":"Zico Kolter","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.764087Z"},"links":{"cited_paper":"/paper/2306.11695","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:fcd2085d3043f1dfd7a1ef5953cdb59b300ff1cbc2829ae075f31d2febb1a87b","observation_id":"68ed08ca-6355-4c80-b319-fb4a6ee67289","resolution":{"observed_at":"2026-08-11T12:17:56.764087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08680","last_updated":"2024-04-08T00:08:29Z","snapshot_observed_at":"2026-08-09T12:48:20.894784Z","submitted_at":"2024-04-08T00:08:29Z","title":"Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08680","snapshot_observed_at":"2026-08-11T12:17:56.767904Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.767904Z"},"links":{"cited_paper":"/paper/2404.08680","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:608f017b02eba24f23e5ab38a556cf9f0f82d1a036d7b030cb921e40ebbef109","observation_id":"5c731372-b90e-4a39-abb0-645ba6acc1a6","resolution":{"observed_at":"2026-08-11T12:17:56.767904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-11T12:17:56.771743Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.771743Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:f4e5c4d3f364e2bd5dcadd290946f4a2be131f1a68765e5f3a1b53fd51e0467a","observation_id":"594f630d-fcc3-4e7d-a6d7-524449b19ec4","resolution":{"observed_at":"2026-08-11T12:17:56.771743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.566103Z","title":null,"venue":null,"work_id":"674c7481-60b1-463c-b3ac-2c19100e9cb5","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.775807Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:b6d91827fdd7ef234eae3ea77a913607e71d586b4631fc17f6fb19ec68c4b02c","observation_id":"f4f178ba-9d3c-4cd1-83bd-e4acd5448f0e","resolution":{"observed_at":"2026-08-11T12:17:57.570300Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.779369Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.779369Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:c01c10969fdab79906c385df8f7242c59fdab5d6f8fc25541ef848d4d1d0515e","observation_id":"f0dde0f9-d1da-4039-9442-fad864f0a71b","resolution":{"observed_at":"2026-08-11T12:17:56.779369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03863","last_updated":"2024-05-23T06:08:37Z","snapshot_observed_at":"2026-08-02T01:09:26.116796Z","submitted_at":"2023-12-06T19:18:42Z","title":"Efficient Large Language Models: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03863","snapshot_observed_at":"2026-08-11T12:17:56.782840Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.782840Z"},"links":{"cited_paper":"/paper/2312.03863","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:d713a36ec6c8b4fec513222ba925228573724ace1875e8f8f215cd3348dca0ca","observation_id":"f367fe39-b176-4c2b-91f3-8b6285a68126","resolution":{"observed_at":"2026-08-11T12:17:56.782840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11441","last_updated":"2024-12-16T07:18:06Z","snapshot_observed_at":"2026-08-09T23:57:36.086097Z","submitted_at":"2024-02-18T03:36:26Z","title":"InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11441","snapshot_observed_at":"2026-08-11T12:17:56.786438Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.786438Z"},"links":{"cited_paper":"/paper/2402.11441","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:de9c93312a944919ad0ca81c1286c6df6a127a4391bfe5d91ae6f984192915ad","observation_id":"9551cab0-254d-45c4-af2b-2bac1d2e7678","resolution":{"observed_at":"2026-08-11T12:17:56.786438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.549294Z","title":null,"venue":null,"work_id":"5ae79cc5-16ad-4d91-ac28-c20ff1fd2fa2","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.791016Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:6ea9f5f0b41f4660161cbdb4256799f0cfd2b862251c8cb980e74035d4631bc4","observation_id":"843a548b-0452-45c1-8534-b7771393c577","resolution":{"observed_at":"2026-08-11T12:17:57.552744Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.538410Z","title":null,"venue":null,"work_id":"47fd40a1-fa61-4ce1-9922-b4f88333346a","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.794530Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:a3cf9a438143fe02822ffc1f1e3b156c7a01ab17fd6fef1f3d1bed4f34d3df66","observation_id":"7e5c9b38-b446-4b10-8099-640f51d7f2af","resolution":{"observed_at":"2026-08-11T12:17:57.542394Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.528180Z","title":null,"venue":null,"work_id":"17421414-0f31-4e94-84d0-1120865beec7","year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.797876Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:1b509927a357e01617340a069041dbf95448215faa441b973320fbe700b1ab62","observation_id":"845cfa8f-c8de-456a-897c-1b35532c3dea","resolution":{"observed_at":"2026-08-11T12:17:57.531706Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.516690Z","title":null,"venue":null,"work_id":"8c24896f-db94-4a7e-a0dc-c98f50ee46c5","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.801242Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:21a7ebd239a81a7ed34ed57ae45115f69041dfc4cb4727f3fc4818a1085eadb4","observation_id":"83e9cf2c-42e4-4833-aea6-9f22f4fbeae1","resolution":{"observed_at":"2026-08-11T12:17:57.520559Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.505960Z","title":null,"venue":null,"work_id":"e056332d-3d12-4c4e-bd2b-2bd13646090e","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.804796Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:ca980080bc2dbc27c8638acd3591d3c2dc043e9903ad66df48a595d7b2a11ac8","observation_id":"ee030299-06df-498a-a0fa-cb1f8474efc4","resolution":{"observed_at":"2026-08-11T12:17:57.509748Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.494204Z","title":null,"venue":null,"work_id":"caab405b-5da6-4a71-9847-3c415cf54d22","year":2020},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.808279Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:eea69d447b44429e619c0f2a6eb4b2922a279b3b163d03ad398cc5a27b4eec9e","observation_id":"38c11f8f-c024-4824-8556-9906d9c6ba7e","resolution":{"observed_at":"2026-08-11T12:17:57.497789Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.482304Z","title":null,"venue":null,"work_id":"4d95fb61-fadb-4e7e-a92d-d27ec5cbced5","year":2020},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.811943Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:30bbe9a3bd3fb9869ddef58abadfaea620a12e8b89b668a2a5a13bf0d8732b2f","observation_id":"007519ed-e948-41df-bc45-59be154e448e","resolution":{"observed_at":"2026-08-11T12:17:57.486122Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.10763","last_updated":"2022-04-22T20:16:12Z","snapshot_observed_at":"2026-07-06T12:10:34.925614Z","submitted_at":"2021-11-21T08:36:31Z","title":"Decentralized Unsupervised Learning of Visual Representations","version":2},"cited_work":{"arxiv_id":"2111.10763","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.10763","snapshot_observed_at":"2026-08-11T12:17:56.965039Z","title":"Decentralized Unsupervised Learning of Visual Representations","venue":"cs.LG","work_id":"b64cb8ea-ea52-4916-bee3-0f28375ec6c1","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.816040Z"},"links":{"cited_paper":"/paper/2111.10763","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:e30ad94867e8cedf1ece3a6d4ae290d11a89ed48f9c4698dad39d7af796774ee","observation_id":"84812da3-0fde-45c8-8409-794494107598","resolution":{"observed_at":"2026-08-11T12:17:56.970817Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.470282Z","title":null,"venue":null,"work_id":"262c3446-ebf4-4081-9d9d-fdf9573ce182","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.820161Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:a74fa57bbfac334bc101a4e698321c1c204f9134a5cdffd00e8cfa963b8651ac","observation_id":"972d6c36-d194-4285-a575-8e9bae01379d","resolution":{"observed_at":"2026-08-11T12:17:57.474054Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.459490Z","title":null,"venue":null,"work_id":"66c6fb92-c36e-4fa0-a735-d5c247a4a1f1","year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.824311Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:850b2595868f92d41359621722e27ff219f3339ee1593b6ac9af224704137e93","observation_id":"6cb63d5b-86ee-40f3-9531-6f69f03e8588","resolution":{"observed_at":"2026-08-11T12:17:57.463094Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.448058Z","title":null,"venue":null,"work_id":"b95bbb2f-c174-464d-abb0-4b95d4baa313","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.828040Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:7728b3f09c844bf71b5b4d30decd07aec5ff585b16034b5849d4aec3d2e77fba","observation_id":"b0fb088b-33c9-42a0-895d-7e90424f25e3","resolution":{"observed_at":"2026-08-11T12:17:57.451714Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.435830Z","title":null,"venue":null,"work_id":"636418f9-2654-4675-bda1-5bd0c97bfa17","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.831708Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:fdcbd5c68469f9646d9758d0ad0e9fd3b937cd676584f75bfc3759e08c2de09b","observation_id":"55b8f7e2-a70d-499d-81fd-e9660cb0f51e","resolution":{"observed_at":"2026-08-11T12:17:57.440765Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.423331Z","title":null,"venue":null,"work_id":"5a7c3902-f636-44ed-88d2-53b0b1d48325","year":2022},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.836714Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:8626e4864b5d27a70e8e2d40b8853ed3e7928910664adbd0dc2acf6e5966a242","observation_id":"a6f2a0d2-813e-4e3e-9db9-84642c68707c","resolution":{"observed_at":"2026-08-11T12:17:57.427710Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.840266Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.840266Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:5e5ddce20a03a8a246b7bf9014322aa51e561a1d34fb932d0856bff809f35855","observation_id":"ec1a0337-694e-41df-b196-081755840f5d","resolution":{"observed_at":"2026-08-11T12:17:56.840266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12749","last_updated":"2024-11-02T03:17:34Z","snapshot_observed_at":"2026-08-11T16:02:29.548414Z","submitted_at":"2024-02-20T06:37:31Z","title":"Me LLaMA: Foundation Large Language Models for Medical Applications","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12749","snapshot_observed_at":"2026-08-11T12:17:56.843858Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.843858Z"},"links":{"cited_paper":"/paper/2402.12749","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:5ee7d19fcd1c5c3dd3420577fe8ef47213d65f149686607028ab69f386c51a85","observation_id":"3a1918fc-8e77-4442-bd06-5dd353be74a5","resolution":{"observed_at":"2026-08-11T12:17:56.843858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17193","last_updated":"2024-02-27T04:18:49Z","snapshot_observed_at":"2026-08-10T16:54:55.723491Z","submitted_at":"2024-02-27T04:18:49Z","title":"When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17193","snapshot_observed_at":"2026-08-11T12:17:56.848531Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.848531Z"},"links":{"cited_paper":"/paper/2402.17193","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:97d257e8e83dd241d892e2e594d1dac812e7c882122909a61f5e07119fc32e76","observation_id":"e28f2532-787e-471c-95f7-31881f8f9748","resolution":{"observed_at":"2026-08-11T12:17:56.848531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18403","last_updated":"2024-08-07T03:30:30Z","snapshot_observed_at":"2026-08-09T12:33:50.387527Z","submitted_at":"2023-05-28T15:15:48Z","title":"LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18403","snapshot_observed_at":"2026-08-11T12:17:56.852294Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.852294Z"},"links":{"cited_paper":"/paper/2305.18403","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:b3f8b50f749ce2c29577427903bc88eebbabca9166dc9103fda4384210ccff3d","observation_id":"8101fbc1-681a-4d21-a857-884d922595ce","resolution":{"observed_at":"2026-08-11T12:17:56.852294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.404727Z","title":null,"venue":null,"work_id":"e295d809-ad4a-4880-914d-28f023865b94","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.856955Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:c2c45b4a746309af3ed474dce9600b4d4f9af7625c441f3a6036a647bd51391b","observation_id":"7d0b60d6-6348-46fe-a089-b6b17270ee24","resolution":{"observed_at":"2026-08-11T12:17:57.408687Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.393287Z","title":null,"venue":null,"work_id":"29d83f9e-9f61-4d56-9cd0-6860d41c18b7","year":2022},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.860220Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:8d6d56a7cee1508ad337d64cd91e8c76510beac05bb3f4c7600eb68c108be5c6","observation_id":"c2a84c4f-14a3-489b-be15-6041bba1b0ea","resolution":{"observed_at":"2026-08-11T12:17:57.397521Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:17:57.382371Z","title":null,"venue":null,"work_id":"8679dbf5-cfc8-442e-ab61-65e4af99e511","year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.865294Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:86c8a2248af11978c4291f4dfb17716d04d2ce5c1013b9c3897ee440f8ba56cc","observation_id":"ec2cbba5-e095-4a16-8547-3db47f66d549","resolution":{"observed_at":"2026-08-11T12:17:57.386051Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15061","last_updated":"2024-12-17T12:45:20Z","snapshot_observed_at":"2026-07-06T17:34:24.337596Z","submitted_at":"2024-02-23T02:24:15Z","title":"Fine-tuning Large Language Models for Domain-specific Machine Translation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15061","snapshot_observed_at":"2026-08-11T12:17:56.869044Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.869044Z"},"links":{"cited_paper":"/paper/2402.15061","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:1f55744dd18cea8d18cc762acd25a2b02fc6869c1e5e503b47f3bfb959786cd8","observation_id":"31959962-6a31-4071-8b1a-b6c1bf7f08ae","resolution":{"observed_at":"2026-08-11T12:17:56.869044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:57.371512Z","title":null,"venue":null,"work_id":"7e8c2e6d-334f-4813-a080-10684b4094a7","year":2021},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.872956Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:d3ca7c8d5dc36d1ca50120cb37a114ca49d0e841a3264a71df9ebbcef292deca","observation_id":"bda1745a-4b41-4103-a7f4-c1e8355b43fc","resolution":{"observed_at":"2026-08-11T12:17:57.375382Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15311","last_updated":"2023-07-28T05:17:11Z","snapshot_observed_at":"2026-08-05T10:16:41.678334Z","submitted_at":"2023-07-28T05:17:11Z","title":"TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15311","snapshot_observed_at":"2026-08-11T12:17:56.876510Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.876510Z"},"links":{"cited_paper":"/paper/2307.15311","citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:f499b69c453380a986c611c42c4bd278953e590b5b9f63384578f6b46a0cd8a4","observation_id":"69d8a12b-4e14-4481-acb8-1336695b4934","resolution":{"observed_at":"2026-08-11T12:17:56.876510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.880801Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.880801Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:257545e065b40f0a2fe8c7f4c508702aa308b310516851619b068b738e30d086","observation_id":"b69eb500-fb4a-4b11-ac2f-e5208a58674e","resolution":{"observed_at":"2026-08-11T12:17:56.880801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:17:56.885258Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-11T12:17:56.885258Z"},"links":{"citing_paper":"/paper/2412.14426"},"observation_digest":"sha256:233305dc741a47febc2dd3a37dc5357215527275c31dddb288bd1a780fa21df9","observation_id":"d2c9f6e7-4143-47d5-868d-4645b877299b","resolution":{"observed_at":"2026-08-11T12:17:56.885258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.14426","last_updated":"2024-12-20T15:57:10Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T12:13:25.271742Z","submitted_at":"2024-12-19T00:41:40Z","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs"},"reference_resolution":{"displayed":70,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":67,"verified_exact":2,"verified_fuzzy":1},"total_outbound_references":70},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2412.14426."}