{"as_of":"2026-08-07T09:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c0a08c5672fc092a2ea628470e956dbfc8e65bd75eb9f3d899dfe005ea6e0d19","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:50:15.749239Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T05:16:35.355130Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20629","snapshot_observed_at":"2026-08-02T05:16:35.355130Z","title":"Plop: Precise lora placement for efficient finetuning of large models.arXiv preprint arXiv:2506.20629,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13425","last_updated":"2026-07-15T04:03:24Z","snapshot_observed_at":"2026-08-03T17:12:53.669172Z","submitted_at":"2026-07-15T04:03:24Z","title":"Data-Efficient Adaptation of LLMs via Attention Head Reweighting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T05:16:35.355130Z"},"links":{"cited_paper":"/paper/2506.20629","citing_paper":"/paper/2607.13425"},"observation_digest":"sha256:aceac963fbd38f4945dd37ac702a4c9450b9e17b464026a2d12681852626d046","observation_id":"b39c9925-8b32-4b65-8f19-cfb6d7b58af3","resolution":{"observed_at":"2026-08-02T05:16:35.355130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20629","snapshot_observed_at":"2026-08-02T02:01:50.780452Z","title":"Plop: Precise lora placement for efficient finetuning of large models.arXiv preprint arXiv:2506.20629, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.14506","last_updated":"2026-07-16T02:42:24Z","snapshot_observed_at":"2026-08-02T02:01:47.032587Z","submitted_at":"2026-07-16T02:42:24Z","title":"Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T02:01:50.780452Z"},"links":{"cited_paper":"/paper/2506.20629","citing_paper":"/paper/2607.14506"},"observation_digest":"sha256:2aab4cf392f35db4929af4f732959df5df0c57becf4601662d1bd416c0d74832","observation_id":"a91d7e92-debe-4d2e-9bb7-a94f201777d4","resolution":{"observed_at":"2026-08-02T02:01:50.780452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20629","snapshot_observed_at":"2026-08-01T17:32:33.530247Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.17620","last_updated":"2026-07-20T07:16:31Z","snapshot_observed_at":"2026-08-06T10:27:53.292028Z","submitted_at":"2026-07-20T07:16:31Z","title":"PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T17:32:33.530247Z"},"links":{"cited_paper":"/paper/2506.20629","citing_paper":"/paper/2607.17620"},"observation_digest":"sha256:3f68131b43d7f7ef27233bb4c458ecd3b89455c8667ddfcf159af62bbab5ab27","observation_id":"ab7739c6-94ab-4006-b8c5-da9c222a1270","resolution":{"observed_at":"2026-08-01T17:32:33.530247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.20629/citation-record","integrity":"/paper/2506.20629/integrity","json":"/paper/2506.20629/citation-record.json","paper":"/paper/2506.20629"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:11.762337Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:11.762337Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:179a21ac4bd95d058b246d4d58e5457e3cd7e389a86435de61614fefc90f1665","observation_id":"b5db60fe-41bd-4e44-9518-a79662231a5a","resolution":{"observed_at":"2026-08-06T22:50:11.762337Z","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":"2026-08-06T22:50:18.557838Z","title":"LoRA+: Efficient low rank adaptation of large models","venue":null,"work_id":"0e388279-5e07-4f41-8a56-571e4bc896c2","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:11.806899Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:214695ff2b13767267f260fb2e8da34e36025ca0e266fd8afb957bfaa53ceccb","observation_id":"bc146791-03eb-470b-ba9f-97bd0c413541","resolution":{"observed_at":"2026-08-06T22:50:18.645387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:18.383214Z","title":"Dora: Weight-decomposed low-rank adaptation","venue":null,"work_id":"aa93b1da-ce13-4622-893d-790948c5b5c7","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:11.866917Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:298de6959774b23cf27d4eff91f20b66c4e596c5187d35615e5e93bf98dd181c","observation_id":"d6846caf-cf64-4ca6-90dd-ea8b981b5217","resolution":{"observed_at":"2026-08-06T22:50:18.456925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:11.916739Z","title":"RA-LoRA: Rank- adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:11.916739Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:b75f1074e02daa379aca3aa0898722f32351b4a62f4c6b949efe6aadce3f1237","observation_id":"afdf1c00-ad44-45e4-ae95-dd2109535032","resolution":{"observed_at":"2026-08-06T22:50:11.916739Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01004","last_updated":"2026-05-27T06:22:29Z","snapshot_observed_at":"2026-08-05T13:03:37.067429Z","submitted_at":"2024-12-01T23:41:42Z","title":"Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01004","snapshot_observed_at":"2026-08-06T22:50:11.955707Z","title":"Adaptive rank, reduced forgetting: Knowledge retention in continual learning vision- language models with dynamic rank-selective lora, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:11.955707Z"},"links":{"cited_paper":"/paper/2412.01004","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:f750b859ef11faedc0c74fe662f703f83ceba32926b07fec6b476898f0465eac","observation_id":"4f14b611-5324-426a-9f6a-8c236fabb752","resolution":{"observed_at":"2026-08-06T22:50:11.955707Z","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":"2026-08-06T22:50:18.189040Z","title":"The impact of initialization on lora finetuning dynamics","venue":null,"work_id":"b20b874b-3bd7-4c98-964c-45894967c130","year":null},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.015542Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:00e55221866e1441b222b669e56245897b715c80f5a464f8fcb85d4b18a9731c","observation_id":"411880e2-f4d0-4922-8149-838dee2eefcb","resolution":{"observed_at":"2026-08-06T22:50:18.283837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10512","last_updated":"2023-12-20T20:56:14Z","snapshot_observed_at":"2026-07-06T15:05:16.471478Z","submitted_at":"2023-03-18T22:36:25Z","title":"AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10512","snapshot_observed_at":"2026-08-06T22:50:12.149846Z","title":"Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.149846Z"},"links":{"cited_paper":"/paper/2303.10512","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:3f9de22606d0d623f70ca484b21220f680a0f9ec9ed688f1712f37a0e8f7157b","observation_id":"32f10d84-83ea-4ff9-8bb6-cbc0cb9fd109","resolution":{"observed_at":"2026-08-06T22:50:12.149846Z","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":"2026-08-06T22:50:17.850421Z","title":"Qlora: Effi- cient finetuning of quantized llms","venue":null,"work_id":"acc5618a-ea06-4ba1-8a77-d08f85ca4a26","year":2023},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.211265Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:96acf7e0da4d27a2e60295dcafc541432c3be437b2a526617d9dbed226627c4b","observation_id":"dad788d7-cbe3-45db-ab3d-6b88285e0e49","resolution":{"observed_at":"2026-08-06T22:50:17.926630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11454","last_updated":"2024-01-16T18:59:22Z","snapshot_observed_at":"2026-07-06T16:34:38.718198Z","submitted_at":"2023-10-17T17:59:46Z","title":"VeRA: Vector-based Random Matrix Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.11454","snapshot_observed_at":"2026-08-06T22:50:12.267784Z","title":"Kopiczko, Tijmen Blankevoort, and Yuki M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.267784Z"},"links":{"cited_paper":"/paper/2310.11454","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:c2da973be7525845e6104d79bc2037b7f14f831a803ea31bf459b6a72c70d301","observation_id":"60eda5be-c04f-4910-a234-c24fe22a6fcd","resolution":{"observed_at":"2026-08-06T22:50:12.267784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03303","last_updated":"2026-05-09T05:30:15Z","snapshot_observed_at":"2026-07-06T16:03:14.694457Z","submitted_at":"2023-08-07T05:12:27Z","title":"LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03303","snapshot_observed_at":"2026-08-06T22:50:12.331356Z","title":"Lora-fa: Memory- efficient low-rank adaptation for large language models fine-tuning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.331356Z"},"links":{"cited_paper":"/paper/2308.03303","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:ed679c5874c380eb5841b7cd4e846a0b99d71f43627a52c2fcf3c75269ffa36e","observation_id":"5c6837c7-1911-4314-905f-1ef8bbbf7e3a","resolution":{"observed_at":"2026-08-06T22:50:12.331356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19245","last_updated":"2024-05-23T15:06:02Z","snapshot_observed_at":"2026-08-05T08:36:16.990741Z","submitted_at":"2024-04-30T04:01:09Z","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19245","snapshot_observed_at":"2026-08-06T22:50:12.357927Z","title":"Hydralora: An asymmetric lora architecture for efficient fine-tuning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.357927Z"},"links":{"cited_paper":"/paper/2404.19245","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:ab71e42880b4554ba325819104aa64607286c8f9487de17713a5272e3ff317a1","observation_id":"ee0ed6d0-85d7-49d5-b489-a2954f848beb","resolution":{"observed_at":"2026-08-06T22:50:12.357927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12130","last_updated":"2024-05-20T15:48:32Z","snapshot_observed_at":"2026-07-06T18:16:51.116432Z","submitted_at":"2024-05-20T15:48:32Z","title":"MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12130","snapshot_observed_at":"2026-08-06T22:50:12.408587Z","title":"Mora: High-rank updating for parameter-efficient fine-tuning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.408587Z"},"links":{"cited_paper":"/paper/2405.12130","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:d5058883cde2ca1d0a102bcf42c5cbdbba05f770b15afffac67b1d316f3b878e","observation_id":"6d5a7cc4-248e-4a91-8ea7-52f11502b57a","resolution":{"observed_at":"2026-08-06T22:50:12.408587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05086","last_updated":"2024-04-07T22:00:50Z","snapshot_observed_at":"2026-08-04T09:19:23.535838Z","submitted_at":"2024-04-07T22:00:50Z","title":"A Note on LoRA","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05086","snapshot_observed_at":"2026-08-06T22:50:12.474879Z","title":"A note on lora","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.474879Z"},"links":{"cited_paper":"/paper/2404.05086","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:a55167d6764bf8d59979f7a69203f195378a805630771113289e8579ae73705c","observation_id":"f9d36fd4-5867-4440-8523-58922a1ec4a0","resolution":{"observed_at":"2026-08-06T22:50:12.474879Z","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":"2026-08-06T09:47:33.389391Z","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":"2026-08-06T22:50:12.504083Z","title":"Towards a unified view of parameter-efficient transfer learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.504083Z"},"links":{"cited_paper":"/paper/2110.04366","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:15eeffbb914a4184bd41ab26a3abb1a49082cedf61c0d73da48f0cada7c8cbc7","observation_id":"9cd9a7cb-07f0-49eb-bc91-7b730ad0733e","resolution":{"observed_at":"2026-08-06T22:50:12.504083Z","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":"2026-08-06T22:50:17.705809Z","title":"The llama 3 herd of models, 2024","venue":null,"work_id":"64dc7f5c-baf1-4a5a-9243-f7d26dbc2983","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.614220Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:c96e73ac9d81cdf92b4c1cfb7fec7999f6c78539bd055cce6cf36a5993d2a140","observation_id":"383b47b1-d223-4a68-a1ec-16c743680418","resolution":{"observed_at":"2026-08-06T22:50:17.756774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08771","last_updated":"2021-09-14T16:07:09Z","snapshot_observed_at":"2026-08-05T18:30:05.719561Z","submitted_at":"2021-04-18T08:41:01Z","title":"Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08771","snapshot_observed_at":"2026-08-06T22:50:12.706998Z","title":"Cross-attention is all you need: Adapt- ing pretrained transformers for machine translation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.706998Z"},"links":{"cited_paper":"/paper/2104.08771","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:8844f4cf9b461358cce667bbe1987369b68cba90de1bda163c84c808016229b7","observation_id":"6102ceff-bbf2-45f1-8b94-602a10a7f861","resolution":{"observed_at":"2026-08-06T22:50:12.706998Z","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":"2026-08-06T22:50:17.479671Z","title":"Gradient-based parameter selection for efficient fine-tuning","venue":null,"work_id":"7d03ef79-3317-44ef-9927-d1f29b10705d","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.818350Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:0239bfbf1b41598af8709a902c9bc96fe5801facd2fe3cc461e98e83f1d6dc49","observation_id":"cd0794ff-9d5f-4fb4-be95-65d80cded92d","resolution":{"observed_at":"2026-08-06T22:50:17.562213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:17.320992Z","title":"Sensitivity-aware visual parameter-efficient fine-tuning","venue":null,"work_id":"cc2f5272-b0cf-4c63-9853-3661f42fd872","year":2023},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.910209Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:61328169e456a7fe216d93b34bf80b292fa4ae6e1ccd78fb2dd0722a8bd1ab2e","observation_id":"2da089bd-1adc-4d98-bc7a-aa29fc568acd","resolution":{"observed_at":"2026-08-06T22:50:17.380197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:17.157021Z","title":null,"venue":null,"work_id":"1202dfae-7cff-4ff2-9df6-444ec21d2581","year":null},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.021420Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:26ebbd8565cc9509a41b4bf0a31c3dd4ff92992fb11c5948204e7ff57a9fe709","observation_id":"ce9e4aa4-272e-4d60-b1a5-fefd85e3a4f3","resolution":{"observed_at":"2026-08-06T22:50:17.238859Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:17.015349Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"da95f519-2415-475e-a5d1-fb519118d58b","year":null},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.218947Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:5f94d6e9d5af5a48d9656c94ac414fa7ea008fa80ab2eb0257f41eb53f55a8bc","observation_id":"ce83a540-3168-4779-9db0-909cbe759f6d","resolution":{"observed_at":"2026-08-06T22:50:17.096221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04434","last_updated":"2018-08-07T18:55:19Z","snapshot_observed_at":"2026-08-06T02:47:02.967969Z","submitted_at":"2018-02-13T02:14:35Z","title":"signSGD: Compressed Optimisation for Non-Convex Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04434","snapshot_observed_at":"2026-08-06T22:50:13.453468Z","title":"signsgd: Compressed optimisation for non-convex problems, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.453468Z"},"links":{"cited_paper":"/paper/1802.04434","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:5a2cc48fe4244ab24f0053a1599c1624f66c6b958552a15b962ad2c41be72d7a","observation_id":"8dc6addd-a51a-4ece-bbfc-5d095e255263","resolution":{"observed_at":"2026-08-06T22:50:13.453468Z","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":"2410.04264","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:16.222658Z","title":"Visualising feature learning in deep neural networks by diagonalizing the forward feature map, 2024","venue":null,"work_id":"123cc28a-2459-4670-858c-b75791b44440","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.578167Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:797934e4fadb5a2d582c0eafdcd237e14800efa5eef98c77e2a3eeb4d77e1bd2","observation_id":"dd1a8385-e7d5-48c7-a0fd-26dbbedfddfe","resolution":{"observed_at":"2026-08-06T22:50:16.273915Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.00938","last_updated":"2021-03-17T00:57:56Z","snapshot_observed_at":"2026-08-06T11:57:11.965103Z","submitted_at":"2020-08-03T15:18:07Z","title":"Implicit Regularization via Neural Feature Alignment","version":3},"cited_work":{"arxiv_id":"2008.00938","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.00938","snapshot_observed_at":"2026-08-06T22:50:15.961398Z","title":"Implicit Regularization via Neural Feature Alignment","venue":"cs.LG","work_id":"fc9f60bc-6107-4380-b2cd-4514b773daff","year":2020},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.659226Z"},"links":{"cited_paper":"/paper/2008.00938","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:dd071fd533633fa12445957898a61eb84d96f0c9902b79492b5343ed2a588021","observation_id":"516add5c-cd9c-420e-80ee-e4bf65044507","resolution":{"observed_at":"2026-08-06T22:50:16.027006Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:16.891268Z","title":"Feature learning and signal propagation in deep neural networks","venue":null,"work_id":"9ae459cf-a19a-4936-baef-916fad689a9e","year":2022},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.754683Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:6a64e07311a2e3aadd5bd312207d18a2f369b269a91404ce82a0b109a6235ea0","observation_id":"75d14942-7f09-45ca-95e0-f31ddb2a1c04","resolution":{"observed_at":"2026-08-06T22:50:16.966923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19279","last_updated":"2024-11-06T22:45:30Z","snapshot_observed_at":"2026-07-06T18:22:05.757775Z","submitted_at":"2024-05-29T17:11:28Z","title":"Understanding and Minimising Outlier Features in Neural Network Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19279","snapshot_observed_at":"2026-08-06T22:50:13.820346Z","title":"Understanding and minimising outlier features in neural network training, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.820346Z"},"links":{"cited_paper":"/paper/2405.19279","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:d95477d81dff21a73b8299af4f84b2c741722f26dbc745d1dfe12735d6b4f541","observation_id":"18e2395b-0685-4b1d-bddd-cbc855c1caa0","resolution":{"observed_at":"2026-08-06T22:50:13.820346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-06T22:50:13.890990Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.890990Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:ac9e9f5f677f3ef5d82fe14dd4f042c077085befbd057db636c96debc73cc203","observation_id":"a23e82c6-11ae-4b8d-9364-b6de3b3eef0b","resolution":{"observed_at":"2026-08-06T22:50:13.890990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-06T22:50:13.961350Z","title":"Evaluating large language models trained on code","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.961350Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:e09e9d67ef00ab6e4a443cd0f248582aa82da2fae3aa6805a6d11e4746846e83","observation_id":"5183aeeb-7db3-4afc-abd7-7ba021bb41d9","resolution":{"observed_at":"2026-08-06T22:50:13.961350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-06T22:50:14.042709Z","title":"Measuring massive multitask language understanding, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.042709Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:f4c3834a162e9dcff70542f1f7f2431e40009ba63a8a5fa619465a37553e3d15","observation_id":"447d596a-cfef-43ab-8d04-ea5ccab57416","resolution":{"observed_at":"2026-08-06T22:50:14.042709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-06T22:50:14.160586Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.160586Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:667184ed9f8fff2004bf83a6f633d3c631b1ec0ade0b9e254963f2c76e46f496","observation_id":"b3b6997b-fa6a-4013-b85e-751b70d60b89","resolution":{"observed_at":"2026-08-06T22:50:14.160586Z","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":"2026-08-06T22:50:16.747898Z","title":"Qwen3 technical report, April 2025","venue":null,"work_id":"a114ed02-8319-45e3-a9a2-d1e4310fab49","year":2025},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.273874Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:05a3e377d5681a3061c787267662ea5cd407845fedd7fbc0a430969bafc0c33d","observation_id":"eb7b65fa-b8be-4540-86d1-5ccfe1ac932e","resolution":{"observed_at":"2026-08-06T22:50:16.813184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:50:14.336198Z","title":"Gemma 3 technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.336198Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:26eb15a54cbe40e39bc25a432b73ce18708fa4025a9d5e82412f4dcbf5d3f0bd","observation_id":"7e6ccd7b-320c-4252-ac65-c033859c8043","resolution":{"observed_at":"2026-08-06T22:50:14.336198Z","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":"2026-08-06T22:50:16.608448Z","title":"Adversarial nli: A new benchmark for natural language understanding","venue":null,"work_id":"c772e9d7-b9ce-4f50-b2aa-e7442aeff776","year":2020},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.417894Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:069075592e69fc1c82dced41fc6c4f6dc9d0887b6f0a7e61092c18cf87f346d7","observation_id":"5db56f86-e897-44f6-9275-bec66881314d","resolution":{"observed_at":"2026-08-06T22:50:16.682332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-02T15:00:50.388422Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12284","snapshot_observed_at":"2026-08-06T22:50:14.482792Z","title":"Metamath: Bootstrap your own mathematical questions for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.482792Z"},"links":{"cited_paper":"/paper/2309.12284","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:638b60f8cd040eaba4202f445bd0db725a743d6bd3c2884abd09df22050aeb85","observation_id":"1f47f458-6a09-4c7a-af59-185ffdbe48e0","resolution":{"observed_at":"2026-08-06T22:50:14.482792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00451","last_updated":"2024-06-17T22:11:49Z","snapshot_observed_at":"2026-07-06T18:08:09.361079Z","submitted_at":"2024-05-01T11:10:24Z","title":"Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00451","snapshot_observed_at":"2026-08-06T22:50:14.576341Z","title":"Lillicrap, Kenji Kawaguchi, and Michael Shieh","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.576341Z"},"links":{"cited_paper":"/paper/2405.00451","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:ffb186675d80d96210d3366af5b559afcc163c5f08ccaaf175dddc696252f66d","observation_id":"e070fe5c-7e87-41b8-8a10-5bf39df4fad6","resolution":{"observed_at":"2026-08-06T22:50:14.576341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T22:50:14.684254Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.684254Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:be616f7b17158036ad5b379d1b14bad9f64a21dc91bfb5138b4cd5a3c923fde0","observation_id":"1be3cd60-22ba-4da2-b15d-2d4d881126db","resolution":{"observed_at":"2026-08-06T22:50:14.684254Z","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":"2026-08-06T22:50:14.881189Z","title":"Rae, Oriol Vinyals, and Laurent Sifre","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:14.881189Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:25a0e14a493607a215dcde94b801359730ecd3d7477b01d81cfe7076810acdf8","observation_id":"f0915151-274c-4caf-ba34-072f6be67ad8","resolution":{"observed_at":"2026-08-06T22:50:14.881189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.01852","last_updated":"2015-02-06T10:44:00Z","snapshot_observed_at":"2026-08-06T07:29:40.861474Z","submitted_at":"2015-02-06T10:44:00Z","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.01852","snapshot_observed_at":"2026-08-06T22:50:15.138441Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:15.138441Z"},"links":{"cited_paper":"/paper/1502.01852","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:f2d63f0906c8754e5ee820d1a0513fd20f993d2f4238f3a12ed536bfb3247778","observation_id":"e33a345d-cbf0-45e2-bc08-32823397018e","resolution":{"observed_at":"2026-08-06T22:50:15.138441Z","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":"2026-08-06T22:50:16.481153Z","title":"On the impact of the activation function on deep neural networks training","venue":null,"work_id":"b91861fb-3124-45a5-bf52-9205e5561b53","year":2019},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:15.503504Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:0aa1f1d08b63733f3ce503d07d76f9f0afe26761ebf68ecff8cab9833668e6b1","observation_id":"885b4b84-f9cf-4666-89b9-1eddf68da088","resolution":{"observed_at":"2026-08-06T22:50:16.534788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-07-06T07:32:46.883175Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-06T22:50:15.749239Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:15.749239Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:7ad7a71e2fbf9ccffb66c469c99ec9393635f5ac0840f46c3c5c9ad371d0cc37","observation_id":"182b4bf2-086e-478b-a7e5-4997fd1f11a4","resolution":{"observed_at":"2026-08-06T22:50:15.749239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T22:50:13.370576Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.370576Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:f45e65435ba5b81ab56880fadf0786e0ebbd17aa21ed2d7b89a6085f25db3e03","observation_id":"a332630d-d8ed-4262-a49f-6f32e235eb1e","resolution":{"observed_at":"2026-08-06T22:50:13.370576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.14522","last_updated":"2022-07-15T17:04:24Z","snapshot_observed_at":"2026-07-06T10:18:51.318056Z","submitted_at":"2020-11-30T03:21:05Z","title":"Feature Learning in Infinite-Width Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.14522","snapshot_observed_at":"2026-08-06T22:50:13.117590Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:13.117590Z"},"links":{"cited_paper":"/paper/2011.14522","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:f0c15393e9fc3d4520c2d090dc148dd1c21717bae699fe7d2b47129efc4670bd","observation_id":"6b15f366-c0a1-41dd-8aa3-21ed137496a3","resolution":{"observed_at":"2026-08-06T22:50:13.117590Z","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":"2026-08-06T22:50:18.009577Z","title":null,"venue":null,"work_id":"29a9e83d-761b-40e3-9875-872ebb073bc1","year":2024},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:12.072489Z"},"links":{"citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:9b2cdd85e4e7463ea7f4cb12e9419a224d685d4037bfe57f926f550eaaff6f37","observation_id":"8decc2cc-64c6-4c65-89a0-bc6e92d4e8e4","resolution":{"observed_at":"2026-08-06T22:50:18.095224Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":2,"verified_fuzzy":12},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2506.20629."}