{"as_of":"2026-08-07T01:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e8c49c9e280e98bf8976b4bc8a241e223cba8ff8a5739fd99524c0649cd8186","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:21:10.899497Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-23T20:45:48.885676Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","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":"2306.09782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","venue":null,"work_id":"353b6558-5218-4e6b-990f-f033b7adb4ad","year":2023},"citing_paper":{"arxiv_id":"2403.03507","last_updated":"2024-06-02T21:24:12Z","snapshot_observed_at":"2026-07-06T17:40:15.746482Z","submitted_at":"2024-03-06T07:29:57Z","title":"GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-16T23:51:50.163520Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2403.03507"},"observation_digest":"sha256:ea4d40188d01cdbcc59c5047fdcff1bdf299eda3dfcf2dec64633c6ac67002a6","observation_id":"3cd73f03-6e99-4b6f-9946-04cdd52c9687","resolution":{"observed_at":"2026-05-16T23:51:50.251397Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","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":"2306.09782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","venue":null,"work_id":"353b6558-5218-4e6b-990f-f033b7adb4ad","year":2023},"citing_paper":{"arxiv_id":"2409.04777","last_updated":"2026-05-20T09:55:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-07T09:37:19Z","title":"Optimization Hyper-parameter Laws for Large Language Models","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-23T20:45:31.427677Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2409.04777"},"observation_digest":"sha256:26ef1dd5d03a6e143b96f482527059449ad92e559878d3cb234d03961878447c","observation_id":"671738de-d351-4a27-b967-585eccb6ef81","resolution":{"observed_at":"2026-05-23T20:45:48.888996Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","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":"2026-08-06T22:21:10.899497Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02938","last_updated":"2025-06-27T04:16:53Z","snapshot_observed_at":"2026-08-06T22:14:16.417610Z","submitted_at":"2025-06-27T04:16:53Z","title":"A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T22:21:10.899497Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2507.02938"},"observation_digest":"sha256:15526e9e4a03fed50e72babc66b8d82f2c8a9b6c1d7657f375ed34c8be1a2c2e","observation_id":"63c724cb-ff8b-4a32-98a9-9ec77507f620","resolution":{"observed_at":"2026-08-06T22:21:10.899497Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-06T19:22:29.189182Z","title":"Full parameter fine-tuning for large language models with limited resources","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05733","last_updated":"2025-07-08T07:26:55Z","snapshot_observed_at":"2026-08-06T19:16:42.621801Z","submitted_at":"2025-07-08T07:26:55Z","title":"When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:22:29.189182Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2507.05733"},"observation_digest":"sha256:09682cd154bf7f07953e5ca368dcfb401098627a6ca2342f9326d9e4abafc752","observation_id":"4d214fb6-5379-4175-8923-b9e70a1cf8b4","resolution":{"observed_at":"2026-08-06T19:22:29.189182Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-06T18:49:25.726546Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07296","last_updated":"2025-07-09T21:43:06Z","snapshot_observed_at":"2026-08-06T18:42:07.995188Z","submitted_at":"2025-07-09T21:43:06Z","title":"Time Series Foundation Models for Multivariate Financial Time Series Forecasting","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T18:49:25.726546Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2507.07296"},"observation_digest":"sha256:c113e5f65e3178f6bf090538cbccda1e651bbcd59ac865d5c4e42f05431fbe04","observation_id":"0372d93c-a3bf-4537-be03-11b1c195e63c","resolution":{"observed_at":"2026-08-06T18:49:25.726546Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-06T18:35:40.193728Z","title":"Full parameter fine-tuning for large language models with limited resources","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08091","last_updated":"2025-07-10T18:04:52Z","snapshot_observed_at":"2026-08-06T18:25:24.570454Z","submitted_at":"2025-07-10T18:04:52Z","title":"Low-rank Momentum Factorization for Memory Efficient Training","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T18:35:40.193728Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2507.08091"},"observation_digest":"sha256:5104c1c2c64d2adddad1ada3f5979dec023582ccb1b22067630bf2447f3d956c","observation_id":"aa8b78af-79f6-4412-8c45-a7c3df309054","resolution":{"observed_at":"2026-08-06T18:35:40.193728Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-06T15:22:33.000104Z","title":"Full parameter fine-tuning for large language models with limited resources,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16203","last_updated":"2025-07-22T03:36:06Z","snapshot_observed_at":"2026-08-06T15:13:10.982642Z","submitted_at":"2025-07-22T03:36:06Z","title":"SVAgent: AI Agent for Hardware Security Verification Assertion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:33.000104Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2507.16203"},"observation_digest":"sha256:0c6a04a2a8a667662e733614c292a06c291326882de55de66d3f299961423b79","observation_id":"34f0cf21-466d-4a48-b1f9-8c0bd254a029","resolution":{"observed_at":"2026-08-06T15:22:33.000104Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-05T11:10:16.458083Z","title":"Full parameter fine-tuning for large language models with limited resources,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03057","last_updated":"2025-09-03T06:40:25Z","snapshot_observed_at":"2026-08-05T11:10:12.923784Z","submitted_at":"2025-09-03T06:40:25Z","title":"Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T11:10:16.458083Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2509.03057"},"observation_digest":"sha256:ba0ce850a936829443390e31283e56bb27c1dc9363e9d47796b076dd4d33b048","observation_id":"4310bf94-4d72-42f8-ba77-0f554f6a5502","resolution":{"observed_at":"2026-08-05T11:10:16.458083Z","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":"2026-07-06T15:43:27.458645Z","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":"2026-08-04T14:44:14.782933Z","title":"Full parameter fine-tuning for large language models with limited resources.arXiv preprint arXiv:2306.09782,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.23730","last_updated":"2026-05-28T14:53:43Z","snapshot_observed_at":"2026-08-06T09:14:25.703476Z","submitted_at":"2025-09-28T08:20:22Z","title":"EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-04T14:44:14.782933Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2509.23730"},"observation_digest":"sha256:f9a2341c40f72580f32d28805a19e8ee07a905be3f7b4b760876ae817e2e18cd","observation_id":"96ef28e7-a304-45d7-97b6-74c9564ba4ed","resolution":{"observed_at":"2026-08-04T14:44:14.782933Z","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":"2026-07-06T15:43:27.458645Z","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":"2306.09782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","venue":null,"work_id":"353b6558-5218-4e6b-990f-f033b7adb4ad","year":2023},"citing_paper":{"arxiv_id":"2604.05807","last_updated":"2026-04-07T12:44:19Z","snapshot_observed_at":"2026-08-03T02:37:09.804633Z","submitted_at":"2026-04-07T12:44:19Z","title":"Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T18:51:18.466788Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2604.05807"},"observation_digest":"sha256:5e8a25e31b80a56b122ce3f6c6dd2c97e05cce2e42b3e9e2835dd2fd10296174","observation_id":"96ac6457-49d0-44e7-b881-7ebf5e6d27e8","resolution":{"observed_at":"2026-05-10T23:50:52.545894Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","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":"2306.09782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","venue":null,"work_id":"353b6558-5218-4e6b-990f-f033b7adb4ad","year":2023},"citing_paper":{"arxiv_id":"2604.17051","last_updated":"2026-04-18T16:13:49Z","snapshot_observed_at":"2026-07-06T23:04:14.688737Z","submitted_at":"2026-04-18T16:13:49Z","title":"Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T06:21:59.735341Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2604.17051"},"observation_digest":"sha256:a15a3c063ffbddb56851c65a810d19b8039e3308f3d05f56e9798af7e63e30e6","observation_id":"75cee458-e7b8-4c30-863d-25ef289863a8","resolution":{"observed_at":"2026-05-10T06:26:27.682081Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","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":"2306.09782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.09782","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources","venue":null,"work_id":"353b6558-5218-4e6b-990f-f033b7adb4ad","year":2023},"citing_paper":{"arxiv_id":"2605.04058","last_updated":"2026-04-10T08:00:28Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-04-10T08:00:28Z","title":"MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning","version":1},"reference_index":161,"source":"arxiv_source","source_observed_at":"2026-05-10T17:19:59.247074Z"},"links":{"cited_paper":"/paper/2306.09782","citing_paper":"/paper/2605.04058"},"observation_digest":"sha256:c68b1ae4c4a034e9bb5d6e8a1179f33caf32e4176c71fa40a99bdd907a71b942","observation_id":"fd9239f6-9844-4350-93d0-bd5729dca9e2","resolution":{"observed_at":"2026-05-11T07:01:11.882878Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.09782/citation-record","integrity":"/paper/2306.09782/integrity","json":"/paper/2306.09782/citation-record.json","paper":"/paper/2306.09782"},"outbound":[],"paper":{"arxiv_id":"2306.09782","last_updated":"2024-06-06T13:22:26Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T15:43:27.458645Z","submitted_at":"2023-06-16T11:37:15Z","title":"Full Parameter Fine-tuning for Large Language Models with Limited Resources"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2306.09782."}