{"as_of":"2026-08-09T20:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:97fad26f011b1e187e61f3a3d92c70b3dcec4cfd17bdecf2b0ad87765f99d83e","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":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T17:01:36.126796Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T10:58:03.105441Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-08-08T17:01:36.126796Z","title":"Scaling laws for forgetting when fine-tuning large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06042","last_updated":"2025-05-26T18:48:37Z","snapshot_observed_at":"2026-08-09T10:18:57.511599Z","submitted_at":"2025-02-09T21:44:27Z","title":"Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T17:01:36.126796Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2502.06042"},"observation_digest":"sha256:a09fd04122ec821b7ce7493cf8219b64323a4fd2087b01f79da1f6316b7847dc","observation_id":"46cd8a44-6f4f-4b39-bed4-e6aae40e8a25","resolution":{"observed_at":"2026-08-08T17:01:36.126796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-08-06T21:36:34.775771Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23762","last_updated":"2025-06-30T12:09:29Z","snapshot_observed_at":"2026-08-06T21:29:49.550137Z","submitted_at":"2025-06-30T12:09:29Z","title":"Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead","version":1},"reference_index":159,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:34.775771Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2506.23762"},"observation_digest":"sha256:509ace848934a495e1282cd22d7974fc51ccecf12920973646c4778315d1a5ed","observation_id":"bd5d357f-9900-4126-ac01-11ef0174c9b4","resolution":{"observed_at":"2026-08-06T21:36:34.775771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2511.01831","last_updated":"2026-04-06T20:32:02Z","snapshot_observed_at":"2026-08-02T08:19:53.364576Z","submitted_at":"2025-11-03T18:39:32Z","title":"Routing-Based Continual Learning for Multimodal Large Language Models","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-18T00:52:36.700027Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2511.01831"},"observation_digest":"sha256:413000b2edd81b2081f1079da89d82639f0cfac94bf9724152bf8fdabb410622","observation_id":"0051e9ee-9054-4ced-801e-be4c11cad423","resolution":{"observed_at":"2026-05-18T00:55:35.315453Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-08-03T04:54:46.402500Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.03846","last_updated":"2026-06-15T22:17:43Z","snapshot_observed_at":"2026-08-09T04:46:21.995253Z","submitted_at":"2026-02-03T18:59:42Z","title":"PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T04:54:46.402500Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2602.03846"},"observation_digest":"sha256:1d09cf7eddf86d872e724d82611af5639b9536e6c6019ef62c5f65a06450b30b","observation_id":"76ff92a5-86e0-47dd-bb79-dbbd608da349","resolution":{"observed_at":"2026-08-03T04:54:46.402500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.01077","last_updated":"2026-05-01T20:22:38Z","snapshot_observed_at":"2026-07-30T10:04:14.236315Z","submitted_at":"2026-05-01T20:22:38Z","title":"Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T18:48:58.169601Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.01077"},"observation_digest":"sha256:5eeccf61aa48b6de3e5cb2bba047426ee20be2a426a107c84396686d83e1519a","observation_id":"d437b9d1-834d-4676-b430-d82212065dbf","resolution":{"observed_at":"2026-05-11T16:06:07.049963Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.10468","last_updated":"2026-05-11T12:34:20Z","snapshot_observed_at":"2026-08-08T10:39:37.475240Z","submitted_at":"2026-05-11T12:34:20Z","title":"Can Muon Fine-tune Adam-Pretrained Models?","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-12T03:53:11.469583Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.10468"},"observation_digest":"sha256:3d56243f83c61a84e2db42026602e1098a49555f2b0ebe51ef1610452bd38557","observation_id":"f789671f-77a5-476e-b3a8-5a82cdd42e54","resolution":{"observed_at":"2026-05-12T06:51:28.706971Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-13T05:00:31.452781Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:3b962359cc48248feb1cee91f453522d0e3cf928a012a7c9fcef5ff566cb658f","observation_id":"4b14271e-7364-499f-a8f3-35d97ffdfac2","resolution":{"observed_at":"2026-05-13T05:07:18.504428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T05:19:05.368681Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:63f3090d3f59ff505450aeeb3cc7fa8252ad3fd941aabee244ed9c50e0438cdc","observation_id":"f8165aca-0c39-4ed8-af0c-11bd68585f94","resolution":{"observed_at":"2026-05-15T05:19:45.635796Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.20005","last_updated":"2026-05-19T15:36:52Z","snapshot_observed_at":"2026-07-06T23:30:39.512029Z","submitted_at":"2026-05-19T15:36:52Z","title":"Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-20T07:14:59.396900Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.20005"},"observation_digest":"sha256:64c5a5a360f8a3655af9f1bcd437eab8c32963aa4d360f3e03c3ffd59a0802b1","observation_id":"7d2fedfe-b223-4017-89a7-81a08ce39143","resolution":{"observed_at":"2026-05-20T07:18:07.083238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2605.27564","last_updated":"2026-05-26T18:36:42Z","snapshot_observed_at":"2026-07-30T04:13:30.453540Z","submitted_at":"2026-05-26T18:36:42Z","title":"The Future of Facts: Tracing the Factual Generation-Verification Gap","version":1},"reference_index":114,"source":"pdf_text","source_observed_at":"2026-06-29T18:31:04.169632Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2605.27564"},"observation_digest":"sha256:fece4bd546f35d4f836854f8e8b5de4d801eff44756e10edf25d3d86e96f4e77","observation_id":"90dbe389-a945-4d05-a168-ac3f0f5a95c1","resolution":{"observed_at":"2026-06-29T18:33:50.403451Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":"2401.05605","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-03T10:58:03.105441Z","title":"Scaling laws for forgetting when fine-tuning large language models","venue":null,"work_id":"81c90535-a2ac-40cd-80f3-ac6a962a3547","year":2024},"citing_paper":{"arxiv_id":"2606.12633","last_updated":"2026-06-10T19:42:03Z","snapshot_observed_at":"2026-08-02T11:20:31.077337Z","submitted_at":"2026-06-10T19:42:03Z","title":"ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-06-27T09:45:35.383450Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2606.12633"},"observation_digest":"sha256:f2468a42450bfa7a18b1f508d9b1951b25007c299f2f496e2f39fa0a8da56581","observation_id":"20ba4a5a-61b1-4d8b-95c9-0fdfb499f4d8","resolution":{"observed_at":"2026-07-03T10:58:03.106935Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-08-02T09:22:00.518886Z","title":"arXiv preprint arXiv:2401.05605 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18293","last_updated":"2026-06-30T19:56:05Z","snapshot_observed_at":"2026-08-09T04:45:41.291747Z","submitted_at":"2026-06-30T19:56:05Z","title":"One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-02T09:22:00.518886Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2607.18293"},"observation_digest":"sha256:df3f18a47c0ae498613984f551549bf76e1c5e9e904d63132320e1e7566c8993","observation_id":"74f0b9a1-2e19-43b5-8c2e-bdbb907f7718","resolution":{"observed_at":"2026-08-02T09:22:00.518886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-07-30T15:13:36.968236Z","title":"Kalajdzievski","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23711","last_updated":"2026-07-26T15:17:41Z","snapshot_observed_at":"2026-08-03T05:34:08.083107Z","submitted_at":"2026-07-26T15:17:41Z","title":"The Intruder Threshold: A Spectral Law for LoRA Fine-Tuning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-07-30T15:13:36.968236Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2607.23711"},"observation_digest":"sha256:7a2d52ca6a5a6d3556fe22c6043e92ff39cff815ddd1205fb99caa9cc2c4a3af","observation_id":"f228ec5f-8425-4b45-b5fe-c3a55b9b09ca","resolution":{"observed_at":"2026-07-30T15:13:36.968236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05605","snapshot_observed_at":"2026-08-01T01:59:49.806815Z","title":"Scaling laws for forgetting when fine-tuning large language models.arXiv preprint arXiv:2401.05605,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25614","last_updated":"2026-07-28T11:45:34Z","snapshot_observed_at":"2026-08-09T01:33:49.293526Z","submitted_at":"2026-07-28T11:45:34Z","title":"MemSFT: Mitigating Alignment Tax with an External Parametric Memory","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T01:59:49.806815Z"},"links":{"cited_paper":"/paper/2401.05605","citing_paper":"/paper/2607.25614"},"observation_digest":"sha256:9e9d9d8d2ad5aac25abf443b7fc38e5c05385d38192074fc3430579800babcb2","observation_id":"2a4cc0e8-e02f-45d7-8bee-36ab3aa6fa80","resolution":{"observed_at":"2026-08-01T01:59:49.806815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.05605/citation-record","integrity":"/paper/2401.05605/integrity","json":"/paper/2401.05605/citation-record.json","paper":"/paper/2401.05605"},"outbound":[],"paper":{"arxiv_id":"2401.05605","last_updated":"2024-01-11T00:44:25Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:14:03.152949Z","submitted_at":"2024-01-11T00:44:25Z","title":"Scaling Laws for Forgetting When Fine-Tuning Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2401.05605."}