{"as_of":"2026-08-23T03:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b90c044734e9d4b17f3fee9e8dc55fb3b99e09302eaf61e39e0fdfcd1a6c16fd","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T10:45:46.618668Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.04969/citation-record","integrity":"/paper/2607.04969/integrity","json":"/paper/2607.04969/citation-record.json","paper":"/paper/2607.04969"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.08905","last_updated":"2024-12-12T03:37:41Z","snapshot_observed_at":"2026-08-20T14:44:18.211453Z","submitted_at":"2024-12-12T03:37:41Z","title":"Phi-4 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08905","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Phi-4 techni- cal report.arXiv preprint arXiv:2412.08905,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2412.08905","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:0b658b3f26f87893b629e170b77562f89072409135e2c5d0430fc90edaaeb756","observation_id":"0a1d000a-3215-432f-be92-848e41b96587","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02737","last_updated":"2025-02-04T21:43:16Z","snapshot_observed_at":"2026-08-14T05:03:05.661970Z","submitted_at":"2025-02-04T21:43:16Z","title":"SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02737","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Smollm2: When smol goes big–data-centric training of a small language model.arXiv preprint arXiv:2502.02737,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2502.02737","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:6a78a813c0c569bb824b4f32bb5aa5d3bd1927eba438eaf0baff68f324f1df3c","observation_id":"21cff844-1be4-4e10-bcde-fd3fc1f8428f","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.07372","last_updated":"2026-07-12T07:41:31Z","snapshot_observed_at":"2026-08-15T16:31:19.460938Z","submitted_at":"2026-01-12T09:54:49Z","title":"Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.07372","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Conditional memory via scalable lookup: A new axis of sparsity for large language models.arXiv preprint arXiv:2601.07372,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2601.07372","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:1e0104c6bac891b757a5910bb1f15d7b7667685eefee13124f0cf93256961996","observation_id":"57b7f3bd-d63e-46e9-8f64-c6b2c8044616","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04235","last_updated":"2025-05-19T11:52:17Z","snapshot_observed_at":"2026-08-20T05:26:03.378193Z","submitted_at":"2025-02-06T17:19:55Z","title":"Reformulation for Pretraining Data Augmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04235","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Reformulation for pretraining data augmentation.arXiv preprint arXiv:2502.04235,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2502.04235","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:83476b2cd3cebabe22eb31a7a210cc8005d07115a01346ca8d9755b616919f94","observation_id":"ac4162bd-e12c-4948-a030-39ba601e5c8c","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10487","last_updated":"2022-05-21T02:14:27Z","snapshot_observed_at":"2026-08-04T13:21:32.121470Z","submitted_at":"2022-05-21T02:14:27Z","title":"Scaling Laws and Interpretability of Learning from Repeated Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10487","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Scaling laws and inter- pretability of learning from repeated data.arXiv preprint arXiv:2205.10487,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2205.10487","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:8b947976064830468b570f98034097d4b5c85da4571ed63b495abbbf36af10fa","observation_id":"0f37e195-1eca-4716-a8b1-1c6d23669b05","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-21T02:12:10.329412Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Train- ing compute-optimal large language models.arXiv preprint arXiv:2203.15556,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:4fb6b1079c3c2eb08b369dfcf05c81fc68a37bc322a10ff121107f8af93df665","observation_id":"0736a376-dae2-4317-820f-6f4f87747bc7","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12364","last_updated":"2025-02-06T09:36:58Z","snapshot_observed_at":"2026-08-20T12:51:23.149888Z","submitted_at":"2024-11-19T09:24:34Z","title":"Ultra-Sparse Memory Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12364","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Ultra- sparse memory network.arXiv preprint arXiv:2411.12364,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2411.12364","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:5e1b62034f7608c87024a0263e11e252ff5eeb020eba429eaa99d378b22b9e0d","observation_id":"e364a06c-7f2d-4cab-aeaf-bc5df9d406ab","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","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":"2026-07-11T10:45:46.618668Z","title":"Ziyue Li, Chenrui Fan, and Tianyi Zhou","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:36fe57544c1fbf7019bc7a05082277a80aaab6c3136fa6de31789ed9399025ad","observation_id":"5b965e8a-d04d-415a-a662-6fe5946470a2","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-17T09:42:34.746112Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Let’s verify step by step.arXiv preprint arXiv:2305.20050,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:032959105d5ad7944cfa7c52b5a976908bb0362fe424df097f59f22b2a08d66f","observation_id":"d0dfc0bd-c81c-49e7-a214-27aa69869332","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24832","last_updated":"2025-06-18T15:27:03Z","snapshot_observed_at":"2026-08-15T05:45:43.601041Z","submitted_at":"2025-05-30T17:34:03Z","title":"How much do language models memorize?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24832","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Smollm2: When smol goes big—data-centric training of a fully open small language model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2505.24832","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:7e8dc8dfcc0fcdd324a303707dbdaa981b65dbd144165e3bd27e98dbe643cc4e","observation_id":"7ddfe9df-2dac-499f-83e3-0f48e186728f","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.13961","last_updated":"2026-04-14T15:12:44Z","snapshot_observed_at":"2026-08-16T16:18:35.731432Z","submitted_at":"2025-12-15T23:41:48Z","title":"Olmo 3","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.13961","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Olmo 3.arXiv preprint arXiv:2512.13961,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2512.13961","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:14f2adb0647b4565fee6b2b10f01edeb1ec6eae7b6027ff4b84dc2294b9198c3","observation_id":"016b0d27-4f1c-4fdf-9077-d0a313695566","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07263","last_updated":"2024-07-09T22:37:59Z","snapshot_observed_at":"2026-08-20T06:41:56.595086Z","submitted_at":"2024-07-09T22:37:59Z","title":"Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07263","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Reuse, don’t retrain: A recipe for continued pretraining of language models.arXiv preprint arXiv:2407.07263,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2407.07263","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:ab7c919ac238226bd40c47ba5db90afdf1c86c8d34ced3f655201428e2a9b663","observation_id":"5fb69b54-f28c-4bea-95ec-b9069f16e924","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.01116","last_updated":"2023-06-01T20:03:56Z","snapshot_observed_at":"2026-08-20T04:37:17.931952Z","submitted_at":"2023-06-01T20:03:56Z","title":"The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.01116","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"The refinedweb dataset for falcon llm: outperforming curated corpora with web data, and web data only.arXiv preprint arXiv:2306.01116,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2306.01116","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:655ca9049c3efa9f48c7a2314a358a3305e264a3af000ac8231754409c7c31fc","observation_id":"9d5df397-5493-4268-bdaa-aa093e709d94","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09085","last_updated":"2022-11-16T18:06:33Z","snapshot_observed_at":"2026-08-17T08:35:32.078396Z","submitted_at":"2022-11-16T18:06:33Z","title":"Galactica: A Large Language Model for Science","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09085","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Qwen3-coder-next technical report","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2211.09085","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:825ac91225e4887d4b7ed7746e920fb4c1989c69ba19f78a69dbf3409478588f","observation_id":"9f79dfb9-1d36-46f9-a59d-b50b33d7fb36","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20534","last_updated":"2026-02-03T04:57:00Z","snapshot_observed_at":"2026-08-16T14:37:33.548231Z","submitted_at":"2025-07-28T05:35:43Z","title":"Kimi K2: Open Agentic Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.20534","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Kimi k2: Open agentic intelligence.arXiv preprint arXiv:2507.20534,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2507.20534","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:814bed6100b9c1161bd7c42cd0b0d29d75696fe010b3f86919ce70f651666d55","observation_id":"c2f585ae-f956-40fb-ba97-e8247b0a8355","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01560","last_updated":"2024-10-05T03:54:22Z","snapshot_observed_at":"2026-08-20T11:08:50.305630Z","submitted_at":"2024-10-02T14:00:09Z","title":"OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01560","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data.arXiv preprint arXiv:2410.01560,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2410.01560","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:ef0fdf66764b42e4ad48cb7b492330c9b199bf80f03e3d1c0cbc1d663783d9c1","observation_id":"72e0f637-ddf4-4168-bb56-6415d1dfcb4d","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14985","last_updated":"2025-03-02T03:27:58Z","snapshot_observed_at":"2026-08-16T13:32:26.549245Z","submitted_at":"2024-07-20T21:24:40Z","title":"Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14985","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Generalization vs memorization: Tracing language models’ capabilities back to pretraining data.arXiv preprint arXiv:2407.14985,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2407.14985","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:6d0ce29495d97acdc430786b973db0057cc2be3286c94838d1780e42fe0f9109","observation_id":"10a4e715-c655-4ca4-aee8-55fcf3ac4764","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","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":"2026-07-11T10:45:46.618668Z","title":"Larger datasets can be repeated more: A theoretical analysis of multi-epoch scaling in linear regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:74af26fcc128799018747670b530b23a8d1e685309a4f94c2795838aba4bcb94","observation_id":"d13ccbe8-d9a3-40c6-90d6-4c078dd88cd3","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","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":"2026-07-11T10:45:46.618668Z","title":"Nicolas Zucchet, Francesco d’Angelo, Andrew K Lampinen, and Stephanie CY Chan","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:995dd9d28a80f0dd97de322088fc96a0db40f49de98b4c721644eefaf95f80d2","observation_id":"920c41c3-595b-4484-8567-053d0cb39d89","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.22448","last_updated":"2025-07-30T07:55:33Z","snapshot_observed_at":"2026-08-16T19:03:00.202162Z","submitted_at":"2025-07-30T07:55:33Z","title":"Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.22448","snapshot_observed_at":"2026-07-11T10:45:46.618668Z","title":"Falcon-h1: A family of hybrid-head language models redefining efficiency and performance.arXiv preprint arXiv:2507.22448,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-11T10:45:46.618668Z"},"links":{"cited_paper":"/paper/2507.22448","citing_paper":"/paper/2607.04969"},"observation_digest":"sha256:25434674ad3f6e71bff16c5e7b4619a96b6214e82821a1fddb389a37fd2f889d","observation_id":"dff1143d-d849-420a-9631-c9316323ac43","resolution":{"observed_at":"2026-07-11T10:45:46.618668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.04969","last_updated":"2026-07-06T11:57:32Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T15:29:37.706087Z","submitted_at":"2026-07-06T11:57:32Z","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":20},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.04969."}