{"as_of":"2026-08-20T10:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a4e8deaf88f8c3ae804502504b3e8505df8b87c3e824ca0aa4b9e79b0a1dab4d","coverage":[{"denominator":74,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":74,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-14T20:37:55.057366Z","state":"measured"},{"denominator":74,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":74,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2605.12944/citation-record","integrity":"/paper/2605.12944/integrity","json":"/paper/2605.12944/citation-record.json","paper":"/paper/2605.12944"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36:55006–55021","venue":null,"work_id":"fcd1e458-347d-4dd7-afdb-10d2bf0d0588","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:1d69cf313d58b2d90be5cd472a050087a32b344f703aa7419d4ee18164029386","observation_id":"4715e4f1-1c65-4fc2-874b-9deef821b6f1","resolution":{"observed_at":"2026-05-15T14:25:56.247872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning","venue":null,"work_id":"58ddb74d-bf7c-43f9-aa69-2daebc197ca7","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:fc771d1a7e3eec7260e71dd27e7e8aab693eceb6109abbd4e190339278e3e1dd","observation_id":"e777c71e-155e-4cd9-969d-ae3ff3e810e4","resolution":{"observed_at":"2026-05-15T14:25:56.244652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Data-juicer: A one-stop data processing system for large language models","venue":null,"work_id":"f3bf8b5f-fa00-4c25-aa3f-59a1c1243373","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:398417f30b62c026d7574de4f4770d0437fcbeb4ef96030853ac49ff4b30ef65","observation_id":"befc2ff9-f85f-424c-83d3-8d8789de3a9f","resolution":{"observed_at":"2026-05-15T14:25:56.241532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"LESS: Selecting influential data for targeted instruction tuning","venue":null,"work_id":"32366490-9da4-4f06-a4c1-1660331d8b32","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:772c679b91616b40158de8ce188389047012552e227b98c0072fd06ccafb7ced","observation_id":"fd3149c3-4587-4a17-b2fe-1543e656cb1c","resolution":{"observed_at":"2026-05-15T14:20:56.295769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Task-specific data selection for instruction tuning via monosemantic neuronal activations","venue":null,"work_id":"55ec7f32-2d2c-45f2-a08b-a12eeb00f549","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:68d43d6c637cf85eecc92195fe05adc478f3564fe26453c624d394c77544ba3c","observation_id":"a17fadca-fe17-4b4e-8dc2-882d30347bcf","resolution":{"observed_at":"2026-05-15T14:25:56.230942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Lead: iterative data selection for efficient llm instruction tuning.Proceedings of the VLDB Endowment, 19(3):426–439","venue":null,"work_id":"5b7e0257-a4e6-463d-8156-15be578a38f4","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:9f8bfe14f79ebd3ccb10b14c6524a4186329a0f3f7cb3120b77129c84a9013d6","observation_id":"294bc0fe-01c7-498f-9d0c-838682de0011","resolution":{"observed_at":"2026-05-15T14:20:56.299194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2602.11089","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Datachef: Cooking up optimal data recipes for llm adaptation via reinforcement learning","venue":null,"work_id":"92011787-f8ac-4ae2-97d5-55abf39f1fd6","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:d3a6f00a1de81eac981d27e3e1743c66a2ad7c15f1d00ae800e7f8b79ddcd64d","observation_id":"b050ce25-53b4-4aa7-8797-45aab7edd99c","resolution":{"observed_at":"2026-05-14T20:39:26.686535Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.20375","last_updated":"2026-05-07T02:38:44Z","snapshot_observed_at":"2026-07-30T12:44:01.703494Z","submitted_at":"2026-01-28T08:37:34Z","title":"LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning","version":2},"cited_work":{"arxiv_id":"2601.20375","doi":null,"metadata_source":"pith","pith_arxiv_id":"2601.20375","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning","venue":"cs.LG","work_id":"7069cbf6-5034-4f97-a653-28a64a6c8d11","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2601.20375","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:62ff53cde6058c3d0a4df710cce6c6207fd8a5dd95e5f9c37d866f4e089a7f32","observation_id":"c2d65200-50c0-4620-a552-455429877e91","resolution":{"observed_at":"2026-05-14T20:39:26.649702Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15963","last_updated":"2025-01-27T11:14:04Z","snapshot_observed_at":"2026-08-16T18:10:21.459256Z","submitted_at":"2025-01-27T11:14:04Z","title":"Evaluating Data Influence in Meta Learning","version":1},"cited_work":{"arxiv_id":"2501.15963","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.15963","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Evaluating data influence in meta learning","venue":null,"work_id":"d67cc026-33ec-4164-9a12-6683ccd81bce","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2501.15963","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:8c77a1289bb912e554a7c416e54db1d8674546d94f1cec7fdbb53fadb957e2b2","observation_id":"97385bba-1145-4265-a1f9-a43d12c021a4","resolution":{"observed_at":"2026-05-14T20:39:26.676425Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning","venue":null,"work_id":"265e318b-e629-4e5d-87d4-352e9e394014","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:2b5c97ce5ef6d1609566d4ccaa72ba66416516a8f96111435d009f5fe15e2308","observation_id":"5f818755-12a0-4f34-b09e-6c859bc29ded","resolution":{"observed_at":"2026-05-15T14:25:56.227970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Deduplicating training data makes language mod- els better","venue":null,"work_id":"efffc842-2254-44b3-ae11-21104a9c10fe","year":2022},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:cd49b1f7401c3713ecce984a51591615d041aab6222cbfc7ec985c4bbfba2d4f","observation_id":"fd598a7e-d805-41dd-bd20-75eb731cc494","resolution":{"observed_at":"2026-05-15T14:20:56.274336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.09540","last_updated":"2023-03-22T17:22:35Z","snapshot_observed_at":"2026-08-06T15:07:40.203199Z","submitted_at":"2023-03-16T17:53:24Z","title":"SemDeDup: Data-efficient learning at web-scale through semantic deduplication","version":3},"cited_work":{"arxiv_id":"2303.09540","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.09540","snapshot_observed_at":"2026-07-09T03:05:54.898931Z","title":"SemDeDup: Data-efficient learning at web-scale through semantic deduplication","venue":"cs.LG","work_id":"492d4320-a8d4-4094-b226-ea8d784560d9","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2303.09540","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:4e394070c30a8b806153b4852397e2ec3a75d0dff12a4ca5a8addc22eec37c3e","observation_id":"d0531286-0e3e-4904-8937-38e1a997ae38","resolution":{"observed_at":"2026-05-18T02:43:31.136179Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Data diversity matters for robust instruction tuning","venue":null,"work_id":"b52858fb-7ca4-4048-9e3e-00c45ee6fbe1","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:d5589c0227705fd87e571a541b8531db02658ab692c6ec40b10de815e7b7a2ed","observation_id":"ee0e6dae-9e7a-4822-a9be-cb7eee2e3804","resolution":{"observed_at":"2026-05-15T14:20:56.283274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.18773","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Automatic configuration of llm post-training pipelines","venue":null,"work_id":"4ab7189d-a67a-447e-a88c-fb1aed518b49","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:4f7072d1223dadec93b162732289e0b56b71140de09988d498060891270bbc35","observation_id":"90d40d11-b0af-4a6a-9b3e-94542dcddbcb","resolution":{"observed_at":"2026-05-14T20:39:26.673700Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Random search for hyper-parameter optimization.Journal of machine learning research, 13(2)","venue":null,"work_id":"259d2cec-d833-4c12-b793-3a609074d49d","year":2012},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:d7699b1021492f5ad7b11f9d03e08fc133b291d8f6c353490d71c4b2344f28c0","observation_id":"2b19e6e7-58dd-47bf-b241-1a1f09fff59c","resolution":{"observed_at":"2026-05-15T14:25:56.233838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.52202/068431-2059","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25","venue":"Advances in Neural Information Processing Systems 35","work_id":"782af98e-2ce3-4e2d-a6ca-c516779c8487","year":2012},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f9b5da1e1f7e1adb651d687599afcb1436ad81e6274df58e859c6ed63a32094d","observation_id":"22669bad-b791-46ac-8efe-760da4b1405b","resolution":{"observed_at":"2026-05-15T14:20:56.256646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Hy- perband: A novel bandit-based approach to hyperparameter optimization.Journal of Machine Learning Research, 18(185):1–52","venue":null,"work_id":"f5d7ccf8-7320-44a7-9a2f-5db7b855b1c4","year":2018},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:67ae3087245ddf8eccc7ea33169852119b1a56cddbbed9d6e65b1a6a65b20d4e","observation_id":"862ae9fd-0111-4df8-8331-e7397dea5988","resolution":{"observed_at":"2026-05-15T14:20:56.249180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"BOHB: Robust and efficient hyperparameter optimization at scale","venue":null,"work_id":"c7b4aad4-de54-4d71-883a-8356b87929de","year":2018},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:efad0ffb0746d82b7561f26df14721c4cc300db9d00527cf24fddae8dc24cdf7","observation_id":"7b36ad39-26d3-4aaa-80b4-665cb1f75143","resolution":{"observed_at":"2026-05-15T14:20:56.243145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Efficient and robust automated machine learning","venue":null,"work_id":"747ba9aa-2601-4016-b552-5352b5d5b7ce","year":2015},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f7c760cb3d83a4b8294a94dbdcfc7719cef6d1278a628f8aee3febad70fd0d8e","observation_id":"69fc331b-c5a4-4f75-acc8-c0d05fda3ae6","resolution":{"observed_at":"2026-05-15T14:20:56.246476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Limit: Less is more for instruction tuning across evaluation paradigms","venue":null,"work_id":"76709b31-3cb0-4ca0-9616-e41311b31f98","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:756696c08996befcf6feb973d240d0c27726a39ca6fde0424b3236b15b0f7560","observation_id":"24698167-87a8-46e8-a31f-b700c0c29721","resolution":{"observed_at":"2026-05-15T14:20:56.251562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants","venue":null,"work_id":"664f74db-c737-46f0-98c5-f657c8ae113a","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:0e929b6ef1928139bc708d98d3cd5c16ca5789d2e50c9dd8e1b4971880834857","observation_id":"5f42c2ab-bab2-4895-b2c6-c58a014a8a6a","resolution":{"observed_at":"2026-05-15T14:20:56.259050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-08T09:34:48.469423Z","title":"Self-instruct: Aligning language models with self-generated instruc- tions","venue":null,"work_id":"40d20eeb-4469-4548-b78d-d83166af6146","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:87ca1d5d2a1600afbff678b1b4d9148b090250e5f3143f3e5354dd62ac332cea","observation_id":"4555efc8-c8d5-4bad-83a8-00d125b0a367","resolution":{"observed_at":"2026-05-15T14:20:56.269020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"384f32e1-14c7-4216-b925-c5603794dff2","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:b800b7bd9ef6d994890dd9000eba09e6e346d62fe01982719b34f7b038ed775c","observation_id":"f1cbe55d-d6b8-488a-888b-56e355e4b465","resolution":{"observed_at":"2026-05-15T14:20:56.236486Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:51b8072cb795df246f69895232c0bffb4bd5123a988b3a8249e2f1bb8ec1f63d","observation_id":"4a07cdc6-dace-42b1-ae8f-ee819d90cf8a","resolution":{"observed_at":"2026-05-14T20:39:26.665574Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Brown, et al","venue":null,"work_id":"a5968517-08eb-4074-8836-2c3441b8e03e","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e9f00f27065905651c75d040a1253e0f36046681655428c741df1e8d623c961e","observation_id":"3e94e300-0cdf-4e7c-ab52-8a428333929d","resolution":{"observed_at":"2026-05-15T14:20:56.240751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":"254bdefb-eeaf-4b05-8eed-f47a57fab568","year":2021},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:8ded3c24a0790b9322b9671eae30c62c2fc936fcb865cb024cd69637a3173193","observation_id":"5f023c1c-6244-45cf-95e0-4b75334ec440","resolution":{"observed_at":"2026-05-15T14:20:56.231248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-08-17T18:50:07.059564Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":"2412.15115","doi":"10.1145/3581783.3612503","metadata_source":"pith","pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5 Technical Report","venue":"cs.CL","work_id":"d8432992-4980-4a81-85c7-9fa2c2b87f85","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:a3c69cbfc4f370642fe824e99a89bd214e8c3b8d03e08e3e27118bd2a9cfdea6","observation_id":"16c900b4-423e-4a77-8f9a-0ffe0fa12e59","resolution":{"observed_at":"2026-05-14T20:39:26.693616Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":"2407.21783","doi":"10.1016/s0749-0720(15","metadata_source":"pith","pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"The Llama 3 Herd of Models","venue":"cs.AI","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:831fac3212a7c34f42bc417487b18477cc6e11400024a8f45f30d9f6248601ef","observation_id":"ecfd9b10-dae6-469b-b359-1a0854fe03d0","resolution":{"observed_at":"2026-05-14T20:39:26.659122Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Graphwiz: An instruction-following language model for graph computational problems","venue":null,"work_id":"218a3af0-21dd-4c5b-85e8-37e470679daa","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f4d8069a0fb031e5632713e825289c7cce105f7e7d693656dfa5446f1cddcce9","observation_id":"eba0f0c1-2fd9-4a3d-bc44-b12c3d448815","resolution":{"observed_at":"2026-05-15T14:20:56.180895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Can language models solve graph problems in natural language?Advances in Neural Informa- tion Processing Systems, 36:30840–30861","venue":null,"work_id":"d5bc9572-c49a-4b58-ae87-3cc2ef288b5b","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:9b8980ca1a2c46ab6032d6f4b547eacf943fd05a1690857437084d603d91733b","observation_id":"747fbccd-d6a8-4a61-9a9e-1ca5d2ca2e26","resolution":{"observed_at":"2026-05-15T14:20:56.178662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2013.229100","doi":"10.1109/tit.2013.2291007","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Optimal lossless data compression: Non-asymptotics and asymptotics.IEEE Transactions on Information Theory, 60(2):777–795","venue":"IEEE Transactions on Information Theory","work_id":"6220394f-ea7d-4323-aea7-274dfa717413","year":2014},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:63bddb114290272de324cd6c6c054c9439e36656b3d893e34a73c8dc95bbbeab","observation_id":"68d15f8b-52cd-40d9-8852-050fe7ec2136","resolution":{"observed_at":"2026-05-14T20:39:26.251914Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"A new generalized varentropy and its properties","venue":null,"work_id":"b9a260b5-d864-4d96-9b9a-79353273c5a9","year":2020},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:1bd61802283791208de84100710ce02805efd1133b73e7af607da02690dd97d7","observation_id":"d8847618-2bdf-4e3b-908f-f05435c9c331","resolution":{"observed_at":"2026-05-15T14:20:56.226425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2026","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T22:16:36.322033Z","title":"Exploring iterative controllable summarization with large language models","venue":null,"work_id":"1d423d36-81f7-451c-a29b-8eaca5046ed4","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:d6c677fd6bdb4b68d2b53bba7a0ef1e5e2d2f3f87e0fe67ed0c7652407dbe938","observation_id":"d951f526-fd7a-4b6c-bdd1-6fc292e7bc0b","resolution":{"observed_at":"2026-05-14T20:39:26.244000Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-07-15T19:20:36.756562+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T19:20:36.756562+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.11056","last_updated":"2026-05-26T10:46:50Z","snapshot_observed_at":"2026-08-11T12:13:30.569226Z","submitted_at":"2026-04-13T06:32:49Z","title":"Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR","version":2},"cited_work":{"arxiv_id":"2604.11056","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.11056","snapshot_observed_at":"2026-07-04T08:59:42.576944Z","title":"Rethinking Token-Level Credit Assignment in RLVR: A Polarity-Entropy Analysis","venue":"cs.LG","work_id":"7d77b568-5627-42a2-8339-dc5da565e809","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2604.11056","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f0ebd9d02e0f5c4554689298d38db1553f3a19459f6f6f9a7d770aed60ce5208","observation_id":"d69dfee8-277d-43fa-b9f4-0b455a7ceaf8","resolution":{"observed_at":"2026-05-14T20:39:26.679983Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5a2ace31-9b8a-48b3-a49b-843d6c86af4a","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f323f18b80d8cba4e55677e29564e75f607efff60ad7ccd01a888505ae26efbd","observation_id":"f796cac9-d3c7-4a80-813b-82987f9540ab","resolution":{"observed_at":"2026-05-15T14:20:56.224115Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3add3d49-a75d-45bb-ae62-21188deca99b","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:aeb95eaa99a27e0b9ec3edb92f7c6800c35249680b4e08c35c405c17e9c3adb7","observation_id":"5623a6be-2872-446e-9a2b-8c721a572eb7","resolution":{"observed_at":"2026-05-15T14:20:56.219079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"The best of both worlds: Bridging quality and diversity in data selection with bipartite graph","venue":null,"work_id":"ce63ef5f-61f2-448b-b11d-bb0f936f727b","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e6ae070343728ae891045bddc91d788e9298277a0793e4b08ce1ea005d0357c9","observation_id":"05f59ea9-39ea-46bb-b6ef-3db85751116e","resolution":{"observed_at":"2026-05-15T14:20:56.221703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05696","last_updated":"2024-02-29T03:04:22Z","snapshot_observed_at":"2026-08-18T10:17:08.498041Z","submitted_at":"2023-08-10T16:58:51Z","title":"A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment","version":2},"cited_work":{"arxiv_id":"2308.05696","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05696","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2308.05696","venue":null,"work_id":"58821c4e-73bd-456c-acbd-7072002f30ac","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2308.05696","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:c75843bdf356e62ba68dfff38a225aff893b6b680700327d86980296a38be646","observation_id":"2e84cacb-1aaa-4718-8d9b-11b0f7d4f26f","resolution":{"observed_at":"2026-05-14T20:39:26.713257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Chasing random: Instruction selection strategies fail to generalize","venue":null,"work_id":"b8bdc81c-27e9-4614-9916-c9f7d9712646","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e5b205093793f26dab318183796836922523421d9fb02e83fd43981da329425e","observation_id":"623a51f1-d8e8-47c3-ab8c-3eca2b712dca","resolution":{"observed_at":"2026-05-15T14:20:56.228883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01807","last_updated":"2025-06-19T04:19:15Z","snapshot_observed_at":"2026-08-16T12:53:29.192200Z","submitted_at":"2025-03-03T18:37:26Z","title":"Large-Scale Data Selection for Instruction Tuning","version":2},"cited_work":{"arxiv_id":"2503.01807","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.01807","snapshot_observed_at":"2026-07-03T19:18:54.703277Z","title":"W., and Dasigi, P","venue":null,"work_id":"bbdaf4c4-707a-48a9-bced-5db8a7e8f129","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2503.01807","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:40353dcf7611a33389f3b5e0055b584c017aba456965e1b43ef57813b98dc188","observation_id":"6e64941c-a5cc-42a1-864f-11bb86ad213a","resolution":{"observed_at":"2026-05-14T20:39:26.646591Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.14696","last_updated":"2026-06-18T11:23:03Z","snapshot_observed_at":"2026-08-17T14:22:15.774101Z","submitted_at":"2026-02-16T12:33:05Z","title":"A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)","version":2},"cited_work":{"arxiv_id":"2602.14696","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.14696","snapshot_observed_at":"2026-07-03T15:18:33.869546Z","title":"A critical look at targeted instruction selection: Disentangling what matters (and what doesn’t)","venue":"cs.LG","work_id":"a40a29b1-1950-4b84-a5cf-1f588c98189d","year":2026},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2602.14696","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:b06725e6a9b1aef6dd79bdecfae441dddbe748f45f33ecbd8e4de52f23517e30","observation_id":"d2b9fb81-7db0-4deb-9cb9-a57f072d564c","resolution":{"observed_at":"2026-06-19T17:11:54.579692Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.findings-acl.623","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Smaller language models are capable of selecting instruction-tuning training data for larger language models","venue":null,"work_id":"7fca8fab-a649-45b8-8044-99277ebd8672","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:3732e424a1d8f290aeae3765d31fab773d027c1462ce2ade95f3696db9287c78","observation_id":"fcecfd25-ccc4-4dca-bd7a-c409106f147f","resolution":{"observed_at":"2026-05-14T20:39:26.250356Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.52202/079017-2655","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Smalltolarge (s2l): Scalable data selection for fine-tuning large language models by summarizing train- ing trajectories of small models","venue":null,"work_id":"2654172b-ad97-4aa9-b893-1682239b7320","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f0173e81e38e21abe52d05e4b5741243a4763b4910a29126acde4fd88e3469ad","observation_id":"bc1cfd25-b92c-4e73-a3da-d5e39504931a","resolution":{"observed_at":"2026-05-14T20:39:26.245598Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Doremi: Optimiz- ing data mixtures speeds up language model pretraining","venue":null,"work_id":"2fa0d798-0039-4b3b-9725-fc83334381a2","year":2023},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:570dc3f25fd413956e8a7526475db1b3a55f15ed046ef098d257ad5d881c6993","observation_id":"c33d70ef-6cf9-4f2c-b4b4-fe43da0ef044","resolution":{"observed_at":"2026-05-15T14:20:56.213941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2501.12948","doi":"10.1016/j.artmed.2024.103001","metadata_source":"pith","pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","venue":"cs.CL","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","year":2025},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f0c08c9a9fef7719f5be754e8415a163c78d8f525b4161614da3e9ebd83f18ce","observation_id":"7445782c-912e-4882-a0ae-135981f0a295","resolution":{"observed_at":"2026-05-14T20:39:26.699806Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Sparse autoencoders find highly interpretable features in language models","venue":null,"work_id":"6dd9cd06-1fe9-4a91-9eb8-464c403583c5","year":2024},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:c95fc4ba1600c841b914583d948662a374f92b07cb85c9880f8db61df34cc3c4","observation_id":"9f722457-dfd2-432d-8a2c-8de4b8e1f89c","resolution":{"observed_at":"2026-05-15T14:20:56.208342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2329.0254","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Operators","venue":null,"work_id":"747d3b5e-b685-41bc-bd14-c9a2b18de8e7","year":1941},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:f57b5c6a87779ee87ad2e005378df72bbdb1c5b4d07ec3a2ac4a10244d409be7","observation_id":"d4234ffa-c54c-4592-a19d-5af3e7582f0b","resolution":{"observed_at":"2026-05-14T20:39:26.652501Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"532021a3-d010-4fd4-8b5c-a0b398a84e80","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e3a43a6c44bfa4329c613f259e7b16833efba646f1a0bae54d6453270bf1e739","observation_id":"8f1fde34-1829-45fd-b9d2-b30f0d6e357e","resolution":{"observed_at":"2026-05-15T14:20:56.210590Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"185b28fe-cb5f-4f48-8129-af1e7964d6d1","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:8a7363a7d622fe67d66b351251c948c2aa3423aeb7ca3d14ab42a6b495d6460e","observation_id":"b9dc190f-84b4-44a8-a1ed-b5366639f008","resolution":{"observed_at":"2026-05-15T14:20:56.216586Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Be direct and quantitative","venue":null,"work_id":"657f2b41-d03f-4324-9508-95b789493321","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:befe6957d42a109a31db16369ffbe8d6a291437af0ce1e1e8f8c6a040339701e","observation_id":"1b9ea0e3-3daa-4b2c-a89d-dae30cf26f63","resolution":{"observed_at":"2026-05-15T14:20:56.234021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Avoid aggressive filtering","venue":null,"work_id":"4bef26fc-f9a7-4d8a-b722-2ccdef22f8d7","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:08850510de942932402fde04b66bcb9b73964c634dee86cad6c2618f30aa5ac7","observation_id":"b91c201f-a11c-4e53-8349-3ba37fc84a94","resolution":{"observed_at":"2026-05-15T14:20:56.264070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Try higher rates or skip it","venue":null,"work_id":"6859ba80-d212-421f-9a81-a71b7b373488","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:feec7207b0df967e4c7edef697e7a4fb9a72f5dd8b4726a8365adca32b52edb4","observation_id":"0643fd05-47a6-4d51-8ffb-90e12b118fd0","resolution":{"observed_at":"2026-05-15T14:20:56.288922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7142dcc4-653b-4885-901c-14d523652c24","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:0731878565138fd6d39b02c247ff7a0edec96875c2e3d54924a05ea82f4eaccb","observation_id":"03807b02-0bc6-4a34-ae98-133238f50c1f","resolution":{"observed_at":"2026-05-15T14:20:56.186805Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1cc2e6c8-40a4-48c2-80ee-d070690223bf","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:b733f2704e0229438e04de043816838193cab74753af7638f5eea1da8a904485","observation_id":"a76790bd-bf26-4b89-a6ff-30a9bc4ad153","resolution":{"observed_at":"2026-05-15T14:20:56.190943Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Do NOT include markdown blocks (‘ ‘‘‘json ‘), just raw JSON","venue":null,"work_id":"5628b2ab-83e4-45bb-9108-658b43cf5b01","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:4cb7e2b9204cec85ebea87436c746077715f8a945779d1e39007b8ae48fd0fe3","observation_id":"6cb88c4b-2863-4350-86a5-e05ce8f5e5de","resolution":{"observed_at":"2026-05-15T14:20:56.196305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"steps\": [ {","venue":null,"work_id":"e2ce4f38-4a05-44fd-9bf2-693ce22924df","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e41d3fbdac322d69be2425f76281b268f629b025e69aa47c26ff681bce9f6021","observation_id":"31189e74-084e-4b06-8e65-96b1bf3afea6","resolution":{"observed_at":"2026-05-15T14:20:56.193407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"- A candidate whose per-task MONA scores improve across multiple benchmarks is a strong positive signal, even if retain_ratio drops","venue":null,"work_id":"cd608a81-acf6-4af5-9d5a-7a3eb343f7a5","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:c42ba7a40cd99a2e92ddf7c54e20caa6e1a98df353e5bcb885eed59bfc436ab3","observation_id":"a106b52e-2fd2-462b-8627-72a80eea87f1","resolution":{"observed_at":"2026-05-15T14:20:56.205162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"- Late search: prefer high mu candidates to refine the best","venue":null,"work_id":"a3aed4ab-655d-4fb2-aaa8-6cda9cc50325","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:718825d48336e103fbeee1c9fd2ab4efee26c9e9a58b7cc185ddf77c6b2f9d2f","observation_id":"51528fe8-7c51-45c0-9941-408fc7d18c03","resolution":{"observed_at":"2026-05-15T14:20:56.199163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"- Historical evidence shows extreme filtering often fails catastrophically","venue":null,"work_id":"ccd0c0ce-9cbe-4962-ae3a-851e20baecbd","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:645f3b427243b499c577f10f218adf8821dd4fac846f3250afd6e2523e1c7795","observation_id":"72ebdc65-76a8-4ba3-bac9-ef2dc2048776","resolution":{"observed_at":"2026-05-15T14:20:56.238613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"- Operators from the same family are often redundant","venue":null,"work_id":"773cc2a1-4c32-4b17-a94c-abd93f4344ae","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:161b6c200ee4c57742429b061c1b759fa76550eaff6df107c6100189ef1b44e5","observation_id":"d80de820-7c54-430c-8d5c-4cdad39d87cb","resolution":{"observed_at":"2026-05-15T14:25:56.239123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f34c7331-777e-4865-886f-4352fa9682c5","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:1c6d3805ac20ce820fd2639fe823158cd76909951ee8c98f6fc70de4c44aca64","observation_id":"3139e63f-f87a-436e-b0c1-27e8f18e1586","resolution":{"observed_at":"2026-05-15T14:20:56.182786Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"- High distribution_drift indicates risky distributional shift","venue":null,"work_id":"fc46e05d-6961-43fd-964e-4307d095ca80","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:6ccdde46f98be1be43b3b630307d1c9a39583287680267c0a6014e8b483eefb3","observation_id":"330ba93c-c32e-4f8f-9d47-c5b9f47f31f5","resolution":{"observed_at":"2026-05-15T14:20:56.266339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"ranking\": [<best_idx>, <2nd_idx>, ..., <worst_idx>]","venue":null,"work_id":"e8f3e55f-2153-4004-8b1f-e28e83f7fa2e","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:9aababe289eb252cd0c9272cb0f970d49fd50a917a0b1473909a86dbee6ba3a4","observation_id":"01657263-269f-4737-bd8c-ca56cbfab960","resolution":{"observed_at":"2026-05-15T14:20:56.184840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7c570eb3-92aa-43fa-8ece-5a954fd210fa","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:eb43b68c340604c00dce960cda0bcec3b4929be434dc56540360a16ffa59ec83","observation_id":"dc25a044-4907-4f9c-89bb-33004a503d30","resolution":{"observed_at":"2026-05-15T14:20:56.261431Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b892ddd5-bd62-4191-9fa3-7b1a0411f6a3","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:fb95f150640f64351acea613a1c34cbb975c9dc4f2e26e9b1ae5761f370059c6","observation_id":"dfe2f6e1-1b38-420d-9fbc-71a25a5ad86b","resolution":{"observed_at":"2026-05-15T14:20:56.188809Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"fa70a329-5159-4c72-85b8-6c6d15437180","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:e21ff2371949901634ecadb98a532206d0221bd151ffca43645e815fa1f8519e","observation_id":"fc32d51f-d51e-4be6-aab5-bc13e2b609b2","resolution":{"observed_at":"2026-05-15T14:20:56.202020Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"operator","venue":null,"work_id":"c4116fe9-e800-4fed-9875-bfad5a89caa8","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:5be02f54fed6a3aed044b356a06a90d971f41b22749b723975a1551243bbc09e","observation_id":"f8d69748-f75c-4405-9581-247fa084c6e9","resolution":{"observed_at":"2026-05-15T14:20:56.271562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ad84e711-3ef0-466c-9534-68d29228b407","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:ec630b592a49519cc926f3732ab0654184aae46356cd36688fbf6bb55ace1032","observation_id":"e5955a55-7ce5-4aad-bd21-075e54ad708b","resolution":{"observed_at":"2026-05-15T14:20:56.292427Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: Which of the following is NOT a function of the cell membrane? A","venue":null,"work_id":"459484d1-3e80-4372-b974-092d91a06f6f","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:17cf3f19b4fb279bb8dff18a1ed3ca61662506db3ff9509fa3a9f78074581678","observation_id":"ffc775ef-774d-45fb-82fd-d3ec0d1e3a24","resolution":{"observed_at":"2026-05-15T14:25:56.236676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2a784dad-8998-4747-836a-52f1464c6a14","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:d54a3352d7d75c7d67ec4d3752dc02f1e06de78aa12e808fe54e13497feac17a","observation_id":"0d78b58c-9e81-49b3-8605-eeabe00e656f","resolution":{"observed_at":"2026-05-15T14:25:56.225194Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Example format: [Your reasoning] Answer: (B) Few-shot turns: User: not ( True ) and ( True ) is Assistant: not ( True ) evaluates to False","venue":null,"work_id":"935baf5f-9d22-4d6e-819c-8ad02ff14cc5","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:1487f62ad3b74f083d480f033cf6441c6787968e558166823a5f27e3426a4a3c","observation_id":"cc5788b8-ea3a-4eb9-af27-f1d179f60a04","resolution":{"observed_at":"2026-05-15T14:20:56.277417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7cc04847-1462-4561-adbe-0f45cd215660","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:780ec709efb8130d7362da79ee76dc7d7382b8181d5e27ed7109a316269eb1f0","observation_id":"196c08b5-f2bb-4a0d-878c-967b6b6456ef","resolution":{"observed_at":"2026-05-15T14:20:56.280448Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0db74218-ebd7-4dea-b6ff-b31ccec67421","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:12db354668e3586789f690ff94ae99b85edd7838c5a45fa67677f9b0e0929728","observation_id":"e8fbfbd8-801b-4789-9f4c-e8f216058977","resolution":{"observed_at":"2026-05-15T14:20:56.285964Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-06-05T21:23:00.469572Z","title":"Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: What is the capital of France? A","venue":null,"work_id":"08bbb8c6-1552-4352-87cf-0e7d4ba6a3a5","year":null},"citing_paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-14T20:37:55.057366Z"},"links":{"citing_paper":"/paper/2605.12944"},"observation_digest":"sha256:82a45209a5c47bfab45cde2c110e8cdd05b7237e21931aadf9e87d3770333f14","observation_id":"7df6c424-88db-4324-87ab-7cb96ddfa3e0","resolution":{"observed_at":"2026-05-15T14:20:56.253964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.12944","last_updated":"2026-05-13T03:27:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T19:45:35.792111Z","submitted_at":"2026-05-13T03:27:21Z","title":"From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning"},"reference_resolution":{"displayed":74,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":1,"unresolved":14,"verified_exact":14,"verified_fuzzy":41},"total_outbound_references":74},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:2605.12944."}