{"as_of":"2026-08-10T12:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:647eaab8882bca7dfb8a924ba519a42f380f170741af6f0223703d8d0ea96754","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:33:55.702118Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T09:19:39.848194Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T09:21:21.452805Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"cited_work":{"arxiv_id":"2505.14826","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14826","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Openrubrics: Contrastive rubric generation for reward models","venue":null,"work_id":"dbfdba3c-b9fd-40f4-96a5-f046ab58a177","year":2025},"citing_paper":{"arxiv_id":"2605.17602","last_updated":"2026-05-20T20:58:48Z","snapshot_observed_at":"2026-08-02T06:34:30.012151Z","submitted_at":"2026-05-17T19:00:44Z","title":"AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-20T12:11:23.775843Z"},"links":{"cited_paper":"/paper/2505.14826","citing_paper":"/paper/2605.17602"},"observation_digest":"sha256:d855306d44bbf228bc5801e00c96bd63d50ed452d41a65d15ba4189ac65600c9","observation_id":"6834289c-1c52-4239-8fce-1a850878b6aa","resolution":{"observed_at":"2026-05-20T12:13:16.128122Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"cited_work":{"arxiv_id":"2505.14826","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14826","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Openrubrics: Contrastive rubric generation for reward models","venue":null,"work_id":"dbfdba3c-b9fd-40f4-96a5-f046ab58a177","year":2025},"citing_paper":{"arxiv_id":"2605.17602","last_updated":"2026-05-20T20:58:48Z","snapshot_observed_at":"2026-08-02T06:34:30.012151Z","submitted_at":"2026-05-17T19:00:44Z","title":"AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-22T09:19:39.848194Z"},"links":{"cited_paper":"/paper/2505.14826","citing_paper":"/paper/2605.17602"},"observation_digest":"sha256:eb15b903e67b85436a80863629582941cd2333f0ce246badc0417b93f33d8855","observation_id":"db9b8703-7e0e-4549-9708-3d3e6b5dc41b","resolution":{"observed_at":"2026-05-22T09:21:21.457051Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.14826/citation-record","integrity":"/paper/2505.14826/integrity","json":"/paper/2505.14826/citation-record.json","paper":"/paper/2505.14826"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:49.231262Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:49.231262Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:bab540a0e2ec531a9887b4e997d71b3e450cff8c138ee1ebe94074ffedc6bb90","observation_id":"dcdd67a7-9a4f-48fe-a4fb-2511c49dbc64","resolution":{"observed_at":"2026-08-07T15:33:49.231262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.09540","snapshot_observed_at":"2026-08-07T15:33:49.406344Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:49.406344Z"},"links":{"cited_paper":"/paper/2303.09540","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:17a2146d891c7248cfb8b0c109f7284094b99265921a343a8b901b8784270afd","observation_id":"424a77be-2830-4c67-badd-cb43a352dbb6","resolution":{"observed_at":"2026-08-07T15:33:49.406344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:03.706146Z","title":"Improved algorithms for linear stochastic bandits","venue":null,"work_id":"6bb80ee6-8931-4367-adaa-ee8bb3ecd17e","year":2011},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:49.527948Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:61a3c6ed3189acd3e0ec7c4a31cfba01294534a2244500f20aebaa1f1386e836","observation_id":"b7fa811c-abfe-4f18-866b-5bcee96213db","resolution":{"observed_at":"2026-08-07T15:34:03.803499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:03.458211Z","title":"P., and Wunder, M","venue":null,"work_id":"82dde7aa-f74d-40a8-bbbe-e03a5e769cb4","year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:49.736418Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:2e46da46dff9834adb8ddbedde1aa15581f9d23daa951f810f4f89cf73504508","observation_id":"9d299939-1bc1-4683-bf7a-e12260681d37","resolution":{"observed_at":"2026-08-07T15:34:03.580345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:03.161123Z","title":null,"venue":null,"work_id":"11ab7d0e-609f-415f-a2d6-5072ef651fbb","year":2009},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:49.868934Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:5aa928854e6cacba12308b8abf368d1f6939513d0faca370b00ef12769cd0030","observation_id":"10456815-691d-4ac8-8d51-eb7b5e19b2cf","resolution":{"observed_at":"2026-08-07T15:34:03.320041Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:02.932696Z","title":"Pattern Recognition and Machine Learning","venue":null,"work_id":"7a3117c5-00ca-4b04-89ba-4ef36087816c","year":2006},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.045825Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:7c9d429205927e5086befd7e530f4c427d08c980e0468239108e4f2129859818","observation_id":"2b3129c8-81ee-493d-ab6c-ba184d858840","resolution":{"observed_at":"2026-08-07T15:34:03.036966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-07T15:33:50.215979Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.215979Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:b8233262be8ad589ed24a2ff4bac70336d7d5efe016426e83625c4d00c8c5ede","observation_id":"440e0be3-022f-4413-ace6-9fee82a33c73","resolution":{"observed_at":"2026-08-07T15:33:50.215979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:02.688064Z","title":"Coresets via bilevel optimization for continual learning and streaming","venue":null,"work_id":"87e36be6-4346-4b68-9d7a-a7e149b570c8","year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.317302Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:cdd5926b83b5bc96297fbc852364b242d5ecb738c071a781912c08d1db2a0aa7","observation_id":"a2b9c314-426e-4062-9f3f-45f48fb1accc","resolution":{"observed_at":"2026-08-07T15:34:02.789773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:02.374922Z","title":null,"venue":null,"work_id":"dd09257d-ac5d-4c82-ba41-4a274fd089e8","year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.422295Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:9957f7649a9328197bac0c00ec020466a824da8a4155d0980dca6112c63f5f20","observation_id":"b21aa86a-3b3f-44c6-bf34-cd3a21d39834","resolution":{"observed_at":"2026-08-07T15:34:02.506276Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1203.3472","last_updated":"2012-03-15T11:17:56Z","snapshot_observed_at":"2026-07-06T02:44:38.546504Z","submitted_at":"2012-03-15T11:17:56Z","title":"Super-Samples from Kernel Herding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1203.3472","snapshot_observed_at":"2026-08-07T15:33:50.595074Z","title":"Super-samples from kernel herding","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.595074Z"},"links":{"cited_paper":"/paper/1203.3472","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:db5c200900979af83a61277d33edee0b47370a107e53c16697927b4f5735af41","observation_id":"43649b05-d5d4-46ca-a0c7-d1c2ffd0d77f","resolution":{"observed_at":"2026-08-07T15:33:50.595074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:02.132321Z","title":"M., Haussmann, E., and Fardet, E","venue":null,"work_id":"3365b95d-149f-4e4a-8bb1-dcab7084ef56","year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.784785Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:1e154c67f9e2019b815e00904249a3f9f7f2e373723942e71af8f815a43e599c","observation_id":"a0f747e9-5d66-46c1-b1db-1038e62a893a","resolution":{"observed_at":"2026-08-07T15:34:02.258315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:50.927740Z","title":"and Shrivastava, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:50.927740Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:7746b46013238ec098598d50e79c940cb19d09b28d9049a9afc2b55b4192999d","observation_id":"14c92296-4c4d-42b5-8064-04250ade4892","resolution":{"observed_at":"2026-08-07T15:33:50.927740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:01.896777Z","title":"Selection via proxy: Efficient data selection for deep learning","venue":null,"work_id":"92405dbd-ffde-4b9e-af4c-c03c24ea5be2","year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.076260Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:200f1dab11a9dd6726f27880be0c41c1ff447820a24ecbfc4b6bc8d040f14aa9","observation_id":"65ea28c3-3bc3-459f-84d4-69e99929c55e","resolution":{"observed_at":"2026-08-07T15:34:02.038794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10500","last_updated":"2025-06-07T16:03:51Z","snapshot_observed_at":"2026-08-09T23:56:03.049175Z","submitted_at":"2024-02-16T08:19:34Z","title":"Active Preference Optimization for Sample Efficient RLHF","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10500","snapshot_observed_at":"2026-08-07T15:33:51.144721Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.144721Z"},"links":{"cited_paper":"/paper/2402.10500","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:b7a5e723cd457915ef31bd7c81f9895ee1d87365cc18c95b70e7adeddbd0c5c1","observation_id":"fc84104b-b3ff-4fd6-aee8-135c2a4557f2","resolution":{"observed_at":"2026-08-07T15:33:51.144721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:01.644501Z","title":"and Zhang, C","venue":null,"work_id":"9f3d868c-d715-4231-8318-1c47b882ac64","year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.262928Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:faba52fe38853b79fcee3d7462af144a1d0c9a631f45d82d831c402ff831cd2c","observation_id":"66056af6-47f9-4bbb-b5bb-6a1465fbfc34","resolution":{"observed_at":"2026-08-07T15:34:01.745072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:01.361540Z","title":"On the mathematical foundations of theoretical statistics","venue":null,"work_id":"4f91b349-1670-4b8d-bd74-e6d2eddf920b","year":1922},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.401001Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:32ce8fab4d192d9b49fbe9b527da2af6620d55fc12bb3a029bc36a3841b5ca71","observation_id":"7de814da-df92-4acb-bb0f-ea1e5cc4ea01","resolution":{"observed_at":"2026-08-07T15:34:01.466564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1406.5638","last_updated":"2014-06-21T17:55:54Z","snapshot_observed_at":"2026-07-06T03:46:58.247552Z","submitted_at":"2014-06-21T17:55:54Z","title":"Minimax-optimal Inference from Partial Rankings","version":1},"cited_work":{"arxiv_id":"1406.5638","doi":null,"metadata_source":"pith","pith_arxiv_id":"1406.5638","snapshot_observed_at":"2026-08-07T15:33:56.514104Z","title":"Minimax-optimal Inference from Partial Rankings","venue":"stat.ML","work_id":"656d1653-4a2a-4a80-9048-c16d56bb3eb1","year":2014},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.500852Z"},"links":{"cited_paper":"/paper/1406.5638","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:4f1e5e77a1a26d49c8e8138d63b48037b284f984d8d700bcc64b8f815774102a","observation_id":"3b88b5e0-1bfd-4e8f-b11c-5dd4c87ee52b","resolution":{"observed_at":"2026-08-07T15:33:56.609259Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:51.671297Z","title":null,"venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.671297Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:92da43669021ced34644f4da8b648de2d94240c5743a1481a2ca30e2128c5612","observation_id":"c516dfb3-4f8d-464c-ad70-4383042b3d25","resolution":{"observed_at":"2026-08-07T15:33:51.671297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:01.097094Z","title":"LoRA : Low-rank adaptation of large language models","venue":null,"work_id":"2ff02256-84f7-4bbb-aca5-47bed79ed973","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.777301Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:3e08eae7a2f0c193d55f169eb3f5fc4eea609439f5dad232ee3e59361a7dd0b1","observation_id":"e9950644-7869-4f40-9e56-0798500eed05","resolution":{"observed_at":"2026-08-07T15:34:01.254404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:51.858503Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.858503Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:6833ab558ed2448493dbef7099be2fc961ca28ad25ea8ada907b07345b1af808","observation_id":"f930385d-710a-4e19-88fa-ecf1990b1214","resolution":{"observed_at":"2026-08-07T15:33:51.858503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:00.782770Z","title":"and Liberty, E","venue":null,"work_id":"48ca16d0-dd6f-449e-800b-6b5d34be9583","year":1975},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:51.923206Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:12b71ca71333a1eeaa2ee76a21b79a83aaf48694cb3e92e44551debb45fb1818","observation_id":"20223e23-d6a5-494f-b997-e27c74baff71","resolution":{"observed_at":"2026-08-07T15:34:00.933462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:00.558857Z","title":"char-rnn","venue":null,"work_id":"0da7fee1-cdc7-47f5-82bf-82eb1dba3f29","year":2015},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.053201Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:3cb69d3f06272807b615684438d343e88086713d6b8ce353f482d3b35a5db470","observation_id":"288ac579-d70f-4519-a0cf-39f96417ac91","resolution":{"observed_at":"2026-08-07T15:34:00.669480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:00.332240Z","title":"and Szepesvari, C","venue":null,"work_id":"83854ee3-bd95-4f9e-ba20-c1df24d9df32","year":2019},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.168727Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:cfe83ba607ae6613db37e7480c07fa6253cfd52b59c32764e6639fcd9b0c21d5","observation_id":"3dc3c016-1e45-4bd0-9568-9e1fa1bc6a41","resolution":{"observed_at":"2026-08-07T15:34:00.446532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13840","last_updated":"2025-07-02T21:53:51Z","snapshot_observed_at":"2026-08-09T13:27:29.163770Z","submitted_at":"2023-06-24T02:25:56Z","title":"Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13840","snapshot_observed_at":"2026-08-07T15:33:52.314478Z","title":"Beyond scale: The diversity coefficient as a data quality metric demonstrates llms are pre-trained on formally diverse data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.314478Z"},"links":{"cited_paper":"/paper/2306.13840","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:c12bd4048a6a350a7f7cd5a93d3bf5a4e233c0b7ce5649a686cf454bedce647b","observation_id":"c632ca69-546e-4b6a-86ea-8a88854e1ca4","resolution":{"observed_at":"2026-08-07T15:33:52.314478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:34:00.003039Z","title":"Deduplicating training data makes language models better","venue":null,"work_id":"10a7797a-7955-4af4-bb4e-e47b4ec3c9ea","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.460469Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:b39bf8e185d7b859e07b40ed19b3332943ac43f3afbea9fb85ece5a975479311","observation_id":"c715a6c3-925b-4021-87a9-c8303ad1fe6d","resolution":{"observed_at":"2026-08-07T15:34:00.165111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02504","last_updated":"2024-12-31T01:36:40Z","snapshot_observed_at":"2026-08-10T00:31:41.481245Z","submitted_at":"2024-10-03T14:09:58Z","title":"Dual Active Learning for Reinforcement Learning from Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02504","snapshot_observed_at":"2026-08-07T15:33:52.622227Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.622227Z"},"links":{"cited_paper":"/paper/2410.02504","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:623009ab36b76689bea234bfdd9901ae6fbd5afe7a8f61aa0b0b46bf1ff2105c","observation_id":"11e2c045-38c1-4fd7-85f6-88d51aca9b29","resolution":{"observed_at":"2026-08-07T15:33:52.622227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:52.795149Z","title":"Peft: State-of-the-art parameter-efficient fine-tuning methods","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.795149Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:2bc2b79a5edccb903856c184f7ff4420bbf1598ab8233e574800e7e227d5b734","observation_id":"df986964-b692-4a24-92f0-95cb5c07d13a","resolution":{"observed_at":"2026-08-07T15:33:52.795149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05922","last_updated":"2022-04-27T18:25:19Z","snapshot_observed_at":"2026-07-06T11:56:56.738395Z","submitted_at":"2021-10-12T12:09:59Z","title":"Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)","version":3},"cited_work":{"arxiv_id":"2110.05922","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.05922","snapshot_observed_at":"2026-08-07T15:33:56.248380Z","title":"Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)","venue":"cs.CV","work_id":"072f2a80-32f2-41ed-842b-259a546151d8","year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:52.929278Z"},"links":{"cited_paper":"/paper/2110.05922","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:abe14e2f66a0fb32f3b8b0fe2e8ae56d3851ee6fa6e75e29579bae86d527c19c","observation_id":"79fcef58-9e46-4540-ac9f-6617a2db6e4d","resolution":{"observed_at":"2026-08-07T15:33:56.330976Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:59.748200Z","title":"S., and Dean, J","venue":null,"work_id":"f7e8abe8-c168-4029-a6a8-363619c48ee3","year":2013},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.004777Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:93a50ae564e297451e2673b3e5fbb2d6adcb9710eb1c20af0aff5a35cccf8de4","observation_id":"b7cd40f1-9e20-4086-b05c-5f5af4492c3e","resolution":{"observed_at":"2026-08-07T15:33:59.839731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:59.547104Z","title":"Prioritized training on points that are learnable, worth learning, and not yet learnt","venue":null,"work_id":"8cfc4642-6c92-446a-a106-d11aeec9e2db","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.114717Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:9c50944371170742636bfa04dfd274277d23659644f008388e57801b06fe6554","observation_id":"1e9e8a58-a9c4-46ae-af55-da9d1fdc3dad","resolution":{"observed_at":"2026-08-07T15:33:59.642934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10623","last_updated":"2023-09-05T16:34:10Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T00:21:04Z","title":"The star-shaped space of solutions of the spherical negative perceptron","version":2},"cited_work":{"arxiv_id":"2305.10623","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.10623","snapshot_observed_at":"2026-08-07T15:33:56.051142Z","title":"The star-shaped space of solutions of the spherical negative perceptron","venue":"cond-mat.dis-nn","work_id":"96f60eb0-0848-4622-9f32-ad974b8d0a8d","year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.224174Z"},"links":{"cited_paper":"/paper/2305.10623","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:0b31e635f6b739271f6ac04cd9119ad7ae5fe50fcd910e0b0fde5fe48847c50a","observation_id":"572be791-6b49-425e-bb1c-fa4610de8ccf","resolution":{"observed_at":"2026-08-07T15:33:56.128852Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:59.294114Z","title":"Optimal design for human preference elicitation","venue":null,"work_id":"1ab9d91d-07e3-421f-add4-c53a6de14252","year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.335769Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:7df23a2086061c82a10ba9ef16ac13e6c4dd9ec6113f6b369399e9480dbdfe22","observation_id":"4cc89c8e-a620-4d37-8b83-0ff9f53369c6","resolution":{"observed_at":"2026-08-07T15:33:59.435562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:59.062331Z","title":"L., Wolsey, L","venue":null,"work_id":"9b695d27-4d9d-40f4-b140-19cc44127a7c","year":1978},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.417518Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:718872cb775c0a8de15a930967e2c476e47d6d73a4d5391b34d06cbf6ea21c34","observation_id":"de061543-acf5-46c6-9b9d-8f06fc21807a","resolution":{"observed_at":"2026-08-07T15:33:59.171920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:58.855002Z","title":"Training language models to follow instructions with human feedback","venue":null,"work_id":"91d0a6b1-3c10-4164-8892-7d9d4dd4a007","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.541745Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:667ee1f63ace58526541f4730e35815b9c2020c8593ecd2df2a76797aec9d199","observation_id":"77fca0d3-9b9e-4280-b16c-95e8b1e666e1","resolution":{"observed_at":"2026-08-07T15:33:58.951173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:58.566036Z","title":null,"venue":null,"work_id":"6dbbcc57-71c4-4a80-a3a8-87e6a3c299f2","year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.646729Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:21b9288e4d82b706e0bc6362eea0379e28e664c467b4766f4af2f54d3e5c544f","observation_id":"b6fea7d4-6d13-46f5-8e20-86f44525c2f7","resolution":{"observed_at":"2026-08-07T15:33:58.699653Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:58.269514Z","title":null,"venue":null,"work_id":"049a503b-ae67-4c06-a92a-aee4305748ce","year":2017},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.761624Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:37abeb8c66c71d9bfabd0db89a14d8393404250a7cd5da8fc2c862b0f6557bbe","observation_id":"13517ba4-ba3e-442a-81ae-5d12ae33a063","resolution":{"observed_at":"2026-08-07T15:33:58.416602Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:58.016125Z","title":"Optimal Design of Experiments, volume 50 of Classics in Applied Mathematics","venue":null,"work_id":"4ab6c3a6-1cc1-46c6-885c-cdca657ea22b","year":2006},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.841150Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:0a14861f28fe47fac6e01a6f5ef7a2e6498a6849476e1dce6c9328f24d5602a1","observation_id":"9d6e6a10-bf62-4d2e-99a2-a92d521ce1e2","resolution":{"observed_at":"2026-08-07T15:33:58.108840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:57.789747Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":"5a493a43-1a12-46f1-b104-66ae12de8695","year":2019},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:53.968315Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:c6106b79476a3afb1b18b8e95e301fc7578ddecbe66cd77b6c6f6569a8bd82a0","observation_id":"94379257-47b7-4a15-8c47-503f79d20f98","resolution":{"observed_at":"2026-08-07T15:33:57.859146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:57.642663Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":"4b1f848d-12d2-4399-bda1-f6dbe08b48ea","year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.059317Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:61b11fe809892f2f373211f446faa9065814b3b87cb41b19be5d1e6876a7a099","observation_id":"db80c7d6-3f9c-4ad1-8d2e-e5bff9fed775","resolution":{"observed_at":"2026-08-07T15:33:57.718365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.04984","last_updated":"2021-07-11T07:00:19Z","snapshot_observed_at":"2026-08-03T02:19:12.379007Z","submitted_at":"2021-07-11T07:00:19Z","title":"SVP-CF: Selection via Proxy for Collaborative Filtering Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.04984","snapshot_observed_at":"2026-08-07T15:33:54.228168Z","title":"SVP - CF : Selection via proxy for collaborative filtering data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.228168Z"},"links":{"cited_paper":"/paper/2107.04984","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:6a76e7af9a50fcb6b9b684152575d79532497c6012233d6ab25977304a6e5e77","observation_id":"05c8e0f8-fa83-4633-9b7b-aa91d74cacef","resolution":{"observed_at":"2026-08-07T15:33:54.228168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09668","last_updated":"2024-02-15T02:27:57Z","snapshot_observed_at":"2026-08-07T11:18:58.227690Z","submitted_at":"2024-02-15T02:27:57Z","title":"How to Train Data-Efficient LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09668","snapshot_observed_at":"2026-08-07T15:33:54.356737Z","title":"H., Caverlee, J., and Cheng, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.356737Z"},"links":{"cited_paper":"/paper/2402.09668","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:bf1551ab8948818df19c8bbe22e93642d82ccccab859b5ffe81f784cfa58aca8","observation_id":"75691a37-0af0-4eb6-8cbb-6578d75e4297","resolution":{"observed_at":"2026-08-07T15:33:54.356737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17055","last_updated":"2024-10-23T12:55:39Z","snapshot_observed_at":"2026-08-03T19:40:12.558709Z","submitted_at":"2024-10-22T14:36:44Z","title":"Optimal Design for Reward Modeling in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17055","snapshot_observed_at":"2026-08-07T15:33:54.491558Z","title":"Optimal design for reward modeling in RLHF","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.491558Z"},"links":{"cited_paper":"/paper/2410.17055","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:e13fa49222ddea017a44a5a75ef78c0d366c6c9960d45f723f5aee19bb6096b3","observation_id":"c58e1577-1621-4ef2-9eb0-c87452a42a56","resolution":{"observed_at":"2026-08-07T15:33:54.491558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:57.485139Z","title":null,"venue":null,"work_id":"af13120f-136f-4132-80eb-55106f388bca","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.652169Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:49ca0315dcad0b80567f477609238e1a7efc6c1b83c38bacf7485c9a63cf0b22","observation_id":"f9715298-d517-417e-b192-2b2340faf1e2","resolution":{"observed_at":"2026-08-07T15:33:57.554704Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:57.350384Z","title":"and Yang, M","venue":null,"work_id":"a8ed7309-a69e-486f-b8fb-f2208e69d991","year":2012},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.730925Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:f5107b7ef4997a9997a9750efc82e758e128fb122304ee3b9432bd8ba8c399a2","observation_id":"16d7da80-b2c7-4f87-8fec-eb8b86ae27cc","resolution":{"observed_at":"2026-08-07T15:33:57.419423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19396","last_updated":"2024-12-27T01:10:17Z","snapshot_observed_at":"2026-08-07T16:49:17.391873Z","submitted_at":"2024-12-27T01:10:17Z","title":"Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19396","snapshot_observed_at":"2026-08-07T15:33:54.880840Z","title":"Comparing few to rank many: Active human preference learning using randomized Frank-Wolfe","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:54.880840Z"},"links":{"cited_paper":"/paper/2412.19396","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:6c0d5b98233220ac6806244d0e41f5e497c11174df4f0809b1c769471cd08268","observation_id":"64413cef-0d91-4807-9ca8-f8e52ee96539","resolution":{"observed_at":"2026-08-07T15:33:54.880840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12284","last_updated":"2023-08-23T17:58:14Z","snapshot_observed_at":"2026-08-02T00:26:43.445762Z","submitted_at":"2023-08-23T17:58:14Z","title":"D4: Improving LLM Pretraining via Document De-Duplication and Diversification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12284","snapshot_observed_at":"2026-08-07T15:33:55.009360Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.009360Z"},"links":{"cited_paper":"/paper/2308.12284","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:6ec8c63ca4b470f94d96841972e3f414cb70eeca1645de64ba87130d008c833a","observation_id":"cc034977-d9fb-489d-9ff7-ad39c71ea6ce","resolution":{"observed_at":"2026-08-07T15:33:55.009360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:57.112991Z","title":"On coresets for support vector machines","venue":null,"work_id":"e4fcac4a-5bbd-402a-a80e-e7e3fa919121","year":2021},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.132746Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:ceb31e61c39d4ef5090bf0c6f6b5e5975941ba816883f7a1e3d389481fa41990","observation_id":"f0bc88ca-ac6d-4833-9c01-9e5f391100cd","resolution":{"observed_at":"2026-08-07T15:33:57.238907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:33:56.913069Z","title":"W., Lester, B., Du, N., Dai, A., and Le, Q","venue":null,"work_id":"5d759157-0d93-4540-84b3-1dcff4a1691f","year":2022},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.280820Z"},"links":{"citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:16fa280d98c456c4d97ec86d98dc4f9314bf73080ed40e4987d780cd78a7a93e","observation_id":"0b35729e-5db2-4b13-8ab3-097ecd82dbef","resolution":{"observed_at":"2026-08-07T15:33:57.000752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00359","last_updated":"2019-11-15T00:03:54Z","snapshot_observed_at":"2026-07-06T08:34:04.911718Z","submitted_at":"2019-11-01T13:09:28Z","title":"CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00359","snapshot_observed_at":"2026-08-07T15:33:55.441839Z","title":"Ccnet: Extracting high quality monolingual datasets from web crawl data","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.441839Z"},"links":{"cited_paper":"/paper/1911.00359","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:5c36789d4fd211be2bf610351221718624561f4b0843103b69e68e687d175d28","observation_id":"253f9be1-f33b-44e4-8048-6bebdc1a3247","resolution":{"observed_at":"2026-08-07T15:33:55.441839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03771","snapshot_observed_at":"2026-08-07T15:33:55.546557Z","title":"L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.546557Z"},"links":{"cited_paper":"/paper/1910.03771","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:41cca120d7dae90d598d28781399ed2236a779abc6fcb2f7f8aad38b8d8667cb","observation_id":"de0b357e-c288-41aa-af0f-e59281d439fe","resolution":{"observed_at":"2026-08-07T15:33:55.546557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11270","last_updated":"2024-02-08T04:16:52Z","snapshot_observed_at":"2026-08-10T00:34:24.901700Z","submitted_at":"2023-01-26T18:07:21Z","title":"Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.11270","snapshot_observed_at":"2026-08-07T15:33:55.702118Z","title":"Principled reinforcement learning with human feedback from pairwise or K -wise comparisons","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T15:33:55.702118Z"},"links":{"cited_paper":"/paper/2301.11270","citing_paper":"/paper/2505.14826"},"observation_digest":"sha256:076f01bacbfc91513999be3f1dec1cb89d08435c6c120cabb5b4a758413cbe2f","observation_id":"8b16b78e-9cf1-419d-94fc-e7029f1716da","resolution":{"observed_at":"2026-08-07T15:33:55.702118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14826","last_updated":"2025-05-20T18:41:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T02:47:54.126071Z","submitted_at":"2025-05-20T18:41:34Z","title":"FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":24},"total_outbound_references":51},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2505.14826."}