{"as_of":"2026-08-15T23:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c7245803d6bc92b80919f31efb0ea97055b29ae256bf0a120c1830c67646dfd3","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T05:38:36.968415Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:13:48.299054Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T14:13:55.156116Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"cited_work":{"arxiv_id":"2412.17365","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.17365","snapshot_observed_at":"2026-08-05T14:13:55.156116Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","venue":"cs.CL","work_id":"d6ac58d1-4867-47bc-8e85-b764810cdd3b","year":2024},"citing_paper":{"arxiv_id":"2508.21589","last_updated":"2025-10-22T11:09:23Z","snapshot_observed_at":"2026-08-11T01:24:04.693782Z","submitted_at":"2025-08-29T12:47:27Z","title":"Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning","version":5},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:13:48.299054Z"},"links":{"cited_paper":"/paper/2412.17365","citing_paper":"/paper/2508.21589"},"observation_digest":"sha256:ae33f6edca2c0c1bc13d0ee6c79f72d28b445a146f95512ae455e7655bd4dd94","observation_id":"661e6b99-0dc0-42b8-a46d-46840bce793d","resolution":{"observed_at":"2026-08-05T14:13:55.210427Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.17365/citation-record","integrity":"/paper/2412.17365/integrity","json":"/paper/2412.17365/citation-record.json","paper":"/paper/2412.17365"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-11T05:38:36.707342Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.707342Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:38f903cf71f800c8d64165d9ba9384c3591ced5c30986efeccd152935338306b","observation_id":"dea4c427-acc5-4573-9b58-f89064dc166e","resolution":{"observed_at":"2026-08-11T05:38:36.707342Z","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":"2026-08-11T05:38:37.463302Z","title":"Enhancing chat language models by scaling high-quality instructional conversations","venue":null,"work_id":"c89db789-7cb9-42b8-9555-5686103dd212","year":2023},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.721294Z"},"links":{"citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:3592724bc11b356653a4254333996ca59ea9d9ead4b4b5232052c1c873148da1","observation_id":"7c50f13a-de21-486a-b239-bc36dcf28681","resolution":{"observed_at":"2026-08-11T05:38:37.522908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T05:38:36.725214Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.725214Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:17e853425333a6f11e04dc07ac93d297e33a084b858bf1d509663274ad4a8809","observation_id":"66ee1c05-f02e-45c2-af2d-614de20ed494","resolution":{"observed_at":"2026-08-11T05:38:36.725214Z","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":"2026-08-11T05:38:37.345556Z","title":null,"venue":null,"work_id":"45e49f63-91aa-4e98-a7e5-69e579b0ebc2","year":2024},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.968415Z"},"links":{"citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:f82186bcf875a50f2b5329f693793e66e3d27c0eecf22fc5bff5d1a0d77ee12f","observation_id":"7ab5d178-59ec-4a6c-bbc1-c530abc6f502","resolution":{"observed_at":"2026-08-11T05:38:37.349665Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10110","last_updated":"2024-06-07T20:23:21Z","snapshot_observed_at":"2026-08-15T12:32:26.805077Z","submitted_at":"2024-02-15T17:06:21Z","title":"Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10110","snapshot_observed_at":"2026-08-11T05:38:36.733063Z","title":"Selec- tive reflection-tuning: Student-selected data recycling for llm instruction-tuning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.733063Z"},"links":{"cited_paper":"/paper/2402.10110","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:45f5f5216b800a6ffc83d2b6981e38ca5e8a50b4fb9078d174f613df6017dc93","observation_id":"d2224adf-1eeb-4070-820c-5e68a8fccbdd","resolution":{"observed_at":"2026-08-11T05:38:36.733063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16705","last_updated":"2025-01-15T08:20:19Z","snapshot_observed_at":"2026-08-13T04:09:55.899267Z","submitted_at":"2024-02-26T16:21:53Z","title":"SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection","version":2},"cited_work":{"arxiv_id":"2402.16705","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.16705","snapshot_observed_at":"2026-08-11T05:38:37.229086Z","title":"SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection","venue":"cs.CL","work_id":"733c0360-d981-474d-8676-ebfae039414d","year":2024},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.737287Z"},"links":{"cited_paper":"/paper/2402.16705","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:dc5b6780a23914b7acbab1866a30f692be2d19e6578f9d3b9298baf8e8f64a1d","observation_id":"0124e764-589f-4e25-8a83-1173690a618e","resolution":{"observed_at":"2026-08-11T05:38:37.233646Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07316","last_updated":"2023-03-19T13:37:01Z","snapshot_observed_at":"2026-08-14T05:00:07.792972Z","submitted_at":"2022-10-13T19:42:08Z","title":"MTEB: Massive Text Embedding Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07316","snapshot_observed_at":"2026-08-11T05:38:36.741434Z","title":"Mteb: Massive text embedding benchmark","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.741434Z"},"links":{"cited_paper":"/paper/2210.07316","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:09b982490321ecf325edb889fa97232920508c6c1aab45e71e4b618e81e3702b","observation_id":"dbf93492-fea8-4a71-be11-c5e598b06202","resolution":{"observed_at":"2026-08-11T05:38:36.741434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.03277","last_updated":"2023-04-06T17:58:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-06T17:58:09Z","title":"Instruction Tuning with GPT-4","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.03277","snapshot_observed_at":"2026-08-11T05:38:36.813136Z","title":"Instruc- tion tuning with gpt-4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.813136Z"},"links":{"cited_paper":"/paper/2304.03277","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:ca0fb958d35fcbe7c141176cdca3f47636f9fc2755162cad56e537c9a04b6e43","observation_id":"2e7bf1b1-b95f-47d3-9d13-6604753160c3","resolution":{"observed_at":"2026-08-11T05:38:36.813136Z","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":"2026-08-11T05:38:37.360588Z","title":"W., Chowdhery, A., Le, Q., Chi, E., Zhou, D., et al","venue":null,"work_id":"70f01138-cb64-4472-918d-89c57c02192b","year":2023},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.894836Z"},"links":{"citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:2343b6839dc11db80d8cecdbd9c2b38dffdad929996d6cf0bdfcce3f8c9a6696","observation_id":"cb6d798a-daf5-481a-aac9-c5f7d9185824","resolution":{"observed_at":"2026-08-11T05:38:37.383544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12845","last_updated":"2024-06-18T17:58:28Z","snapshot_observed_at":"2026-08-12T23:39:50.330661Z","submitted_at":"2024-06-18T17:58:28Z","title":"Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12845","snapshot_observed_at":"2026-08-11T05:38:36.946910Z","title":"Wang, H., Xiong, W., Xie, T., Zhao, H., and Zhang, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.946910Z"},"links":{"cited_paper":"/paper/2406.12845","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:f5f5fad3a1b552967f7276873c4bd68606289c9a303c85036301f317617c4399","observation_id":"4bbd1de7-89bb-4245-845b-a3ce80ee060e","resolution":{"observed_at":"2026-08-11T05:38:36.946910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.07705","last_updated":"2022-10-24T07:00:15Z","snapshot_observed_at":"2026-08-13T16:00:55.684745Z","submitted_at":"2022-04-16T03:12:30Z","title":"Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.07705","snapshot_observed_at":"2026-08-11T05:38:36.959856Z","title":"S., Naik, A., Stap, D., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.959856Z"},"links":{"cited_paper":"/paper/2204.07705","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:0f364bdf7aa13061b96e0d4f7428c00d27b8f3d26ba1ef2a2babdec5ab1cb98d","observation_id":"15ec9f82-7834-42a4-86cc-02f1d68b9585","resolution":{"observed_at":"2026-08-11T05:38:36.959856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12458","last_updated":"2025-05-26T19:41:16Z","snapshot_observed_at":"2026-08-12T22:21:57.195463Z","submitted_at":"2024-10-16T11:16:34Z","title":"The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph","version":2},"cited_work":{"arxiv_id":"2410.12458","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.12458","snapshot_observed_at":"2026-08-11T05:38:37.015588Z","title":"The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph","venue":"cs.CL","work_id":"77082c2e-b23f-4fc6-a2b9-5be925feff67","year":2024},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.964434Z"},"links":{"cited_paper":"/paper/2410.12458","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:e200e40565b286aad57f678a7260eafdf8c1baed4b2d61acd8f3c485c50da84c","observation_id":"54943a2a-9207-4879-9c2d-3b13c1df7575","resolution":{"observed_at":"2026-08-11T05:38:37.022829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.06290","last_updated":"2024-07-26T18:09:11Z","snapshot_observed_at":"2026-08-15T01:55:26.051681Z","submitted_at":"2023-07-12T16:37:31Z","title":"Instruction Mining: Instruction Data Selection for Tuning Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.06290","snapshot_observed_at":"2026-08-11T05:38:36.697541Z","title":"Instruction min- ing: Instruction data selection for tuning large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.697541Z"},"links":{"cited_paper":"/paper/2307.06290","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:fd3a09d3d335c33cd2141c949baa5a1c376a950e33968b85034789cd07a48222","observation_id":"6c8a38b3-a57a-4f0f-8789-2c22a981b988","resolution":{"observed_at":"2026-08-11T05:38:36.697541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-11T05:38:36.716507Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.716507Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:7761404a6d56f9199c41281c478f7a4a9e1a9e1814e1ba720a996c733a4cce92","observation_id":"a367526e-15a3-4901-8359-826e59fc0ac4","resolution":{"observed_at":"2026-08-11T05:38:36.716507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06094","last_updated":"2024-02-08T23:02:04Z","snapshot_observed_at":"2026-08-15T18:51:51.302605Z","submitted_at":"2024-02-08T23:02:04Z","title":"Rethinking Data Selection for Supervised Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06094","snapshot_observed_at":"2026-08-11T05:38:36.853572Z","title":"Rethinking data selection for supervised fine- tuning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.853572Z"},"links":{"cited_paper":"/paper/2402.06094","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:01420fd5778d2228c11ec6b83d2ea25dcc0a29655e5e94318876f2c5dfd92088","observation_id":"380a5976-f76b-4e3e-aaea-3f1b5d27e457","resolution":{"observed_at":"2026-08-11T05:38:36.853572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-08-14T19:36:07.505691Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-11T05:38:36.711724Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.711724Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:c4703f1b27267d715ccd8b89b47b52803e9f1a34e12c1c0f76f7ccbc45c749f2","observation_id":"e4742e01-864e-4fb6-bf66-49649684e893","resolution":{"observed_at":"2026-08-11T05:38:36.711724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07316","last_updated":"2023-03-19T13:37:01Z","snapshot_observed_at":"2026-08-14T05:00:07.792972Z","submitted_at":"2022-10-13T19:42:08Z","title":"MTEB: Massive Text Embedding Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07316","snapshot_observed_at":"2026-08-11T05:38:36.777241Z","title":"URL https://arxiv.org/ abs/2210.07316","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.777241Z"},"links":{"cited_paper":"/paper/2210.07316","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:48edff52ee8a2766a6e8c6f857643eac40e66fc0cdc00fe326e97639058a4256","observation_id":"eacee8db-6283-492b-acc1-77ff1dd99474","resolution":{"observed_at":"2026-08-11T05:38:36.777241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08701","last_updated":"2024-02-13T18:37:25Z","snapshot_observed_at":"2026-08-13T10:54:00.274565Z","submitted_at":"2023-07-17T17:59:40Z","title":"AlpaGasus: Training A Better Alpaca with Fewer Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08701","snapshot_observed_at":"2026-08-11T05:38:36.702391Z","title":"Alpagasus: Training a better alpaca with fewer data.arXiv preprint arXiv:2307.08701,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.702391Z"},"links":{"cited_paper":"/paper/2307.08701","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:da256817e56fd4d8ab2b4ace64b42323e7ddef6bbaee7540335582d55b2e924e","observation_id":"bf7ef1f6-2c6d-4bc7-845f-05a273af0b08","resolution":{"observed_at":"2026-08-11T05:38:36.702391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-08-14T08:11:36.232487Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17428","snapshot_observed_at":"2026-08-11T05:38:36.729400Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:36.729400Z"},"links":{"cited_paper":"/paper/2405.17428","citing_paper":"/paper/2412.17365"},"observation_digest":"sha256:dd8ca13449345a8f3f94084f7b35ef5a82827adcb3252724eb30bbc77a1cca09","observation_id":"c2a8bbae-4664-4735-98a8-2a84d0b01aed","resolution":{"observed_at":"2026-08-11T05:38:36.729400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.17365","last_updated":"2024-12-23T08:01:24Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T23:06:30.791900Z","submitted_at":"2024-12-23T08:01:24Z","title":"Boosting LLM via Learning from Data Iteratively and Selectively"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":19},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2412.17365."}