{"as_of":"2026-08-19T21:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed7cfdf6270ea3dcb01f5be1c6dc541f66e9e4bfe2712abb672d643113bc6996","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:28:23.993007Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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":"2026-08-01T10:48:51.218298Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07833","snapshot_observed_at":"2026-07-30T19:59:23.665213Z","title":"arXiv preprint arXiv:2506.07833 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26825","last_updated":"2026-07-30T15:41:13Z","snapshot_observed_at":"2026-08-18T19:21:12.868419Z","submitted_at":"2026-07-29T12:18:36Z","title":"From Found to Designed: Concepts as a Design Axis for Large Language Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-07-30T19:59:23.665213Z"},"links":{"cited_paper":"/paper/2506.07833","citing_paper":"/paper/2607.26825"},"observation_digest":"sha256:94c37ea4c1d9bea57d317bf7a0297ccfb8687d77c9df9a1055e6d99184110398","observation_id":"18e95850-7f60-4ca1-be1e-affab7bc3de7","resolution":{"observed_at":"2026-07-30T19:59:23.665213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07833","snapshot_observed_at":"2026-08-01T10:48:51.218298Z","title":"arXiv preprint arXiv:2506.07833 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26825","last_updated":"2026-07-30T15:41:13Z","snapshot_observed_at":"2026-08-18T19:21:12.868419Z","submitted_at":"2026-07-29T12:18:36Z","title":"From Found to Designed: Concepts as a Design Axis for Large Language Models","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T10:48:51.218298Z"},"links":{"cited_paper":"/paper/2506.07833","citing_paper":"/paper/2607.26825"},"observation_digest":"sha256:c8a412a929f9a058a245d08e828262da2481cac1c49e5b630f68e6b14e473404","observation_id":"4a404dbe-02ea-447c-9fab-c2d34e51bf7c","resolution":{"observed_at":"2026-08-01T10:48:51.218298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.07833/citation-record","integrity":"/paper/2506.07833/integrity","json":"/paper/2506.07833/citation-record.json","paper":"/paper/2506.07833"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:24.635399Z","title":"A dataset and benchmark for hospital course summarization with adapted large language models // Journal of the American Medical Informatics Association","venue":null,"work_id":"9135274f-fd2d-4773-a4b6-f06a219880fd","year":2025},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.800060Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:0ad6f4b21b0332cdaf939a0c000b7da80cd9d2b9010ba348302f6b40551d4fea","observation_id":"12561326-0cdf-431d-b61e-676f88fec198","resolution":{"observed_at":"2026-08-07T05:28:24.638398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-15T17:40:38.050939Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-07T05:28:23.803956Z","title":"Program synthesis with large language models // arXiv preprint arXiv:2108.07732","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.803956Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:9736e2f4341f0d5b193cb8e402efa26e7f3546e1a8cebe2a8e9a1bb6eef594f5","observation_id":"2e509308-4d88-4def-82c6-2044a8741aed","resolution":{"observed_at":"2026-08-07T05:28:23.803956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06963","last_updated":"2025-07-29T03:57:01Z","snapshot_observed_at":"2026-08-16T14:10:51.973960Z","submitted_at":"2024-03-11T17:47:30Z","title":"The pitfalls of next-token prediction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06963","snapshot_observed_at":"2026-08-07T05:28:23.808234Z","title":"The pitfalls of next-token prediction // arXiv preprint arXiv:2403.06963","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.808234Z"},"links":{"cited_paper":"/paper/2403.06963","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:9fb7e142ff3452057ad99651db9fab16388678886c24bf4d014e035efd7cec9c","observation_id":"7bb6ceb4-7e4c-48e3-9b5c-12a1b4ca336c","resolution":{"observed_at":"2026-08-07T05:28:23.808234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.08821","last_updated":"2024-12-15T21:20:12Z","snapshot_observed_at":"2026-08-16T08:10:37.041600Z","submitted_at":"2024-12-11T23:36:20Z","title":"Large Concept Models: Language Modeling in a Sentence Representation Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08821","snapshot_observed_at":"2026-08-07T05:28:23.811740Z","title":"Large Concept Models: Language Modeling in a Sentence Representation Space // arXiv preprint arXiv:2412.08821","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.811740Z"},"links":{"cited_paper":"/paper/2412.08821","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:90370316fad8a32879806016a61ba6b022d0c78a9abe390bfdd7616031e1ef88","observation_id":"a5e4d91c-382e-4730-9b7d-304e832701c4","resolution":{"observed_at":"2026-08-07T05:28:23.811740Z","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":"2026-08-07T05:28:24.626933Z","title":"Language models are few-shot learners // Advances in neural information processing systems","venue":null,"work_id":"e5c6e0e8-f14c-4a47-be87-9dfb5ffff203","year":2020},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.815181Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:1d52290d48fc1ff34172b90f94c8809e0ba98b02f2bdb722a30a4e7e48879eeb","observation_id":"2f54c252-cd71-4bf5-9b1c-21a3f18d2952","resolution":{"observed_at":"2026-08-07T05:28:24.629858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10774","last_updated":"2024-06-14T23:32:32Z","snapshot_observed_at":"2026-08-17T10:13:54.763177Z","submitted_at":"2024-01-19T15:48:40Z","title":"Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10774","snapshot_observed_at":"2026-08-07T05:28:23.818196Z","title":"Medusa: Simple llm inference acceleration framework with multiple decoding heads // arXiv preprint arXiv:2401.10774","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.818196Z"},"links":{"cited_paper":"/paper/2401.10774","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:93a61c496a309c2b7a97be8bd8de6eaa60dccf41fbc9aa0a37963ac3a93031b7","observation_id":"d98e0efc-fb5f-45e0-a1ac-e19d643a7ef1","resolution":{"observed_at":"2026-08-07T05:28:23.818196Z","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":"2026-08-07T05:28:24.618689Z","title":"Code Alpaca: An Instruction-following LLaMA model for code generation","venue":null,"work_id":"6daa8e8a-94b0-47d2-a7b8-12120e846a87","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.822255Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:c84241dcd85787be0098ac6593e50c25600b1bbae9fe4640a71d71e2a1dd7368","observation_id":"6c3af472-0ab6-4a1c-8c78-f7698a88b29e","resolution":{"observed_at":"2026-08-07T05:28:24.621486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T05:28:23.824947Z","title":"Evaluating large language models trained on code // arXiv preprint arXiv:2107.03374","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.824947Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:4c032200994e61ee951b1d58f5a2795f5c777662545cdb4983b433a18b2f4710","observation_id":"95597ad8-9af0-4e64-bf5c-9a0ac1b6edad","resolution":{"observed_at":"2026-08-07T05:28:23.824947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14851","last_updated":"2025-05-09T05:26:43Z","snapshot_observed_at":"2026-08-16T20:15:33.207365Z","submitted_at":"2025-01-24T15:49:10Z","title":"JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.14851","snapshot_observed_at":"2026-08-07T05:28:23.828084Z","title":"JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models // arXiv preprint arXiv:2501.14851","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.828084Z"},"links":{"cited_paper":"/paper/2501.14851","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:77deb6f22ab335735612df37829ed14c6f14383b761fffb17ff89d11f1e4a373","observation_id":"9d8e7afd-0c1b-42ec-ad31-7d2a76d554fc","resolution":{"observed_at":"2026-08-07T05:28:23.828084Z","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":"2026-08-07T05:28:23.831576Z","title":"Training verifiers to solve math word problems // arXiv preprint arXiv:2110.14168","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.831576Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:3adfc6e4f3beb0505572b3254c16f775948220e4cd49d4205524002a3095f982","observation_id":"e01e88be-cb18-44f6-acb1-bbf9388ab8fd","resolution":{"observed_at":"2026-08-07T05:28:23.831576Z","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":"2026-08-07T05:28:24.610709Z","title":"Qlora: Efficient finetuning of quantized llms // Advances in neural information processing systems","venue":null,"work_id":"dddfa76c-292f-4a9c-83ea-7e2e224075bb","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.834860Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:2afa2a586a289fb9275b94835aed7956649fca35960fb4e8b2447eff233cb50e","observation_id":"ccc39df4-742d-4c36-9534-98762cdf700d","resolution":{"observed_at":"2026-08-07T05:28:24.614002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.602561Z","title":null,"venue":null,"work_id":"5d5dbfb1-f5d0-4d0d-b696-ef2fa86290f2","year":2019},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.838627Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:4b6f8c29076e39a98712ab73bfeaab2efbc6f09208b96f842cf54abdc33f0932","observation_id":"810448c8-774f-45db-b0b6-2fef15a9bd89","resolution":{"observed_at":"2026-08-07T05:28:24.605669Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.593987Z","title":"Faith and fate: Limits of transformers on compositionality // Advances in Neural Information Processing Systems","venue":null,"work_id":"6e0589da-8879-402e-a250-478ff5d407a6","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.841245Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:06aaa4a1ec5eff33468433a04faa3c4355dde369624df0db602e0e637ffdb57a","observation_id":"d03d0764-a594-41c4-9010-28e2f145fdb0","resolution":{"observed_at":"2026-08-07T05:28:24.597930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00791","last_updated":"2024-07-04T17:21:48Z","snapshot_observed_at":"2026-08-16T14:16:05.743714Z","submitted_at":"2024-02-22T20:11:24Z","title":"L+M-24: Building a Dataset for Language + Molecules @ ACL 2024","version":2},"cited_work":{"arxiv_id":"2403.00791","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.00791","snapshot_observed_at":"2026-08-07T05:28:24.301360Z","title":"L+M-24: Building a Dataset for Language + Molecules @ ACL 2024","venue":"cs.CL","work_id":"73417735-ab45-429e-b3bb-6567942bf5ae","year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.843997Z"},"links":{"cited_paper":"/paper/2403.00791","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:de6c3a8bacee0aa624a3d6c2a8e71734d67758decc88bda6f50803da0609ad54","observation_id":"48a57128-16aa-4d84-8b01-12a3247ef01f","resolution":{"observed_at":"2026-08-07T05:28:24.304529Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08018","last_updated":"2024-03-04T12:49:31Z","snapshot_observed_at":"2026-08-16T15:24:15.973686Z","submitted_at":"2023-06-13T14:35:34Z","title":"Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.08018","snapshot_observed_at":"2026-08-07T05:28:23.847016Z","title":"Mol-instructions: A large-scale biomolecular instruction dataset for large language models // arXiv preprint arXiv:2306.08018","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.847016Z"},"links":{"cited_paper":"/paper/2306.08018","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:df73774c742fb0f7c82f578fb4952702e6d5cd3cf575e36e64e58708ef9e3d60","observation_id":"3aac82e0-b5f5-4dae-ac7a-0f0f676c0d49","resolution":{"observed_at":"2026-08-07T05:28:23.847016Z","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":"2026-08-07T05:28:24.585394Z","title":"Bridging the data gap between children and large language models // Trends in Cognitive Sciences","venue":null,"work_id":"6f82be00-8c3f-438c-8b5e-55e2389da351","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.850635Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:f542e8fa429bf6842b62de41cf6b1a0918d56458d94be43d1440d7c28700aacc","observation_id":"4b3fcfab-0b43-4181-83bd-9a7d94dcaf30","resolution":{"observed_at":"2026-08-07T05:28:24.588568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19737","last_updated":"2024-04-30T17:33:57Z","snapshot_observed_at":"2026-08-16T23:36:44.216328Z","submitted_at":"2024-04-30T17:33:57Z","title":"Better & Faster Large Language Models via Multi-token Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19737","snapshot_observed_at":"2026-08-07T05:28:23.853884Z","title":"Better & faster large language models via multi-token prediction // arXiv preprint arXiv:2404.19737","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.853884Z"},"links":{"cited_paper":"/paper/2404.19737","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:b617e2b97044017540ccd5caa0b13cfd907da43f1c12c9fdd44dd4dde89faf24","observation_id":"a2ec83bb-6e7c-4cd3-b8ec-5fa6b607a700","resolution":{"observed_at":"2026-08-07T05:28:23.853884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06265","last_updated":"2024-06-22T16:00:49Z","snapshot_observed_at":"2026-08-16T14:11:12.034173Z","submitted_at":"2024-03-10T17:02:53Z","title":"Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06265","snapshot_observed_at":"2026-08-07T05:28:23.856911Z","title":"Unpacking tokenization: Evaluating text compression and its correlation with model performance // arXiv preprint arXiv:2403.06265","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.856911Z"},"links":{"cited_paper":"/paper/2403.06265","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:d6c5740b1d43e10b17d1f178796dc3c761846f24055cca97f9090716f62001d3","observation_id":"2b14736c-197a-4e1b-8386-6d34f635b68d","resolution":{"observed_at":"2026-08-07T05:28:23.856911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-07T05:28:23.860206Z","title":"The llama 3 herd of models // arXiv preprint arXiv:2407.21783","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.860206Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:16ead10310ed40bfe956667ffcd273a83ed83acd4f94540d985043bb2cfaa98a","observation_id":"46b7bc9e-1fc9-4998-9824-9ed03e7ec06e","resolution":{"observed_at":"2026-08-07T05:28:23.860206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06769","last_updated":"2025-11-03T00:53:34Z","snapshot_observed_at":"2026-08-17T06:12:37.525438Z","submitted_at":"2024-12-09T18:55:56Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06769","snapshot_observed_at":"2026-08-07T05:28:23.862979Z","title":"Training large language models to reason in a continuous latent space // arXiv preprint arXiv:2412.06769","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.862979Z"},"links":{"cited_paper":"/paper/2412.06769","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:d495bf1606d8baee7a10242fc445a7bb01cb9125931d353164fe37e34478a0d8","observation_id":"54344d67-9455-4cde-b527-59167f4d67b9","resolution":{"observed_at":"2026-08-07T05:28:23.862979Z","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":"2026-08-07T05:28:24.577690Z","title":"Amino acid substitution matrices from protein blocks","venue":null,"work_id":"154780b9-faaa-42b8-813d-703c16c12f95","year":1992},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.867044Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:5f3c09a5a0cf84dd44161819f6fad485e9a0025200e140def238b586a608cef3","observation_id":"bb01dbcc-54cc-49b6-9794-a36e472f14ba","resolution":{"observed_at":"2026-08-07T05:28:24.580485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.569820Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":"9cc2de78-2238-47ae-aa43-5bd8ef642d5a","year":2022},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.869954Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:6ff15e0279ea01d05088d46677e363f2022a7c2d0b054beff1a6a75e9916f7f4","observation_id":"9c6c07c1-31a9-43c9-8ac1-b479c4a820cc","resolution":{"observed_at":"2026-08-07T05:28:24.572625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.561705Z","title":"MIMIC-IV, a freely accessible electronic health record dataset // Scientific data","venue":null,"work_id":"01859062-8cf2-4fe1-99dd-bb6b97c8e8c7","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.873002Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:34566d523d9011d43d5e3b6cf5fb7112b1c7096c45738b0ca5a2f444dce152f9","observation_id":"d06001be-5d2d-42d2-b35c-3610d3db8d73","resolution":{"observed_at":"2026-08-07T05:28:24.565075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.554237Z","title":"Highly accurate protein structure prediction with AlphaFold // nature","venue":null,"work_id":"d94f3fb6-4c90-44c0-985b-392db57bc5cb","year":2021},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.875849Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:509fba1c056a63e5ce99e6f4de8a5cfa64880544e280bf6ffba1810e496b9eb7","observation_id":"33605d40-1617-436a-a068-1db1fd2ec00c","resolution":{"observed_at":"2026-08-07T05:28:24.557082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.546585Z","title":"Concept bottleneck models // International conference on machine learning","venue":null,"work_id":"7030f974-2e26-46af-907b-981e1b581b00","year":2020},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.878521Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:6b64e25f81a1460916c423d0a999d0e25e015cfdbcaad9d03568fc2f867b05dd","observation_id":"0ccf116c-f6be-47a5-bfc4-12f83c2af567","resolution":{"observed_at":"2026-08-07T05:28:24.549762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.538428Z","title":"De novo protein design—From new structures to programmable functions // Cell","venue":null,"work_id":"8a7b9ecc-f0d9-4b59-814a-6aca84dede53","year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.881209Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:c94c531bfc2cfb01bb4976af3bfa2337cb36d7c2b7e471fa283d93d2a5761cd6","observation_id":"fb45db1c-1a6e-4d29-9076-06a4175826fa","resolution":{"observed_at":"2026-08-07T05:28:24.541629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.530461Z","title":"Attribute and simile classifiers for face verification // 2009 IEEE 12th international conference on computer vision","venue":null,"work_id":"5669206f-8e66-4ae1-b648-c03225315367","year":2009},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.884044Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:8991671c9423f3a5634a5d9a3e1e02ae698171c0642f1dfd94e972d45d7e12c1","observation_id":"b220b0a3-5a00-4cb2-a857-7b450ecd4710","resolution":{"observed_at":"2026-08-07T05:28:24.533481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07204","last_updated":"2021-05-28T11:41:35Z","snapshot_observed_at":"2026-08-16T18:31:30.265836Z","submitted_at":"2021-04-15T02:36:49Z","title":"Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models","version":2},"cited_work":{"arxiv_id":"2104.07204","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.07204","snapshot_observed_at":"2026-08-07T05:28:24.255064Z","title":"Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models","venue":"cs.CL","work_id":"2eace81c-b847-4f50-82ab-0ec2899fec6e","year":2021},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.887064Z"},"links":{"cited_paper":"/paper/2104.07204","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:7767ad6d286a7c67801ca1980bbe21576f708c5bffb2e0219ac3fa91f0c5c359","observation_id":"f7d58bae-cd38-473b-a1a0-93c34149fbd0","resolution":{"observed_at":"2026-08-07T05:28:24.260138Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15124","last_updated":"2025-04-14T22:39:09Z","snapshot_observed_at":"2026-08-17T14:11:00.232598Z","submitted_at":"2024-11-22T18:44:04Z","title":"Tulu 3: Pushing Frontiers in Open Language Model Post-Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15124","snapshot_observed_at":"2026-08-07T05:28:23.889757Z","title":"T \" ulu 3: Pushing frontiers in open language model post-training // arXiv preprint arXiv:2411.15124","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.889757Z"},"links":{"cited_paper":"/paper/2411.15124","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:a8f696936ce1d33d85bee851889d7b456d14bd457f2a1caaf70bac7fcfe540c4","observation_id":"7ff18b06-e29f-4b0b-afcf-349bc3cad08b","resolution":{"observed_at":"2026-08-07T05:28:23.889757Z","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":"2026-08-07T05:28:24.522422Z","title":"Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions // Hugging Face repository","venue":null,"work_id":"3898fbae-53bf-45e8-b6f3-235289b15662","year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.893315Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:a403d56b177e5cc82476d029dc1b9e0788cd803e21057fc0589835ef01e994a0","observation_id":"57556b8b-1303-4685-b8b3-88840a7c24d0","resolution":{"observed_at":"2026-08-07T05:28:24.525278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.513901Z","title":"Pre-trained language models for text generation: A survey // ACM Computing Surveys","venue":null,"work_id":"45abfa12-7d73-4522-9ddb-5e19b763b0a3","year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.896151Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:57d3d9e55aa46d7d2ea44fba0473d4cdb343e61ef06a033022017fdaa644cd9f","observation_id":"c5c12e27-8930-47c2-9e25-75c49badcd49","resolution":{"observed_at":"2026-08-07T05:28:24.516936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09263","last_updated":"2023-10-13T17:20:56Z","snapshot_observed_at":"2026-08-16T14:52:16.665727Z","submitted_at":"2023-10-13T17:20:56Z","title":"Table-GPT: Table-tuned GPT for Diverse Table Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09263","snapshot_observed_at":"2026-08-07T05:28:23.899093Z","title":"Table-gpt: Table-tuned gpt for diverse table tasks // arXiv preprint arXiv:2310.09263","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.899093Z"},"links":{"cited_paper":"/paper/2310.09263","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:8081ac994917a709dd5ae025c7e6c45805381758336be537b6053ecdf5e8b98b","observation_id":"4acc6a26-db0c-4dbb-a05c-7b2ade845432","resolution":{"observed_at":"2026-08-07T05:28:23.899093Z","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":"2026-08-07T05:28:24.506123Z","title":"Let's verify step by step // The Twelfth International Conference on Learning Representations","venue":null,"work_id":"afb62990-d53b-43a7-8645-c51fa9cc9414","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.903466Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:cb07b510d1c67b1d9db720ec4504691092c317042edcc6d1f4b164ef8e5f02c9","observation_id":"4bddd19e-cb96-4f12-9864-0fe9f2de6e5c","resolution":{"observed_at":"2026-08-07T05:28:24.509070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.498915Z","title":null,"venue":null,"work_id":"3f5f7414-01c0-48b7-9fa8-95d05bf222bc","year":2003},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.906170Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:93d84ac9eb5ef13172fc9bdf768878ca732ea9765d36d1a8252cec1ee628280b","observation_id":"9e67d8cf-1143-491f-b98c-b7d200f93880","resolution":{"observed_at":"2026-08-07T05:28:24.501465Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.490425Z","title":"Ben, Zimmerman Sam, Rivoire Kelley, Conerly Thomas, Olah Chris, Batson Joshua","venue":null,"work_id":"229292da-fd1f-473e-ad8d-c9dae6b2734d","year":2025},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.909468Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:cbacc1a600f14f0bb1d13ee2c64c65b91cda9ca05b27ba0e399beda84c94bb95","observation_id":"3a5d22d4-a9d6-45c7-b9a3-31492d550b35","resolution":{"observed_at":"2026-08-07T05:28:24.493837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-08-18T18:18:37.449517Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T05:28:23.912496Z","title":"Deepseek-v3 technical report // arXiv preprint arXiv:2412.19437","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.912496Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:dec5232a44a7d28fe387744ce267bb485c0e2835ed13fbe550838be2238736fa","observation_id":"ba30d372-02d6-4d85-8a3c-b23fa7c9cf54","resolution":{"observed_at":"2026-08-07T05:28:23.912496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13423","last_updated":"2025-08-26T18:43:00Z","snapshot_observed_at":"2026-08-19T21:16:24.269986Z","submitted_at":"2025-03-17T17:53:23Z","title":"SuperBPE: Space Travel for Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.13423","snapshot_observed_at":"2026-08-07T05:28:23.915536Z","title":"Superbpe: Space travel for language models // arXiv preprint arXiv:2503.13423","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.915536Z"},"links":{"cited_paper":"/paper/2503.13423","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:9d3c78afea551bdcef34f018978a2bdd5b77adf5cc167732ab5ffb2d8b9644cd","observation_id":"7961aade-6835-4bc4-9b9d-822bfa6e7d89","resolution":{"observed_at":"2026-08-07T05:28:23.915536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15084","last_updated":"2025-01-17T07:12:55Z","snapshot_observed_at":"2026-08-15T17:39:14.326195Z","submitted_at":"2024-12-19T17:29:44Z","title":"AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15084","snapshot_observed_at":"2026-08-07T05:28:23.918291Z","title":"Acemath: Advancing frontier math reasoning with post-training and reward modeling // arXiv preprint arXiv:2412.15084","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.918291Z"},"links":{"cited_paper":"/paper/2412.15084","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:6ca73b1bc07a4941cc5ca0b6e79c3cc966183b30da2caec2d6641b607cbc02c0","observation_id":"30b95365-5af2-40c3-b92b-c5556d29a205","resolution":{"observed_at":"2026-08-07T05:28:23.918291Z","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":"2026-08-07T05:28:24.482625Z","title":"The flan collection: Designing data and methods for effective instruction tuning // International Conference on Machine Learning","venue":null,"work_id":"4c3d8764-6e65-40fd-bb88-88ddeeb84cf7","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.922291Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:da0b2ab0ae95e2f0076251313a774c114fdf7eb4c8f7b2da4749b71b78207985","observation_id":"8d22a078-2413-41ea-a72a-d57144ee9889","resolution":{"observed_at":"2026-08-07T05:28:24.485923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.475419Z","title":"WizardCoder: Empowering Code Large Language Models with Evol-Instruct","venue":null,"work_id":"56d02e0b-fd32-46fd-95bb-eda9b0d684d8","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.925063Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:0a7c989cb1784c19c37215d44278cc39d78e7884257fadcc24bcb184cb5f4f67","observation_id":"91ac9bee-9584-4856-ad42-c8805314820f","resolution":{"observed_at":"2026-08-07T05:28:24.478303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.468339Z","title":"De novo molecular design and generative models // Drug discovery today","venue":null,"work_id":"ce804df6-ebbd-4c22-ae05-9811e71281fa","year":2021},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.927655Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:a99d74c73d604a2d77205ef60517fab867ca7e2062d4604b9372e5dd0fb8b728","observation_id":"2cb5da11-23d5-41e0-9628-7d753a9a623c","resolution":{"observed_at":"2026-08-07T05:28:24.471051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.460304Z","title":"Levenshtein distance: Information theory, computer science, string (computer science), string metric, damerau? Levenshtein distance, spell checker, hamming distance","venue":null,"work_id":"2e578976-b409-4575-bf21-1565b8a5d4cf","year":2009},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.930666Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:9d6b7ebc2e2681958f72632acd5334bd109926b31a9eb7d8e61c7da2cca5813b","observation_id":"58843eac-80f5-49fe-9198-09608ee574cc","resolution":{"observed_at":"2026-08-07T05:28:24.463328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.453493Z","title":"ColabFold: making protein folding accessible to all // Nature methods","venue":null,"work_id":"4cb90dab-81b9-467f-bd23-89a12c683b81","year":2022},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.933846Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:791b310a435f3d47adfd82bd1bb0efa4359b0d35beca16ca0f9b0117e4efb58f","observation_id":"e923f9a5-1a4b-4ddc-8873-3ba5f8316e3f","resolution":{"observed_at":"2026-08-07T05:28:24.456118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.445729Z","title":"A general method applicable to the search for similarities in the amino acid sequence of two proteins // Journal of molecular biology","venue":null,"work_id":"42961470-dac1-4391-a359-cc8ba8500f68","year":1970},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.936635Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:ec45cec57b2775a50e3a0179410f08ba999b2a20175eca86296b478563b03f70","observation_id":"b1f27967-4015-45cc-b342-e5e1036f6186","resolution":{"observed_at":"2026-08-07T05:28:24.448351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.438120Z","title":"Training language models to follow instructions with human feedback // Advances in neural information processing systems","venue":null,"work_id":"0d6d30e4-8ec1-4eb9-80fa-2157920d0a71","year":2022},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.939458Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:dff28ccb30892e537ed28d278f6b9a5ee5e772cce98d5d7a72dde9a4e113253d","observation_id":"a688db36-6b30-45e1-9ddf-780c162b8007","resolution":{"observed_at":"2026-08-07T05:28:24.441391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.430539Z","title":"Improving language understanding by generative pre-training.(2018)","venue":null,"work_id":"1518f454-6838-41cb-a6bc-7ddc37b9dce9","year":2018},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.942065Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:c0319ba5db434c6e022789886382207ef3bff18fd2e6f970c1700c5a708173ee","observation_id":"f6751e21-240e-48bc-972c-a31ea6e0162e","resolution":{"observed_at":"2026-08-07T05:28:24.433463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.422073Z","title":"Twilight zone of protein sequence alignments // Protein engineering","venue":null,"work_id":"3edf80f1-c7e6-4d88-a0c4-ebf0bbf68a62","year":1999},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.944448Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:931aadf387947b91bbbe339950c2c290dde9b4043a5dfa3cdd3723758aae35e9","observation_id":"b8d9c750-7432-4b1f-b6f0-feebbe8a93e3","resolution":{"observed_at":"2026-08-07T05:28:24.425033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.413791Z","title":"Get Your Atoms in Order: An Open-Source Implementation of a Novel and Robust Molecular Canonicalization Algorithm // Journal of chemical information and modeling","venue":null,"work_id":"19344425-cf4a-48b1-bbf3-30da9ef9e958","year":2015},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.947132Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:658ad6d62c2a7fd5d9556a37a58b8ad1ed8d4d514f01bb863913ce8c659cc8a5","observation_id":"d71c7a00-7ef5-4bf4-bdc2-60f1526f3e4a","resolution":{"observed_at":"2026-08-07T05:28:24.416899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1508.07909","last_updated":"2016-06-10T14:45:08Z","snapshot_observed_at":"2026-08-17T07:28:37.146698Z","submitted_at":"2015-08-31T16:37:31Z","title":"Neural Machine Translation of Rare Words with Subword Units","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.07909","snapshot_observed_at":"2026-08-07T05:28:23.950123Z","title":"Neural machine translation of rare words with subword units // arXiv preprint arXiv:1508.07909","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.950123Z"},"links":{"cited_paper":"/paper/1508.07909","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:def9ab65c9d6a25811cab33ef43e00a2ef46ad4eba1969e770190c486da03cde","observation_id":"07928385-739e-4eff-8c82-72f6a5d54992","resolution":{"observed_at":"2026-08-07T05:28:23.950123Z","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":"2026-08-07T05:28:24.405358Z","title":"A mathematical theory of communication // The Bell system technical journal","venue":null,"work_id":"cfbaa0bd-f31b-4009-abe2-aa52db53680a","year":1948},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.953395Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:ba73f89e03a28cb95395ef7f265e058eee74185dd46345dfcbd4488be5520641","observation_id":"2c1425b3-a0ea-4650-87da-03f84fd969ff","resolution":{"observed_at":"2026-08-07T05:28:24.408503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14903","last_updated":"2024-02-22T18:14:09Z","snapshot_observed_at":"2026-08-18T14:23:11.964976Z","submitted_at":"2024-02-22T18:14:09Z","title":"Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14903","snapshot_observed_at":"2026-08-07T05:28:23.956370Z","title":"Tokenization counts: the impact of tokenization on arithmetic in frontier llms // arXiv preprint arXiv:2402.14903","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.956370Z"},"links":{"cited_paper":"/paper/2402.14903","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:e368c7e1e355af8ae203d61642bb118998aacb5b0c2c2ac675dbff722fc5a069","observation_id":"260d899d-5546-4b5b-9352-9fc8864fd913","resolution":{"observed_at":"2026-08-07T05:28:23.956370Z","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":"2026-08-07T05:28:24.396455Z","title":"Blockwise parallel decoding for deep autoregressive models // Advances in Neural Information Processing Systems","venue":null,"work_id":"c567730a-91e5-40fb-b47f-6f49db4db8db","year":2018},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.959519Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:6a838e6d74bd373a81e51b1421cb4ff9b9ba58b9594d9baf9fdd275e2b9912fb","observation_id":"2068eae3-0832-459d-8509-9a1cdad9c3d3","resolution":{"observed_at":"2026-08-07T05:28:24.399779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13623","last_updated":"2024-11-01T02:41:36Z","snapshot_observed_at":"2026-08-16T13:33:00.004600Z","submitted_at":"2024-07-18T15:58:54Z","title":"Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13623","snapshot_observed_at":"2026-08-07T05:28:23.962571Z","title":"Scaling laws with vocabulary: Larger models deserve larger vocabularies // arXiv preprint arXiv:2407.13623","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.962571Z"},"links":{"cited_paper":"/paper/2407.13623","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:c802a9cb2cb55edb474264609cc12324823af5e78ea988057549a44f736ea2cb","observation_id":"9393101f-8d37-4692-8299-ce5998ef4b9e","resolution":{"observed_at":"2026-08-07T05:28:23.962571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01560","last_updated":"2024-10-05T03:54:22Z","snapshot_observed_at":"2026-08-16T13:13:21.673239Z","submitted_at":"2024-10-02T14:00:09Z","title":"OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01560","snapshot_observed_at":"2026-08-07T05:28:23.965512Z","title":"OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data // arXiv preprint arXiv:2410.01560","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.965512Z"},"links":{"cited_paper":"/paper/2410.01560","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:abcfdf2d01929c7c13c44a9b7bfc5876b7496d15c82662a6d390876e905cf95d","observation_id":"8393c317-7ac5-4d48-9760-cee7642c1ecd","resolution":{"observed_at":"2026-08-07T05:28:23.965512Z","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":"2026-08-07T05:28:24.388164Z","title":"Attention is all you need // Advances in neural information processing systems","venue":null,"work_id":"302a64f2-97af-4427-98f0-1dbbcd2ad34c","year":2017},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.968288Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:a0b5a47753e16d7cb457a7a2187de37814c8cd87ef94c4f21df84a325d13f7ee","observation_id":"4b6d841d-186f-4680-8dff-abc097a4c8c7","resolution":{"observed_at":"2026-08-07T05:28:24.391096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:23.970765Z","title":"Sciriff: A resource to enhance language model instruction-following over scientific literature // arXiv preprint arXiv:2406.07835","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.970765Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:c4411829010cab90b685180f4281ef041635ef8c925897afc5c69b7b7a340b59","observation_id":"5395233b-013c-442a-9754-809b735b346c","resolution":{"observed_at":"2026-08-07T05:28:23.970765Z","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":"2026-08-07T05:28:24.380329Z","title":"Transformers: State-of-the-art natural language processing // Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations","venue":null,"work_id":"1fd6f85d-958e-43cd-9229-65f9daa62757","year":2020},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.973501Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:294fdc5e3449e3bb9c80e7fcfdbcf9096701f90a454811b0ebe566a7b03930e6","observation_id":"4b334a26-05c3-4280-9e93-106571b0c114","resolution":{"observed_at":"2026-08-07T05:28:24.383404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-07T05:28:24.372215Z","title":"WizardLM: Empowering large pre-trained language models to follow complex instructions // The Twelfth International Conference on Learning Representations","venue":null,"work_id":"0e55de60-4830-46e4-8631-54ae23bae364","year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.977022Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:347267fa351a1182b4c704ac9dfe2948fa8a80641b2368af2259475cc7614928","observation_id":"06a09b59-b44d-4459-8ab6-d8c233df379f","resolution":{"observed_at":"2026-08-07T05:28:24.375507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-13T10:57:13.012119Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12284","snapshot_observed_at":"2026-08-07T05:28:23.979753Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models // arXiv preprint arXiv:2309.12284","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.979753Z"},"links":{"cited_paper":"/paper/2309.12284","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:52d9d39dffcbac3f0a5c0fbc4e6cf039bf5dab3212a4714ac46d55047a463fa8","observation_id":"32334a21-c525-4fb1-ba2d-079f7117e89d","resolution":{"observed_at":"2026-08-07T05:28:23.979753Z","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":"2026-08-07T05:28:24.362772Z","title":"Scoring function for automated assessment of protein structure template quality // Proteins: Structure, Function, and Bioinformatics","venue":null,"work_id":"a9ea4d8d-3318-4989-ae3d-5f15d10d1a49","year":2004},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.983016Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:eba18d69b7007d94442474396870b280704aecd083b57c7cd83cf2d55fddd657","observation_id":"701a2e50-71c7-4c70-96d5-67a2ff2e39fd","resolution":{"observed_at":"2026-08-07T05:28:24.366097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15778","last_updated":"2025-05-21T17:29:15Z","snapshot_observed_at":"2026-08-16T17:14:52.645395Z","submitted_at":"2025-05-21T17:29:15Z","title":"Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15778","snapshot_observed_at":"2026-08-07T05:28:23.986449Z","title":"Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space // arXiv preprint arXiv:2505.15778","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.986449Z"},"links":{"cited_paper":"/paper/2505.15778","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:b0a6c39b40dbca018f140da92b4f90e4cfe8e1a07c7330c786f5148dc5de8924","observation_id":"fe723ed0-ed49-458e-aa61-deba29655f78","resolution":{"observed_at":"2026-08-07T05:28:23.986449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01470","last_updated":"2024-05-02T17:00:02Z","snapshot_observed_at":"2026-08-17T00:12:52.305769Z","submitted_at":"2024-05-02T17:00:02Z","title":"WildChat: 1M ChatGPT Interaction Logs in the Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01470","snapshot_observed_at":"2026-08-07T05:28:23.989799Z","title":"Wildchat: 1m chatgpt interaction logs in the wild // arXiv preprint arXiv:2405.01470","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.989799Z"},"links":{"cited_paper":"/paper/2405.01470","citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:32638c055eb3b7c72499f3c1bf78f6279f841f12c325038fed52f63dead05b0c","observation_id":"11839ef5-e3bd-4061-84c5-acadd2c53706","resolution":{"observed_at":"2026-08-07T05:28:23.989799Z","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":"2026-08-07T05:28:24.354586Z","title":"P, Zhang Hao, Gonzalez Joseph E., Stoica Ion","venue":null,"work_id":"5c7a29b2-32a9-414c-9cf0-b3d14c19e4b8","year":2023},"citing_paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:23.993007Z"},"links":{"citing_paper":"/paper/2506.07833"},"observation_digest":"sha256:866952aa92e8597cb0f1bb5773caa82f4f6f05f16d31b8ef9d061e94243368cc","observation_id":"7299156b-02c5-4ea9-8cc0-da7200d721c9","resolution":{"observed_at":"2026-08-07T05:28:24.357953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07833","last_updated":"2025-06-13T17:24:38Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T14:05:11.635506Z","submitted_at":"2025-06-09T14:55:00Z","title":"Improving Large Language Models with Concept-Aware Fine-Tuning"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":2,"verified_fuzzy":34},"total_outbound_references":63},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2506.07833."}