{"as_of":"2026-08-06T18:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0f1e637f703fa4b739243e28f5ac9270365548003605dca03905647e4cea6e53","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":"2026-08-06T15:21:58.369911Z","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":"2026-08-06T06:34:29.942622+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":"2026-08-03T20:12:52.473205Z","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":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16178","snapshot_observed_at":"2026-08-03T20:12:52.473205Z","title":"Llm data selection and utilization via dynamic bi-level opti- mization.arXiv preprint arXiv:2507.16178, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.21056","last_updated":"2026-06-22T20:57:57Z","snapshot_observed_at":"2026-08-05T23:59:38.714370Z","submitted_at":"2025-11-26T04:48:33Z","title":"Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-03T20:12:52.473205Z"},"links":{"cited_paper":"/paper/2507.16178","citing_paper":"/paper/2511.21056"},"observation_digest":"sha256:f71471fc40cad3e238e4354a76a151d5c269770efc987189ce14a5bc4ee7beff","observation_id":"fea06d46-0954-45c0-8340-9f2ba7c05b2d","resolution":{"observed_at":"2026-08-03T20:12:52.473205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.16178/citation-record","integrity":"/paper/2507.16178/integrity","json":"/paper/2507.16178/citation-record.json","paper":"/paper/2507.16178"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.16827","last_updated":"2024-08-02T17:59:31Z","snapshot_observed_at":"2026-08-03T03:15:21.035418Z","submitted_at":"2024-02-26T18:54:35Z","title":"A Survey on Data Selection for Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16827","snapshot_observed_at":"2026-08-06T15:21:56.891192Z","title":"M., Longpre, S., Lambert, N., Wang, X., Muennighoff, N., Hou, B., Pan, L., Jeong, H., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:56.891192Z"},"links":{"cited_paper":"/paper/2402.16827","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:5eec67313bfe02d13fa02b612c15e28eb84c8e0f00e84145b920d1864594bb52","observation_id":"da8ac168-8b41-493d-a11c-895b55eab91f","resolution":{"observed_at":"2026-08-06T15:21:56.891192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-07-06T07:55:12.121264Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-06T15:21:57.188884Z","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.188884Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:f07fd419669a64984865ca124343dec6c4cee032ae88e6d8cb33722d784f90fc","observation_id":"b5b2d5b7-abd7-44a7-b74d-8d184de03e32","resolution":{"observed_at":"2026-08-06T15:21:57.188884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12926","last_updated":"2024-01-23T17:22:00Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T17:22:00Z","title":"DsDm: Model-Aware Dataset Selection with Datamodels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12926","snapshot_observed_at":"2026-08-06T15:21:57.354915Z","title":"org/abs/2401.12926,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.354915Z"},"links":{"cited_paper":"/paper/2401.12926","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:7a22b2b3d7c6342b7944e784425cf59b07f94476b67c03a4455b711bd32af3a3","observation_id":"e1f7f25b-9568-4247-8625-65dc0d06be9e","resolution":{"observed_at":"2026-08-06T15:21:57.354915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-07-06T12:54:11.616335Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-06T15:21:57.467246Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.467246Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:8514526fbb065f3948875d2a08bafcc7d99a775fe6f763c3b372169461380f3f","observation_id":"840d96b7-544c-4daa-a23e-2019b5ac28fa","resolution":{"observed_at":"2026-08-06T15:21:57.467246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:21:57.654502Z","title":"Crafting papers on machine learning","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.654502Z"},"links":{"citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:dfe4ce8179e9727a25f6cd6f967524e0ce6f6c7c409ff81ec1e4117212f66b19","observation_id":"4b96db73-2d87-4086-8533-a8a0e9cb5fbd","resolution":{"observed_at":"2026-08-06T15:21:57.654502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.08124","last_updated":"2020-07-16T05:52:16Z","snapshot_observed_at":"2026-07-06T09:38:41.713097Z","submitted_at":"2020-07-16T05:52:16Z","title":"LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.08124","snapshot_observed_at":"2026-08-06T15:21:57.729432Z","title":"Logiqa: A challenge dataset for machine reading comprehension with logical reasoning","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.729432Z"},"links":{"cited_paper":"/paper/2007.08124","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:5c448f61cf82e1c0d45f46963910f78ba2e511f3e5014764d104d4f37edd18a8","observation_id":"f8fe7129-b9e8-437a-826b-b5557455153c","resolution":{"observed_at":"2026-08-06T15:21:57.729432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01492","last_updated":"2025-01-23T17:35:43Z","snapshot_observed_at":"2026-07-06T18:39:43.472708Z","submitted_at":"2024-07-01T17:31:03Z","title":"RegMix: Data Mixture as Regression for Language Model Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01492","snapshot_observed_at":"2026-08-06T15:21:57.795468Z","title":"Regmix: Data mixture as regression for language model pre-training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.795468Z"},"links":{"cited_paper":"/paper/2407.01492","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:1f56595032cfd580a2ff28b89dcf1e896eb733caeb87e2c69487be1a57d8b24e","observation_id":"3842d0bf-c82e-41a3-9182-7df0dc644310","resolution":{"observed_at":"2026-08-06T15:21:57.795468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.02789","last_updated":"2018-09-08T11:47:16Z","snapshot_observed_at":"2026-07-06T06:59:57.168051Z","submitted_at":"2018-09-08T11:47:16Z","title":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.02789","snapshot_observed_at":"2026-08-06T15:21:57.864570Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.864570Z"},"links":{"cited_paper":"/paper/1809.02789","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:71014ab33cbf6a39f69a333bfcaafc45622b5872358e2e1ba3f8e266cf00ed7e","observation_id":"1df8ba64-dd46-4828-a44a-600687fc98fc","resolution":{"observed_at":"2026-08-06T15:21:57.864570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T15:21:57.953937Z","title":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.953937Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:02aa83f22f15d5d8c3c3a84f5426ac5f6dfc62ae697f70fcb79bda1cfb715914","observation_id":"4661ad6c-0431-47e4-a2ef-8032ceeb6a6b","resolution":{"observed_at":"2026-08-06T15:21:57.953937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09739","last_updated":"2024-07-17T20:50:11Z","snapshot_observed_at":"2026-07-06T17:30:25.359458Z","submitted_at":"2024-02-15T06:36:07Z","title":"QuRating: Selecting High-Quality Data for Training Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09739","snapshot_observed_at":"2026-08-06T15:21:58.039374Z","title":"Qurating: Selecting high-quality data for training language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:58.039374Z"},"links":{"cited_paper":"/paper/2402.09739","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:fbe37420e7688372d757994c4485c307042adbbbfe3dbe0cc0675e31d1308c4c","observation_id":"22547dd7-86c5-417b-91b3-1093554466e4","resolution":{"observed_at":"2026-08-06T15:21:58.039374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06046","last_updated":"2024-11-16T02:59:22Z","snapshot_observed_at":"2026-08-06T14:51:55.681794Z","submitted_at":"2024-06-10T06:27:42Z","title":"MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06046","snapshot_observed_at":"2026-08-06T15:21:58.101569Z","title":"Mates: Model-aware data selection for efficient pretraining with data influence mod- els","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:58.101569Z"},"links":{"cited_paper":"/paper/2406.06046","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:17637c04df980282bbc8043d14609a83f837473d0c955c09f9b5d206e88b9a70","observation_id":"c91ef90a-3734-44ab-832d-846d511e3051","resolution":{"observed_at":"2026-08-06T15:21:58.101569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-07-31T00:09:56.948833Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-06T15:21:58.207626Z","title":"Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:58.207626Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:d3b4548143716b188fc06aacbfd4db07a7203e7ff7bba716880a3c92e67fd8ef","observation_id":"9e3f7be7-67d6-4cf4-bef0-7f20a902df7e","resolution":{"observed_at":"2026-08-06T15:21:58.207626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T18:34:53.559874Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-06T15:21:58.285598Z","title":"X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y ., Min, Y ., Zhang, B., Zhang, J., Dong, Z., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:58.285598Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:079570739a61bf9643e2e79d162c33fd317d86309f3bd2edf7c21a90a0802a2d","observation_id":"891f0a4d-7516-4e9d-a388-af9d74be7344","resolution":{"observed_at":"2026-08-06T15:21:58.285598Z","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":"2026-08-06T15:21:58.878239Z","title":null,"venue":null,"work_id":"37ab0df2-7b27-491a-aa83-6831dc7d6584","year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:58.369911Z"},"links":{"citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:b0c4652e3e32e0931dded1cbb0574dce57ec1816d9415a3a6e180aaacb89954f","observation_id":"d3fd7d19-7280-40dd-b866-126d0c136e23","resolution":{"observed_at":"2026-08-06T15:21:58.999718Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-06T15:21:57.285070Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.285070Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:102f2c83dfec403a4fe9bd3b134f5cae1971ee994cf4f3758edcd1d24495a331","observation_id":"55d6b536-8e2c-44a6-a5f4-1b10bea0555d","resolution":{"observed_at":"2026-08-06T15:21:57.285070Z","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":"2026-08-06T15:21:59.090662Z","title":"D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al","venue":null,"work_id":"b6008603-2afc-4488-b9c6-a3af4617b77c","year":1901},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.123738Z"},"links":{"citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:7b5acbdd0183906c0f784b87b230a88b15307eb134915e7db5a134ef7b886b57","observation_id":"26e686b9-6da6-4ff9-b1d1-15907f551d07","resolution":{"observed_at":"2026-08-06T15:21:59.148794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-06T15:21:57.569561Z","title":"B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.569561Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:08cf72f7aabb965755a63a8f6edeb9027255ff1034689f531b8d94f875ca6707","observation_id":"94ac5923-685e-4055-91b6-f4c94a784b0c","resolution":{"observed_at":"2026-08-06T15:21:57.569561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08102","last_updated":"2025-06-08T17:32:07Z","snapshot_observed_at":"2026-07-06T19:31:18.802964Z","submitted_at":"2024-10-10T16:45:28Z","title":"Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08102","snapshot_observed_at":"2026-08-06T15:21:57.057372Z","title":"H., Peng, J., Zhuang, X., Zhang, C., Wu, L., Qiu, J., Zhang, W., Yuan, B., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:57.057372Z"},"links":{"cited_paper":"/paper/2410.08102","citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:d30a792db632e560974bfaef0d2db0220ae612b791f67538bdb7d81f3d431d7a","observation_id":"e1d6dee8-17da-4930-953e-732455697afd","resolution":{"observed_at":"2026-08-06T15:21:57.057372Z","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":"2026-08-06T15:21:59.223343Z","title":"L., and Paul, M","venue":null,"work_id":"92ccce6d-deea-41d0-acca-b8ad0af9cb14","year":2024},"citing_paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T15:21:56.982463Z"},"links":{"citing_paper":"/paper/2507.16178"},"observation_digest":"sha256:5452c619d2fff82e2a707506f60b2a230f4de01c415dfcb433b2cf5f512cc2c9","observation_id":"5c44c51a-7f91-4007-8b8d-71462f0e8d56","resolution":{"observed_at":"2026-08-06T15:21:59.280698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.16178","last_updated":"2025-07-22T02:47:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:14:23.580355Z","submitted_at":"2025-07-22T02:47:12Z","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2507.16178."}