{"as_of":"2026-08-14T07:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:da893a1bd6a7ab8b12f1d9c6d4caac9eac9e350337b44c6e5f1680d0a45ee8ac","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:54:30.317387Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.12612/citation-record","integrity":"/paper/2507.12612/integrity","json":"/paper/2507.12612/citation-record.json","paper":"/paper/2507.12612"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:54:36.395819Z","title":"Achille, M","venue":null,"work_id":"e7221a0a-d68b-43cb-ae40-88a7d2ca922c","year":2019},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:25.810159Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:8d2bdc5ac2b4259c4d115c1bcc9d54b9c501f144e84882c6f6fae765892987cb","observation_id":"88cf1c0f-639f-4264-91e3-b2ce8acbe740","resolution":{"observed_at":"2026-08-06T16:54:36.511247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:36.171420Z","title":"Agarwal, K","venue":null,"work_id":"ca63f230-0a34-46f3-99ad-c6278d59018a","year":2025},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:25.885810Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:a4d3081a6f6efa12304d1156f7ee4f4a5376afe527d4c179d35ec5859ded8d58","observation_id":"9b223c7b-30dd-49ae-9b40-8f72c45f8789","resolution":{"observed_at":"2026-08-06T16:54:36.300084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:35.972612Z","title":"Alvarez-Melis and N","venue":null,"work_id":"c9955a6d-7491-40ba-9956-9e8ec6f8d0f2","year":2020},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.041440Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:078b8af23192d17e8ec0c6f0d7e9fdc1a661614d22b46316d9db69bddd924927","observation_id":"45424dd5-2189-4c4e-93e3-fdfc2f10cf7c","resolution":{"observed_at":"2026-08-06T16:54:36.057490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:26.156947Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.156947Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:6e8363539fa9306469a93c197a15b51961699b4f002fdc5df8eb73627d7dbb31","observation_id":"e8591ec4-e18e-450a-a344-ed594f69cc82","resolution":{"observed_at":"2026-08-06T16:54:26.156947Z","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":"2026-08-06T16:54:26.290308Z","title":"Brown, B","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.290308Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:c113de94b4dc5be45a3323794312d781afeb3b61e6880dab2024f03134c5af1f","observation_id":"0fc5b3ae-d75f-45cc-8f7a-b68972dde19f","resolution":{"observed_at":"2026-08-06T16:54:26.290308Z","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":"2026-08-06T16:54:35.700069Z","title":null,"venue":null,"work_id":"8580a72a-52ec-4c25-8e8c-243b5d72aa76","year":2009},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.434940Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:02c08acc08d1367fe490236c2809a5b1b0e9ca722e959222151dd5f134476c70","observation_id":"967b595e-4b66-4fef-80ee-eea36bd4f5f2","resolution":{"observed_at":"2026-08-06T16:54:35.840032Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:35.460228Z","title":null,"venue":null,"work_id":"a79b7cc6-817f-4569-ae38-011def6e03e4","year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.588710Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:187a1f3d75b6c61ca2b811a69087c1db582cba8436a80974469eff30e489b128","observation_id":"8dc72f5b-4c4e-4e73-880a-625793c53262","resolution":{"observed_at":"2026-08-06T16:54:35.573940Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12147","last_updated":"2025-01-21T14:00:43Z","snapshot_observed_at":"2026-08-13T09:59:37.491838Z","submitted_at":"2025-01-21T14:00:43Z","title":"Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities","version":1},"cited_work":{"arxiv_id":"2501.12147","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.12147","snapshot_observed_at":"2026-08-06T16:54:31.189897Z","title":"Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities","venue":"cs.CL","work_id":"32b5f4d4-2f79-43ea-bd5b-67674f289959","year":2025},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.744749Z"},"links":{"cited_paper":"/paper/2501.12147","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:4c83627b03c4934a91576494d0aea1428e9e0f543a376de4b7574070664e6909","observation_id":"13bf518e-69fb-47eb-9727-0096ac2e3537","resolution":{"observed_at":"2026-08-06T16:54:31.248787Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:35.177973Z","title":"Duchi, S","venue":null,"work_id":"8b82a71e-2091-4a9d-b226-e2243dff1a71","year":2008},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:26.885769Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:e2df29c9fd34d3c7b83a0921d87bf5510b75b3574097c5b3fa368efa00d16498","observation_id":"a1a8d904-7bc2-434c-bf2f-838112bb689c","resolution":{"observed_at":"2026-08-06T16:54:35.297834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:34.947055Z","title":"Hwang, Y","venue":null,"work_id":"cfd34aa7-de8c-4089-b13f-24863c7486b3","year":2020},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.014573Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:be4429d93de678652d8400fb1c975985414a068ec3e0e1dd251649d7f0ddf193","observation_id":"5a2e13ad-8e1a-4616-8bce-9cbf9a5ae826","resolution":{"observed_at":"2026-08-06T16:54:35.049207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:34.715679Z","title":"Killamsetty, X","venue":null,"work_id":"b795b300-e06d-4509-aec2-d33b7309a1f8","year":2021},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.185893Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:9f11bb46eaf973509e181425025310fe972982fc3b42c54a019cc63972f9ca09","observation_id":"5c350660-ace2-48b1-999c-8d10af4a2e92","resolution":{"observed_at":"2026-08-06T16:54:34.828284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:34.513472Z","title":null,"venue":null,"work_id":"05dd6b29-77ba-4f26-bc23-104a21ee6d65","year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.317722Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:80ae363568ff848c1ff14f15d8348038147d616f467984ac0390ab2cdf7f8e3a","observation_id":"cc43f4bd-4338-4409-9f86-7eca1f98616b","resolution":{"observed_at":"2026-08-06T16:54:34.640857Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:34.267891Z","title":"Kindermann and J","venue":null,"work_id":"4501f839-5f69-4120-b43c-8be2cae468a0","year":1980},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.438105Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:f114c27746ad16b57ab12dfe0bb941940a78654e7b2fa1490ad13d9d79c10f84","observation_id":"52958cfb-130f-4a68-b58c-eeef36058f1f","resolution":{"observed_at":"2026-08-06T16:54:34.382229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:27.561840Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.561840Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:930c52027c49e2c105ffd0edd963de42585e0fdc10400c647cf74b9bcfd5276a","observation_id":"b6a731ef-4019-4643-853e-4f233d05a1d1","resolution":{"observed_at":"2026-08-06T16:54:27.561840Z","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":"2026-08-06T16:54:34.082011Z","title":"Kuhn and A","venue":null,"work_id":"07dde92a-51e6-4fb0-89c6-01a9cf670d06","year":1951},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.698857Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:4e735f55a076d7e92974e0f6c9b0a5083bc8399366aba72b96d3baaafba9bc7d","observation_id":"5fb6e712-1a87-4439-9d06-00ef19be8653","resolution":{"observed_at":"2026-08-06T16:54:34.180438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15124","last_updated":"2025-04-14T22:39:09Z","snapshot_observed_at":"2026-08-10T16:05:13.426341Z","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":"2026-08-06T16:54:27.826638Z","title":"Lambert, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.826638Z"},"links":{"cited_paper":"/paper/2411.15124","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:1f96c8cff37cd4f7b0823309231761d28fb244362d7478426db55861d3fda026","observation_id":"05df662c-0994-415a-bc30-a197e0e01a3a","resolution":{"observed_at":"2026-08-06T16:54:27.826638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.11953","last_updated":"2025-08-16T07:28:39Z","snapshot_observed_at":"2026-08-12T17:19:27.084816Z","submitted_at":"2025-08-16T07:28:39Z","title":"Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.11953","snapshot_observed_at":"2026-08-06T16:54:27.921929Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:27.921929Z"},"links":{"cited_paper":"/paper/2508.11953","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:597ef783cf8182c4858f20c59a5bdd5fd161ec7639b55f7bdd18142bcc7f659f","observation_id":"111e95f3-43c0-47a8-8f13-77ceac3ef911","resolution":{"observed_at":"2026-08-06T16:54:27.921929Z","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":"2026-08-12T23:31:04.473877Z","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":"2026-08-06T16:54:28.022180Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.022180Z"},"links":{"cited_paper":"/paper/2407.01492","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:1d3fb356b789cbf33b050655de07447545f941253f65819e8841b3797f19d525","observation_id":"1dc607a7-58cc-4395-a982-852553487513","resolution":{"observed_at":"2026-08-06T16:54:28.022180Z","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":"2026-08-06T16:54:33.897851Z","title":null,"venue":null,"work_id":"86dd9730-fce8-49a8-a07b-8eb925ad7d0f","year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.099799Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:0110a9640cbda987b07b6eccf4bbde5c340570cd26b1b0939192cf75ae9d081e","observation_id":"eb09f32b-6092-4a17-9746-7276e7f659e5","resolution":{"observed_at":"2026-08-06T16:54:33.980814Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:33.698965Z","title":"Longpre, L","venue":null,"work_id":"2626a478-1f0e-456a-aa65-fbb59c224721","year":2023},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.198450Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:d79e571d323a0a09e4f43f1acfe57262c50343a294a15cc6852066f4dc58796a","observation_id":"44876557-a577-47f2-bcc0-d053e24297b5","resolution":{"observed_at":"2026-08-06T16:54:33.786577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07931","last_updated":"2023-10-11T23:01:29Z","snapshot_observed_at":"2026-08-13T05:51:44.084236Z","submitted_at":"2023-10-11T23:01:29Z","title":"D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07931","snapshot_observed_at":"2026-08-06T16:54:28.284721Z","title":"Maharana, P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.284721Z"},"links":{"cited_paper":"/paper/2310.07931","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:a45ecbfe69aa527ef23c3f8b465ab9f002bea706f3ecd42cfec69d798f92d2bf","observation_id":"15071aaa-eb02-42cb-abec-2b1e6955a5ab","resolution":{"observed_at":"2026-08-06T16:54:28.284721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12827","last_updated":"2023-11-21T18:43:43Z","snapshot_observed_at":"2026-08-13T14:21:44.188543Z","submitted_at":"2023-05-22T08:39:25Z","title":"Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12827","snapshot_observed_at":"2026-08-06T16:54:28.347464Z","title":"Ortiz-Jimenez, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.347464Z"},"links":{"cited_paper":"/paper/2305.12827","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:87053cfbaa73a84edd8a59ba5119e6c8d0ab67427fe3268fbdacb940a677c966","observation_id":"98fe3553-768b-4d52-97ae-32970e91bc89","resolution":{"observed_at":"2026-08-06T16:54:28.347464Z","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":"2026-08-06T16:54:33.521583Z","title":null,"venue":null,"work_id":"8da22a10-cabe-45bc-85cc-c0ff31e8f1ce","year":2021},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.431050Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:958e55d3a138795ca7e1bdf214665aaa2d2e6b7694921426fb888e0ca562675e","observation_id":"f79e9035-ee4f-49ed-85e2-dd686d5bbc4c","resolution":{"observed_at":"2026-08-06T16:54:33.624615Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:28.510290Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.510290Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:ec10007486057b7c8c5c10c4958c91b0f85a0349f450398875048bdee6833ff3","observation_id":"a0e79e35-113a-4db4-8d43-c0aa7a583d9b","resolution":{"observed_at":"2026-08-06T16:54:28.510290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.08207","last_updated":"2022-03-17T17:53:01Z","snapshot_observed_at":"2026-08-14T04:21:28.978034Z","submitted_at":"2021-10-15T17:08:57Z","title":"Multitask Prompted Training Enables Zero-Shot Task Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.08207","snapshot_observed_at":"2026-08-06T16:54:28.618124Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.618124Z"},"links":{"cited_paper":"/paper/2110.08207","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:de2fb7d5253c1053015e57df0bd99795b541dd820c9c0277aa996fac5c3f0ce2","observation_id":"e51f19fc-f3a0-495e-89f2-12621d0bb678","resolution":{"observed_at":"2026-08-06T16:54:28.618124Z","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":"2026-08-06T16:54:33.342412Z","title":"Sener and S","venue":null,"work_id":"98f27464-7b92-4bd2-8a48-6bbd7b7c1339","year":2018},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.746369Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:c34595b9aa93eff82a50c4bb0553d4b951594bd667374459dad6f3a510da412d","observation_id":"bf489a80-e78e-45c6-9324-1afd2fb5dccd","resolution":{"observed_at":"2026-08-06T16:54:33.408136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:33.150921Z","title":"Toneva, A","venue":null,"work_id":"c19197ff-70ef-4de4-a0df-e9c30f1b0dd3","year":2019},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:28.930033Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:76080373f3cc53739544309c95f282877854956aa7f859bd5bfa9cb9f839f5cb","observation_id":"ddaba4b7-03d2-486b-ac8f-09775e93a87f","resolution":{"observed_at":"2026-08-06T16:54:33.215210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-06T16:54:29.078509Z","title":"Touvron, T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.078509Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:4bab39271c4a510e666e7429d4116aba224282a091c14d20002ed05cb719053e","observation_id":"23eee2aa-5ff1-404e-9351-85fc2d7493fd","resolution":{"observed_at":"2026-08-06T16:54:29.078509Z","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":"2026-08-06T16:54:32.993579Z","title":null,"venue":null,"work_id":"614ac6a5-37d4-4431-ad30-cd2acee9309e","year":2018},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.175597Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:ef9ca92c0b8638140fae21e85c1150342be7e76b5370bde5f801b3d5be5dcfd0","observation_id":"d4b8e00a-b35f-4360-b686-2ab00f27e59f","resolution":{"observed_at":"2026-08-06T16:54:33.066821Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:32.781492Z","title":null,"venue":null,"work_id":"005eaccf-8699-428f-b04c-a2e0f13c477c","year":2019},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.298509Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:f122dff4de8df9a32cae4724040cd1eee871f2939a50b06091eb3703e8ab9abb","observation_id":"6a32e556-054c-46b4-ac60-eac0fe8a27ad","resolution":{"observed_at":"2026-08-06T16:54:32.904128Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:32.555528Z","title":null,"venue":null,"work_id":"c9332a7c-f40f-4bef-b38d-fb4aa15f6749","year":2022},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.391324Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:3a46215953d620d3eef9f31354e468d6f94b3d9024a25fd2eb0e57dc3391e09f","observation_id":"f438e3b8-bffa-462f-86ce-445d8341f032","resolution":{"observed_at":"2026-08-06T16:54:32.658427Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:29.470800Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.470800Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:139c316087bb23d26a1a73cf7466593dad9ed569b06f3d9c8c89ae1ab754f4c9","observation_id":"e85e89b3-4781-4538-b153-56eca92263d8","resolution":{"observed_at":"2026-08-06T16:54:29.470800Z","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":"2026-08-06T16:54:32.340902Z","title":null,"venue":null,"work_id":"6e03ea1d-c34e-4526-900a-de5fa54e6cf1","year":2005},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.560158Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:89052b1b732bf4ad852f0150ce4ab2c4beffd65ffce10c528013e1d2debf9959","observation_id":"e66f0797-0b00-41c3-a8e8-2d520abead57","resolution":{"observed_at":"2026-08-06T16:54:32.417336Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:32.101912Z","title":null,"venue":null,"work_id":"ae154e3f-5559-49f2-bacf-287c325dc19e","year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.624281Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:4c9123caf3587c098c6dbc7516e69a7fb8cfe82bcb33af6a2b1c84ae66c416f8","observation_id":"2a1520d2-c10f-4f66-a254-95f6c64b8ae3","resolution":{"observed_at":"2026-08-06T16:54:32.235648Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-06T16:54:29.696081Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.696081Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:caf1d26c9b3c97d2e6a612b8aa417d3d31fe0c454e3b14e3fa044e1fa30c225f","observation_id":"2bc21424-e7e7-4801-a534-5b396e0b711b","resolution":{"observed_at":"2026-08-06T16:54:29.696081Z","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":"2026-08-06T16:54:31.870595Z","title":"Zhang, J","venue":null,"work_id":"4c70614a-abb9-4434-b99d-a3dbcb1b711b","year":2025},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.795757Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:6978f522dca830192342f9fc22cf464b6d8bcccf9d1a9dfa7520dc6c19902f87","observation_id":"084373b5-9dd5-484a-83a1-af8df1f99c39","resolution":{"observed_at":"2026-08-06T16:54:31.992601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:31.670526Z","title":"Zheng, R","venue":null,"work_id":"e905fa55-c1f7-4cd3-8cb1-f7bb71aedb56","year":2023},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.858110Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:77dfc3b8ec3c647e9062bd2843ff5b9646204b52252ef249c672e81bccbdfc83","observation_id":"f2bba78c-4207-4016-9753-2eb7f1726fbd","resolution":{"observed_at":"2026-08-06T16:54:31.768412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:29.948653Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:29.948653Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:8a8108919642e993a796b5bb5f73cb6b1b2d64849bc416bc6d15231cef5410d2","observation_id":"9c06241e-4886-497f-84d2-fc965b9a1d10","resolution":{"observed_at":"2026-08-06T16:54:29.948653Z","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":"2026-08-06T16:54:30.010395Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:30.010395Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:ddd197f3fe535e75c7599093eb4dcbb5b36a0e25df9d1bd5e2abe629cf2fe45e","observation_id":"b481338f-a42a-4174-bbf0-520c1966a5cf","resolution":{"observed_at":"2026-08-06T16:54:30.010395Z","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":"2026-08-06T16:54:30.093229Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:30.093229Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:6e712613214458b5f94f2ce712e7a81f40cc61a2e648a8a912e97502c800b72e","observation_id":"94a6de6b-3e78-4e1b-a403-c3d2d0d7bc06","resolution":{"observed_at":"2026-08-06T16:54:30.093229Z","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":"2026-08-06T16:54:31.356160Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":"6e81c5be-466b-4a4b-880e-af4305653053","year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:30.191849Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:10f3a9a05b53f90fd66bad4f0e953ac437101abbb842e63ce3d43ddbb5d6e833","observation_id":"9469ca55-e591-41bf-a5f4-e2a03710a7bd","resolution":{"observed_at":"2026-08-06T16:54:31.515414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T16:54:30.254554Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:30.254554Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:b4ae1ca7c8b86370037cbd70d37ee2b64d2e5fce932216e40eba2ae8cf119605","observation_id":"37ab4773-d7ea-4833-8d47-43ced6d387b7","resolution":{"observed_at":"2026-08-06T16:54:30.254554Z","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":"1003.80859","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:54:30.891098Z","title":"sibling model","venue":null,"work_id":"c0188b87-a226-42ba-9284-5d9d3701fe29","year":2025},"citing_paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T16:54:30.317387Z"},"links":{"citing_paper":"/paper/2507.12612"},"observation_digest":"sha256:50e1eb135db0401ee7ddf96b33d26991cbc9dbe287a1311e9f0a20d372bdffbe","observation_id":"4fa80da5-750e-4a4b-b253-ddfeecd5eea5","resolution":{"observed_at":"2026-08-06T16:54:30.971305Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.12612","last_updated":"2026-06-06T03:35:14Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T04:01:47.203194Z","submitted_at":"2025-07-16T20:14:55Z","title":"Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":27,"verified_exact":1,"verified_fuzzy":14},"total_outbound_references":43},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.12612."}