{"as_of":"2026-08-20T10:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d9a12c2c6778000a4466bac93554ea7423c02408fa857d0878007c2376acbbf","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:28:53.436332Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2505.09792/citation-record","integrity":"/paper/2505.09792/integrity","json":"/paper/2505.09792/citation-record.json","paper":"/paper/2505.09792"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:28:53.323747Z","title":"URL: \" 'urlintro :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.323747Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:b41131ffb3083fbda152b09b3205829fa7ce858a23fab7e6445af961bf691647","observation_id":"942b229f-67ba-4860-9c3f-06b74fc66dd7","resolution":{"observed_at":"2026-08-15T21:28:53.323747Z","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":"2026-08-15T21:28:53.327814Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.327814Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:7da013390791f1c80e16244bcd26ef00149026f9c197fc110d7c5299526082b4","observation_id":"967d36a6-8686-42d3-b335-20b50a78fdc1","resolution":{"observed_at":"2026-08-15T21:28:53.327814Z","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":"2026-08-15T21:28:53.331152Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.331152Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:c54a4f65915dfa0cf99c5c1cce9d31e52599c4f2bde264267ccdb20c7db8b7cf","observation_id":"50287575-07f2-4ae9-97cc-8e6a6faa4846","resolution":{"observed_at":"2026-08-15T21:28:53.331152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-15T21:28:53.334014Z","title":"Peters, and Arman Cohan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.334014Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:55b3026ed9b4aa045e5e2373f670ae06d2a2b94d7219c317533699bdafa8a88e","observation_id":"930e2490-7463-41fc-bc55-2b3dcbbe7581","resolution":{"observed_at":"2026-08-15T21:28:53.334014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14119","last_updated":"2021-07-29T15:02:43Z","snapshot_observed_at":"2026-07-06T09:59:50.601893Z","submitted_at":"2020-09-29T16:08:19Z","title":"Asymmetric Loss For Multi-Label Classification","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14119","snapshot_observed_at":"2026-08-15T21:28:53.338144Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.338144Z"},"links":{"cited_paper":"/paper/2009.14119","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:d3e10c8e9df413dcad9bac5df61977266058885d62f623427a4aa9ac033d4088","observation_id":"0056cfe8-4f4d-4559-8445-e208a894e660","resolution":{"observed_at":"2026-08-15T21:28:53.338144Z","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":"2026-08-15T21:28:53.698716Z","title":null,"venue":null,"work_id":"fa406bd3-0205-4799-a537-1a2b8afededf","year":2011},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.341629Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:ea65780dc2830a249946eeba1a07ec4c62a0bc1fb32d2fd4761582af3db0dabf","observation_id":"cdde8361-29ca-47a2-962e-f6fc18586ebf","resolution":{"observed_at":"2026-08-15T21:28:53.702661Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.688516Z","title":null,"venue":null,"work_id":"9ce2aa1a-9f5a-412c-aa6e-a294fa450db6","year":2012},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.345855Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:87abc895326240a9fb67e4ff1b413c0d3009c5f903aef9246aff26128f52fcf8","observation_id":"78da83e2-5d86-41fe-aec5-51dab1c655e9","resolution":{"observed_at":"2026-08-15T21:28:53.691567Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.05847","last_updated":"2021-11-24T22:40:27Z","snapshot_observed_at":"2026-08-17T12:07:03.336063Z","submitted_at":"2021-07-13T04:55:47Z","title":"Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.05847","snapshot_observed_at":"2026-08-15T21:28:53.349295Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.349295Z"},"links":{"cited_paper":"/paper/2107.05847","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:3c27ce5f98eaa3581d13229edf073a117aaaf3899226df246cb98f2204d5ceb5","observation_id":"d02e3461-2bae-490d-bbc2-acb349e5f022","resolution":{"observed_at":"2026-08-15T21:28:53.349295Z","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":"2026-08-15T21:28:53.679593Z","title":null,"venue":null,"work_id":"01851fc0-bcd6-45f9-b156-9609324ff7ab","year":2024},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.352625Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:9a8665f41f8e4916933b0f5a3e0c41f9d3f48308f4ebe66538f4a006114729a3","observation_id":"fe3f46ec-3112-47c2-b46a-b65a34431f18","resolution":{"observed_at":"2026-08-15T21:28:53.682903Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.355040Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.355040Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:9bbfbf6fc6ce0645a51c00d42eb910b3864299a6f700f95114f18e8c670f72ba","observation_id":"6272e715-fdf8-4ec8-9c0f-59b79be11950","resolution":{"observed_at":"2026-08-15T21:28:53.355040Z","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":"10.18653/v1/2021.eacl-main.319","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:28:53.465248Z","title":null,"venue":null,"work_id":"95331d1e-23c2-40e0-86cf-7a9da71589f1","year":2021},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.357423Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:863983d6fcb77f4ff1c027c27d0953a3c1dbef424fd958ef3ef6fd6ca3b6fc27","observation_id":"e5ddedc9-ed6c-4149-b70f-0df3efcf93ff","resolution":{"observed_at":"2026-08-15T21:28:53.469388Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.667499Z","title":null,"venue":null,"work_id":"b3705c40-8e40-4b0c-8f95-69121d5c10e2","year":2022},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.359908Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:ca0d0a68baca375dbaf91c779a13fa38f6bdb9e3c1d12dec7990ff427620b5ed","observation_id":"ef4ad4b0-a654-41de-9548-367f8c7260ec","resolution":{"observed_at":"2026-08-15T21:28:53.670453Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.659619Z","title":null,"venue":null,"work_id":"f4d68035-8be5-40d8-9056-fb1dc30decfd","year":2007},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.362747Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:2d2930b347d6cb33493e2eb6a47a600802f7ef5fef7f5841f8cfc4212d590b2f","observation_id":"8ad57752-9eb9-4ecb-9d70-87b9b8cb069c","resolution":{"observed_at":"2026-08-15T21:28:53.662680Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02811","last_updated":"2018-07-08T13:06:26Z","snapshot_observed_at":"2026-08-18T00:58:48.914606Z","submitted_at":"2018-07-08T13:06:26Z","title":"A Tutorial on Bayesian Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.02811","snapshot_observed_at":"2026-08-15T21:28:53.366890Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.366890Z"},"links":{"cited_paper":"/paper/1807.02811","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:7ae241c9ac4d02014109dde7608f176e910a52ca51bbc4fd208a9a6b7f968f6e","observation_id":"abc2f37c-e475-4205-8504-8b69b9aa20e5","resolution":{"observed_at":"2026-08-15T21:28:53.366890Z","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":"2026-08-15T21:28:53.650272Z","title":"Flach, and C","venue":null,"work_id":"3392b0e0-b7c1-4ffc-bdcc-9ccd277f8ab3","year":2012},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.370768Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:4a93df865a959df955082a694486abb0722d10251e38f644d523708c7142f66a","observation_id":"ec440239-416b-48e8-8735-181bad84a94c","resolution":{"observed_at":"2026-08-15T21:28:53.653906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.373757Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.373757Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:058ad114feb0e1599cf229a92b113cfecacddd3330758896229c1841e1ead833","observation_id":"c3b5fcf4-a197-4098-9b9e-48cf4303716d","resolution":{"observed_at":"2026-08-15T21:28:53.373757Z","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":"2026-08-15T21:28:53.377621Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.377621Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:926844221363148dd4e90467defe52e0a1129fc1fcde27ec69f9c3d8fae8705e","observation_id":"b884f21f-cfa2-42f1-bd81-07b759df60ca","resolution":{"observed_at":"2026-08-15T21:28:53.377621Z","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":"2026-08-15T21:28:53.638519Z","title":null,"venue":null,"work_id":"355866a2-9efe-44f9-9faa-02891451a83d","year":2016},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.380243Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:9778f866128c26759b3b72d17116bde8764245bf0c715579ad99a8f25cdb5ae7","observation_id":"b9d47e3e-20db-4a57-bbf4-3c85f1024a49","resolution":{"observed_at":"2026-08-15T21:28:53.641132Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.630664Z","title":null,"venue":null,"work_id":"c5bd332e-0904-4cb2-a05a-bb58b85dfef2","year":2017},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.383008Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:f48efa15c445e216863981009f7c298efc66e0698b82087388b473202aa41ea7","observation_id":"14eae53c-ac24-4310-b67a-e1a6d7a35495","resolution":{"observed_at":"2026-08-15T21:28:53.633361Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.622362Z","title":null,"venue":null,"work_id":"dd6fbda9-de13-4d65-9ad4-2cc51b325ff6","year":2018},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.386112Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:88b7e3ffd2b7dcc836d52a43e61b7108b7c0da03631347c2037dc62ea40160cb","observation_id":"4ec7a005-82ee-48ef-b55f-2d1eaaee073d","resolution":{"observed_at":"2026-08-15T21:28:53.625543Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.06560","last_updated":"2018-06-18T23:01:43Z","snapshot_observed_at":"2026-08-19T22:12:57.636119Z","submitted_at":"2016-03-21T19:51:04Z","title":"Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.06560","snapshot_observed_at":"2026-08-15T21:28:53.389487Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.389487Z"},"links":{"cited_paper":"/paper/1603.06560","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:26d2699eccc2d4826bce602f7eb29028bf1df1e12ba6849fbf77b0f10a7e9b3b","observation_id":"44a26f4e-a867-4a11-81aa-3ef2c7cc62b3","resolution":{"observed_at":"2026-08-15T21:28:53.389487Z","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":"2026-08-15T21:28:53.613878Z","title":"Girshick, Kaiming He, and Piotr Doll \\'a r","venue":null,"work_id":"9e9267b0-8c63-48b3-9758-29a33f584e9b","year":2020},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.393049Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:8550003f4fa103781079882ea716671171c778bc0d5b77b67a0b8f9ca25262b4","observation_id":"9e3f5f81-ec01-4bf8-9580-306c897de73b","resolution":{"observed_at":"2026-08-15T21:28:53.617617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.606243Z","title":null,"venue":null,"work_id":"3d891c6c-37a8-43e9-80d3-649af9149325","year":2016},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.396708Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:77c5245b8f4b437b4bec83b49e044398cc7927588500c76300b53357a70efdcb","observation_id":"4938d2e9-80b5-4ce2-9282-8e544a91dea9","resolution":{"observed_at":"2026-08-15T21:28:53.608762Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.597015Z","title":null,"venue":null,"work_id":"92588687-827d-4f0b-abf6-7672a73cf21b","year":1960},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.399727Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:ecab21c10b98d340b9895aeda7e0baebda635ab4d2cc6608cae8ae5da4a71c49","observation_id":"e7b9a49a-78f9-4af4-a380-fec6ad46cb79","resolution":{"observed_at":"2026-08-15T21:28:53.599862Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.588121Z","title":null,"venue":null,"work_id":"427f89e9-a41e-46c4-a319-4e79045c548d","year":2022},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.403780Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:89d2a9703db31cbd988c0f5ea474e4fe2cf5f73b0fab86781f94618182c45ce4","observation_id":"15e1d385-04cf-4271-853e-2528118dd820","resolution":{"observed_at":"2026-08-15T21:28:53.591540Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.580105Z","title":null,"venue":null,"work_id":"bfadca3f-fdfa-4f54-b047-f1e72fb16ef6","year":1989},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.407808Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:2adf877b47a13c6685ca259720f647f654fea01a7149012a7e8b7a106db89caa","observation_id":"d92b7009-4f0a-416a-8f31-747916326ef0","resolution":{"observed_at":"2026-08-15T21:28:53.583364Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.571447Z","title":null,"venue":null,"work_id":"ad594d5b-b2f0-42ed-856b-1450e1440692","year":2016},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.410399Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:d53ea9dde99346a1321735883e90ed0fc943d89bc2553aeba36036c25614a0a1","observation_id":"d46f6eb4-0fb0-43e9-bb09-ff82187a0b72","resolution":{"observed_at":"2026-08-15T21:28:53.574570Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.563569Z","title":null,"venue":null,"work_id":"64514e60-4f0f-4300-bfdc-8b4108f9ac25","year":2006},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.413572Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:8fe8bc7a19f9d91c9882276f4b0de785867915a4f0f7e19561b08cf2692d84b8","observation_id":"b35f7673-4d94-40ed-bcb8-24ca66091eb7","resolution":{"observed_at":"2026-08-15T21:28:53.566526Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1206.2944","last_updated":"2012-08-29T06:36:23Z","snapshot_observed_at":"2026-08-18T04:25:34.706968Z","submitted_at":"2012-06-13T21:23:15Z","title":"Practical Bayesian Optimization of Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.2944","snapshot_observed_at":"2026-08-15T21:28:53.416865Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.416865Z"},"links":{"cited_paper":"/paper/1206.2944","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:6cb296dc6a4fa96ed3d33ab69e9ffa63378092d5b9554192eda47a25e81490bf","observation_id":"fe32818c-e7ef-45de-8ab9-86e91461629f","resolution":{"observed_at":"2026-08-15T21:28:53.416865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12696","last_updated":"2023-06-16T05:44:07Z","snapshot_observed_at":"2026-08-18T21:38:05.188263Z","submitted_at":"2022-05-25T11:54:48Z","title":"Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12696","snapshot_observed_at":"2026-08-15T21:28:53.419647Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.419647Z"},"links":{"cited_paper":"/paper/2205.12696","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:ccf98186c4180c64b985d759b53c6293ffb921a303da58c0f96b26ffcaa342ab","observation_id":"1a5f0b6f-fca6-4915-89e6-a727c8b0e616","resolution":{"observed_at":"2026-08-15T21:28:53.419647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11127","last_updated":"2026-05-31T18:23:52Z","snapshot_observed_at":"2026-08-16T15:38:38.818961Z","submitted_at":"2023-04-21T17:02:38Z","title":"Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11127","snapshot_observed_at":"2026-08-15T21:28:53.422328Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.422328Z"},"links":{"cited_paper":"/paper/2304.11127","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:bbd82e99e4f1081fff6a5fecba458fa247e862bdb72c8762a68ec5310ae24b21","observation_id":"d3d0d30d-d453-4b11-a684-5a599837728e","resolution":{"observed_at":"2026-08-15T21:28:53.422328Z","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":"2026-08-15T21:28:53.425981Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.425981Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:61c52d8174ce5196f58e47e98171ff8c0544e1eaa90f2503a50ac1cc305013da","observation_id":"742d27f7-3af0-4a09-bff4-be9cb1e6eed3","resolution":{"observed_at":"2026-08-15T21:28:53.425981Z","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":"2026-08-15T21:28:53.555120Z","title":null,"venue":null,"work_id":"c605e953-908a-4e03-810f-716060d03adf","year":2001},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.428683Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:6c5e63d956e7ab3ea01e2ab6f52b8faffa378df1a131fe7d5e1b7d8008058636","observation_id":"c185024c-e3cd-45d3-ba73-b97edb7226eb","resolution":{"observed_at":"2026-08-15T21:28:53.558644Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06127","last_updated":"2019-08-09T07:42:54Z","snapshot_observed_at":"2026-08-17T17:38:25.588094Z","submitted_at":"2019-06-14T11:12:20Z","title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06127","snapshot_observed_at":"2026-08-15T21:28:53.431125Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.431125Z"},"links":{"cited_paper":"/paper/1906.06127","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:e4b66cabae10935971a8d19d38f60ffcd064aed9f829f0a48ffd1dbb248a473d","observation_id":"332fc44d-8f3b-49fb-9482-8baecb9584a6","resolution":{"observed_at":"2026-08-15T21:28:53.431125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03845","last_updated":"2023-09-08T00:23:45Z","snapshot_observed_at":"2026-08-16T15:56:55.697737Z","submitted_at":"2023-02-08T02:38:26Z","title":"Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset","version":2},"cited_work":{"arxiv_id":"2302.03845","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.03845","snapshot_observed_at":"2026-08-15T21:28:53.478903Z","title":"Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset","venue":"cs.LG","work_id":"2d56efec-12d2-4490-9206-aa4774ca1f57","year":2023},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.433670Z"},"links":{"cited_paper":"/paper/2302.03845","citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:e0963d43a36313c572805671343e69ee18426af38a9b79378ae4053c0adeff02","observation_id":"e05564d2-7d89-4987-b84d-7c89c4ab4571","resolution":{"observed_at":"2026-08-15T21:28:53.483955Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T21:28:53.436332Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T21:28:53.436332Z"},"links":{"citing_paper":"/paper/2505.09792"},"observation_digest":"sha256:b14918ed9f96ec2a01d2ea60a01aca8398a15280cf89797aeba300094883c406","observation_id":"6a6c2fa1-cc0b-46a5-8442-68208fb10292","resolution":{"observed_at":"2026-08-15T21:28:53.436332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.09792","last_updated":"2025-05-14T20:38:44Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-20T09:33:00.875046Z","submitted_at":"2025-05-14T20:38:44Z","title":"Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":2,"verified_fuzzy":2},"total_outbound_references":36},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.09792."}