{"as_of":"2026-08-05T09:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bd866f572eade173f96bc78bd979e89d5aa189e55bb1104334448a173da58b4d","coverage":[{"denominator":118,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T22:06:48.152555Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:56:35.079982Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-04T08:49:42.329847Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"cited_work":{"arxiv_id":"2604.01328","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.01328","snapshot_observed_at":"2026-07-04T08:49:42.329847Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","venue":"cs.LG","work_id":"cbe24980-6f8c-4c7f-9913-10be1de66587","year":2026},"citing_paper":{"arxiv_id":"2606.22425","last_updated":"2026-06-21T10:24:27Z","snapshot_observed_at":"2026-08-05T01:45:17.050239Z","submitted_at":"2026-06-21T10:24:27Z","title":"SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T10:56:35.079982Z"},"links":{"cited_paper":"/paper/2604.01328","citing_paper":"/paper/2606.22425"},"observation_digest":"sha256:3d0513362eadcdea14ccaaf9879e5dc32c5df375830b721ab5a7e96518c4b66b","observation_id":"8a1b57c0-0754-4601-af0c-6146a66e637f","resolution":{"observed_at":"2026-07-04T08:49:42.331192Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2604.01328/citation-record","integrity":"/paper/2604.01328/integrity","json":"/paper/2604.01328/citation-record.json","paper":"/paper/2604.01328"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Looping in the Human: Collaborative and Explainable Bayesian Optimization","venue":null,"work_id":"c8674ae5-adf2-4a8e-907a-6b91d768d0c2","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:db3f6850d5d8ebf7a03a1bb38a473862dafa2555aca7ff060cee2ff9760e3120","observation_id":"c1212e75-ebdc-4dfb-9860-df6aadd5e475","resolution":{"observed_at":"2026-05-13T22:08:21.081711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Using Conﬁdence Bounds for Exploitation-Exploration Trade-offs","venue":null,"work_id":"0db0a9f3-0d44-4203-9d5e-35797d482b78","year":2002},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:eb7fba688abd6c3e3b5084eef707cef33a055ddfeb9e90e27745cbcfb12e87eb","observation_id":"fc7f92d7-7d77-4caa-9c86-15e2030b3e74","resolution":{"observed_at":"2026-05-13T22:08:21.086865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"BoTorch: A framework for efﬁcient Monte-Carlo Bayesian optimization","venue":null,"work_id":"0d414729-3b90-4677-8d3c-6a99e786a53b","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:db7a925afea4b28deec8c1bea68e7d6e85ffde0850760924cc95a3fb310c4197","observation_id":"2f18c643-09a5-4d32-8213-f0713b998ff7","resolution":{"observed_at":"2026-05-13T22:08:21.095057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Max-V alue Entropy Search for Multi-Objective Bayesian Optimization","venue":null,"work_id":"19d78849-6149-4414-ac7a-8ab45f863513","year":2019},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:f7d4b705606e4446cefac252d23657338a95acb9e2fd500d257f497860f80f52","observation_id":"876c74d6-f043-415a-9770-f5d1160b7c1e","resolution":{"observed_at":"2026-05-13T22:08:21.078311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.25080/majora-8b375195-003","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hyperopt: A Python Library for Optimizing the Hyperparam- eters of Machine Learning Algorithms","venue":"Proceedings of the Python in Science Conferences","work_id":"64b6f477-996c-4588-b1da-68d498d52adb","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:601997923a19a5c0b7b38a720417facad84bac13e3f79c74dc0ff9e55e7b20a7","observation_id":"87c17720-782c-4485-b099-611c0ef99c3e","resolution":{"observed_at":"2026-05-13T22:08:20.218064Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Algorithms for Hyper-Parameter Optimization","venue":null,"work_id":"8c227c79-f460-47bf-80fb-7eda36b20876","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b03d3b970fba5a8d2f9bfbcd064a9031036cd5eeb05dd6182076bb196b8350d0","observation_id":"da40e0ca-002e-421a-857b-5928bc4b326e","resolution":{"observed_at":"2026-05-13T22:08:21.068067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/nchem.1243","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"R., Paolini, G","venue":"Nature Chemistry","work_id":"c299a91c-616f-4a90-8bb6-bf819da740a4","year":2012},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:68b83e7fc9e2279fc26f538ae76562833aee988bf807b8368a352b74d7967c0d","observation_id":"889d0e74-b354-4589-90fc-bdfd45481db1","resolution":{"observed_at":"2026-05-13T22:08:20.242145Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-12T18:20:25.776224+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T18:20:25.776224+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/j.2517-6161","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T16:15:48.805642Z","title":"1974.tb00999.x","venue":null,"work_id":"a9ec2119-fb12-4ff9-ac4a-7c70a437ffee","year":1974},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:ac7dc52c38fae5972057c9487e5de913f7d7101fba67921be35bdfe3f74f4867","observation_id":"687638a8-b476-4d4b-a7e9-f6802324fb0e","resolution":{"observed_at":"2026-05-13T22:08:20.254419Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-020-2442-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A mobile robotic chemis t","venue":"Nature","work_id":"1d4d7037-1937-4300-9c78-0392cf78bb0f","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9bb73393132c89c96446373cc975cc0266dc6bb6f35c36da9e1a264fe32a5839","observation_id":"4c170d47-d7c2-49ce-84d2-0414c657e418","resolution":{"observed_at":"2026-05-13T22:08:20.260746Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08757","last_updated":"2021-05-24T08:52:07Z","snapshot_observed_at":"2026-07-06T09:48:41.077000Z","submitted_at":"2020-08-20T03:48:14Z","title":"On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization","version":2},"cited_work":{"arxiv_id":"2008.08757","doi":"10.48550/arxiv.2008.08757","metadata_source":"arxiv_reference","pith_arxiv_id":"2008.08757","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimiza- tion","venue":"arXiv (Cornell University)","work_id":"78618fa5-f0e4-45cc-93bd-d004ad36d1d9","year":2021},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2008.08757","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:975c359cbc4015fc240fc32d057c15d46c1cec53b6b28e2a17a6bc8a7de75106","observation_id":"96a2b6f0-60ef-4e9d-a49b-5614a765f074","resolution":{"observed_at":"2026-05-13T22:08:20.272080Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys","venue":null,"work_id":"3f04aff2-8be1-4c4c-85ff-021515bbf6ef","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b095d760ccd7c1ac114fbf62f22750ef6f7903e9eef51705c7732cc44d0b2d1e","observation_id":"e641d6a2-bdb0-4084-bd80-3fdb26fdb202","resolution":{"observed_at":"2026-05-13T22:08:21.085190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"cited_work":{"arxiv_id":"2601.06820","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.06820","snapshot_observed_at":"2026-07-10T01:18:57.261370Z","title":"Bgolearn: a Uniﬁed Bayesian Optimization Framework for Accelerating Materials Discovery","venue":null,"work_id":"186bd267-8d4d-47f7-b90f-f8cedd0959b2","year":2026},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2601.06820","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:7370cca1ccf865cff273ced429737ccfc49b654a13af9021a769be469b933793","observation_id":"db8f16ab-4910-484d-aaed-4071e229af09","resolution":{"observed_at":"2026-07-10T01:18:57.261370Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Spatial-adaptive active learning identiﬁes ultra-durable and highly active catalysts for acidic oxygen evolution reaction","venue":null,"work_id":"127ed427-1f4e-4a7a-9633-d5be42c3e383","year":2025},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:a716e5693ad9d80bf7555cde70536fda67813797b6a03834d182d487b893f0b7","observation_id":"a2dad229-87b1-49de-a10e-d083fa47eb6f","resolution":{"observed_at":"2026-05-13T22:08:21.139521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05250","last_updated":"2024-12-06T12:40:53Z","snapshot_observed_at":"2026-07-06T18:27:24.526210Z","submitted_at":"2024-06-07T20:22:36Z","title":"LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation","version":3},"cited_work":{"arxiv_id":"2406.05250","doi":"10.48550/arxiv.2406.05250","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.05250","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LLM-Enhanced Bayesian Optimization for Efﬁcient Analog Layout Constraint Generation","venue":"arXiv (Cornell University)","work_id":"c8afa8df-d8c0-4b03-ae2f-35107ddb9938","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2406.05250","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:757b1e96944028a47631b05b7164bde0313b63cded89081bf7d0599d63b255a3","observation_id":"6dbcef0a-7249-4c90-89ad-01680d9e0b5f","resolution":{"observed_at":"2026-05-13T22:08:20.194168Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection","venue":null,"work_id":"5c90cb47-fb39-496a-9cdf-a7dde33a5a63","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b4711c95eb46fa61bc7d4a52c689e50448c166997ad3fa076a88420f1f080303","observation_id":"939bf5a7-c681-4cb1-9537-794895a6f9cc","resolution":{"observed_at":"2026-05-13T22:08:21.130349Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2501.16224","doi":"10.48550/arxiv.2501.16224","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Language-Based Bayesian Optimization Research Assistant (BORA)","venue":"ArXiv.org","work_id":"67c874bc-57b6-4afe-bdcb-59cd3daa8661","year":2025},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:3f60d3dc666acd68a1fffa69a14db87f204155ae151bfc80983e4bd80ccd3302","observation_id":"78e9c683-3f15-4dbe-9a82-60e12db3cec7","resolution":{"observed_at":"2026-05-13T22:08:20.263839Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11229-017-1544-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"On Serendipity in Science: Discovery at the Intersection of Chance and Wisdom","venue":"Synthese","work_id":"2731176f-0726-440f-852f-5dabe88289c7","year":2019},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:edcc806d4a525646ac633547190e470d05e3b742c08bbc3fb2184a660eae96b0","observation_id":"4ca63884-c1e9-4f58-ad1d-0e145ed2fd98","resolution":{"observed_at":"2026-05-13T22:08:20.209478Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.03826","last_updated":"2022-05-25T14:33:04Z","snapshot_observed_at":"2026-07-06T10:21:26.704390Z","submitted_at":"2020-12-07T16:21:12Z","title":"HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation","version":6},"cited_work":{"arxiv_id":"2012.03826","doi":"10.48550/arxiv.2012.03826","metadata_source":"arxiv_reference","pith_arxiv_id":"2012.03826","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Cowen-Rivers et al","venue":"arXiv (Cornell University)","work_id":"cbc716a8-bd70-4171-8fee-f6fc3a9bd03f","year":2012},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2012.03826","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:20b6ad5de796f4e3210fc24691ab502df657c501384264b8d419ff40fde06d3e","observation_id":"60f94775-d8c3-4cfe-9be7-6c2576df74c9","resolution":{"observed_at":"2026-05-13T22:08:20.207267Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization Meets Bayesian Optimal Stopping","venue":null,"work_id":"bb438243-2792-4c76-b62e-00b23a13d067","year":2019},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:230740aa3c60a992527e26e7d9b0a023dbbacc67dd5480b8a846864752e986c4","observation_id":"ae146402-24de-4b29-85cc-6a4b6632a142","resolution":{"observed_at":"2026-05-13T22:08:21.099948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8912.305266","doi":"10.1145/3038912.3052660","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BOA T: Building Auto-Tuners with Structured Bayesian Optimization","venue":"arXiv (Cornell University)","work_id":"ec5f0c1a-1cb7-4550-968b-f7a5f0f94027","year":2017},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:7cdbdc8826a69cbc6bffa84d80ad5cc74aead36b062445f36fc9646171b9dc0c","observation_id":"7628351f-e5ea-4550-8005-5373abec2c2d","resolution":{"observed_at":"2026-05-13T22:08:20.151214Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07549","last_updated":"2022-06-03T17:43:39Z","snapshot_observed_at":"2026-07-06T12:38:05.979914Z","submitted_at":"2022-02-15T16:33:48Z","title":"Robust Multi-Objective Bayesian Optimization Under Input Noise","version":4},"cited_work":{"arxiv_id":"2202.07549","doi":"10.48550/arxiv.2202.07549","metadata_source":"arxiv_reference","pith_arxiv_id":"2202.07549","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Daulton, S","venue":"arXiv (Cornell University)","work_id":"46560303-2376-491a-854d-5edb7fd0e24d","year":2022},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2202.07549","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:153953674af0bdee38cf79c6287b990edaeef92232f2a957e423e2832a57e2f0","observation_id":"df928731-74bd-408a-9f3b-959f45248a89","resolution":{"observed_at":"2026-05-13T22:08:20.269080Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"High-Dimensional Gaussian Process Bandits","venue":null,"work_id":"c700c364-6d2f-4ea3-a734-ea3e8e2c1ec0","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:05b715321d6c92f2b94d2329afc804a62224558d77a249cb1fc19897a70fb362","observation_id":"1bd28fdc-75b7-4e3e-8285-410d996b6ee9","resolution":{"observed_at":"2026-05-13T22:08:21.057026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1302.4922","last_updated":"2013-05-13T13:10:31Z","snapshot_observed_at":"2026-07-06T03:06:51.745085Z","submitted_at":"2013-02-20T14:53:13Z","title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","version":4},"cited_work":{"arxiv_id":"1302.4922","doi":"10.48550/arxiv.1302.4922","metadata_source":"pith","pith_arxiv_id":"1302.4922","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","venue":"stat.ML","work_id":"e287450c-7711-4601-82ae-3f87ac95537b","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/1302.4922","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:fb1a138776731ade68cabb670008473715c3fc0470543e7dd65e5bdfff6cea96","observation_id":"7556d0ec-6faf-4311-bbb3-648e4bf89f3b","resolution":{"observed_at":"2026-05-13T22:08:20.200666Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Scalable Global Optimization via Local Bayesian Optimization","venue":null,"work_id":"1bc82d65-fb88-47cb-935d-7ba74c8cb820","year":2019},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:02aa16f4b8fd61196e2016cf456138312c0a6ecf932c972b18290b4d36f25e9b","observation_id":"211322b2-28b5-4cc2-bc31-249e35d7b915","resolution":{"observed_at":"2026-05-13T22:08:21.079984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02811","last_updated":"2018-07-08T13:06:26Z","snapshot_observed_at":"2026-07-06T06:48:58.340685Z","submitted_at":"2018-07-08T13:06:26Z","title":"A Tutorial on Bayesian Optimization","version":1},"cited_work":{"arxiv_id":"1807.02811","doi":"10.48550/arxiv.1807.02811","metadata_source":"pith","pith_arxiv_id":"1807.02811","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A Tutorial on Bayesian Optimization","venue":"stat.ML","work_id":"5a5d3b4a-33eb-4952-9f9b-7687b04e6323","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/1807.02811","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:27f0dd9e36a671e1a9ae0dfd7b8529e04826df371ab623805e71425d79d5827e","observation_id":"dd0a28d3-f4aa-4ccd-8951-5068412ae9cd","resolution":{"observed_at":"2026-05-13T22:08:20.188488Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-20T09:22:45.243908+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T09:22:45.243908+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.12411","last_updated":"2022-10-09T18:15:38Z","snapshot_observed_at":"2026-07-06T13:24:27.607054Z","submitted_at":"2022-06-22T20:36:49Z","title":"Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization","version":2},"cited_work":{"arxiv_id":"2206.12411","doi":"10.48550/arxiv.2206.12411","metadata_source":"arxiv_reference","pith_arxiv_id":"2206.12411","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sample Efﬁciency Matters: A Benchmark for Practical Molecular Optimization","venue":"arXiv (Cornell University)","work_id":"e2133642-6218-42d6-9b59-41b9bd7c95b3","year":2022},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2206.12411","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9b59702bcd38a4fb8f5fe4b01cfbf19f928017bec46bfc0d183f471b3add75e4","observation_id":"61f317a6-307a-4efe-80e6-378570164589","resolution":{"observed_at":"2026-05-13T22:08:20.162465Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization with Inequality Constraints","venue":null,"work_id":"5a6eead7-9631-4749-b03b-ae6d0807a157","year":2014},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:e8bc77d3c0789c4ae66083db17b77829c5c634e603ccb6503b6f81ec050b4114","observation_id":"3ef5627d-d351-4785-8079-b79a85832fbc","resolution":{"observed_at":"2026-05-13T22:08:21.050552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelera- tion","venue":null,"work_id":"3a5f625c-118c-4823-a4e3-cc981a689f37","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:ab65035ce8d04adc73fc30f4708cede7c131be848eed988d4dbf6a94170a304c","observation_id":"61bb4a81-3421-4093-a3f7-def14e3898c6","resolution":{"observed_at":"2026-05-13T22:08:21.048891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization","venue":null,"work_id":"39cbec55-2b57-4ea8-b9f0-c11ef46e2a69","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:082728f3bdd58b5b3cc9d4c6bcd78d01b6a1ef1451396c9dcaa8f8f0cc33ef52","observation_id":"088446d6-d36b-496b-8236-1bac57b9a43d","resolution":{"observed_at":"2026-05-13T22:08:21.052096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Kriging Is Well-Suited to Parallelize Optimiza- tion","venue":null,"work_id":"b51bd0ff-b14f-42b6-a73c-b97d3c243038","year":2010},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:56f02730bac911a3150d4f50b04c7ea1b4c9147c7ab3e3ab9154e2e09afe3af6","observation_id":"71c20a54-3a3c-4fc6-aacd-74f01a9476d1","resolution":{"observed_at":"2026-05-13T22:08:21.062406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A Multi-points Criterion for Deterministic Par- allel Global Optimization Based on Gaussian Processes","venue":null,"work_id":"cf2bc760-ceed-41b2-bc93-82fef651fe14","year":2008},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:5727a36d4ef71c10236bdbe6bf047b484d819e0a7cdc13dac89223b55bf58902","observation_id":"e2cf5317-6ddb-4dc8-95cd-9d385f582aeb","resolution":{"observed_at":"2026-05-13T22:08:21.073210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acsmedchemlett.8b00359","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Decision Making in Medicinal Chemistry: The Power of Our Intuition","venue":"ACS Medicinal Chemistry Letters","work_id":"45c9de8d-6335-4591-b7ef-464ab7f571a4","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:bdddbeee3fbf9ba090c454feaf7cebd013ffd4e9b43e1fc23b72f1a0307c3342","observation_id":"8c044792-f569-419a-855b-e870161c06ee","resolution":{"observed_at":"2026-05-13T22:08:20.266002Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"GLASSES: Relieving The Myopia Of Bayesian Opti- misation","venue":null,"work_id":"dd7d1ae2-4d68-4bf3-9723-87f63937fb9c","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:7a1181fde4d8212d13d8ab0319c853305c02faaecb82eaeb86f3532fc8b76b4e","observation_id":"08161958-0f38-48da-bcd2-ea5527e8b10d","resolution":{"observed_at":"2026-05-13T22:08:21.060746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Batch Bayesian Optimization via Local Penalization","venue":null,"work_id":"5349d317-42e1-43d9-945e-728b8b0c47cd","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:02ac4e5ee690cb963707e31e2c6645a69678c685b796c54b60268e5a75967abd","observation_id":"ed229490-65c5-4139-a80c-f97d5e7fee82","resolution":{"observed_at":"2026-05-13T22:08:21.053669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04739","last_updated":"2024-11-04T10:02:09Z","snapshot_observed_at":"2026-07-06T18:26:58.940425Z","submitted_at":"2024-06-07T08:39:40Z","title":"A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences","version":2},"cited_work":{"arxiv_id":"2406.04739","doi":"10.48550/arxiv.2406.04739","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.04739","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A Survey and Benchmark of High-Dimensional Bayesian Optimization of Dis- crete Sequences","venue":null,"work_id":"667a09ef-8507-4e0c-8efd-a4c0c6683b2c","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2406.04739","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:bcf097637e520437e29821dd0c83f13208b8ba9d248f3b6ea2cab742816c15a2","observation_id":"297a7cfa-0a0a-407d-84d8-bec6f313b4fd","resolution":{"observed_at":"2026-05-13T22:08:20.183084Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"oso/9780199","doi":"10.1093/acprof:oso/9780199202973.001.0001","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scale-Free Networks: Complex Webs in Nature and Technology","venue":null,"work_id":"e46cfa5e-c56e-4eac-8e83-4e064828157d","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:ee1f97cecb0c28b373c08272eb39c561f0e1028d1d0755c17f5d6a343f48d980","observation_id":"7e7b8d9a-7968-41a5-b702-ee9bff59a2fe","resolution":{"observed_at":"2026-05-13T22:08:20.236099Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.12127","last_updated":"2021-05-28T22:47:31Z","snapshot_observed_at":"2026-08-02T10:21:43.659767Z","submitted_at":"2020-03-26T19:52:32Z","title":"Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge","version":2},"cited_work":{"arxiv_id":"2003.12127","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.12127","snapshot_observed_at":"2026-07-04T14:29:53.418934Z","title":"Gryfﬁn: An Algorithm for Bayesian Optimization of Categorical V ariables Informed by Expert Knowledge","venue":null,"work_id":"e9868fdf-0ae8-412e-9be8-b1622239509e","year":2003},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2003.12127","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:54bf1a0b1399bae47986b381df21b142ef5c75a42befd22e1b8939e0bc1ebd95","observation_id":"ceb86983-75aa-47a2-9a6e-7460a47595b1","resolution":{"observed_at":"2026-05-13T22:08:20.661276Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acscentsci.8b00307","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Phoenics: A Bayesian Optimizer for Chemistry","venue":"ACS Central Science","work_id":"f7a5f62c-6fa8-4815-a7b7-2c146919ecad","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:79058ae5ea03d4fb3723405bfadce0cb13a406e0b992a469b6ea7fdf57d53a27","observation_id":"b5afcc39-02d9-4bb2-a504-eeacc167034e","resolution":{"observed_at":"2026-05-13T22:08:20.237873Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"MCMC for V ariationally Sparse Gaussian Processes","venue":null,"work_id":"537b082f-7936-403d-88dc-1dcd8a3b0ecf","year":2015},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9a6a08b17e87a45b5a39cd032c83e59f7411d794b8658969172120ea5e03c092","observation_id":"6b43c560-0ec5-42b0-9076-4bf4ab9e5b9b","resolution":{"observed_at":"2026-05-13T22:08:21.096726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Scientiﬁc Method","venue":null,"work_id":"f2370e85-08ef-4010-a943-0689e6d45be7","year":2026},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:5c73f2c2d346f8c00f1b09332d2ca145c34bc3fd108a0af68efda8ba3813ba24","observation_id":"0b78756c-652e-4f9a-8ca9-7b8116f61b54","resolution":{"observed_at":"2026-05-13T22:08:21.091735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Decentralized High-Dimensional Bayesian Optimization With Factor Graphs","venue":null,"work_id":"576cf482-ffc7-44ab-9af0-a803d003783b","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:f88267723b4242e3ad637ef5094d4a6b2c543133ca0db4890cc3a630db693257","observation_id":"3ead9ceb-5cc5-42d0-aa29-fdf201667726","resolution":{"observed_at":"2026-05-13T22:08:21.143082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Modular Mechanisms for Bayesian Optimization","venue":null,"work_id":"0bd5461d-986e-41b9-9d8e-c06c264f6006","year":2014},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9a300099f6ace41b79864cabb1f8f3287eedf34e35265b0020feebdc891fe547","observation_id":"45644326-f647-4e4b-affa-e17e35910305","resolution":{"observed_at":"2026-05-13T22:08:21.135803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T14:16:16.764150Z","title":"In: Proc","venue":null,"work_id":"452f975d-5e1f-47e0-967f-db1ed9da0d80","year":2025},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b4188e9d732fe89b5c1be4d340c7672ca4048ef2f651b253a2c1620005691808","observation_id":"521e92d3-fb45-4970-b7e8-3090530ddb73","resolution":{"observed_at":"2026-05-13T22:08:20.223641Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acs.jcim.0c00675","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery","venue":"Journal of Chemical Information and Modeling","work_id":"0b83f9cc-d702-47c8-8e72-2d640a500d1f","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:0dcf71961818c6ad89f8fca04fd41b4b455702cdcee7a98c063febc55c6f76ed","observation_id":"4dd41ccc-f886-49ca-a358-4556bc368e59","resolution":{"observed_at":"2026-05-13T22:08:20.233393Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-18T07:51:10.352238+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-18T07:51:10.352238+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1023/a:1008306431147","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficient Global Optimization of Expensive Black-Box Functions","venue":"Journal of Global Optimization","work_id":"c9ed8122-721d-40fc-bc15-eec82762a325","year":1998},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:bb37b6b8f15f2932ced44856e02def6fbf7b844a0dc085ec0c606943a8c8dba3","observation_id":"c9c912cd-e6cb-42cb-a70d-bc7ec81bc4f4","resolution":{"observed_at":"2026-05-13T22:08:20.246259Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-13T13:19:47.869203+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T13:19:47.869203+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12570","last_updated":"2022-10-14T18:31:22Z","snapshot_observed_at":"2026-08-05T03:33:20.678794Z","submitted_at":"2022-01-29T12:03:04Z","title":"AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation","version":4},"cited_work":{"arxiv_id":"2201.12570","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2201.12570","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Opti- misation","venue":null,"work_id":"acb30eaa-b14c-480b-8849-46b328103ba0","year":2022},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2201.12570","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:352975f525742f88f7d1c20db8705b7abd398e7a1105382d6e9dcfbf26500783","observation_id":"a3fee357-7c80-4f8a-9d4b-1f2cfa57e17e","resolution":{"observed_at":"2026-05-13T22:08:20.678029Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Contextual Gaussian Process Bandit Optimization","venue":null,"work_id":"46129f5a-584d-480e-af51-fc5b12dd8430","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:2e37591fb289a010670d2acff0c6f96087e7f3f1881b1108ce439b00da5d8358","observation_id":"291c30dc-e588-4f8e-a5f7-fb6fc76e7367","resolution":{"observed_at":"2026-05-13T22:08:21.144924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A Statistical Approach to Some Basic Mine V aluation Problems on the Witwatersrand","venue":null,"work_id":"d50d23a9-9dad-44fc-b7d3-e86b280efc2e","year":1951},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:885a3e9cda084e9ec2a1726fee9a0f5cdc0dceefea45b5a40a5f640ec3807bea","observation_id":"6ca3aeb9-4362-457b-b970-b7c588ea4808","resolution":{"observed_at":"2026-05-13T22:08:21.120038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06459","last_updated":"2024-06-10T16:53:58Z","snapshot_observed_at":"2026-07-06T18:28:16.301122Z","submitted_at":"2024-06-10T16:53:58Z","title":"How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?","version":1},"cited_work":{"arxiv_id":"2406.06459","doi":"10.48550/arxiv.2406.06459","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.06459","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Useful Is Intermittent, Asynchronous Expert Feedback for Bayesian Optimiza- tion? June 2024","venue":"arXiv (Cornell University)","work_id":"3ad4de6c-35cd-41e3-94c7-e6fb5559dacc","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2406.06459","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:8227311b4e3ab1b33b57ea346a259a04b483ad6020cca5b9b520084aee2d600b","observation_id":"7cde912b-c475-4abf-9ab1-6d8ec8c0fce0","resolution":{"observed_at":"2026-05-13T22:08:20.221035Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1126/science.136.3518.760","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Historical Structure of Scientiﬁc Discovery: To the Historian Discovery Is Seldom a Unit Event Attributable to Some Particular Man, Time, and Place","venue":"Science","work_id":"59a07ce9-eb2e-4dd1-b40d-8db8116ab17e","year":1962},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:8a7c6da00ec269dd553746171d6d406232416521c96bcff0c8522c44fa0e170b","observation_id":"40ed8042-f1b4-430a-a3a5-7d7c5f4f4deb","resolution":{"observed_at":"2026-05-13T22:08:20.258891Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d58ecfc8-c7a9-44ad-bd1b-324abef87852","year":1994},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:8201d4a830c42cc5c0386ee422bbff58dc1b8efc2545da8ddf14b604510dea0d","observation_id":"dcf982c6-fd97-473b-8f23-00dc514f943c","resolution":{"observed_at":"2026-05-13T22:08:21.114817Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A generalized probability density function for double-bounded random pro- cesses","venue":null,"work_id":"900b229a-9a16-4b87-bcf5-eddded3548e5","year":1980},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:cf6eec9c55edfddded23449fe2328b5694dd78c3f69b556305c51e964937973b","observation_id":"3aaa6ce2-1eec-41ab-abf6-982188ce7e08","resolution":{"observed_at":"2026-05-13T22:08:21.116510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1115/1.3653121","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise","venue":"Journal of Basic Engineering","work_id":"a640fe77-53ef-4941-ade2-7c7e988e0ef7","year":1964},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:da307da66658dc39e5912d6b190e1fbb96ae7d3d3d9ac3cc83a8b4bf75f50397","observation_id":"d42b71bf-6ce2-4de5-96d3-460724e2249f","resolution":{"observed_at":"2026-05-13T22:08:20.214065Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-20T12:23:06.211232+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T12:23:06.211232+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1017/9781108571401","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Bandit Algorithms","venue":"Cambridge University Press eBooks","work_id":"695cfcbe-74c3-488b-bde9-50a4da9f205f","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:4a3decf8b3a8c84ac27318701defe9cbf7fb3e490ffc317f62a2398c928be7d8","observation_id":"5d0bb14b-ffa5-437e-963e-49478dad102f","resolution":{"observed_at":"2026-05-13T22:08:20.164874Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Sparse Spectrum Gaussian Process Regression","venue":null,"work_id":"dd43dbc1-1d5b-4daf-bf80-034ffd9aec90","year":2010},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:3dfe96becf88fd8b43aab2c63dba5f1dda2248d83e871a8f7ec1dcf9fd487338","observation_id":"f49d5123-6163-463b-8b4b-8a1ee1267720","resolution":{"observed_at":"2026-05-13T22:08:21.111333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41524-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T13:32:15.311735Z","title":"Kaufmann, D","venue":null,"work_id":"a5b69607-955f-46a8-8778-97e6bbc8b0af","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:983cc01205e631f07f70467a460a2ca55eececb99b0de65ee83df6b75eeb4afd","observation_id":"a835c16f-6c7d-4816-b6a3-ea5467dd9c56","resolution":{"observed_at":"2026-05-13T22:08:20.174922Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks","venue":null,"work_id":"e50645e0-fb2a-451a-8579-7b30d09adf9d","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:a9770d70ce8bbc9f9d2225d99a56cb24c380278168f42b404d9cc28b52fca4ce","observation_id":"8505242a-dfdc-4d0c-9903-be4bf2d2c7c3","resolution":{"observed_at":"2026-05-13T22:08:21.107989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1039/c5ob00709g","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Current Complexity: A Tool for Assessing the Complexity of Organic Molecules","venue":"Organic & Biomolecular Chemistry","work_id":"7d72c79e-a37f-41ef-82f6-709a7b441089","year":2015},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:fca71f3391e3d1fb0293d8dc7b0921ec63390f754f10e9eb9e5f3c7627a0d8c8","observation_id":"7e766564-e822-4ba3-933e-3058327713c3","resolution":{"observed_at":"2026-05-13T22:08:20.243970Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Optimize the quantum yield of G-quartet-based circularly polarized luminescence materials via active learning strategy-BgoFace","venue":null,"work_id":"3b7a52fc-fd11-42db-9a9a-6bb9a1b875db","year":2025},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:6aa0f61e4edca87463f0c86dab5bd07036acb930f563319530f7f827ee9236bc","observation_id":"8f07e827-e4b5-4b35-916e-c2b59d24f9bb","resolution":{"observed_at":"2026-05-13T22:08:21.113199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41524-021-00656-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"npj Computational Materials , author =","venue":"npj Computational Materials","work_id":"ee9c8449-809e-47ae-b4cb-c2c701eaa3dc","year":2021},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:41eaa8a269e311544c1a0da9e5ccf804fb6c03726dd042a4d291ed10cb45b380","observation_id":"e507218a-ffa4-44bb-aa2b-54411654dfbd","resolution":{"observed_at":"2026-05-13T22:08:20.216126Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-14T21:50:37.017309+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T21:50:37.017309+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Epistemology","venue":null,"work_id":"840876f5-db6b-4c15-8e8a-80cd1a80fd49","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:4688042b6416aa7cee67f77fe176e41221b23531ad1be0ce1128a774e547e14c","observation_id":"8f1b049d-d8ea-42c2-8e5a-447ebad5f93f","resolution":{"observed_at":"2026-05-13T22:08:21.103136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03921","last_updated":"2024-03-08T12:23:56Z","snapshot_observed_at":"2026-07-06T17:26:10.790336Z","submitted_at":"2024-02-06T11:44:06Z","title":"Large Language Models to Enhance Bayesian Optimization","version":2},"cited_work":{"arxiv_id":"2402.03921","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03921","snapshot_observed_at":"2026-07-04T17:40:00.788123Z","title":"Large language models to enhance bayesian optimization","venue":null,"work_id":"6cb5de09-0ec3-42ab-9a85-2cf54055bd03","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2402.03921","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:ddfbdd12b73f4879c0cea3722cf5b25fbc7f8e21976cdbff55b18328b27360b9","observation_id":"66bcc05a-1744-4fc6-8bfe-58d49f903104","resolution":{"observed_at":"2026-05-13T22:08:20.670892Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Text Classiﬁcation Using String Kernels","venue":null,"work_id":"e99a0fcc-a6e5-4f65-87c9-af4c27920986","year":2000},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:3bfbdbf032a551d4cbd8e3b5ec320f4d674b2169745bf8bd8a675d01d6078891","observation_id":"8bd22e93-5aae-45b0-897f-e79c49530955","resolution":{"observed_at":"2026-05-13T22:08:21.123528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Active learning in materials science with emphasis on adaptive sampling using uncer- tainties for targeted design","venue":null,"work_id":"eb9c1291-448a-4472-a8ad-4f9f34a8b5e3","year":2019},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:956e0d3381e4478d223ea4b17146244211036276b74812fdc1e7c809291cf4de","observation_id":"a513bbc7-2043-449f-920b-ba68827bc932","resolution":{"observed_at":"2026-05-13T22:08:21.148596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1126/sciadv.aaz8867","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Self-driving labor atory for accelerated discovery of thin-ﬁlm materials","venue":"Science Advances","work_id":"f3e67303-2477-4b12-97ee-d4630d60ae94","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:f42cdf5db640ce0831c495f30013512fc07ecf359d8440ad8b61eac6454425d9","observation_id":"24cb47bc-4451-45bc-abe2-5ecd508e1969","resolution":{"observed_at":"2026-05-13T22:08:20.212023Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20302","last_updated":"2025-01-02T23:08:47Z","snapshot_observed_at":"2026-07-06T19:40:17.497219Z","submitted_at":"2024-10-27T00:50:30Z","title":"Sequential Large Language Model-Based Hyper-parameter Optimization","version":3},"cited_work":{"arxiv_id":"2410.20302","doi":"10.48550/arxiv.2410.20302","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.20302","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sequentiallargelanguagemodel-basedhyper-parameter optimization","venue":"arXiv (Cornell University)","work_id":"487f0aa1-fed8-4d6c-a789-6bdde5b39a38","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2410.20302","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:07e0278906eeb35c3fb48de1f63f1bceb0f69701af916284a0b23dcf04319ae0","observation_id":"31c896e1-c2a7-4325-a955-85ca33f8eddd","resolution":{"observed_at":"2026-05-13T22:08:20.197448Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s1474-6670(17)67769-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"On the Bayes Methods for Seeking the Extremal Point","venue":"IFAC Proceedings Volumes","work_id":"da97ba2f-4304-4ffc-96b0-03705c5fc91d","year":1975},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:10cd969bb9c11d327d827ef8f4a5c43f6b539e91ac4c8da0a8dc199297d1fa3e","observation_id":"f2a2749a-7299-4500-aefc-0804c2a288c2","resolution":{"observed_at":"2026-05-13T22:08:20.156337Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"BOSS: Bayesian Optimization over String Spaces","venue":null,"work_id":"98737d20-fb0a-4c73-9650-ce61ce80086f","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:8a5bd559269da245d3f79004f6118a4c266b453c1f52bc6802a4465d18037a4a","observation_id":"1b0c4a52-9f3d-4843-a03b-62814e7061d2","resolution":{"observed_at":"2026-05-13T22:08:21.090191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"PFNs4BO: In-Context Learning for Bayesian Optimization","venue":null,"work_id":"16443c89-5f3d-42a2-8e45-5521c43b5391","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:ffd9dc97325665035f8d7924f1b0dab8d50d6a5425f252045a9bce314774b773","observation_id":"af95d899-e1c8-434e-b6df-59d7f443d9c1","resolution":{"observed_at":"2026-05-13T22:08:21.098321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Murphy.Machine Learning: A Probabilistic Perspective","venue":null,"work_id":"378c3375-3c97-4690-999a-0200a01c878b","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b4ef19fdb96708243531753ecbc6a1a0d72ce0da2c241bb6f2cd64fc5f7a5823","observation_id":"750993cb-8005-489d-9e85-953877c5efe4","resolution":{"observed_at":"2026-05-13T22:08:21.074912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-1-4612-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:03:29.325092Z","title":"Balay, W","venue":null,"work_id":"f9f2945e-dd6c-439c-8b4a-d4e043ab9a44","year":1996},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:1fd9465798d53d570589edacc864e83cba5f8055316081d59e4ec64bf318b5a5","observation_id":"27c84559-76ac-47f6-91f1-d89548cd3655","resolution":{"observed_at":"2026-05-13T22:08:20.240101Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s43586-023-00226-x","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Photocatalytic Water Splitting","venue":"Nature Reviews Methods Primers","work_id":"daa76bb0-02d8-427c-aa9d-15d17d9e4193","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:fe65b27e6a0af37603bce5e9281ea93afa6f8ab9cfd36217e03c684993b51f2c","observation_id":"4f137f4c-8af4-4910-a4a5-e1d611cfd4a3","resolution":{"observed_at":"2026-05-13T22:08:20.180297Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"794d6485-430d-4891-b9a1-4b4252d61598","year":2006},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:a439121f9b6a559b40d05c996fffc07c10446e64eba327d661bd7fa6d377c2e5","observation_id":"1ebc20fe-f220-4017-a39e-cf78ebf2eb60","resolution":{"observed_at":"2026-05-13T22:08:21.069694Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization: A Python Implementation of Global Optimization with Gaussian Processes","venue":null,"work_id":"8510f9e0-516f-4501-a1a0-2b625c01d51f","year":2014},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:91bf2e5281e009f263b7afa668d72d0ecc9cf853653a68dda60faecf2547e16d","observation_id":"02cf7a6b-f8e2-4ceb-b758-7351e1f69fcb","resolution":{"observed_at":"2026-05-13T22:08:21.071426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Scikit-learn: Machine Learning in Python","venue":null,"work_id":"74ad872a-ac85-4b68-8a29-72242a6f0ea0","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:f20458797064c95b7a1af6a9bdc4d111eaaf1fb2437a227a066056ac757620dd","observation_id":"9ac2c15a-7e39-43b9-b227-7f36e8fed657","resolution":{"observed_at":"2026-05-13T22:08:21.076573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization under Mixed Constraints with a Slack-V ariable Augmented La- grangian","venue":null,"work_id":"b4a11019-d5f2-406f-9658-ce5d3f9d64b2","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:07e2412dd6e70edd81f686b89d509fc1ccce8e7c301b62866d739dc06d72d2df","observation_id":"d14dcfb5-5186-4e80-9758-7a7545f6dcaa","resolution":{"observed_at":"2026-05-13T22:08:21.083446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2013.1508","doi":"10.1089/big.2013.1508","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Data Science and Its Relationship to Big Data and Data-Driven Decision Making","venue":"Big Data","work_id":"6f26be29-f328-40fd-83ce-c43ff298d23a","year":2013},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:808f43a538bdcb837422d951234baa7e051111c9f32e156f735c4adc83d7755d","observation_id":"be3b7311-8801-4470-9401-6b7baa029af2","resolution":{"observed_at":"2026-05-13T22:08:20.250750Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14545","last_updated":"2023-11-29T10:20:19Z","snapshot_observed_at":"2026-07-06T14:56:47.509018Z","submitted_at":"2023-02-28T13:10:04Z","title":"Modern Bayesian Experimental Design","version":2},"cited_work":{"arxiv_id":"2302.14545","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.14545","snapshot_observed_at":"2026-07-03T16:28:38.760226Z","title":"Modern Bayesian Experimental Design","venue":null,"work_id":"09b2ea5f-e4f9-494b-909d-d4280a09013f","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2302.14545","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:51cb34b9a8af553ae2be89e2ea315cc3cc041f442a72a0930f15ee49b2590c60","observation_id":"5587ef4c-1943-4bdd-a1a1-858e054a3440","resolution":{"observed_at":"2026-05-13T22:08:20.665993Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05341","last_updated":"2025-05-15T00:12:04Z","snapshot_observed_at":"2026-07-06T15:14:28.574778Z","submitted_at":"2023-04-11T17:00:35Z","title":"Bayesian Optimization of Catalysis With In-Context Learning","version":2},"cited_work":{"arxiv_id":"2304.05341","doi":"10.48550/arxiv.2304.05341","metadata_source":"arxiv_reference","pith_arxiv_id":"2304.05341","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2304.05341 , year=","venue":"arXiv (Cornell University)","work_id":"6aa8079e-9869-4d5b-be8a-ee92be6f7356","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2304.05341","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:5498f46263d2a0f831da9dd50b418312d7cb217c9ace8226b133b9391cb2b4e9","observation_id":"6c8f3a76-9543-40e9-a8a7-ab0a4c9aae92","resolution":{"observed_at":"2026-05-13T22:08:20.191361Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"https://www.rdkit.org/","venue":null,"work_id":"a695df48-4478-4d0f-b13c-5467a24ce53d","year":null},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:079a2cfaa7f815e18fc4ab455098491d1348568ac30d51de9136a7577d949ae8","observation_id":"4ea1c593-f70b-4cf9-9210-c227b6a51395","resolution":{"observed_at":"2026-05-13T22:08:21.064161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.02038","last_updated":"2020-07-14T22:22:04Z","snapshot_observed_at":"2026-08-01T20:52:42.520348Z","submitted_at":"2017-07-07T05:22:16Z","title":"A Tutorial on Thompson Sampling","version":3},"cited_work":{"arxiv_id":"1707.02038","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1707.02038","snapshot_observed_at":"2026-07-01T20:36:11.676128Z","title":"A Tutorial on Thompson Sampling","venue":null,"work_id":"a0342702-4ce2-4a86-8679-5c35897fa34f","year":2020},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/1707.02038","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:b4106178e2199724b0f8b9e7030cf5f3e66a5dbe9af3f766d29495996ca7873c","observation_id":"4bc358bf-9905-4bb8-85e3-4cb113e9e296","resolution":{"observed_at":"2026-05-13T22:08:20.663634Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02852","last_updated":"2023-12-05T16:09:31Z","snapshot_observed_at":"2026-07-06T16:57:13.744607Z","submitted_at":"2023-12-05T16:09:31Z","title":"Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems","version":1},"cited_work":{"arxiv_id":"2312.02852","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.02852","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Expert-Guided Bayesian Optimisation for Human-in- the-loop Experimental Design of Known Systems","venue":null,"work_id":"1c59182a-3a99-427e-8de1-1b7799b30898","year":2023},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2312.02852","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:c930e008fce265b025557dda2ae79fb12ca7a5f3a2eac7730cdc247c97aeca04","observation_id":"02f2f430-d5b5-4604-8355-51469f46f2f6","resolution":{"observed_at":"2026-05-13T22:08:20.668413Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5314.0002","doi":"10.3998/ergo.12405314.0002.006","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scientiﬁc Discovery: That-Whats and What-Thats","venue":"Ergo an Open Access Journal of Philosophy","work_id":"418e6bd9-c3c4-4035-8f29-ef201a934ec2","year":2015},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:f1b1de6ef004096e84ba2007d44a022617b0597f153dd9697c6f6e0c491f3f4b","observation_id":"b2b554b8-265a-4472-b5e9-f6eca0ad9d42","resolution":{"observed_at":"2026-05-13T22:08:20.256999Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/100801275","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The Correlated Knowledge Gradient for Simulation Opti- mization of Continuous Parameters Using Gaussian Process Regression","venue":"SIAM Journal on Optimization","work_id":"85ef03c1-753e-4b39-93bc-80850c7a3768","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:15c89468645b660a46b77965d7b67184ef9d28640ee2f2df5432a60879b5996c","observation_id":"5e221bb7-2427-46a9-ad35-ec55ad226dce","resolution":{"observed_at":"2026-05-13T22:08:20.204638Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions","venue":null,"work_id":"46cff397-c3d2-4676-952e-3567f0af3947","year":2015},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:2bb2a92d68f84d2abfb588b582e40e7521a42677f0583a0b5bd78bffa024c74f","observation_id":"e3726581-464c-48b9-9773-f32ced047605","resolution":{"observed_at":"2026-05-13T22:08:21.150418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Student-t Processes as Alternatives to Gaussian Pro- cesses","venue":null,"work_id":"93627cb5-718e-4506-b0de-fcfb35894f6e","year":2014},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:32d8c97612bf1c0eb7f710a9de659e286d923a0f4a2b6d883b87c65f8848e34a","observation_id":"fec86bc5-954a-4b9a-9099-5f27e2aee750","resolution":{"observed_at":"2026-05-13T22:08:21.126764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2015.249421","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T15:26:18.458129Z","title":"Taking the human out of the loop: A review of Bayesian optimization","venue":null,"work_id":"5f93d301-6a08-462a-88b6-b9a9a82e3522","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9a1f4f8ed3a06e369a83990b0d2ec4e2ebd185be8636dfc4c466060e2dfb5294","observation_id":"062d1f71-4190-4614-84d0-b3bc2f2680d9","resolution":{"observed_at":"2026-05-13T22:08:20.178187Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-021-03213-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Shields, Jason M","venue":"Nature","work_id":"97db6d55-0b9d-41f5-9806-9310835a5f34","year":2021},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:3b3e7c519785a127832234a7e748619cd5ef255bba29bcc157a77dca13aa2149","observation_id":"d3c308ed-8b65-4d35-91b9-15258a4d71e7","resolution":{"observed_at":"2026-05-13T22:08:20.225768Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-14T21:50:44.707996+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T21:50:44.707996+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Accelerating Material Discovery with a Threshold-Driven Hybrid Acquisition Policy-Based Bayesian Optimization","venue":null,"work_id":"47bdf047-6655-4317-8667-bc3e79b7aaed","year":2024},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:cdb7eccff14aefcf09c0ed6be6891fbd755b2a4c4ab413c9a8b33c5403d45b47","observation_id":"678ff525-cd05-4289-acdb-38865047ce01","resolution":{"observed_at":"2026-05-13T22:08:21.118296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Sparse Gaussian Processes Using Pseudo-inputs","venue":null,"work_id":"d18dec0b-753a-4325-a9cd-5b8878cc9308","year":2005},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:177e5fd45b2318ef8e8a65aaa1173da8e1e3b49d8e1e75c3cf07bf4649ce8dfc","observation_id":"eb56b477-c229-4697-be84-c0c56ac81e87","resolution":{"observed_at":"2026-05-13T22:08:21.121901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Warped Gaussian Processes","venue":null,"work_id":"96709e21-1fd4-48f5-8247-b8afe35d59f2","year":2003},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:d10de2c2b1eed50fbb0ee2df044ce37b2e8ced8075cde9a9768a430bb5205cf5","observation_id":"803d579e-c651-4490-b90c-4db35a16d3b2","resolution":{"observed_at":"2026-05-13T22:08:21.125099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"JasperSnoek/spearmint","venue":null,"work_id":"8469ee41-7f87-4c58-ba36-70fb4a85fce6","year":2015},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:1747394f34774f8b59f7bd9f1230860bc095496f1a4e11436c3f376923f35639","observation_id":"5a4a2007-14fd-4168-9521-9b8805a4dc77","resolution":{"observed_at":"2026-05-13T22:08:21.106341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Practical bayesian optimization of machine learning algorithms","venue":null,"work_id":"b68b0b79-c01e-455c-b127-b98f2afb63f8","year":2012},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:de1ef411417b83366b3c38af2deabfee7d8a944a7926aefc4d09b1f2ba51a3fb","observation_id":"449b5c5c-745b-44b3-87d1-57350726765d","resolution":{"observed_at":"2026-05-13T22:08:21.104772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Optimization with Robust Bayesian Neural Networks","venue":null,"work_id":"130453f3-9c74-47c3-8a0a-05e28245f4e4","year":2016},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:bf01658755e8d9b74f8e72da2c6c12a187f042b430f762ef9f35eac1c95340e2","observation_id":"ac40d292-c5fa-4da4-8126-d746a8d1a641","resolution":{"observed_at":"2026-05-13T22:08:21.109680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design","venue":null,"work_id":"913c5366-1558-490d-b92d-1768f619e4e6","year":2010},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:a5eaff9fda36641d4b442c908b413e9bc3bb5a83d473be4f3c904c7393530723","observation_id":"65f8011a-a417-4bcd-9fe6-8afe3f02cc9e","resolution":{"observed_at":"2026-05-13T22:08:21.128583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2011.218203","doi":"10.1109/tit.2011.2182033","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Srinivas, A","venue":"IEEE Transactions on Information Theory","work_id":"4f720937-0fb4-429d-b4e0-4aac9e0ce1d1","year":2011},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:d4e6fbd200e39da7a20158109f17c929a48a791ce9c5b786a082fafe2017266c","observation_id":"c2d23034-73f3-4ad2-bc91-88dad5df2d50","resolution":{"observed_at":"2026-05-13T22:08:20.228447Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A Non-myopic Utility Function for Statistical Global Optimization Algo- rithms","venue":null,"work_id":"eeaed3f8-7519-4daa-8335-285355cced39","year":1999},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:4226607e05afd295e1146b91a58e4ec78d00de3c822a62cde7ec02d76bc4fe46","observation_id":"b21c7ef3-7dd5-469a-84e6-c50a57aa1583","resolution":{"observed_at":"2026-05-13T22:08:21.093429Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/anie.202111540","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Is Organic Chemistry Really Growing Expo- nentially?","venue":"Angewandte Chemie International Edition","work_id":"2cffdfef-dda8-4756-83b5-59c2c1dcec28","year":2021},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:47b8ce0319483b8039532f873af9f682a42c236c92ce8da7c392ba02293e0313","observation_id":"54f20191-9b92-490c-9015-7d669467bc16","resolution":{"observed_at":"2026-05-13T22:08:20.202689Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1039/d4qo02363c","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AI Molecular Catalysis: Where Are We Now?","venue":"Organic Chemistry Frontiers","work_id":"19b2f2a0-1d38-411f-a246-11a92dd24b1c","year":2025},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:9556cc8ea00bcfd07aa7912438e2b53c1ecde2f5677276157a2b2f5ac501b987","observation_id":"3df69dfa-6098-4a5e-b95d-a337e3970cb0","resolution":{"observed_at":"2026-05-13T22:08:20.252492Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-07-11T04:03:03.760038+00:00","source":"paper_reference_links","state":"open"},"event_date":"2025-02-26","event_type":"correction","notice_doi":"10.1039/d5qo90021b","provenance":{"observed_at":"2026-07-11T03:03:40.607765+00:00","source":"crossref","source_record_id":"10.1039/d5qo90021b->10.1039/d4qo02363c:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Uncertainty Estimation for Batch Normalized Deep Networks","venue":null,"work_id":"88a36c9c-cac1-42ab-ae6e-a44be1361b00","year":2018},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:7778c44ee5844c41bc6e7b359be64b72ddccc88951996d667c28a009e1d37e72","observation_id":"93de4bf2-e9ad-4f81-8266-76374c966d52","resolution":{"observed_at":"2026-05-13T22:08:21.101577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":6,"metadata_mismatch":9,"parse_uncertain":0,"unresolved":2,"verified_exact":36,"verified_fuzzy":47},"total_outbound_references":118},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 100 of 118 outbound references and 1 inbound Pith citation observation for arXiv:2604.01328."}