{"as_of":"2026-08-15T00:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1e1f7c5f4923b2c54487528348f2e005b9f119f208e7d40cac1ceadf08f00b10","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T21:57:09.816674Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.23448/citation-record","integrity":"/paper/2607.23448/integrity","json":"/paper/2607.23448/citation-record.json","paper":"/paper/2607.23448"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.577749Z","title":"Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.577749Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:b13bd80d8fdd4943911e304eb9e1921511ce97979b5f35759aa2cd47405a6b95","observation_id":"a07e0911-a147-4a09-a318-e5d77b1b07c9","resolution":{"observed_at":"2026-07-30T21:57:09.577749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.582852Z","title":"Classification Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.582852Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:3e1e8f5c6c7c64d85165815ea25943d5c48768193e8390a13b5fc7fb80416eeb","observation_id":"614ef0a3-ec9f-4b80-9e01-b5636c9bed4c","resolution":{"observed_at":"2026-07-30T21:57:09.582852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.589735Z","title":", title =","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.589735Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:0cf640151a81271602c7568087808c950b71e096a9151df1d8f93594caceacea","observation_id":"23ad29c5-540b-446a-acd7-237e33d2f765","resolution":{"observed_at":"2026-07-30T21:57:09.589735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.595161Z","title":"New Ways to Make Microcircuits Smaller---Duplicate Entry","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.595161Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:98d693c5df0c735dccddb0c5771c89b539085480660f38a030046d6fc276adda","observation_id":"348e7ce2-70d6-480d-a446-82b2a620bc1e","resolution":{"observed_at":"2026-07-30T21:57:09.595161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.600364Z","title":"Clancey and Glenn Rennels , abstract =","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.600364Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:22268189f900e0fe3d97ea711063b4dfbacd78e31950bd3d1283bb22be4bd948","observation_id":"928d9beb-4311-43aa-89a3-fae35b89e8b1","resolution":{"observed_at":"2026-07-30T21:57:09.600364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.605657Z","title":"and Rennels, Glenn R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.605657Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:0a1cde5f84d46a05f63e06d55834f4e26b7b5a7b3c5980206a2b57a1c61457ac","observation_id":"60decd1a-a19c-4ebe-b4d7-57d5d4d4c8c9","resolution":{"observed_at":"2026-07-30T21:57:09.605657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.611151Z","title":"Poligon: A System for Parallel Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.611151Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:caa37f9c2a67a3dc91f1ac1387398790392a83a4496802ff22ab1793c2aed6c0","observation_id":"c6a8b80a-0d0c-4379-af11-cd2c3c1a53ca","resolution":{"observed_at":"2026-07-30T21:57:09.611151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.615907Z","title":"Transfer of Rule-Based Expertise through a Tutorial Dialogue","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.615907Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:5b648babffb87142eb886b0ae98612d11ce70de07eef8cb065bc991631bc7a24","observation_id":"f6e5fa67-7079-409a-9fb1-f9a2338fede6","resolution":{"observed_at":"2026-07-30T21:57:09.615907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.621756Z","title":"The Engineering of Qualitative Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.621756Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:812a152b610e54ef907a3de7f9a3fb6c8983119062db1213e152f2099f39fd12","observation_id":"0c26eb3e-abd8-415b-9e25-c80c8ac14449","resolution":{"observed_at":"2026-07-30T21:57:09.621756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.627453Z","title":"2017 , eprint=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.627453Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:d4665980424e395ccf7fcb5fbac06bc0cc084b17481ea0f7b14a76f2c2bd5349","observation_id":"34ab0130-516e-4b46-969c-f603d8f13111","resolution":{"observed_at":"2026-07-30T21:57:09.627453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.632658Z","title":"Pluto: The 'Other' Red Planet","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.632658Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:0b7733ee7cfc8749ba12894ca49dc1478f4c5316d7eeafa5a747b95e80f1fe59","observation_id":"c0e05073-c986-412e-bbe5-6733ffee08a2","resolution":{"observed_at":"2026-07-30T21:57:09.632658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.637737Z","title":"and de Freitas, Nando , journal=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.637737Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:f0b61da91a83bdeb83c60a7d027df1b746565b2b6d6577d9dcb563783f89d779","observation_id":"5d27264c-347a-46c3-8faf-7bf2da6e770d","resolution":{"observed_at":"2026-07-30T21:57:09.637737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.642450Z","title":"International conference on artificial intelligence and statistics , pages=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.642450Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:55fadae9bee859b58700e37b0931ebfe27f44c5150b4ce5b63b8d5a101307458","observation_id":"0b679054-809d-4f59-8438-801aff975426","resolution":{"observed_at":"2026-07-30T21:57:09.642450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.647761Z","title":"Recent advances in optimization and modeling of contemporary problems , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.647761Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:d5fb5eedbd595e6f21a71e47647a064daf843b8b19daefebb36d0f72a62f51a6","observation_id":"36628711-3663-4b0e-92e3-7d10f4557327","resolution":{"observed_at":"2026-07-30T21:57:09.647761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.652921Z","title":", author=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.652921Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:fe94a8594ce2595eb536118e9b50e481eb0ddba47edba0de1bb51d49aab1d9c3","observation_id":"81e4fd2a-6eea-452f-b7d8-2cac3ad849c7","resolution":{"observed_at":"2026-07-30T21:57:09.652921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.657612Z","title":"Bayesian Analysis , number =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.657612Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:952dcbba11560ede1f8f21049e5bc8dd5be2c281e9eceacb0e04c5c70738f808","observation_id":"e3bf2587-8874-41f1-a874-d009d9da3d2e","resolution":{"observed_at":"2026-07-30T21:57:09.657612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.662620Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.662620Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:114ea6a080dee4ce64937f910a12c7ebda781420771963eacfb3c9e30d3219bc","observation_id":"8facfe6b-ddcf-45af-9d9b-cabd1d52497f","resolution":{"observed_at":"2026-07-30T21:57:09.662620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.667310Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.667310Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:5019a4002a6541e8f627f15071db607beb1c50452deef9d0c4d6f9edaaf1c6ef","observation_id":"4bb05a07-0d75-4d4b-a739-6ba23c964627","resolution":{"observed_at":"2026-07-30T21:57:09.667310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.672501Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.672501Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:afc0b9fc97ba6000801b9ec3dc4aac93cff616bea55eacad401fe99ce002270f","observation_id":"bf1b913f-9c0f-4417-a22e-8836ef631439","resolution":{"observed_at":"2026-07-30T21:57:09.672501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.04037","last_updated":"2025-05-07T00:33:14Z","snapshot_observed_at":"2026-08-14T10:41:55.704000Z","submitted_at":"2025-05-07T00:33:14Z","title":"Learning based convex approximation for constrained parametric optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.04037","snapshot_observed_at":"2026-07-30T21:57:09.677303Z","title":"arXiv preprint arXiv:2505.04037 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.677303Z"},"links":{"cited_paper":"/paper/2505.04037","citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:756d3b1df2104927557129d392f48a3eab650c3ae75fd77b61ff2b2bc2561e24","observation_id":"4f91fcf0-cfc9-4547-b9ef-45413ab5f8a5","resolution":{"observed_at":"2026-07-30T21:57:09.677303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.682397Z","title":"arXiv preprint arXiv:2512.20270 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.682397Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:bd09967ee2b34aec8c890133c09e125857b0a4cde9d74088b396c887329b4f2a","observation_id":"b92c0615-f6cc-43d7-9154-c4b012f3e328","resolution":{"observed_at":"2026-07-30T21:57:09.682397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.687654Z","title":"IEEE Communications Letters , volume=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.687654Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:bf5be245dd02763078c4f781574d0ba94d69d9ad9310d5020f7aeb35763b1e27","observation_id":"ac9a6269-672d-4fe4-9cdc-ac0fabf69689","resolution":{"observed_at":"2026-07-30T21:57:09.687654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.693303Z","title":"A Distributed Surrogate-Assisted Evolutionary Algorithm for Heterogeneously Expensive Constrained Optimization , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.693303Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:af21a0767b64cf3207b55849783f9b19874473d4088348ad0c435fb0115f903f","observation_id":"81d9bf38-4afc-4f57-99cf-687b5251709f","resolution":{"observed_at":"2026-07-30T21:57:09.693303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.698201Z","title":"A Surrogate-Assisted Evolutionary Framework With Regions of Interests-Based Data Selection for Expensive Constrained Optimization , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.698201Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:f0a2ff66bd331265868577f5301cc18de4ce7740fd1059a1ac12c7079d4449a8","observation_id":"5cc86d8e-c269-4f54-899f-33aabea46251","resolution":{"observed_at":"2026-07-30T21:57:09.698201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.702942Z","title":"Constrained Bayesian Optimization: A Review , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.702942Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:c31e9d1362568d44f3247c897bb08a106e348fee366a4a095695ec66a1932a68","observation_id":"0d96267b-b0bc-4478-b471-69ec9361b478","resolution":{"observed_at":"2026-07-30T21:57:09.702942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1403.5607","last_updated":"2014-03-22T03:35:00Z","snapshot_observed_at":"2026-08-14T23:40:22.424565Z","submitted_at":"2014-03-22T03:35:00Z","title":"Bayesian Optimization with Unknown Constraints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1403.5607","snapshot_observed_at":"2026-07-30T21:57:09.707620Z","title":"arXiv preprint arXiv:1403.5607 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.707620Z"},"links":{"cited_paper":"/paper/1403.5607","citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:34dfd1eec626ef4577b016c6dc613b78e8caccd519bf5461468926b947697da7","observation_id":"6e236483-e480-4448-850d-6db90c325a9e","resolution":{"observed_at":"2026-07-30T21:57:09.707620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.713012Z","title":"The computer journal , volume=","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.713012Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:d60b587c6b2d61454b3c8b4cfb12037f02eecb38c94343b67649d2a8968276ab","observation_id":"b4b79ca1-6e9d-4c4b-960d-55f13c81ae6f","resolution":{"observed_at":"2026-07-30T21:57:09.713012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.718173Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.718173Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:6293c4559958ddea5a24b1c7e2cba13560eb755a0e9435522e4a025d8f24e177","observation_id":"67a286d4-8db1-429f-8e9b-cd81ef2e744b","resolution":{"observed_at":"2026-07-30T21:57:09.718173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.722854Z","title":"Biometrika , volume=","venue":null,"work_id":null,"year":1933},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.722854Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:332f131ab2614c04729cd89a3286715549c8cead54302494757bced3f713f874","observation_id":"5f72db85-6c44-42cf-9019-52b13505cd06","resolution":{"observed_at":"2026-07-30T21:57:09.722854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.728487Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.728487Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:7a28e050fcae667e6a646759c4bb7abfdf82451fd433cc29240c6f0c1f1f3900","observation_id":"19088172-2508-4903-b7a5-5e11306ad576","resolution":{"observed_at":"2026-07-30T21:57:09.728487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0912.3995","last_updated":"2010-06-09T23:24:13Z","snapshot_observed_at":"2026-08-12T12:05:15.501614Z","submitted_at":"2009-12-21T00:08:19Z","title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0912.3995","snapshot_observed_at":"2026-07-30T21:57:09.733021Z","title":"arXiv preprint arXiv:0912.3995 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.733021Z"},"links":{"cited_paper":"/paper/0912.3995","citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:ad2d60eed2de30c69b9e5bfcb94765162ade604d87dfa3612c6062d4e3f64486","observation_id":"cfc23e08-9b9b-4d48-bb18-70cfe46ea704","resolution":{"observed_at":"2026-07-30T21:57:09.733021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.12214","last_updated":"2020-04-25T19:21:14Z","snapshot_observed_at":"2026-08-14T07:13:17.364901Z","submitted_at":"2020-04-25T19:21:14Z","title":"Learning to Guide Random Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.12214","snapshot_observed_at":"2026-07-30T21:57:09.738154Z","title":"arXiv preprint arXiv:2004.12214 , year=","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.738154Z"},"links":{"cited_paper":"/paper/2004.12214","citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:b5bdf82bcc1ef8e2193291880758cf07f404899dfdd61924a69b25d1d5d7b15d","observation_id":"30ac0585-346a-4eaf-962d-ea7806c1c5c4","resolution":{"observed_at":"2026-07-30T21:57:09.738154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.743797Z","title":"Proceedings of the IEEE conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.743797Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:1346fc90cc1b5ff0954078495b6ebd7b7a0e67ba3b3e11d62b290adcf940ed0c","observation_id":"9e53b915-a178-468b-97a2-4d80c58bbf0c","resolution":{"observed_at":"2026-07-30T21:57:09.743797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.748539Z","title":"Technometrics , volume=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.748539Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:6169105ca21c613806732b23f0bf6fb62dcb2e06c337381e5afec8563523090a","observation_id":"b1bcc8ac-d3e8-4946-99af-5ef072cad3f0","resolution":{"observed_at":"2026-07-30T21:57:09.748539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.753850Z","title":"Towards a new evolutionary computation: Advances in the estimation of distribution algorithms , pages=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.753850Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:ab5b22d8c4c8c9809f9317fa89299ab7b161a6df67464e507aad4a55b3159037","observation_id":"fddcbe6b-e000-4bbe-8faf-67c54ee5e8fe","resolution":{"observed_at":"2026-07-30T21:57:09.753850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.758729Z","title":"Proceedings of the 14th annual conference on Genetic and evolutionary computation , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.758729Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:a225dbaa1fe2cb01b01c20070714a8048195b4e9b574454473b2ed1d847ad6a0","observation_id":"d6415763-5c0c-44e6-a014-92703040b1d2","resolution":{"observed_at":"2026-07-30T21:57:09.758729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.763502Z","title":"Journal of Applied Mechanics , volume=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.763502Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:d2b9b320cd640ea1fe37a0d680ef87a3518f34a6af768e114c27ce67891e8430","observation_id":"5bd0aed0-cf54-4fa8-97cc-175838a90365","resolution":{"observed_at":"2026-07-30T21:57:09.763502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.768309Z","title":"Nanyang Technological University, Singapore , volume=","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.768309Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:71bc732706b9c98ff2df82e81a06facc51ce0b353b3622d83d0bc1604063b5fd","observation_id":"bcbb507c-f9f6-4fbd-abef-13ee03fc7adb","resolution":{"observed_at":"2026-07-30T21:57:09.768309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.773048Z","title":"Yu et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.773048Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:8290dfe0086c72a9fba1f349ee70156767fceff2e4610279a861c73194027df6","observation_id":"fd0f99fe-dabb-4b5f-8fdd-f1126e172bed","resolution":{"observed_at":"2026-07-30T21:57:09.773048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.778057Z","title":"Proceedings of the conference on adaptive computing in engineering design and control , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.778057Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:c652adce6b0cf66a52daa44e9d1be805fa7e652fd0e99bd6ec3dbd7e5c6e12f7","observation_id":"b9b68e85-5586-4c5c-94ee-b5216e6579ae","resolution":{"observed_at":"2026-07-30T21:57:09.778057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.782934Z","title":"Computers & Structures , volume=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.782934Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:a062d3a0320b503e25628e0e027d0089b2ef64e864bb61cc65bcb663939c2fcc","observation_id":"fd95abcc-bb09-43a2-ae11-48332484e52b","resolution":{"observed_at":"2026-07-30T21:57:09.782934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.787727Z","title":"Engineering computations , volume=","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.787727Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:a756615ec5e8c08b44cb9b598b22195c0fe9b66f90d8cc525766d9c879718833","observation_id":"223e1402-0de7-4fba-9333-595b0bba328f","resolution":{"observed_at":"2026-07-30T21:57:09.787727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.792764Z","title":"Journal of Mechanical Design , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.792764Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:6534aa8fafec6e7b77f9734aed10faafc5d3f7ad3dd265a273f85db7100b8ced","observation_id":"c267ff01-3ffa-4cef-bb00-16e519b346be","resolution":{"observed_at":"2026-07-30T21:57:09.792764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.797361Z","title":"International journal of vehicle design , volume=","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.797361Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:24dbc4d73c71d79fdeaa60391496238a73357aa5fa3267f6ac9255ba31027e05","observation_id":"5e590b0a-fe70-4eca-92a5-5cd015bab552","resolution":{"observed_at":"2026-07-30T21:57:09.797361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.802542Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.802542Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:0d3ff7321880f75a711320ecf3641ef726b43118358104be683e07b063be9d14","observation_id":"d32ae1a5-ba3b-4a3b-aad6-0666e92a9abf","resolution":{"observed_at":"2026-07-30T21:57:09.802542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.807420Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.807420Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:945d8296d4bed2757ee5e4c6e79e9c34d03d00ee1f76b3c0060ba3439902609e","observation_id":"3c3dd18d-c6ea-4831-8049-783ceab67db6","resolution":{"observed_at":"2026-07-30T21:57:09.807420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T21:57:09.812141Z","title":"IEEE Transactions on Evolutionary Computation , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.812141Z"},"links":{"citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:fbb4fc38cf2b0ca8eb4bc793c38913506244964a3027a920a30f212b15129c88","observation_id":"15e24d04-76b6-400f-9495-17029f101c3e","resolution":{"observed_at":"2026-07-30T21:57:09.812141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15386","last_updated":"2022-05-09T15:39:11Z","snapshot_observed_at":"2026-08-13T16:13:27.695184Z","submitted_at":"2022-03-29T09:26:22Z","title":"Pareto Set Learning for Neural Multi-objective Combinatorial Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15386","snapshot_observed_at":"2026-07-30T21:57:09.816674Z","title":"arXiv preprint arXiv:2203.15386 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-07-30T21:57:09.816674Z"},"links":{"cited_paper":"/paper/2203.15386","citing_paper":"/paper/2607.23448"},"observation_digest":"sha256:6caea884b9c0984ba5328dada85753db9ab91afc097ba84a042b99aef334d7a5","observation_id":"a2534b5d-bb09-4dee-88b3-a82becf063fa","resolution":{"observed_at":"2026-07-30T21:57:09.816674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.23448","last_updated":"2026-07-26T04:13:43Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-08T19:30:27.436950Z","submitted_at":"2026-07-26T04:13:43Z","title":"Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2607.23448."}