{"as_of":"2026-08-14T00:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1fd19dd58f9ae8fd9878b4483cf12534f9e7990627179b7c35f52b43ef9bd7d3","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:54:36.505091Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2502.07435/citation-record","integrity":"/paper/2502.07435/integrity","json":"/paper/2502.07435/citation-record.json","paper":"/paper/2502.07435"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.753214Z","title":null,"venue":null,"work_id":"5268700b-0ec1-43a7-bb51-7cfd5aecbce3","year":2006},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.426860Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:3e12f8e35ff878c2b6e3d67599fe22d4d70453d66318f361c19e4dd2b2ca35b8","observation_id":"ea8feb09-f3f4-4686-8ec4-7b9c55577038","resolution":{"observed_at":"2026-08-08T12:54:36.756373Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.744262Z","title":"Derivative-Free and Blackbox Optimization","venue":null,"work_id":"690d76b1-be98-466c-a3ce-dee063111205","year":2017},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.431006Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:6e4fe98b82c0b983f6be28d99b7c8014b0e2481fa5e2c4140307516422da5cba","observation_id":"9638d992-ba15-4bc9-bb6e-86607a30a069","resolution":{"observed_at":"2026-08-08T12:54:36.747588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.735231Z","title":null,"venue":null,"work_id":"6ebe8f53-a1b5-447b-b955-7823bde57d95","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.434538Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:874e29981fae6b1e49990d37b1fde6a2ebc6d162300cb4f52c12d2cb4cad6a22","observation_id":"8334d3ea-e492-448b-80ac-f3657191318d","resolution":{"observed_at":"2026-08-08T12:54:36.738534Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.725788Z","title":"Abdalla, Azizallah Izady, Mohammad Reza Nikoo, and Ali Al- Maktoumi","venue":null,"work_id":"a775fab6-2b98-4963-bdc2-a604b093a9df","year":2020},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.438206Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:08dc77cfe484351513853aa43268b13322d8ca7e523b6d6ff389d1a534dc3bc0","observation_id":"bccd6f03-4cfe-4c2e-bba0-e6ed4a7e8f7f","resolution":{"observed_at":"2026-08-08T12:54:36.729371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.716425Z","title":"Conn, Katya Scheinberg, and Luis N","venue":null,"work_id":"19b1028d-1f43-4b6c-b9e8-975b500a40a6","year":2009},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.442643Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:ac63740151339396095ba181f1057375ff84daae40bdabbd4cb8c9727f48d779","observation_id":"ee4728af-4cfc-4e05-b6cf-a548abd34d60","resolution":{"observed_at":"2026-08-08T12:54:36.719432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.707984Z","title":"Conn, Katya Scheinberg, and Lu ´ ıs N","venue":null,"work_id":"a2464892-33eb-422f-b854-d404d9f9ceba","year":2009},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.446441Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:285c4311914d46bfa4cc50410e855b096970dad2c72a6cc11ba5d011e5b84df3","observation_id":"55a85610-a202-4fc2-a4a1-f1dab3262328","resolution":{"observed_at":"2026-08-08T12:54:36.711040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-08-08T12:54:36.450793Z","title":"Sobolev Training for Neural Networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.450793Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:4e8c8c8d06d32af85bb6d1060dad65b92e1a1051aa9122d39c4f9d69f79634b5","observation_id":"99d6841b-4b93-48d0-9e20-5c685ff5a5bd","resolution":{"observed_at":"2026-08-08T12:54:36.450793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.01349","last_updated":"2015-06-03T18:56:19Z","snapshot_observed_at":"2026-07-06T04:19:49.938336Z","submitted_at":"2015-06-03T18:56:19Z","title":"Bayesian optimization for materials design","version":1},"cited_work":{"arxiv_id":"1506.01349","doi":null,"metadata_source":"pith","pith_arxiv_id":"1506.01349","snapshot_observed_at":"2026-08-08T12:54:36.554539Z","title":"Bayesian optimization for materials design","venue":"stat.ML","work_id":"c610e0e5-9d52-4b03-b429-bb6c7c7131b2","year":2015},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.454162Z"},"links":{"cited_paper":"/paper/1506.01349","citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:680d2222b98ca6cbba911eb1b2b4b484ab55065a60ad9a449f21c80672beebca","observation_id":"3999a1be-d210-4009-a10b-a6cc981a83cc","resolution":{"observed_at":"2026-08-08T12:54:36.558101Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12253","last_updated":"2023-11-21T00:21:15Z","snapshot_observed_at":"2026-08-13T05:20:48.880141Z","submitted_at":"2023-11-21T00:21:15Z","title":"The limitation of neural nets for approximation and optimization","version":1},"cited_work":{"arxiv_id":"2311.12253","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.12253","snapshot_observed_at":"2026-08-08T12:54:36.542630Z","title":"The limitation of neural nets for approximation and optimization","venue":"cs.LG","work_id":"eba65472-e5eb-4e26-852a-a83bca477fde","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.457407Z"},"links":{"cited_paper":"/paper/2311.12253","citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:ffadc2b04badc70ad8272cb676a9ce6e39e91573baf0572a39a40abfe8fbc80f","observation_id":"3dbd8a0b-1a33-4118-93d2-926c838da55e","resolution":{"observed_at":"2026-08-08T12:54:36.546021Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.699627Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":"029c5d35-db72-497a-8533-da1b5c24b0f9","year":2010},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.460616Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:9c8af3f22ef5dc2c87e67bd70e5d1f043ccf180b46207949d307669dbd595b52","observation_id":"282e7a1a-ceea-41e3-8f61-d2e16787968e","resolution":{"observed_at":"2026-08-08T12:54:36.702695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.691316Z","title":null,"venue":null,"work_id":"e5ea2cce-e72c-47ac-9285-8656a0898cfe","year":2015},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.463445Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:b4fcc6a82468f73a39a7617bbfd6f76b697539515af541e840d8dd0071f633f9","observation_id":"cd8d0008-b177-4015-8bda-cf60aff72645","resolution":{"observed_at":"2026-08-08T12:54:36.694282Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.683381Z","title":"Grapiglia","venue":null,"work_id":"da938108-67f5-4577-be3d-68d4f935f834","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.466280Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:6783949db78ac3d4598a48a34c7a28ef7d92e8e7d5f0681ac40d67e5649ffc6b","observation_id":"dabdd6e6-7d0b-4723-87a8-9327274f3b1f","resolution":{"observed_at":"2026-08-08T12:54:36.686140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.674522Z","title":"Grapiglia","venue":null,"work_id":"78feee43-c78c-4b09-9c9f-6f7f2a00e122","year":2024},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.469223Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:16f70b1598eba9f0bb4fd5f08fb9f6acec71e8cac80c8240a3b7b777bef29774","observation_id":"da152210-e6ab-4626-9c13-ee1437ec714f","resolution":{"observed_at":"2026-08-08T12:54:36.677712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05636","last_updated":"2025-01-16T14:54:27Z","snapshot_observed_at":"2026-08-13T17:18:03.864114Z","submitted_at":"2021-12-10T16:10:00Z","title":"OPM, a collection of Optimization Problems in Matlab","version":2},"cited_work":{"arxiv_id":"2112.05636","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.05636","snapshot_observed_at":"2026-08-08T12:54:36.527081Z","title":"OPM, a collection of Optimization Problems in Matlab","venue":"math.OC","work_id":"6ff2d699-fefd-4137-b00b-2e7f0475a21e","year":2021},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.472645Z"},"links":{"cited_paper":"/paper/2112.05636","citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:be91a7877e4eb66b5747774d04562b612ce4462010a8398833379b8a95fe0b95","observation_id":"eb4f6952-1c4d-4c3e-97d7-bfe1eb83993e","resolution":{"observed_at":"2026-08-08T12:54:36.532460Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.665425Z","title":"Temperature modeling of creep- feed grinding processes for nickel-based superalloys with variable heat flux distribution","venue":null,"work_id":"33d4141e-7f54-478b-b05b-c55c45c81511","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.475910Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:f10c480ccd07fad7d2c86533b32b2a3ebc36c413bd8df4e5ab0c6231569b7b68","observation_id":"100d3070-ef28-4d82-979c-7992a8451c06","resolution":{"observed_at":"2026-08-08T12:54:36.668866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.656043Z","title":"Delving Deep into Rectifiers: Sur- passing Human-Level Performance on ImageNet Classification","venue":null,"work_id":"d89f82af-5cab-4e17-b128-db94702378fd","year":2015},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.478749Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:e34e75d80a17964a3a27fe9d0a56f66a81b7558477883a56272093749e40dfc0","observation_id":"bddb47af-9d1b-490f-9a18-e290d579e597","resolution":{"observed_at":"2026-08-08T12:54:36.659621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.647323Z","title":"Neural Network Accelerated Implicit Fil- tering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Op- timization Methods","venue":null,"work_id":"75d92a82-1ee9-4a46-8f10-17b7eee2bf6d","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.481560Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:71040422f080b3aef5d6c456fb958a31244dd0637eee646bd24d816dd3b44876","observation_id":"80238972-98a3-4c57-802c-6df1a3f74408","resolution":{"observed_at":"2026-08-08T12:54:36.650521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.638644Z","title":null,"venue":null,"work_id":"3e75e595-b473-439b-ac68-32dca17fe35e","year":2011},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.484495Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:7b8078ffaae5a17e462a2485251fe27f28dfd75a56feab42ced28df4c6e7365b","observation_id":"838d4321-8aba-4176-b99f-6fdfe26dc924","resolution":{"observed_at":"2026-08-08T12:54:36.641784Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.629538Z","title":"ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations","venue":null,"work_id":"704a4c1c-9937-44c6-ae95-e38fc96d6fc6","year":2020},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.487336Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:0480e436a1d9daeadd78c5a04bc2d016b0afbb73d76bdef13b73387bfc23fc30","observation_id":"cb9031a7-99f2-4fc2-b76b-06749ed9447f","resolution":{"observed_at":"2026-08-08T12:54:36.632885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.619548Z","title":null,"venue":null,"work_id":"d0dc9f52-74a1-4fe9-8ada-3a710c6559c5","year":2019},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.490230Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:145a10301a052d009eb9a104006daa62e43789eb0be682621a0c3617ebb2dbc7","observation_id":"74096393-6583-4638-b1ae-0d655b1cf527","resolution":{"observed_at":"2026-08-08T12:54:36.623642Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.610231Z","title":"Benchmarking Derivative-Free Optimization Algorithms","venue":null,"work_id":"d1ec8df7-dd34-4354-b636-e6c21fb9fdec","year":2009},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.493166Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:ceb13087e9dc2dd3953d01c52f7b1f4e9cea2edb73a90f2a087ce9b7312393b1","observation_id":"32488efc-2989-4af3-bcf7-efacf4482f03","resolution":{"observed_at":"2026-08-08T12:54:36.613334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.600789Z","title":null,"venue":null,"work_id":"90774edb-8cab-4606-a494-f4b10a41c26f","year":2006},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.496180Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:727580d424137afc5c9e61a30bd33ccb97b70fd8b10eab1b5c928ef598297c0e","observation_id":"9d35e6c5-2944-42a2-a15b-305e21741093","resolution":{"observed_at":"2026-08-08T12:54:36.604547Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.591884Z","title":"On the numerical performance of finite-difference-based methods for derivative-free optimization","venue":null,"work_id":"32d7de9b-0912-4993-acd4-0000b9326321","year":2023},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.499074Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:5933dfa354c1a0c7f0c9288b4da95ca5b1cae3b98f6d9d47ab22857533285a1f","observation_id":"a5c5e414-d471-4ec1-b5cf-f4ead9917467","resolution":{"observed_at":"2026-08-08T12:54:36.595025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.582976Z","title":null,"venue":null,"work_id":"dde12832-fb5f-4689-a9a8-e0d17857920e","year":2013},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.502064Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:20d69387f2bb5a77bcaea8992610f148ff46c1526224b01ae50d3c87e3cdca97","observation_id":"3d806e2c-50fc-41c2-86de-df93a702409d","resolution":{"observed_at":"2026-08-08T12:54:36.585934Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:36.573237Z","title":"Accelerating Discovery of Polyimides with Intrinsic Microporosity for Membrane-Based Gas Separation: Synergizing Physics-Informed Performance Metrics and Active Learning","venue":null,"work_id":"979b0b4b-33dd-4dfc-905c-33f34c275812","year":2024},"citing_paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T12:54:36.505091Z"},"links":{"citing_paper":"/paper/2502.07435"},"observation_digest":"sha256:022fd3c06d0c9508964d17487f859e813f57d17c2933e2911a74cd9ef108fe04","observation_id":"c7bdf671-cc96-40df-a556-25dc6f04c74e","resolution":{"observed_at":"2026-08-08T12:54:36.576687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.07435","last_updated":"2025-02-11T10:24:35Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-13T18:20:46.645809Z","submitted_at":"2025-02-11T10:24:35Z","title":"Enhancing finite-difference based derivative-free optimization methods with machine learning"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":2,"verified_fuzzy":14},"total_outbound_references":25},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2502.07435."}