{"as_of":"2026-08-08T16:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d17c26b841ccbd7b4dc19e74e5d170ddf23ea11e8cd71d00b0fe0b0d5f94329d","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T18:37:52.256296Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2502.10330/citation-record","integrity":"/paper/2502.10330/integrity","json":"/paper/2502.10330/citation-record.json","paper":"/paper/2502.10330"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:53.217450Z","title":"Learning warm-start points for ac optimal power flow","venue":null,"work_id":"90f40e90-fc78-46eb-9b9a-06063d860620","year":2019},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.007453Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:aca708850200477f6852b31369b4d681378ac2f4acf56bfc578395cb7776878e","observation_id":"1b7392b9-0b81-47a4-8636-c4ae6816ac54","resolution":{"observed_at":"2026-08-07T18:37:53.222304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.202182Z","title":"Bellman, Rand Corporation, and Karreman Mathematics Research Collection","venue":null,"work_id":"ce247023-f53b-4955-9847-58d85f3b3054","year":1957},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.013298Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:ae48bdb94c3565259602a183e268db9dcbd9a181a10a01e9b90e35b3f5e224db","observation_id":"22f55b80-7ec9-456f-969c-e79a73a0105e","resolution":{"observed_at":"2026-08-07T18:37:53.207746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.187083Z","title":"Anjos, and Sébastien Le Digabel","venue":null,"work_id":"d798a8cc-face-4ded-9ed9-553e74d2f324","year":2018},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.018630Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:46113134f2f556c8c1cc0b8b8402a8348ea82257908ea42ace69374b4158d294","observation_id":"b66831f3-86c2-4488-a9e4-65e7a6c9f94b","resolution":{"observed_at":"2026-08-07T18:37:53.192123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.171064Z","title":"Diffusion policies for generative modeling of spacecraft trajectories","venue":null,"work_id":"a712560c-2247-4da3-96b4-9303feb4614c","year":2025},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.025107Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:6ac89be4deede229cc41d4a5b2619af34856bdc7bee9fe1ce3b6075f803008ff","observation_id":"a443fff1-f9a4-4a93-b3bf-280852637774","resolution":{"observed_at":"2026-08-07T18:37:53.176188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.155374Z","title":"Predict and constrain: Modeling cardinality in deep structured prediction","venue":null,"work_id":"03eae3bc-0df5-4009-aec7-e754a6915169","year":2018},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.030298Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:f9f99b271dea22233f263a477b860edc1ada787b72fc36d44ab7767c3b990cd9","observation_id":"45d809fa-4c85-4317-bf66-4b9a02040ef3","resolution":{"observed_at":"2026-08-07T18:37:53.160338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.139345Z","title":"History of optimal power flow and formulations","venue":null,"work_id":"bb1bc5e0-2996-4e7b-a219-bcd84b1f5a41","year":2012},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.036109Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:c5863b989e70fde92b607e3eebbf575c9dd4d182bd9fdca57d8c4ad79d5bbce8","observation_id":"4a73c814-53df-4d34-b705-4bb6723d993c","resolution":{"observed_at":"2026-08-07T18:37:53.144462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.123434Z","title":"Neural networks for portfolio analysis with cardinality constraints","venue":null,"work_id":"ce214665-89e8-463d-a7c1-110c2b284d98","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.041844Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:db2a6a65960620159e4bcbe67c87cbc4b2d4832844e0ddda377f6122ffb556d2","observation_id":"3cc5481c-6662-46cf-ad76-d42471491b8e","resolution":{"observed_at":"2026-08-07T18:37:53.128585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16356","last_updated":"2020-06-29T20:22:16Z","snapshot_observed_at":"2026-07-31T01:26:19.887327Z","submitted_at":"2020-06-29T20:22:16Z","title":"High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.16356","snapshot_observed_at":"2026-08-07T18:37:52.046834Z","title":"High- fidelity machine learning approximations of large-scale optimal power flow","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.046834Z"},"links":{"cited_paper":"/paper/2006.16356","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:1879fa87d3af92f296208565b9277c0a935d858c72fd7ff7f441c601b98fa712","observation_id":"2fe92e2b-49e4-4c57-8f3d-4a0f919dc109","resolution":{"observed_at":"2026-08-07T18:37:52.046834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.04137","last_updated":"2024-03-14T04:36:31Z","snapshot_observed_at":"2026-08-03T00:02:57.373313Z","submitted_at":"2023-03-07T18:50:03Z","title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.04137","snapshot_observed_at":"2026-08-07T18:37:52.051939Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.051939Z"},"links":{"cited_paper":"/paper/2303.04137","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:81294ca9dd7f2e9019852ab61b82de4c1fc1aef4ebaa92a246252f1df6f0c706","observation_id":"99efcea3-13b3-48e3-ba2b-497cfb1e122d","resolution":{"observed_at":"2026-08-07T18:37:52.051939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:53.100773Z","title":"Constrained synthesis with projected diffusion models","venue":null,"work_id":"325be2c7-03d0-4055-9511-824f373bdf42","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.056909Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:1ed66f8906866cda5b0d0b2abe56a46f1c33f39ddd1c10b372005e12045ffab0","observation_id":"a2ff3e62-2e5f-4183-b1b6-9f643b4d72cc","resolution":{"observed_at":"2026-08-07T18:37:53.105628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.084215Z","title":"Cover and Bradley Efron","venue":null,"work_id":"00d7ce0c-e6b3-4302-8636-3f936da31de8","year":1967},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.061545Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:160d6333c30231822123e892f648afe77fd04d1ae38972f366dcec33d401bd6b","observation_id":"c95fcdff-803a-4725-b4d5-faf46fa1b1d3","resolution":{"observed_at":"2026-08-07T18:37:53.089563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16173","last_updated":"2024-12-16T15:42:46Z","snapshot_observed_at":"2026-08-08T00:54:50.275251Z","submitted_at":"2024-05-25T10:45:46Z","title":"Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16173","snapshot_observed_at":"2026-08-07T18:37:52.066423Z","title":"Diffusion-based reinforcement learning via q-weighted variational policy optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.066423Z"},"links":{"cited_paper":"/paper/2405.16173","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:4256f0a414fb8e6431e3adf7580b0719c16dcde71f874705dddf8f1e590db4ff","observation_id":"d2d223cc-4f1c-428a-8e52-3e14cb117f46","resolution":{"observed_at":"2026-08-07T18:37:52.066423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:53.067414Z","title":"Reduced policy optimization for continuous control with hard constraints","venue":null,"work_id":"64e26fc3-650c-4754-b513-e12fda50718f","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.071548Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:20b50f9de362335344463fc7c028813fd53b8a69ddaaeda7b71e8c257425f04d","observation_id":"82f04e73-7bdf-4ed2-85da-dedd72357d68","resolution":{"observed_at":"2026-08-07T18:37:53.072338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.051836Z","title":"Smart-pgsim: Using neural network to accelerate ac-opf power grid simulation","venue":null,"work_id":"f4310f4b-9f36-4e8f-9dce-55a92d431b03","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.076173Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:b3885f36a05853a8dee76779015bc77ead8e5b15e86e68cab4734e737e50a85f","observation_id":"b3fe1384-cb41-417a-bb87-7e705674f260","resolution":{"observed_at":"2026-08-07T18:37:53.057153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.12225","last_updated":"2021-04-25T18:21:59Z","snapshot_observed_at":"2026-08-07T17:21:05.516838Z","submitted_at":"2021-04-25T18:21:59Z","title":"DC3: A learning method for optimization with hard constraints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.12225","snapshot_observed_at":"2026-08-07T18:37:52.081050Z","title":"Dc3: A learning method for optimization with hard constraints","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.081050Z"},"links":{"cited_paper":"/paper/2104.12225","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:82708ef4d3fc7e09d0dbca47f4d635fc5869ea8ed16dd73dbdabdece9ff2dbb0","observation_id":"a7463cd1-e87b-4e9f-8a7d-e5b6549f79e4","resolution":{"observed_at":"2026-08-07T18:37:52.081050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:53.036520Z","title":"Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization","venue":null,"work_id":"107a31e0-2c8a-4a7f-9fbe-cb0fd4225a08","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.085923Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:b4dd6876d77e5b07cd94d4a39454fd014307d202fcb675b1fa39d7cccb1b2d9f","observation_id":"fc659565-70c3-4e86-88a4-b86bfb4ec8e0","resolution":{"observed_at":"2026-08-07T18:37:53.041399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.020950Z","title":"Lenssen, Christopher Morris, Jonathan Masci, and Nils M","venue":null,"work_id":"8a53bf59-b050-44c1-82da-9066ff980c96","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.090730Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:60bb32ef13598a7768ecd1bd552679955e89e3a21c1ed78460993bdf4de37cb8","observation_id":"6b5caf45-29ff-48da-8510-7bd3526e61e5","resolution":{"observed_at":"2026-08-07T18:37:53.025994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:53.005241Z","title":"Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods","venue":null,"work_id":"b9f7b293-a268-49f5-ab16-811e06402bbf","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.095098Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:dfb623060645272869424cb317a4b1d00e22f02b2a3045fdd044c0aa61a4fb38","observation_id":"e9f417e2-13d6-4afe-8050-661d1f9b73f7","resolution":{"observed_at":"2026-08-07T18:37:53.010070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04436","last_updated":"2024-03-07T12:10:41Z","snapshot_observed_at":"2026-08-07T19:12:09.744702Z","submitted_at":"2024-03-07T12:10:41Z","title":"Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04436","snapshot_observed_at":"2026-08-07T18:37:52.099458Z","title":"Learning human-to-humanoid real-time whole-body teleoperation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.099458Z"},"links":{"cited_paper":"/paper/2403.04436","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:0abddc4d4ab81188904937763a41a17b1b85b104726d8cd640f1b1d817ad5d3f","observation_id":"797baaaf-a44b-4434-b75c-7e003e30665f","resolution":{"observed_at":"2026-08-07T18:37:52.099458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.104666Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.104666Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:68a3695aa34d1fcd430dd4ef8ba8938f6e23ee9d1e78410dcc65cb5535feff5a","observation_id":"04c809e6-22c4-47c8-88ad-b74a9866d833","resolution":{"observed_at":"2026-08-07T18:37:52.104666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.979373Z","title":"Advancements and future directions in the application of machine learning to ac optimal power flow: A critical review","venue":null,"work_id":"7b072709-7b84-425e-8bf0-9d805c234aee","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.109284Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:c581783667a0d1e0e727d57a3ada6b50ee218e61acc373bc600df01a9b4c09ca","observation_id":"c1bb70f9-a676-460c-b359-ab6d7aa5a5cf","resolution":{"observed_at":"2026-08-07T18:37:52.984725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.113817Z","title":"Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.113817Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:8840b27bb178057905d30052e37ce71efc01f88c50b29e884ed4d21e70b64104","observation_id":"fb07f71f-3f88-4af1-8c3b-dd6a4900dd6a","resolution":{"observed_at":"2026-08-07T18:37:52.113817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01758","last_updated":"2024-05-02T21:50:26Z","snapshot_observed_at":"2026-08-08T07:57:46.554965Z","submitted_at":"2024-05-02T21:50:26Z","title":"CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning","version":1},"cited_work":{"arxiv_id":"2405.01758","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.01758","snapshot_observed_at":"2026-08-07T18:37:52.550918Z","title":"CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning","venue":"cs.RO","work_id":"33aa19e8-61e7-40bb-8cd5-4371281ec40a","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.118277Z"},"links":{"cited_paper":"/paper/2405.01758","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:5e1b091a04cf1fdb22980ae89758074e38d18a8adfce81c5da35f1c5022e96c5","observation_id":"714e0175-6b59-4aa9-a00b-af22ace0eaa4","resolution":{"observed_at":"2026-08-07T18:37:52.556396Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.01939","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.521464Z","title":"Equality constrained diffusion for direct trajectory optimiza- tion","venue":null,"work_id":"3d6028c5-49ce-4434-a999-883cd4bd30e5","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.123085Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:68d22dafa6ef2f5f3ea8cafc8f903a3f36d569f64db8efcdb87b74b8242852b8","observation_id":"60409483-a94b-4183-b696-6caa1f5a8cec","resolution":{"observed_at":"2026-08-07T18:37:52.531952Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.953973Z","title":"Efficient and guaranteed-safe non-convex trajectory optimization with constrained diffusion model","venue":null,"work_id":"5f75235c-86d0-4346-8118-c6e1efc7953f","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.127822Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:4dc7d1426a942eddb5b602a89434739fe646631bb656b78c527e6824ccb6dc14","observation_id":"384414fa-fcf1-486e-83ff-955c6c5e91c5","resolution":{"observed_at":"2026-08-07T18:37:52.959135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.937671Z","title":"Amortized global search for efficient preliminary trajectory design with deep generative models","venue":null,"work_id":"ab54744d-e8b1-4251-8574-983c87e4bee8","year":2023},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.132419Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:46f447f37c6d4b47ccd8fc6056c510d6be3ab716d48a0e4bf9f3d8ab4f25fb81","observation_id":"8e1ee214-9a52-46a0-aec3-feab3bccb2c0","resolution":{"observed_at":"2026-08-07T18:37:52.943343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01885","last_updated":"2016-06-06T19:50:47Z","snapshot_observed_at":"2026-08-02T12:24:31.765445Z","submitted_at":"2016-06-06T19:50:47Z","title":"Learning to Optimize","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01885","snapshot_observed_at":"2026-08-07T18:37:52.137398Z","title":"Learning to optimize","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.137398Z"},"links":{"cited_paper":"/paper/1606.01885","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:92b54c93b529553baa79c879da8f114dbba29a2ce38c45c229531f3e152694fa","observation_id":"971a7549-bbe3-4285-9e5f-f65ec0175a71","resolution":{"observed_at":"2026-08-07T18:37:52.137398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.920974Z","title":"Gauge flow matching for efficient constrained generative modeling over general convex set","venue":null,"work_id":"fce520b1-94c6-4e6c-9cf4-7356c023a69d","year":2025},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.142481Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:90f81de84bcfe7ac571c9049da8b5a84c1e85ded14faf93dd636b0b193c7e97e","observation_id":"c1a3ef4d-b1bc-47dc-8800-12bc1c0632cd","resolution":{"observed_at":"2026-08-07T18:37:52.926255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.147152Z","title":"From distribution learning in training to gradient search in testing for combinatorial optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.147152Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:bc2dcd71fbaebedd133e9dc8dd7b1f4a0d3bac180872c5cbf110f80d803631d0","observation_id":"4c746da7-adb8-4bb6-8162-ab93c3358ed6","resolution":{"observed_at":"2026-08-07T18:37:52.147152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.895068Z","title":"Fast t2t: Optimization consistency speeds up diffusion-based training-to-testing solving for combinatorial optimization","venue":null,"work_id":"093d4bac-09a8-45c9-aaae-133eeb8d1d39","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.152074Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:44d5e7924e4ca256e1381880c49fa9d06a92ced826f8e7d8e9cdb3dcbb6a2bb0","observation_id":"66e298cb-9791-4bf5-9fb2-30fc01744596","resolution":{"observed_at":"2026-08-07T18:37:52.899932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.879555Z","title":"Generative learning for solving non-convex problem with multi-valued input-solution mapping","venue":null,"work_id":"23c142c7-e188-43dd-af68-2b6899b9450d","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.156841Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:d646165e3e22c650800c764092c0add5471e89d394500ca5fe614225653cce59","observation_id":"2e8c0a87-4f59-4521-b17e-dbd1b885e5d9","resolution":{"observed_at":"2026-08-07T18:37:52.884724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.864474Z","title":"Learning to search in local branching","venue":null,"work_id":"21c7ae21-6bc8-4929-9fe9-aa2fbcd81f7c","year":2022},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.161295Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:a8ac33b57c39c2537350aec21461994e3ee191382d569c98969b1fd9f2cb07bb","observation_id":"2ceab260-e4c9-406c-ba2a-a79a31370a1f","resolution":{"observed_at":"2026-08-07T18:37:52.869438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.848764Z","title":"Numerical optimization","venue":null,"work_id":"e4eef349-9a9f-488d-a69d-53548a7d6135","year":1999},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.166025Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:ee9f60efeb4e446ead61d2063bde427cd4af4e2b7ac281e6f3abfa65348f2d74","observation_id":"0600aea3-00f0-4898-a042-50f33b166bb8","resolution":{"observed_at":"2026-08-07T18:37:52.854062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01573","last_updated":"2024-05-28T22:14:25Z","snapshot_observed_at":"2026-07-06T18:39:47.662209Z","submitted_at":"2024-05-28T22:14:25Z","title":"Model-Based Diffusion for Trajectory Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01573","snapshot_observed_at":"2026-08-07T18:37:52.170794Z","title":"Model-based diffusion for trajectory optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.170794Z"},"links":{"cited_paper":"/paper/2407.01573","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:2078fc1d076e6d8bd415daa06a5bf8022b069a55cf35528717c17298100a5b21","observation_id":"1d1592bc-b034-4fce-9b2c-bc604f622782","resolution":{"observed_at":"2026-08-07T18:37:52.170794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.832543Z","title":"Deepopf: A deep neural network approach for security-constrained dc optimal power flow","venue":null,"work_id":"8f18c7f1-74f6-4f90-953c-a2388af98f76","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.175971Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:ea4313dac88e56b3b996ba1b03222e8bb9ed865d8e49bdec60442e95738dabad","observation_id":"2bd76098-ea58-4f40-ac69-06a0fa27803f","resolution":{"observed_at":"2026-08-07T18:37:52.838238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.815070Z","title":"Self-supervised primal-dual learning for constrained optimization","venue":null,"work_id":"2415224a-ac02-470a-8d34-6a16a5210ca0","year":2023},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.180819Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:6a5c5d435200a7f626f39d2854cbc9285372ee7200b38a76ee92b89f698f0004","observation_id":"deb2497e-c7a6-4f19-8849-c0204896ea09","resolution":{"observed_at":"2026-08-07T18:37:52.820725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.798028Z","title":"Can push-forward generative models fit multimodal distributions? Advances in Neural Information Processing Systems, 35:10766–10779, 2022","venue":null,"work_id":"c42149b4-fdc9-4dd5-9691-c83a26c9c8b8","year":2022},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.185464Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:edb2febbf48ec96dd32c6013ec596a465558858fe838540d5877ab29acb14b34","observation_id":"e66f631f-3672-4115-8b76-777f91cf9695","resolution":{"observed_at":"2026-08-07T18:37:52.803758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08696","last_updated":"2025-07-08T14:03:25Z","snapshot_observed_at":"2026-08-07T23:54:31.021106Z","submitted_at":"2025-02-12T18:59:55Z","title":"Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08696","snapshot_observed_at":"2026-08-07T18:37:52.189968Z","title":"Scalable discrete diffusion samplers: Combinatorial opti- mization and statistical physics","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.189968Z"},"links":{"cited_paper":"/paper/2502.08696","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:cb0273a116eda4a09f6686aa676b3e21fb7c804aa03cb83f63f6ee10b3b9ad35","observation_id":"4f0b2698-42c8-4de5-b9d2-d756bdcfbdba","resolution":{"observed_at":"2026-08-07T18:37:52.189968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01661","last_updated":"2025-08-22T08:40:58Z","snapshot_observed_at":"2026-07-06T18:24:38.958815Z","submitted_at":"2024-06-03T17:55:02Z","title":"A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01661","snapshot_observed_at":"2026-08-07T18:37:52.194633Z","title":"A diffusion model framework for unsupervised neural combinatorial optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.194633Z"},"links":{"cited_paper":"/paper/2406.01661","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:b7a1d89edcbc69acef1928d06b6775911730027cf8d105eef51e3a4bd5cf023f","observation_id":"a02435d1-a156-4b9c-bd55-13fe602a7359","resolution":{"observed_at":"2026-08-07T18:37:52.194633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.782105Z","title":"Global optimization for optimal power flow over transmission networks","venue":null,"work_id":"2f62c5e4-9044-4ea5-a2a1-75ab7c443be1","year":2017},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.199555Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:38b406aeb59d80fc62bfc4402874a69dbdc824bbc012549b528b2dc79a7f7a66","observation_id":"e7271c55-e8b2-44f0-b292-3aef9e9ff855","resolution":{"observed_at":"2026-08-07T18:37:52.787146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-07T18:37:52.204201Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.204201Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:f18ac3b6f310b9f19b4521b7997a48ae44c2cb6529d85eb67943d0b6e24f033e","observation_id":"dff5c4a1-466d-41b9-b9cd-02beb469978f","resolution":{"observed_at":"2026-08-07T18:37:52.204201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-07T18:37:52.209159Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.209159Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:41fbc95a70a36da1488ae87fd39de469af60d305804915ce3bdf0101ccc16014","observation_id":"96bd9cb2-7d30-4d17-8d47-a1b3a44fe682","resolution":{"observed_at":"2026-08-07T18:37:52.209159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.766105Z","title":"Reinforcement learning for integer program- ming: Learning to cut","venue":null,"work_id":"f9e0e62b-17b7-47c6-98fc-f7af3e2cb73e","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.213689Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:980492d82ec98937bf1c09ac56fc66ab2a3771fbd390d4f2387ed7b136f5eeb3","observation_id":"acb6a9f4-8bb9-4832-ba2b-47d7f0e8c0c2","resolution":{"observed_at":"2026-08-07T18:37:52.771023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.750813Z","title":"On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming","venue":null,"work_id":"ebc51940-f8e9-49d2-9dad-75b7f7006c41","year":2006},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.218461Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:5a80387e73047de81dcff76f365f9ced0dede715f75c727abfb679170fe97e8b","observation_id":"02b20109-b442-4343-a2fd-aee4249bc2f3","resolution":{"observed_at":"2026-08-07T18:37:52.755955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.735334Z","title":"Towards one-shot neural combinatorial solvers: Theoretical and empirical notes on the cardinality-constrained case","venue":null,"work_id":"ac7bcc77-51e5-458c-be8d-a9ab6479f322","year":2023},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.223069Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:2263ba7091d9992a312b6d17d31aa3f6eb2308d83a6e89b3700c969d5284e37a","observation_id":"71834285-dca2-4db1-bed8-c69da9885962","resolution":{"observed_at":"2026-08-07T18:37:52.740448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.227930Z","title":"Learning combinatorial embedding networks for deep graph matching","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.227930Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:13efa98ea45e8e5f72c8da5c697fa6da68292c7321ae52092b6bed29ba3176ca","observation_id":"18e0ff2b-9ab9-47a6-9971-197ca11b040a","resolution":{"observed_at":"2026-08-07T18:37:52.227930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.709140Z","title":null,"venue":null,"work_id":"e3bb59a7-5937-4d41-b855-6f43114a5423","year":1962},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.232555Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:26ab4f057389c51b82e44820564378142a706c903a7d5074263b4ff0a45fc3f7","observation_id":"9d12cd11-52bf-42ca-b371-684114b0b59e","resolution":{"observed_at":"2026-08-07T18:37:52.713682Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.693782Z","title":"Zamzam and Kyri Baker","venue":null,"work_id":"49c873a0-bc63-497e-8748-8bb2a8223471","year":2020},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.237492Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:90948e61c32a9e1f01fd6d3263da9578d0fcc27e1ba9ab033a07a4216a9b4589","observation_id":"20592112-5eee-452f-b95a-eee97e54ca7d","resolution":{"observed_at":"2026-08-07T18:37:52.698825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.678423Z","title":"Learning to solve the ac optimal power flow via a lagrangian approach","venue":null,"work_id":"2dd58c77-4066-4257-8057-be351bae57cd","year":2022},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.242068Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:e0262a28ac6971be1c3f99fd65658542baf235cfc456a1c4d569766a457dc072","observation_id":"e0bec4da-e0a6-4701-9172-7af39c99162a","resolution":{"observed_at":"2026-08-07T18:37:52.683432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02543","last_updated":"2026-05-09T04:14:32Z","snapshot_observed_at":"2026-07-06T19:27:03.209340Z","submitted_at":"2024-10-03T14:47:46Z","title":"Diffusion Models are Evolutionary Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02543","snapshot_observed_at":"2026-08-07T18:37:52.246505Z","title":"Diffusion models are evolutionary algorithms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.246505Z"},"links":{"cited_paper":"/paper/2410.02543","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:849384c7730cd8182f48304393b97eb8ab99922c0c2e06acc9148099de9630d9","observation_id":"ea4e5d2d-4d6b-45ca-8b1f-c5e474539825","resolution":{"observed_at":"2026-08-07T18:37:52.246505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:37:52.662801Z","title":"Synergizing machine learning with acopf: A comprehensive overview, 2024","venue":null,"work_id":"a5bc355c-e2fe-4129-999c-8fb06599caf6","year":2024},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.251268Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:98902cf88ccff287baa725b3cb1e09a65bbe8d364bee1d61ef994260bad23229","observation_id":"0c925ad4-8339-4689-8f7c-b90759b7e095","resolution":{"observed_at":"2026-08-07T18:37:52.667927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:37:52.646795Z","title":"Diffusion","venue":null,"work_id":"869b0dfe-7a4a-4b64-b179-0c5fd5d429f2","year":2011},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.256296Z"},"links":{"citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:0a84d93c57d8722d1f3cbc42f970dd1e27b27917c146b379a6fc78ef1c26a758","observation_id":"c8263d34-2651-47e7-8bd9-e7421f9185ab","resolution":{"observed_at":"2026-08-07T18:37:52.651691Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":32},"total_outbound_references":52},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2502.10330."}