{"as_of":"2026-08-08T18:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff96558a5b28b92e9946a5a670f7655a3f0441592ad877be62f79a873ca51754","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:06:59.194060Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T14:37:03.869128Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-08-07T12:06:59.194060Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00478","last_updated":"2025-05-31T09:07:19Z","snapshot_observed_at":"2026-08-07T12:02:04.160770Z","submitted_at":"2025-05-31T09:07:19Z","title":"Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:06:59.194060Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2506.00478"},"observation_digest":"sha256:df1af6f044a1fe7b78f932fee9c1c64d8ee4734887ef9663ef0487264bfbd827","observation_id":"7fbbfe0e-323a-4372-bc25-7f06ed587665","resolution":{"observed_at":"2026-08-07T12:06:59.194060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-08-05T16:02:32.161178Z","title":"Available: https://arxiv.org/abs/2403.17660","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.19083","last_updated":"2025-08-26T14:43:10Z","snapshot_observed_at":"2026-08-08T09:59:59.604269Z","submitted_at":"2025-08-26T14:43:10Z","title":"A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T16:02:32.161178Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2508.19083"},"observation_digest":"sha256:b93693408f1c3344124e28645186034e94badb7bafb7bf2e7449ccfa2eb6b813","observation_id":"207898e9-c24a-4388-8b1a-b2ef71e08823","resolution":{"observed_at":"2026-08-05T16:02:32.161178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-08-05T13:25:26.794918Z","title":"Canos: A fast and scalable neural ac-opf solver robust to n-1 perturbations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00655","last_updated":"2025-08-31T01:29:53Z","snapshot_observed_at":"2026-08-08T14:36:00.904372Z","submitted_at":"2025-08-31T01:29:53Z","title":"Revisiting Deep AC-OPF","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T13:25:26.794918Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2509.00655"},"observation_digest":"sha256:ba4a5bfa7b9ad38854004b72eca35764b92909665c89c91e850f76009e0296fc","observation_id":"0b455a42-602a-4b9b-b053-0a2f17beedc8","resolution":{"observed_at":"2026-08-05T13:25:26.794918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":"2403.17660","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-07-02T14:37:03.869128Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","venue":null,"work_id":"491b6828-97f5-4eae-9002-0358d981e5f4","year":2024},"citing_paper":{"arxiv_id":"2510.06860","last_updated":"2026-04-21T10:08:33Z","snapshot_observed_at":"2026-07-06T22:32:03.314860Z","submitted_at":"2025-10-08T10:28:46Z","title":"Towards Generalization of Graph Neural Networks for AC Optimal Power Flow","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-18T08:49:17.172538Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2510.06860"},"observation_digest":"sha256:7c5822dc52052063c31186ff01e102393641dcbf5a789aea973ae68f3f4903ed","observation_id":"e2a77339-68f0-4482-8006-72a19febd335","resolution":{"observed_at":"2026-05-18T08:51:08.890121Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":"2403.17660","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-07-02T14:37:03.869128Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","venue":null,"work_id":"491b6828-97f5-4eae-9002-0358d981e5f4","year":2024},"citing_paper":{"arxiv_id":"2605.02026","last_updated":"2026-05-03T19:23:37Z","snapshot_observed_at":"2026-08-06T15:27:02.307657Z","submitted_at":"2026-05-03T19:23:37Z","title":"Towards Systematic Generalization for Power Grid Optimization Problems","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-09T17:20:33.456156Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2605.02026"},"observation_digest":"sha256:b832903913ca46fe0c6fa9d52e9da775e06f9ec5e629b7ee1d44809a30f3d7fc","observation_id":"7a751cb4-1be8-4e42-bdca-6727b506e3b8","resolution":{"observed_at":"2026-05-11T16:21:08.632325Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":"2403.17660","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-07-02T14:37:03.869128Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","venue":null,"work_id":"491b6828-97f5-4eae-9002-0358d981e5f4","year":2024},"citing_paper":{"arxiv_id":"2605.02133","last_updated":"2026-05-04T01:25:16Z","snapshot_observed_at":"2026-08-04T04:22:12.229079Z","submitted_at":"2026-05-04T01:25:16Z","title":"LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-09T16:44:23.681475Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2605.02133"},"observation_digest":"sha256:23b6909205f71e92121549fe29664c425c8056b59477fef8e7d0698c49ee5f55","observation_id":"ec8d9606-a147-45ec-9b3e-316ac013d1df","resolution":{"observed_at":"2026-05-11T16:26:09.901018Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":"2403.17660","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-07-02T14:37:03.869128Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","venue":null,"work_id":"491b6828-97f5-4eae-9002-0358d981e5f4","year":2024},"citing_paper":{"arxiv_id":"2605.11102","last_updated":"2026-05-11T18:06:58Z","snapshot_observed_at":"2026-07-31T07:46:26.973726Z","submitted_at":"2026-05-11T18:06:58Z","title":"Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-13T07:10:46.493063Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2605.11102"},"observation_digest":"sha256:30214a16465b48ef9713a6f5d1a8b885239be1d8b5c5e7c2f9a67a3ac852634e","observation_id":"36d7c584-e613-4acb-bf8f-1df18811d0d2","resolution":{"observed_at":"2026-05-13T07:12:28.018435Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","version":1},"cited_work":{"arxiv_id":"2403.17660","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17660","snapshot_observed_at":"2026-07-02T14:37:03.869128Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","venue":null,"work_id":"491b6828-97f5-4eae-9002-0358d981e5f4","year":2024},"citing_paper":{"arxiv_id":"2606.05772","last_updated":"2026-06-04T06:57:17Z","snapshot_observed_at":"2026-08-07T14:19:00.805175Z","submitted_at":"2026-06-04T06:57:17Z","title":"Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-28T00:19:07.001137Z"},"links":{"cited_paper":"/paper/2403.17660","citing_paper":"/paper/2606.05772"},"observation_digest":"sha256:2ba73a275c150a4adea8e4eec6bd14169ea249872305aaa2e1669a8e185955e9","observation_id":"7aa7693a-d3cf-485d-9e96-dd851db48ccb","resolution":{"observed_at":"2026-07-02T14:37:03.870737Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2403.17660/citation-record","integrity":"/paper/2403.17660/integrity","json":"/paper/2403.17660/citation-record.json","paper":"/paper/2403.17660"},"outbound":[],"paper":{"arxiv_id":"2403.17660","last_updated":"2024-03-26T12:47:04Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T09:42:27.960000Z","submitted_at":"2024-03-26T12:47:04Z","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2403.17660."}