{"as_of":"2026-08-18T00:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f88182f2bab85b49b46f547fc44fa75056fa0b2d960c13f6c5053bd94a037c9","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:28:38.401661Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2508.21431/citation-record","integrity":"/paper/2508.21431/integrity","json":"/paper/2508.21431/citation-record.json","paper":"/paper/2508.21431"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:28:44.309507Z","title":"Agnostic federated learning,","venue":null,"work_id":"00268562-a30c-43a4-8e76-affa920d0b25","year":2019},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.512132Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:06af734dac865b026ec08039970a95a9ab49d3c85470fb7eb853efd0ec461066","observation_id":"ecb99654-8c76-405a-b01b-9e4f87cad347","resolution":{"observed_at":"2026-08-05T14:28:44.380302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T14:28:34.612349Z","title":"Certifyin g some distributional robustness with principled adversarial tr aining,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.612349Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:e128de3c7ff7b1981879e7f0d9a3047f1850ccd9a6e7027fa3e605fa3aa262d6","observation_id":"dfdb64a4-401b-45da-b8db-89bf88fe795b","resolution":{"observed_at":"2026-08-05T14:28:34.612349Z","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":"2026-08-05T14:28:44.049063Z","title":"Game theory for autonomy: From min-max optimization to equilibrium and bounded ratio nality learning,","venue":null,"work_id":"ac5d071f-ce37-403e-b4b8-a437eb83f875","year":2023},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.733717Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:ed57829f7bd40cf62916bff042cb974cb0eb31307e5151ae21c84fba2eb12196","observation_id":"b297cd9e-418e-487f-b6b0-a699a7fa9ec4","resolution":{"observed_at":"2026-08-05T14:28:44.195301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:43.879984Z","title":"Generative adversar ial nets,","venue":null,"work_id":"5c49fd66-9352-4ea8-a8f3-a12a3d3639b6","year":2014},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.846052Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:b3864eb95fa0eefc31cec7f1bc67a8011f0e163fc164de4d319c6f82f3944348","observation_id":"c465dbf3-c95a-4df8-b979-ef0a3e32909e","resolution":{"observed_at":"2026-08-05T14:28:43.946096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:43.646943Z","title":"Improved training of wasserstein gans,","venue":null,"work_id":"d67fba5d-fdf0-4281-a7cd-435987ef7f34","year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.956899Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:0ff0d588fdd892b9b7b606e572620da5f937612d6afae76412c86d30b60bee1a","observation_id":"b6b08e21-41b2-4665-aece-98388a1b4c8f","resolution":{"observed_at":"2026-08-05T14:28:43.775581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02527","last_updated":"2016-10-08T13:25:15Z","snapshot_observed_at":"2026-08-17T09:22:54.525990Z","submitted_at":"2016-10-08T13:25:15Z","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02527","snapshot_observed_at":"2026-08-05T14:28:35.087025Z","title":"Federated optimization: Distributed machine learning for on-device intelligence,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.087025Z"},"links":{"cited_paper":"/paper/1610.02527","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:dae6b750ca887365ef92c83b024582fcbbdd7e36f0c448d81fcf385d936bbc18","observation_id":"80545b20-4383-4c86-b66a-50b3bc77e575","resolution":{"observed_at":"2026-08-05T14:28:35.087025Z","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":"2026-08-05T14:28:43.491105Z","title":"Distributed optimization for control,","venue":null,"work_id":"f06ca46d-3413-4619-864a-4e56dcfae59a","year":2018},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.223319Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:29903669f2353b70596fb9c92a2dd34fee965dade935bf195be570ec9383f327","observation_id":"b8870ab7-dbe4-4b0b-9e39-d52bca447eb1","resolution":{"observed_at":"2026-08-05T14:28:43.545629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:43.330982Z","title":"Can decentralized algorithms outperform centralized algorit hms? a case study for decentralized parallel stochastic gradient desc ent,","venue":null,"work_id":"dfb6c174-d286-4a6c-a482-ab5405ff531b","year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.348866Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:2131f7ffdf25d3bb6aebe73bf9b9e173f6ba87904584b1f9e4e9c52758992e7b","observation_id":"d452d4a2-a156-4292-a8ad-8f412277cac3","resolution":{"observed_at":"2026-08-05T14:28:43.390350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:43.128429Z","title":"A survey of distributed optimiza tion and control algorithms for electric power systems,","venue":null,"work_id":"f4922062-87ae-4ec4-9ec4-07dba9ffee63","year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.430142Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:0ebb1496dfc5d05d10474d9cd342af738228f45986e3b2f4522e3c9e9206c1b2","observation_id":"f08cdb00-0bff-4e55-868b-7aa94295b276","resolution":{"observed_at":"2026-08-05T14:28:43.216003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:42.942150Z","title":"A decentralized parallel algorithm for training generati ve adversarial nets,","venue":null,"work_id":"8abd48cb-bfaf-4a56-a949-3cdbc73b5392","year":2020},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.553668Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:470e82c296b5a2de6593d16e0f5575a9dd853a95e470d113ecce3c1f112efd82","observation_id":"beeba10e-f808-4250-95ac-21d4cb64fbcc","resolution":{"observed_at":"2026-08-05T14:28:43.020744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:42.773040Z","title":"Achieving near -optimal convergence for distributed minimax optimization with ada ptive step- sizes,","venue":null,"work_id":"3cbe852a-b6fe-443b-9524-21735c3e27a6","year":2024},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.663044Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:784aeb5af6d11bb9b4447b047774c47329986cd3a8a7ec06cbdca2294db5c0f5","observation_id":"2837bf9f-bfbd-4281-8345-f019801595eb","resolution":{"observed_at":"2026-08-05T14:28:42.864799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:42.558195Z","title":"Numerical methods for finding saddle points,","venue":null,"work_id":"88ed6f03-8866-47eb-b3df-c2402aa51430","year":1972},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.811187Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:ab96c200ceb9fb6868dc486d7a2e9ffa4be956a761aefc4afecd31c86f8b3289","observation_id":"64aefe79-d2b4-4db6-a6df-ad941bb1ede0","resolution":{"observed_at":"2026-08-05T14:28:42.644910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00141","last_updated":"2018-02-13T16:33:55Z","snapshot_observed_at":"2026-08-15T08:33:58.087956Z","submitted_at":"2017-10-31T23:09:08Z","title":"Training GANs with Optimism","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00141","snapshot_observed_at":"2026-08-05T14:28:35.922267Z","title":"Tra ining gans with optimism,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:35.922267Z"},"links":{"cited_paper":"/paper/1711.00141","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:ae7ce2e9e2f25862dd1cd84d4ddad040eeca9bd69f6db646a3062c1aa80e40c2","observation_id":"537fa163-ccf0-4467-beb9-55987997a384","resolution":{"observed_at":"2026-08-05T14:28:35.922267Z","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":"2026-08-05T14:28:42.374928Z","title":"The extragradient method for findin g saddle points and other problems,","venue":null,"work_id":"ca1dbb00-1a7b-4544-93c8-c5e458a5c4aa","year":1976},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.084906Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:65576ab02a69373651b9cbed71b6e8fc80d889e5dca21dbefec07e9a87ef4444","observation_id":"c86437d8-20bb-427d-8164-ff91be62600b","resolution":{"observed_at":"2026-08-05T14:28:42.484106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:42.152073Z","title":"Distributed average cons ensus with time-varying metropolis weights,","venue":null,"work_id":"53ef259f-1a3b-456c-b5c9-8ea76bb3878a","year":2006},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.210690Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:c9b9f871b5fd56d130c3de377af338011cd7509b11cb69cfb4fa7956efc78dd0","observation_id":"ebdb543c-6fc0-4170-ab46-0ae088659bb5","resolution":{"observed_at":"2026-08-05T14:28:42.260455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:41.925087Z","title":"Local stochastic gradient desc ent ascent: Convergence analysis and communication efficiency,","venue":null,"work_id":"521f57ce-fd05-46b7-ac05-9750834642a3","year":2021},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.325618Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:a79279d53003ff80588575434a36d725a2db3646f89e8b2ac4b16e702e8c0b8d","observation_id":"27b6edbb-6ec3-4793-a51b-5b5d26d300af","resolution":{"observed_at":"2026-08-05T14:28:42.033162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:41.657045Z","title":"Communication-efficient learning of deep networks from de central- ized data,","venue":null,"work_id":"26334217-de59-4319-894f-b8001e25903e","year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.454481Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:7d235d26109cb09fd0d5a6262bddc6e9ac74da5bf9c177a2a72c5f47f0969287","observation_id":"61c04127-a1a1-4815-863c-a709a11d81b9","resolution":{"observed_at":"2026-08-05T14:28:41.783778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:41.414589Z","title":"An efficient stochastic algor ithm for decentralized nonconvex-strongly-concave minimax op timization,","venue":null,"work_id":"af8c2dc3-af05-41cd-87fb-97e946a0349e","year":1990},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.589626Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:3d3b09c439e6f46038d3faa876ca4b2499e9cb25fdaa90958d429ab4c88280c4","observation_id":"874cd4e1-f44d-4e6a-9efd-665d812c3e88","resolution":{"observed_at":"2026-08-05T14:28:41.503671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:41.153886Z","title":"A unified an alysis of extra- gradient and optimistic gradient methods for saddle point p roblems: Proximal point approach,","venue":null,"work_id":"1a9f9e1b-38e1-4deb-85b8-ec69a71a639f","year":2020},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.720002Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:1213570f0f25c6592360a84aa942eedda859474df51f632e4cff8e0f9a08613e","observation_id":"4c82735e-a44b-4446-a98e-afea59f798dc","resolution":{"observed_at":"2026-08-05T14:28:41.286274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:40.885573Z","title":"A tight and unified analysis of gradient-based methods for a whole sp ectrum of differentiable games,","venue":null,"work_id":"c2ed084f-9c07-4d20-8ff9-cce45ce6e7cd","year":2020},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.830414Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:45e9eb97b45501ab4825ef100a34d702b8ebe5e7ed7fec6acd786fe3c857575a","observation_id":"5380faea-a4db-489d-8d6e-e35d22ca9bb2","resolution":{"observed_at":"2026-08-05T14:28:41.020459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:40.663182Z","title":"Interaction matters: A note on n on-asymptotic local convergence of generative adversarial networks,","venue":null,"work_id":"6707293a-66ad-4094-b8da-9e3a360194dc","year":2019},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:36.965104Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:cd3953275cda17284129fc35d148b4de9da7d2971c956e05139623fb184c583c","observation_id":"7e48777a-0a44-4d61-89eb-d8ae48cf58fd","resolution":{"observed_at":"2026-08-05T14:28:40.784807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.13112","last_updated":"2025-04-25T08:07:28Z","snapshot_observed_at":"2026-08-16T19:10:48.371977Z","submitted_at":"2020-10-25T13:13:44Z","title":"Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.13112","snapshot_observed_at":"2026-08-05T14:28:37.091682Z","title":"Distribu ted saddle- point problems: Lower bounds, near-optimal and robust algo rithms,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.091682Z"},"links":{"cited_paper":"/paper/2010.13112","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:accdcbe87b6f774c8fb4c64bb39eb0a619dbc449364365c2d2941f5cc88e13ed","observation_id":"4beb1588-ab4c-48f2-9d82-d7fb2013c468","resolution":{"observed_at":"2026-08-05T14:28:37.091682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.14380","last_updated":"2019-10-31T11:24:21Z","snapshot_observed_at":"2026-08-14T00:25:39.862388Z","submitted_at":"2019-10-31T11:24:21Z","title":"A Decentralized Proximal Point-type Method for Saddle Point Problems","version":1},"cited_work":{"arxiv_id":"1910.14380","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.14380","snapshot_observed_at":"2026-08-05T14:28:38.544021Z","title":"A Decentralized Proximal Point-type Method for Saddle Point Problems","venue":"math.OC","work_id":"4054090f-bc4e-4557-a8c3-c2266e34a8a2","year":2019},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.238084Z"},"links":{"cited_paper":"/paper/1910.14380","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:7f920bb17b35d0b9f82d4d9a18c9818cf2d780c87b8148b97abc4a9ed8f562a9","observation_id":"27442e3e-5045-4855-9a16-4012cfab0f40","resolution":{"observed_at":"2026-08-05T14:28:38.653026Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:40.442904Z","title":"Diffusion stoc hastic optimization for min-max problems,","venue":null,"work_id":"08544da0-629f-45ba-9cae-b170ce3704a7","year":2024},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.379355Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:a643d6bf8557a9f0c958998c0c92cf6328eefc31eab59a2e4faf5c4fb44cae1f","observation_id":"9a9c623a-8b9d-4ba5-bbdf-7639413b5020","resolution":{"observed_at":"2026-08-05T14:28:40.542236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:40.242370Z","title":"Augmented distribut ed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,","venue":null,"work_id":"0e08d21e-6ac7-43c0-8fba-9196433e224f","year":2015},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.461027Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:6c4c8e96665a76b28815ebbcec6c91f9e960d9e2119e2995dc21bc9b6790bfb6","observation_id":"71dbe344-f9d7-4ac3-9bee-01d147dde152","resolution":{"observed_at":"2026-08-05T14:28:40.320933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:37.571154Z","title":"Distributed stochastic gradient tracking methods,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.571154Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:b11d0c4ddab92303252ff4f1301bf5d620cccf1c9fdc8ea85140584617c9fa44","observation_id":"eb9d75ee-b752-451c-aa96-e6496adc3b5c","resolution":{"observed_at":"2026-08-05T14:28:37.571154Z","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":"2026-08-05T14:28:39.999436Z","title":"Multi-agent re in- forcement learning via double averaging primal-dual optim ization,","venue":null,"work_id":"6d0b88c3-457f-4b72-99c0-031b247d648d","year":2018},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.764452Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:2997ff49e0799a794a229837811c30f2c152c25ae86f97104cbe69ee94c48956","observation_id":"e9a60b0c-a495-4f4c-9de4-5236c2ea5895","resolution":{"observed_at":"2026-08-05T14:28:40.110064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:39.804986Z","title":"A decentralized algo rithm for large scale min-max problems,","venue":null,"work_id":"1331ff7d-b7c9-41f3-b656-9bae25f81f2e","year":2020},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:37.884186Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:73aab0cb29dd6d7b7cb1a0e89ffa8eb11e1e40327387d7c224400cec6903d48e","observation_id":"0122bfd8-1e24-4ce8-9c03-237504e99b12","resolution":{"observed_at":"2026-08-05T14:28:39.901034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:39.580622Z","title":"Distributed opt imization based on gradient tracking revisited: Enhancing convergen ce rate via surrogation,","venue":null,"work_id":"0ce7b2bb-4c5b-4916-a5d9-2786efd422ab","year":2022},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:38.002649Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:9be2b44d8ba6d7dddc0c0f1e234e3632acc39c5362f06613a22c2c643e88f8f5","observation_id":"070993b7-678f-4360-8567-d1e5c70138e9","resolution":{"observed_at":"2026-08-05T14:28:39.723651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:39.406674Z","title":"Distributed saddle-point problems under data similarity,","venue":null,"work_id":"317a2e24-49ab-4b1f-b6e9-73b0f3685d78","year":2021},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:38.106551Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:f21827ac54c19c8d899bfd532803d2150565f2933494280fe466c770cca5c3d4","observation_id":"53b716f3-deff-42a2-bb7b-f3d0ffb7c8f2","resolution":{"observed_at":"2026-08-05T14:28:39.492122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:39.092569Z","title":"Near-optimal distributed mi nimax optimization under the second-order similarity,","venue":null,"work_id":"6528c114-d11b-45b6-9508-b515fcde511c","year":2024},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:38.235926Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:46731930c7b3ef886e8418d67d1f87790be6633d57d1010581ac122ffc84cfd9","observation_id":"5a6ad848-a12c-4682-ab23-41dcbc64cce8","resolution":{"observed_at":"2026-08-05T14:28:39.262884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T14:28:38.898253Z","title":"Accelerated linear iterations f or distributed averaging,","venue":null,"work_id":"bcc64549-4d0f-4c1f-97bd-5235423d8eee","year":2011},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:38.401661Z"},"links":{"citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:234a8b11dc142ed58d9c41459bb69430f421e67641dfb5a98140f2be3992cfdf","observation_id":"7a01fa43-8072-4d50-8769-7fee9b9e5fbd","resolution":{"observed_at":"2026-08-05T14:28:38.967573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":32},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2508.21431."}