{"as_of":"2026-08-23T16:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9517b33c49cd26527afacb67ec654603448b8526a134de7e72751c5a676fb7b3","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:46:53.545461Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2505.09012/citation-record","integrity":"/paper/2505.09012/integrity","json":"/paper/2505.09012/citation-record.json","paper":"/paper/2505.09012"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:46:53.436557Z","title":"Cascading failure prediction in power grid using node and edge attributed graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.436557Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:cb4a32c31dbc747f4ed633b71363ac01422b89f06636748a169a3e3d943320eb","observation_id":"5909be20-12c3-41fa-942e-cec8d808c00d","resolution":{"observed_at":"2026-08-15T21:46:53.436557Z","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":"2026-08-15T21:46:53.442725Z","title":"Mitigation of cascading outages using a dynamic interaction graph-based optimal power flow model","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.442725Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:a494668975e5b7f818345dabc9280ba0be991317385b2406b1791b03a079fe54","observation_id":"f49bc386-639b-4347-a6f9-99aef815d074","resolution":{"observed_at":"2026-08-15T21:46:53.442725Z","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":"2026-08-15T21:46:54.637809Z","title":"A critical review of cascading failure analysis and modeling of power system","venue":null,"work_id":"18f1e210-c588-4060-8b84-d2e2ace82c6d","year":2017},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.448730Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:52f0a5de0bdd116499a7bf14b008ffae136e90900700f63af5117e7779230017","observation_id":"1a6dc9aa-04a2-498b-ad06-b823e473792e","resolution":{"observed_at":"2026-08-15T21:46:54.644379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T21:46:53.454732Z","title":"Analysis and mitigation of cascading outages using an interaction graph addressing transient stability","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.454732Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:22b6674da418be788769fd539d171ed8689f6bcdb6ed679b444fb9b6b9dedbba","observation_id":"1b87199a-4e99-4a81-a77f-5a667cabf5c2","resolution":{"observed_at":"2026-08-15T21:46:53.454732Z","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":"2026-08-15T21:46:53.459849Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.459849Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:31edf3d67257356f469db1fbc8fab8dcf9059504080fe43e03687232bcc2dd08","observation_id":"f703aa45-9218-4683-b468-207a743fdca2","resolution":{"observed_at":"2026-08-15T21:46:53.459849Z","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":"2026-08-15T21:46:53.465331Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.465331Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:ac22ff1f3bfda267ca3a45730a8d27ad0216110a4cb870d90704873417c04578","observation_id":"f88ce9b0-1e87-450c-a7ea-5b6e32bff6df","resolution":{"observed_at":"2026-08-15T21:46:53.465331Z","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":"2026-08-15T21:46:53.471413Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.471413Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:f6e9450094c6298dc71fa89a92532a548a81e536d355ffd605c306032fa2f21a","observation_id":"732103ed-8d13-4906-8d65-720c20549441","resolution":{"observed_at":"2026-08-15T21:46:53.471413Z","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":"2026-08-15T21:46:53.476951Z","title":"Cascading failure propagation and mitigation strategies in power systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.476951Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:67123af713ae6073fce37d3c9f65ed1c25e50d699c51dbc0325c5c5479395eaf","observation_id":"7890bb45-c364-4fd4-835b-97f1bcec6fbd","resolution":{"observed_at":"2026-08-15T21:46:53.476951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.02971","last_updated":"2019-07-05T10:47:27Z","snapshot_observed_at":"2026-08-16T22:06:26.835611Z","submitted_at":"2015-09-09T23:01:36Z","title":"Continuous control with deep reinforcement learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.02971","snapshot_observed_at":"2026-08-15T21:46:53.481980Z","title":"Lillicrap, Jonathan J","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.481980Z"},"links":{"cited_paper":"/paper/1509.02971","citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:f3208af802b1910ee1f3c08d4286e768f5a5826a75c0c269bfe8ed4c648e848b","observation_id":"75e7a099-dd47-43e4-a649-e9ff37b2ace2","resolution":{"observed_at":"2026-08-15T21:46:53.481980Z","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":"2026-08-15T21:46:53.487751Z","title":"Cascading failure model of cyber-physical power systems considering overloaded edges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.487751Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:4f0ea6b85869fecaf337dd3c06dbc4b91f9150a2f4e075b7fbaf3dcc9e79ceb4","observation_id":"0a32a6cb-cdc0-4f04-8437-b884680b4158","resolution":{"observed_at":"2026-08-15T21:46:53.487751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.01783","last_updated":"2016-06-16T16:38:45Z","snapshot_observed_at":"2026-08-17T11:02:06.117758Z","submitted_at":"2016-02-04T18:38:41Z","title":"Asynchronous Methods for Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.01783","snapshot_observed_at":"2026-08-15T21:46:53.493674Z","title":"Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.493674Z"},"links":{"cited_paper":"/paper/1602.01783","citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:b2c7a8fdb2c827ccdd91fa9453aca2c8be54d5c9d1e1d86eb79206697024f2f2","observation_id":"a1668cc2-f56f-402d-8c0c-05eb98f56aae","resolution":{"observed_at":"2026-08-15T21:46:53.493674Z","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":"2016.25776","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:46:53.966331Z","title":"Estimating the propagation of interdependent cascading outages with multi-type branching processes","venue":null,"work_id":"5bd79ef9-1821-4a52-936f-fbffb7ffdb80","year":2017},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.499840Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:85ca3d61b9b0c2395dc2a5a7a49ada345f4a35fe8020474ee5a3b312d989fcae","observation_id":"25db410a-9951-48ca-b40c-95b5b2fb5a21","resolution":{"observed_at":"2026-08-15T21:46:53.977886Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T21:46:53.506138Z","title":"Resp: A real-time early stage prediction mechanism for cascading failures in smart grid systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.506138Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:bc886128955729cf8977317a7fcae385b80ce88961d85d5afd0a629b7d6274a9","observation_id":"7a9e2a89-1cf6-4ee5-b6b1-8f8acdaef614","resolution":{"observed_at":"2026-08-15T21:46:53.506138Z","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":"8826.2023","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:46:53.803755Z","title":"A comparative study of data-driven power grid cascading failure prediction methods","venue":null,"work_id":"c56d53ff-7b5f-4219-bf8c-fce22de2c696","year":2023},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.511709Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:2a370054cf70d5b257d37f9cd781c7564b7688d647e3c3f5ea9bf035ec8e4960","observation_id":"c94b0cef-a417-4863-9e43-c53381592295","resolution":{"observed_at":"2026-08-15T21:46:53.817757Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T21:46:53.517348Z","title":"Real-time excitation control-based voltage regulation using ddpg considering system dynamic performance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.517348Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:647cdcbab834a06bd4a903829b9fda806036a2c8bfb6ef8b5516192fcc47765a","observation_id":"8b53f470-3aea-4440-8376-b374569ec5e7","resolution":{"observed_at":"2026-08-15T21:46:53.517348Z","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":"8120.2023","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:46:53.648763Z","title":"Power system resilience assessment considering the occurrence of cascading failures","venue":null,"work_id":"0f7da60d-e170-47a8-989c-0fc862e95dac","year":2023},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.523670Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:72b46b7cace194503dc82d6f7f79b0c45fef1eaa0498fa957f64e0976021d3a7","observation_id":"f2b97a7b-d557-46f4-bcaf-adf71f4a68c0","resolution":{"observed_at":"2026-08-15T21:46:53.660808Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T21:46:54.618058Z","title":"Power grid cascading failure mitigation by reinforcement learning","venue":null,"work_id":"1d886772-1781-42f7-a925-d5458f96f687","year":2021},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.528748Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:74dec82d7b968f3794e794dec898cd038158c72767602febe6bb307a1bc3886c","observation_id":"bdef7e4a-9fb7-4269-9565-b41057891734","resolution":{"observed_at":"2026-08-15T21:46:54.623617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T21:46:53.533812Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.533812Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:b8b841e8ac83d2b5a815e5e34401fae4de0403821e7f8c1a2d4744a35b20832a","observation_id":"c1cb7832-4e57-42ec-a8fa-7e0b3a27f60e","resolution":{"observed_at":"2026-08-15T21:46:53.533812Z","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":"2026-08-15T21:46:53.540092Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.540092Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:1b0d36baa148da69e472b0787122e280b4f220871767c39e79b10d6fcbcf68fd","observation_id":"bf3cec22-ae69-471c-85d4-0ec6edc1f837","resolution":{"observed_at":"2026-08-15T21:46:53.540092Z","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":"2026-08-15T21:46:54.572143Z","title":"@ T#`*5s;<F._V -?/Ku? Ւp T L`n֋hH,8 A Gݭ ! kViK f, *KO;IgRf ЀQ5Oߍ ,up4 pxP xzJq>a [ M gΫ 8 *o |c t Uh#n; ^H ҉3; = T Ã |?/ a ЉӔ ?K36 ' lƃ[ \\@ < i!ʠ4oT^&aÔ V H. =No ^'ַ/F C_[ TAG ,","venue":null,"work_id":"0c9f17bc-daff-4fb1-81ec-ac7842c4aa43","year":2025},"citing_paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T21:46:53.545461Z"},"links":{"citing_paper":"/paper/2505.09012"},"observation_digest":"sha256:58a1c26576489ed449607850a0005312bf67214ff5b936b1df6ad4172d8924dd","observation_id":"0227476d-cad3-4ab4-a32b-e2149ce283a4","resolution":{"observed_at":"2026-08-15T21:46:54.579401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.09012","last_updated":"2025-05-13T23:01:34Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-21T22:59:27.473260Z","submitted_at":"2025-05-13T23:01:34Z","title":"Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":14,"verified_exact":2,"verified_fuzzy":3},"total_outbound_references":20},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2505.09012."}