{"as_of":"2026-08-22T16:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45b604a163abd76204d69e92c762b7bab119a8b7efbeda27c72df889efa6b95c","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:09:01.163434Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2507.11726/citation-record","integrity":"/paper/2507.11726/integrity","json":"/paper/2507.11726/citation-record.json","paper":"/paper/2507.11726"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:09:04.350475Z","title":"Corrective switching algorithm for relieving overloads and voltage violations,","venue":null,"work_id":"a3e6ba6e-06d3-46a3-ab75-77b9a76e9a27","year":2005},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:08:59.458501Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:de7175463b365c8cdf9cd9b29ee618d2a3e79ddf4d623158aadc27e4b6edf0ec","observation_id":"34ca3564-8500-474a-b921-3a12874d1744","resolution":{"observed_at":"2026-08-06T17:09:04.467299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:04.051070Z","title":"Optimal transmission switching considering voltage security and n-1 contingency analysis,","venue":null,"work_id":"2dc5c32b-5844-440c-9f56-6b471db03c86","year":2012},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:08:59.551415Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:2b2d683be25019611c2e2599983adebfb80a87c44d9c7461135075d40afc8969","observation_id":"39361177-9341-4cb3-a50e-bc3f82482729","resolution":{"observed_at":"2026-08-06T17:09:04.190972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:03.797069Z","title":"Optimal network reconfiguration for congestion management by deterministic and genetic algorithms,","venue":null,"work_id":"530116ea-3945-49fb-b168-053b73447324","year":2006},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:08:59.734953Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:47eeff58da275f87405c21367553c44e2c19741552c6eebf2269944217780461","observation_id":"78aae21b-b53d-4eef-8de1-83ca91e5d599","resolution":{"observed_at":"2026-08-06T17:09:03.934916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:03.610460Z","title":"Optimal transmission switching,","venue":null,"work_id":"461a9f66-7af6-4482-9b9e-fde945a2d498","year":2008},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:08:59.843652Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:47c95ba92a7afad6fdff32e6f68a81b65bd0297cd2a79ba421678851b752527d","observation_id":"cb051ec8-7bec-45b9-9c94-5cb5e1b8f59a","resolution":{"observed_at":"2026-08-06T17:09:03.725983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:03.267510Z","title":"Loss reduction by network switching,","venue":null,"work_id":"5454fafe-9403-46dc-bfb3-2f9a5e9196cd","year":1988},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:08:59.976904Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:439a405d0ce982e39e1b07182f0a6234bef9e4b7d34154cf6f18c33ba4fe3871","observation_id":"6c761f84-405d-4649-b908-2723c559667a","resolution":{"observed_at":"2026-08-06T17:09:03.441342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:02.957178Z","title":"Transmission switching with connectivity-ensuring constraints,","venue":null,"work_id":"4795fb27-a0e9-4833-9102-bef4871d263b","year":2014},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.113168Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:899a268d1ce0ac26366f6f68e9487b8efc507a6ec58bf593bcdff090c7be758b","observation_id":"ccbbe0ef-7d28-4ae2-aae5-ade7f358478c","resolution":{"observed_at":"2026-08-06T17:09:03.101929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:02.696029Z","title":"Optimal transmission switching considering probabilistic reliability,","venue":null,"work_id":"f0d8931e-3a5d-4e04-95ab-34e5a88d72e4","year":2013},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.249687Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:17d8a76fb81bab2ec8d9db5627fb8f60a9703d3e63b28118143aed75356c949e","observation_id":"12576c50-6365-4718-95db-149c4b68ae12","resolution":{"observed_at":"2026-08-06T17:09:02.832450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:02.372661Z","title":"Congestion management using optimal transmission switching,","venue":null,"work_id":"815d3782-4f14-4004-8e4e-92231b23df14","year":2018},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.359169Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:7aa3b8a0f3eb930c52074efdd91f8b5d95a702914672712d87365a0155a2289f","observation_id":"9a095373-e84b-4e6a-b519-5e38ebb33b24","resolution":{"observed_at":"2026-08-06T17:09:02.521428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:02.134922Z","title":"Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks,","venue":null,"work_id":"657d2f70-4319-4534-8d88-9a8dd88b67b4","year":2020},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.481776Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:7883c574fb614845bfa4976f2894709c4509d92c1b72467fc80b9ba514477219","observation_id":"2844943a-487d-4d43-b042-7ae158e1e4e7","resolution":{"observed_at":"2026-08-06T17:09:02.249064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:01.899106Z","title":"Model-free real-time ev charging scheduling based on deep reinforcement learning,","venue":null,"work_id":"27289129-edef-4f29-bf94-83c65a1f6bd6","year":2019},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.593981Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:87203adcd0746114ce11bb42e3e20176d40068c49bd5993e411ae51d566e52d4","observation_id":"8e8fb142-c841-4b3b-8b0d-80f0fd5bfd2d","resolution":{"observed_at":"2026-08-06T17:09:02.014446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:01.579503Z","title":"Incentive-based demand response for smart grid with reinforcement learning and deep neural network,","venue":null,"work_id":"21ac1632-4a57-4119-917d-37451ce818f4","year":2019},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.712341Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:f294c0e8e84d057d7dbf78ba6701b1b6db3fde77a87f359045c14ea60aadc6c1","observation_id":"2afe2d34-3470-4e01-8565-7d0b5a23fc2f","resolution":{"observed_at":"2026-08-06T17:09:01.750056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:09:01.349964Z","title":"Data-driven load frequency control for stochastic power systems: A deep reinforcement learning method with continuous action search,","venue":null,"work_id":"ab8fb70d-30d9-49cb-bd09-f05f79cad1ea","year":2019},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:00.843494Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:1a51b41ddf0610040840fe6af294619008d85188f2f0d7743bbd697a32786631","observation_id":"f096af96-1ede-40fe-916b-267648ec8382","resolution":{"observed_at":"2026-08-06T17:09:01.453999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-06T17:09:01.026343Z","title":"Soft actor-critic algorithms and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:01.026343Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:02d12b94071f83766315b32d9ae2b3f761730eb42da37986c00fb851fb5e9594","observation_id":"d6f0c3d9-e885-45a1-afec-35257997050e","resolution":{"observed_at":"2026-08-06T17:09:01.026343Z","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":"2026-08-06T17:09:01.163434Z","title":"Dueling network architectures for deep reinforcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:01.163434Z"},"links":{"citing_paper":"/paper/2507.11726"},"observation_digest":"sha256:68bc0c77b4f758443b4c807e56715ee98dbd098cb9c21056d8cfcef787e2cd7b","observation_id":"dca900f8-71b5-4247-b6fd-a94be787f7cd","resolution":{"observed_at":"2026-08-06T17:09:01.163434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.11726","last_updated":"2025-07-15T20:49:48Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-18T00:30:14.732260Z","submitted_at":"2025-07-15T20:49:48Z","title":"A Deep Reinforcement Learning Method for Multi-objective Transmission Switching"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":14},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2507.11726."}