{"as_of":"2026-08-18T07:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c5b4457002c91d50256477894bd60d2c53c8a5dbe26a008cafa3ed2c6608efc6","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T17:17:47.899341Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2501.12362/citation-record","integrity":"/paper/2501.12362/integrity","json":"/paper/2501.12362/citation-record.json","paper":"/paper/2501.12362"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:17:47.792635Z","title":"Power system resilience: Current practices, challenges, and future directions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.792635Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:7b076261b4ae6af3af5e86d777689fb732772e86824e695e4ada3c0db78ede5b","observation_id":"39f38f24-e8c6-4534-95eb-33bc9da78f94","resolution":{"observed_at":"2026-08-10T17:17:47.792635Z","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":"2026-08-10T17:17:48.322543Z","title":"Enabling systems engineers and program managers to select the most useful assessment methods,","venue":null,"work_id":"5ee28d12-9551-4060-bcb0-0da53d0845fa","year":2018},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.796189Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:2ddce0861d3c54d1337df00ab6c64559469d2d4e8d0f4cd0b326361c2a217644","observation_id":"db2fe00a-b537-46f1-834d-e8477b9db4bb","resolution":{"observed_at":"2026-08-10T17:17:48.326167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.312228Z","title":"Cyber resiliency metrics and scoring in practice,","venue":null,"work_id":"3b3757b7-c26b-4b74-bb44-f01e1dc1ab9d","year":2018},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.799547Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:e90c287664d85eb774a950bdd159f05c90a1f2e3578adc29b47f5d97d587bcc0","observation_id":"bab1a69e-f2c0-42d9-b62d-bebdaae30b85","resolution":{"observed_at":"2026-08-10T17:17:48.315936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.302367Z","title":"Resilience metrics for cyber systems,","venue":null,"work_id":"c80f1eb2-76e1-4fa2-af55-1c5edf1a8fc5","year":2013},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.803309Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:a118d82a859c15be437de9ed7e1329fd7318cdacc57ecc8a0bf8ae065ea919b4","observation_id":"0a373d52-6f58-4225-a142-bf6526cfd79b","resolution":{"observed_at":"2026-08-10T17:17:48.305836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.292922Z","title":"Cp-tram: Cyber-physical transmission resiliency assessment metric,","venue":null,"work_id":"38a2f771-4c93-46c2-add1-bd9fee999b2b","year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.806825Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:ad6d84d1748e69bcb116432df7fc941a5f69a77eeb2f97081727cff89ac2bdb3","observation_id":"1f5e8469-0da8-4df0-b92f-004f7475caec","resolution":{"observed_at":"2026-08-10T17:17:48.296403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.283570Z","title":"CP-SAM: Cyber- physical security assessment metric for monitoring microgrid resiliency,","venue":null,"work_id":"f205a095-16e9-4d48-99eb-8fbc8b4f92b4","year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.810631Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:5a73c3f1ec60afbed9692dfe61150599b0579d638c161978555beccec8868ea7","observation_id":"00f01268-fbec-4092-a5af-2bb3aa7a7126","resolution":{"observed_at":"2026-08-10T17:17:48.286921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.274547Z","title":"Scalabil- ity in Multiobjective Optimization (Dagstuhl Seminar 20031),","venue":null,"work_id":"f7e8a629-e810-4167-81bd-ce240f5d2932","year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.813749Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:da2a1cb3544854ed6d5deda765dbd974787717ed7baa989eaf92685c023bebfe","observation_id":"3a6e17d4-6f39-4237-ac12-6934361e9837","resolution":{"observed_at":"2026-08-10T17:17:48.277717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00783","last_updated":"2021-12-07T17:21:57Z","snapshot_observed_at":"2026-08-16T18:12:52.166899Z","submitted_at":"2021-07-02T01:08:45Z","title":"Reinforcement Learning for Feedback-Enabled Cyber Resilience","version":2},"cited_work":{"arxiv_id":"2107.00783","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.00783","snapshot_observed_at":"2026-08-10T17:17:48.075810Z","title":"Reinforcement Learning for Feedback-Enabled Cyber Resilience","venue":"cs.CR","work_id":"3fe28b7a-021d-4d48-b2b0-09aa9e9bbdbf","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.817002Z"},"links":{"cited_paper":"/paper/2107.00783","citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:4256af7aa4acb87ff8f96e4ba653fba673c874f251c78e240f53dcc0ed07bc3b","observation_id":"9004d74a-3834-4147-b176-ff0d1a6745d7","resolution":{"observed_at":"2026-08-10T17:17:48.079950Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.265410Z","title":"To improve cyber resilience, measure it,","venue":null,"work_id":"1e9fcd41-680d-4cff-acef-a2fb92c2523c","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.820658Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:cf1db57b6d7731bbf5dddb109c2d9cee0faedfd5afc406b8af83efe485ba60fd","observation_id":"44bccfd9-b01b-4e90-8a97-a336bcd2be46","resolution":{"observed_at":"2026-08-10T17:17:48.268778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:47.823807Z","title":"Metrics and quantification of operational and infrastruc- ture resilience in power systems,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.823807Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:3b503d9142aa063911a3f3dbd56317188c654e9e82e98030648f2dc469e60b9e","observation_id":"18fa9582-0d06-4c57-b4f8-ebc300909788","resolution":{"observed_at":"2026-08-10T17:17:47.823807Z","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":"2026-08-10T17:17:48.251690Z","title":"Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience,","venue":null,"work_id":"284519fa-6dad-44a1-b5c7-0b130f7a702c","year":2019},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.827246Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:845372206bc33c1fa0a1fbe8ddc6b7ee8730421d866a7c39be3b4b31a22e6d40","observation_id":"4a172ae2-bff8-49b5-bd11-79ef7572dd07","resolution":{"observed_at":"2026-08-10T17:17:48.255111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.242402Z","title":"Resilient scheduling of networked microgrids against real-time fail- ures,","venue":null,"work_id":"7a0baddb-acf3-4fba-8785-ffd70290c7e4","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.830569Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:0a186450d42e7776cd6061e2ec748594e422004ad12b943ed7d28fc05e622856","observation_id":"8576811b-3204-4cc8-b84a-1bb499eed331","resolution":{"observed_at":"2026-08-10T17:17:48.245832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.233324Z","title":"Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges,","venue":null,"work_id":"e8f3bdb5-bc01-4b01-b7e6-a383ebf58d2f","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.833638Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:c3e23469f9849f0c0d69147573060d5d23bab308bd5268cf528c8434dbe1e518","observation_id":"cc12dd14-49b0-490b-8281-7bcfa765222a","resolution":{"observed_at":"2026-08-10T17:17:48.236707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.224297Z","title":"Quantifying the system-level resilience of thermal power generation to extreme temper- atures and water scarcity,","venue":null,"work_id":"ad15cb77-50fa-46c3-9407-b77d0be5e80f","year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.837100Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:aca0dd498e5938a70d1c45211caa163457ba0a2bc373b7dce220215496e4b295","observation_id":"ead738d7-05a9-4ed0-b47d-1e02eb068438","resolution":{"observed_at":"2026-08-10T17:17:48.227930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.215158Z","title":"Optimizing dynamics of integrated food–energy–water systems under the risk of climate change,","venue":null,"work_id":"2ed14d35-540a-42e4-bfe3-07f114ebdf08","year":null},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.840298Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:c52c28f2b893eefdd1e8db1441154749daeac2002609d3e36ac30ae03b024dbf","observation_id":"4f4af1bc-2069-49c0-8402-7f69bb4b0263","resolution":{"observed_at":"2026-08-10T17:17:48.218659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.206170Z","title":"Performance- based cyber resilience metrics: An applied demonstration toward moving target defense,","venue":null,"work_id":"eb2ef3d9-c057-4d46-9f87-7db8cd518625","year":2018},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.846609Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:2aad3a5d77170e868b38b3394bd48d46973635a75398ed92d71a6a3d63f319bf","observation_id":"605dba27-b605-42ff-a685-8f3c31fd843b","resolution":{"observed_at":"2026-08-10T17:17:48.209681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.337831Z","title":"Resilience of cyber-physical systems: an experimental appraisal of quantitative measures,","venue":null,"work_id":"43a2da0d-d9a9-4d80-a55e-37debd378bce","year":2019},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.849516Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:d67d5bf540f2f241da32f2d2c2752a1230bca89c7ec1ca398eb2bd5074546eb0","observation_id":"03e32a6d-1781-406b-b3e6-859305175341","resolution":{"observed_at":"2026-08-10T17:17:48.341711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.196303Z","title":"To- wards a resilience metric framework for cyber-physical systems,","venue":null,"work_id":"dead52a2-89c0-447d-bcda-086293511632","year":2016},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.851970Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:b15f0671720b9656d39a5ee0aa3a2f8a5ce5df3b669ead40b0acdccc94b6bce3","observation_id":"e5f01307-ef3e-47dc-96c4-f98794387b57","resolution":{"observed_at":"2026-08-10T17:17:48.199971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.186816Z","title":"Emotional deep learning programming controller for automatic voltage control of power systems,","venue":null,"work_id":"232c7e51-b82a-4065-9d92-ccb7b4150653","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.855316Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:1613f51a5bf9275ee3ed3c90cbf4aa02798f74784f9fa914a74263462a0e590d","observation_id":"890b226f-6542-46b6-81ef-d1dadb4dd398","resolution":{"observed_at":"2026-08-10T17:17:48.190423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.177417Z","title":"A data-driven method for fast ac optimal power flow solutions via deep reinforcement learning,","venue":null,"work_id":"217cb788-da3b-4a41-adb0-0643fdef5dd5","year":2020},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.858459Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:50ab86665d15bfb817ee47a67ab0cddd7a7a46bb19033dcf8ce24e3fcfdcc9fb","observation_id":"3d973430-6a2d-4cde-885c-a35a329fa98c","resolution":{"observed_at":"2026-08-10T17:17:48.180991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.167476Z","title":"Online learning and distributed control for residential demand response,","venue":null,"work_id":"ac64644d-cfe2-4fab-9fee-c18c5f8c1946","year":2021},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.861184Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:6733b28f91298d1979492e91e472d1df7812ab8d41a96a9f9685056c0a743da5","observation_id":"defb83a3-d461-4b25-ab74-946f81b6a335","resolution":{"observed_at":"2026-08-10T17:17:48.171196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.157875Z","title":"Self-organizing map-based resilience quan- tification and resilient control of distribution systems under extreme events,","venue":null,"work_id":"f25df05b-4def-48d4-9dd5-fa465fb2f6d2","year":1923},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.864156Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:ff1683bc9e668b5a500aa4027769e65e52ce76e88bd07c441b5b0cb4ed598ac8","observation_id":"9557f815-7d49-4ece-82bd-c1d59cad941b","resolution":{"observed_at":"2026-08-10T17:17:48.161475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.148290Z","title":"Reinforcement learning environment for cyber-resilient power distribution system,","venue":null,"work_id":"2e1cf747-322f-4e63-a15d-2e5c6d5378db","year":2023},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.866907Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:d657a5aab19c883889fcdfcfc55c945f41861bacc1a52ed41ee88502ba0e6a5f","observation_id":"fc5e325e-0bae-4754-a913-dbd10ca123b9","resolution":{"observed_at":"2026-08-10T17:17:48.151724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.137926Z","title":"An irl approach for cyber-physical attack intention prediction and recovery,","venue":null,"work_id":"a1b618f7-95bf-4d4c-9cca-b7b5608f26a5","year":2018},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.869838Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:1a166c50d769f5da0ed890b909fde73dbdc4d14a4aafcfad9346e114ccfa35bc","observation_id":"e6beead0-4d8c-4de7-855e-ba954f0f8f45","resolution":{"observed_at":"2026-08-10T17:17:48.141451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.128484Z","title":"A survey of inverse reinforcement learning,","venue":null,"work_id":"d29b7cb6-901f-432e-a9f5-6448eb4428f4","year":2022},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.872644Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:34ad55d06fda40a2cb1acab3016588cdf9d0b3db344b9bfaf5d17fa62b6f8d34","observation_id":"a7eb938e-eb79-4711-a9f6-0c47b439680c","resolution":{"observed_at":"2026-08-10T17:17:48.131800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.118785Z","title":"Bayesian inverse reinforcement learn- ing,","venue":null,"work_id":"1ce734e3-091d-4e50-9a6d-a79ca9d1291e","year":2007},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.875654Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:1be22920cfddabf2470fb328ceef0d6b749091f871fb72150c3a2e0846aa2cf1","observation_id":"47025f2b-10f4-436e-8861-5fbef3b4ff4c","resolution":{"observed_at":"2026-08-10T17:17:48.122543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1011.0686","last_updated":"2011-03-16T18:51:21Z","snapshot_observed_at":"2026-08-15T05:04:07.152443Z","submitted_at":"2010-11-02T17:55:55Z","title":"A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1011.0686","snapshot_observed_at":"2026-08-10T17:17:47.878969Z","title":"A reduction of imitation learning and structured prediction to no-regret online learning,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.878969Z"},"links":{"cited_paper":"/paper/1011.0686","citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:35153167571963e4177890cfaf067a02067368cd22e4acc3ca5741d7204e45ed","observation_id":"6adc7a49-1335-4193-bf75-930ef71633b8","resolution":{"observed_at":"2026-08-10T17:17:47.878969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03476","last_updated":"2016-06-10T20:51:29Z","snapshot_observed_at":"2026-08-14T21:53:15.131684Z","submitted_at":"2016-06-10T20:51:29Z","title":"Generative Adversarial Imitation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.03476","snapshot_observed_at":"2026-08-10T17:17:47.882398Z","title":"Generative adversarial imitation learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.882398Z"},"links":{"cited_paper":"/paper/1606.03476","citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:355b3bc806416f7018f5215e445ab18af369e237543f5085b9f154f9e089c569","observation_id":"1444a373-5933-4f11-8005-23e00388b894","resolution":{"observed_at":"2026-08-10T17:17:47.882398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.11248","last_updated":"2018-08-13T18:33:24Z","snapshot_observed_at":"2026-08-14T20:18:41.596978Z","submitted_at":"2017-10-30T21:22:28Z","title":"Learning Robust Rewards with Adversarial Inverse Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.11248","snapshot_observed_at":"2026-08-10T17:17:47.885971Z","title":"Learning robust rewards with adversarial inverse reinforcement learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.885971Z"},"links":{"cited_paper":"/paper/1710.11248","citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:0166bfd600d71300513e5c07ea5f54980a384a30fb977d94b4285bc4e0594fcb","observation_id":"1f792156-f2ce-462c-90b2-a76bc77e93f2","resolution":{"observed_at":"2026-08-10T17:17:47.885971Z","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":"capsule/4520283","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:17:48.030726Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning,","venue":null,"work_id":"006c55b1-0682-4c66-838e-6e0327d018b9","year":2024},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.889656Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:bbfc93ea528259ecf78496e52d71eec1ba7437e7426a2653bc950f392986cfc0","observation_id":"d6b6a148-f74c-4021-9412-170dd5652434","resolution":{"observed_at":"2026-08-10T17:17:48.036943Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.108816Z","title":"Distribution system restoration with microgrids using spanning tree search,","venue":null,"work_id":"ea89fc22-af83-44c7-a2eb-d19d2425b812","year":2014},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.892853Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:541d8e421c20a52df6fbe6e770b407b6e9523a4777f6ac336122ecf766c2fbef","observation_id":"d5233994-24e5-4d7c-acb5-dbd1588cd8c9","resolution":{"observed_at":"2026-08-10T17:17:48.112525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.097900Z","title":"Con- vex neural networks,","venue":null,"work_id":"5319f0a1-36ff-4c7a-ab6d-62986ffbde57","year":2005},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.896139Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:16c305f81820430ad2655bc0da05801a8a24a7f085a27ba756700456b0a31ced","observation_id":"501df63e-7a37-4511-b49b-f68ffcb70faa","resolution":{"observed_at":"2026-08-10T17:17:48.101650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T17:17:48.086791Z","title":null,"venue":null,"work_id":"89e94402-7ad3-4a73-83fd-d34dda0015b8","year":2015},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.899341Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:80d116d9234250c79b25757e9ee2540f57d13ddecf679fe205fb8d1f12bbb780","observation_id":"bfd61969-1d8d-424c-bf3c-46b21e290ed7","resolution":{"observed_at":"2026-08-10T17:17:48.090430Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1748-9326/ab2104","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:17:47.925729Z","title":"Available: https://doi.org/10.1088/1748-9326/ab2104","venue":null,"work_id":"465e3828-ead3-4dfe-83d1-14587d2ebbd0","year":null},"citing_paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-10T17:17:47.843545Z"},"links":{"citing_paper":"/paper/2501.12362"},"observation_digest":"sha256:e1ce4f5438f0b0523e795ef747b75a0041d5077dee82c44e316c7d9404538ff4","observation_id":"dc44c319-9b4e-4e7e-9314-b5cd419dff71","resolution":{"observed_at":"2026-08-10T17:17:47.930720Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.12362","last_updated":"2025-01-21T18:43:02Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-18T00:45:07.359642Z","submitted_at":"2025-01-21T18:43:02Z","title":"ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":3,"verified_fuzzy":25},"total_outbound_references":34},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2501.12362."}