{"as_of":"2026-08-10T23:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:131d130c2c9754b5c0b9508ca22f47667033dc7dc977a4ca6e832cb6d6069ecb","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T16:42:57.021901Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2607.18359/citation-record","integrity":"/paper/2607.18359/integrity","json":"/paper/2607.18359/citation-record.json","paper":"/paper/2607.18359"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T16:42:53.428330Z","title":"Multi-agent deep reinforcement learning for large-scale traffic signal control,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.428330Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:a90548dca7bcca717ab2c6c81db88bc1553d4f0b8284ce3207cb1c930e424a4f","observation_id":"99a24c57-dfd5-49ca-91f5-c3d99bf15b03","resolution":{"observed_at":"2026-08-01T16:42:53.428330Z","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":"2026-08-01T16:42:53.479325Z","title":"Multi-agent reinforcement learning for active voltage control on power distribution networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.479325Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:77b078bcc3a20ddcfb91cbf6b1881c121b548c74fb05160301c7dc55bbd34d25","observation_id":"679c5586-3aac-463d-93ae-8f5812e2fe39","resolution":{"observed_at":"2026-08-01T16:42:53.479325Z","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":"2026-08-01T16:42:53.577903Z","title":"Deep reinforcement learning challenges and opportunities for urban water systems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.577903Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:f858608c0f7e539996c6a2fcec789e5b9704906a07a62a6644ab3e0a6f124c42","observation_id":"59bc3cda-b78b-4078-9d89-b63c563b6c67","resolution":{"observed_at":"2026-08-01T16:42:53.577903Z","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":"2026-08-01T16:42:53.664694Z","title":"Guide to industrial control systems (ICS) security,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.664694Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:bd0addbc6e54e4a7ce7d9e2d07d786aacb0ebfe63a304e13031064120dd97586","observation_id":"a5c27378-4e48-41d0-b03e-bb8d06f959f6","resolution":{"observed_at":"2026-08-01T16:42:53.664694Z","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":"2026-08-01T16:42:53.699421Z","title":"Cascading failures in interconnected power-to-water networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.699421Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:8986ba10aa85a14a5f2fca298f8c1268174be78d68fd2d2def8ad0979de16a23","observation_id":"ca5fbaca-639a-4fa0-9a92-e5734347aac3","resolution":{"observed_at":"2026-08-01T16:42:53.699421Z","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":"2026-08-01T16:42:53.782756Z","title":"Measuring network relia- bility and repairability against cascading failures,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.782756Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:1a79e37b2973d51ae6206124639fa5d289acce64eafe3f82c833d2322a9a34bc","observation_id":"d9d25d8c-fcbc-4979-884e-549e41c41011","resolution":{"observed_at":"2026-08-01T16:42:53.782756Z","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":"2026-08-01T16:42:53.845435Z","title":"A game-theoretical approach to cyber-security of critical infrastructures based on multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.845435Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:abc407c316ba9c9935655f4cf6491da2b367e12f2be503ab006330c204e42758","observation_id":"db037fb7-f54b-4ddc-83f1-143d719724b9","resolution":{"observed_at":"2026-08-01T16:42:53.845435Z","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":"2026-08-01T16:42:53.886545Z","title":"Prioritizing postdisaster recovery of transportation infrastructure systems using multiagent reinforcement learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.886545Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:efad104fc04105ad05f070f6136f3928706d17c0a11f2fa4aa16ce194222f56c","observation_id":"48bc9a19-c781-4d60-9afd-3ef1b2fbb22d","resolution":{"observed_at":"2026-08-01T16:42:53.886545Z","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":"2026-08-01T16:42:53.984759Z","title":"Review on modeling and simulation of interdependent critical infrastructure systems,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:53.984759Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:f452d38ba27b30ad2c5bc1cdd1739a106430006c9cd6c8afc1b18243e4d48b46","observation_id":"50c1b920-ed27-4529-b133-56e988f05574","resolution":{"observed_at":"2026-08-01T16:42:53.984759Z","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":"2026-08-01T16:42:54.064748Z","title":"Multi-agent actor-critic for mixed cooperative-competitive environ- ments,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.064748Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:26be19436f2530b974c8cf55182210da0fbe7a25af749ce0d1621a2362907c78","observation_id":"0173a4e9-0711-4ef0-8619-05db645ed7ad","resolution":{"observed_at":"2026-08-01T16:42:54.064748Z","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":"2026-08-01T16:42:54.124752Z","title":"Cross-border information sharing for critical infrastructure resilience: Requirements and platform architecture,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.124752Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:07fcba175f87b86341786a525c5539f480478fc4c7cde85c7e9a4bceb0d65d77","observation_id":"f94f7d7f-9e08-4b2d-9382-4686bbb11b21","resolution":{"observed_at":"2026-08-01T16:42:54.124752Z","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":"2026-08-01T16:42:54.206585Z","title":"Asynchronous methods for deep rein- forcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.206585Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:8afdad737aac6a726827c62abaf2139e4847dc858d4005968cd37bf979f88afd","observation_id":"273d0152-9caa-446b-821f-28aa6ccfef27","resolution":{"observed_at":"2026-08-01T16:42:54.206585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-01T16:42:54.314741Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.314741Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:36538d3bb8c3161c3e98b67041d05c284a472a9c2732458e0edcd10e432622e3","observation_id":"b89939c7-0e77-4718-a514-17b31375486b","resolution":{"observed_at":"2026-08-01T16:42:54.314741Z","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":"2026-08-01T16:42:54.469745Z","title":"Human-level control through deep reinforcement learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.469745Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:69d2390c39ecbca532085f92839b4ac2a33702f029169b30bcdad3cf475dd364","observation_id":"998878e7-cdef-4dd3-b4c5-00efe535772b","resolution":{"observed_at":"2026-08-01T16:42:54.469745Z","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":"2026-08-01T16:42:54.564761Z","title":"Fully decentralized multi-agent reinforcement learning with networked agents,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.564761Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:d77693671d47dcfb1e2654fb8e2947c8a607fed10ea9cea0a61682e0052bd6d3","observation_id":"d624e010-2d80-4f94-b216-b053107f4126","resolution":{"observed_at":"2026-08-01T16:42:54.564761Z","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":"2510.19199","doi":"10.48550/arxiv.2510.19199","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A communication-efficient decentralized actor-critic algorithm,","venue":"ArXiv.org","work_id":"939eb62a-20f6-4364-9faf-72322a5a1680","year":2025},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.684750Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:3e0b5868eb5d093548183753b9c99024ea456c691b9668546cf261a4ed53e12e","observation_id":"092f1a1f-e187-49ad-872a-0fa93581a55d","resolution":{"observed_at":"2026-08-01T16:43:21.563484Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-01T16:42:54.860258Z","title":"Communication-efficient actor-critic methods for homogeneous markov games,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.860258Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:96edd9bf3cf24bc0d07506b08a2fe68bc103445b29460f22a856a7453fd57440","observation_id":"c8684ec9-a50c-4394-a801-136253374f36","resolution":{"observed_at":"2026-08-01T16:42:54.860258Z","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":"2026-08-01T16:42:54.991894Z","title":"Neighbor-based decentralized training strategies for multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:54.991894Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:d0b8c2adc0cf54f3c5d5dab10cc1fc4edf99e057b6b71c50a38d592457a10aae","observation_id":"67205982-4c7f-4cd8-950e-a2292f56fb2b","resolution":{"observed_at":"2026-08-01T16:42:54.991894Z","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":"2026-08-01T16:42:55.124300Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.124300Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:2c6c68780a229d5b6382c2bc6144890c6f65d2b0ee5ad78ce7357879a84a43a0","observation_id":"3fd3a965-7eb0-4af7-abf5-5c2cb161b685","resolution":{"observed_at":"2026-08-01T16:42:55.124300Z","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":"2026-08-01T16:42:55.237384Z","title":"Efficient and scalable reinforcement learning for large-scale network control,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.237384Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:e7c5cdcfca9a1d38c7ed89a0afdf1856f67a196c22031acafcfa771cfc559ef4","observation_id":"e0a5d9b2-7fd5-4df3-a68c-a79f861f10e6","resolution":{"observed_at":"2026-08-01T16:42:55.237384Z","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":"2026-08-01T16:42:55.324754Z","title":"Holarchic struc- tures for decentralized deep learning: A performance analysis,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.324754Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:800b4d507833e77e7059a8f3c5f157f44493155dbec67e6c1543bee73aa50791","observation_id":"904bd52a-a58e-4b76-84be-a567a683b002","resolution":{"observed_at":"2026-08-01T16:42:55.324754Z","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":"2026-08-01T16:42:55.434746Z","title":"Security and privacy challenges in the smart grid,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.434746Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:5a9221a4d9ad2ec8d1d44ddfa45996b509fe03df5aa82d70aa2bd6043fc35911","observation_id":"4c9bb723-e076-4271-b9e0-686f60615756","resolution":{"observed_at":"2026-08-01T16:42:55.434746Z","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":"2026-08-01T16:42:55.554748Z","title":"Decentralized collective learning for self-managed sharing economies,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.554748Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:13555138dd80d73c68cb9a78d0e80c533d45f1cc5c9a92b53903944c28129af3","observation_id":"101e548f-5ac7-4ac7-b3ac-0bc79e3336f1","resolution":{"observed_at":"2026-08-01T16:42:55.554748Z","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":"2026-08-01T16:42:55.665643Z","title":"Resilience of critical infras- tructure elements and its main factors,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.665643Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:db348749dce8fb0a2deecac31ef075fb260be78c7c9ae283a497d84ae4566a43","observation_id":"31929c54-aab4-43d1-8420-41daf9946b6d","resolution":{"observed_at":"2026-08-01T16:42:55.665643Z","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":"2026-08-01T16:42:55.734744Z","title":"Optimization under attack: Resilience, vulnerability, and the path to collapse,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.734744Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:7f9eb6057297a921e4f152bcea16c53021e2826d198c1e4599f2a72588594ebf","observation_id":"4735df01-22ff-4a61-a46d-d54c6a7becb7","resolution":{"observed_at":"2026-08-01T16:42:55.734744Z","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":"2026-08-01T16:42:55.884764Z","title":"Learning decentralized traffic signal controllers with multi- agent graph reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:55.884764Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:4153176f5cad74597eb761cdd0cf1d9863d6a6b2d9edea27f25e966bb796bda4","observation_id":"216bb6ba-835f-4e5b-9ee1-fccb78cda716","resolution":{"observed_at":"2026-08-01T16:42:55.884764Z","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":"2026-08-01T16:42:56.042377Z","title":"Self-repairable smart grids via online coordination of smart transformers,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.042377Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:feba78a8e1f69d474493ba29fb18aa126a09c356db5038376270be88c67899df","observation_id":"e118ffb2-d7bb-486b-9720-8844fdba4da2","resolution":{"observed_at":"2026-08-01T16:42:56.042377Z","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":"2026-08-01T16:42:56.104912Z","title":"A survey on federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.104912Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:be27b79dbe6d11d6b4475bfbd5143e73a2d05ec6eda9879465a569b98d987f80","observation_id":"e2a1efc5-8adc-4191-b8a8-19dfc53f49da","resolution":{"observed_at":"2026-08-01T16:42:56.104912Z","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":"2026-08-01T16:42:56.224749Z","title":"Gossip learning as a decen- tralized alternative to federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.224749Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:658f1351aec39ca5ccecc42ca8a4d27064ee190f0bd633571bb5fb925fc8b7c3","observation_id":"f97b7607-c56a-48f0-8090-8763701f7309","resolution":{"observed_at":"2026-08-01T16:42:56.224749Z","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":"2026-08-01T16:42:56.404752Z","title":"Counterfactual multi-agent policy gradients,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.404752Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:759119e2f0fec7841a5de63796602d441d6f8d9b091b27bff0f1725c8b8be73f","observation_id":"551b120e-c403-42de-8f53-4ffbd94d77d7","resolution":{"observed_at":"2026-08-01T16:42:56.404752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04295","last_updated":"2025-02-07T10:48:22Z","snapshot_observed_at":"2026-08-02T02:46:54.388016Z","submitted_at":"2024-08-08T08:18:05Z","title":"Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04295","snapshot_observed_at":"2026-08-01T16:42:56.684749Z","title":"Assign- ing credit with partial reward decoupling in multi-agent proximal policy optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.684749Z"},"links":{"cited_paper":"/paper/2408.04295","citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:955112d23ebdd6fb47f04ef4ecc24c2e8980c6471285421256753ec85f287e5c","observation_id":"6c150b95-a12f-4b8a-a716-8a16fcd92d2e","resolution":{"observed_at":"2026-08-01T16:42:56.684749Z","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":"2026-08-01T16:42:56.865241Z","title":"A survey of multi-agent deep reinforcement learning with communication,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:56.865241Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:11639f8696439921b91a71c9ef4cb05121d096457a2f27ec450110b23ba5e555","observation_id":"34676645-5b15-42b4-b718-dfef20d3824c","resolution":{"observed_at":"2026-08-01T16:42:56.865241Z","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":"2026-08-01T16:42:57.021901Z","title":"Discrete-choice multi-agent optimization: Decentralized hard constraint satisfaction for smart cities,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T16:42:57.021901Z"},"links":{"citing_paper":"/paper/2607.18359"},"observation_digest":"sha256:db8f8fde2f2a4b94e1217196ba15e91b6d033d243c26031ed50be03c51fff3aa","observation_id":"cda167d2-4cdb-4485-a0cb-b70109d893a0","resolution":{"observed_at":"2026-08-01T16:42:57.021901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.18359","last_updated":"2026-07-20T12:43:54Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-07T20:28:25.513607Z","submitted_at":"2026-07-20T12:43:54Z","title":"Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":33},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.18359."}