{"as_of":"2026-08-14T11:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d0ec8ff8502463c9847676235ce74491580a59ea474bae85fde423ad725e8647","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T18:04:01.765007Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2412.08293/citation-record","integrity":"/paper/2412.08293/integrity","json":"/paper/2412.08293/citation-record.json","paper":"/paper/2412.08293"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.enbuild.2007.03.007","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:01.993359Z","title":"P´ erez-Lombard, J","venue":null,"work_id":"9b261a30-de86-4d93-9129-555a29bd43e2","year":2008},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.501001Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:5f0810a9ca77befe99db071fe7cca7993096b5a85762f7b66714775974841ef7","observation_id":"f70b23e8-3186-4bc3-81a4-0d518bae1f16","resolution":{"observed_at":"2026-08-11T18:04:01.998242Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.506552Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.506552Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:64e31448f22a7252c2bb3ff7d9372d0f2cb008c0ff053f39b2abab9b8a83fdce","observation_id":"c2a17b50-5e81-42af-9720-ecdd548cb7dc","resolution":{"observed_at":"2026-08-11T18:04:01.506552Z","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":"2021.12043","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:05.415276Z","title":null,"venue":null,"work_id":"7de364e0-0323-43aa-80a3-633bf2a6307d","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.512154Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:73eacfdcdbc84969731a08fa5934760ef670835c95624424d1aa086d1c28a764","observation_id":"48acad1c-6b5e-4a71-a7b3-8aa8969707f6","resolution":{"observed_at":"2026-08-11T18:04:05.422762Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.517846Z","title":"Gholamzadehmir, C","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.517846Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:00e0a0e226786c2e21cddafe50258db5de23613bb672414b1deb86d1a65d5ca9","observation_id":"66fe4074-e5ca-4ca2-b362-d6a941721b90","resolution":{"observed_at":"2026-08-11T18:04:01.517846Z","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":"2026-08-11T18:04:01.523816Z","title":"Rolnick, P","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.523816Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:6dcfa75bda6b0bc71e725ad1a96ba8761b76bfe84a40c489f25b2b61b66281f3","observation_id":"e3d5b105-7b27-4163-881d-9a75275ed2db","resolution":{"observed_at":"2026-08-11T18:04:01.523816Z","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":"2026-08-11T18:04:01.529163Z","title":"Findeis, F","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.529163Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:f0bf99429619bab675888e396a8eafaec31ae0337fa9ba85123d16e1f1bb2bfa","observation_id":"c164704c-b28e-42ee-9daa-e5f1cd87e3e6","resolution":{"observed_at":"2026-08-11T18:04:01.529163Z","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":"2026-08-11T18:04:01.534820Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.534820Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:a1175117809c35b6adc7710815add975a363dbbddf3ca203826369bee9fc48e4","observation_id":"eb77de42-68dc-4b49-ad76-fa7f9cf72e00","resolution":{"observed_at":"2026-08-11T18:04:01.534820Z","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":"2026-08-11T18:04:05.701491Z","title":"Sutton, A","venue":null,"work_id":"3a1545f3-88b2-4c1f-8870-116b218d4c39","year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.539887Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:060406dfa11c2aca6a6c5f5d01f57ea25cc0cfa4c18a66324149f8c56ecff6b8","observation_id":"39dbb395-3cac-4c48-867b-5161c344c633","resolution":{"observed_at":"2026-08-11T18:04:05.706535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.544594Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.544594Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:30fc288f2146e1bfe1d76be3a6d81823d970ccedbb36ba1ddbd897c527325021","observation_id":"51e41244-7619-49f0-89a0-88988c7839d3","resolution":{"observed_at":"2026-08-11T18:04:01.544594Z","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":"2026-08-11T18:04:01.549269Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.549269Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:3f958259abcd67f8f24e3846c9ec7232c407efad84aea9af1beeb6ab95bcdb62","observation_id":"2aeaf028-92c4-4b5b-a3d0-f5d7b74540a4","resolution":{"observed_at":"2026-08-11T18:04:01.549269Z","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":"10.1016/j.compeleceng.2019.07.019","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:01.959329Z","title":"Mason, S","venue":null,"work_id":"61b98ccf-fa87-4786-a750-4723086f5c72","year":2019},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.554653Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:b5faa6856def093e67dfd129260fc6040c72f4327b2e8215d1ec4aa052579a51","observation_id":"9f85ef4a-7f43-4258-8ea9-96466605989d","resolution":{"observed_at":"2026-08-11T18:04:01.964021Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.560748Z","title":"Zhang, X","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.560748Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:d772df9e9ec46bc714526a5bc8b09209e0912e46f7628d27ba2495c4cbebc9be","observation_id":"044ac111-6d0a-470b-89a3-b84c70af0f28","resolution":{"observed_at":"2026-08-11T18:04:01.560748Z","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":"2026-08-11T18:04:01.565811Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.565811Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:bdc0589ee0121d9b09b820368ddc45245839956c5c50d0ebf71f7f4eada87144","observation_id":"1c36e0c1-5ef3-4908-a020-736e9d4d00b7","resolution":{"observed_at":"2026-08-11T18:04:01.565811Z","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":"2026-08-11T18:04:01.571873Z","title":"Brandi, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.571873Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:6fe8322190c4f7b852e806aa543f91f0f0aae6156d35d565b4ae7e49d522a4e5","observation_id":"a3814d2c-121e-4ec5-b29f-cd08fad32433","resolution":{"observed_at":"2026-08-11T18:04:01.571873Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T18:04:01.578851Z","title":"Azuatalam, W.-L","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.578851Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:cd10b619e3aeb7790b76fc59e34f5aeccc0c77a011b04922335a04431fc434ef","observation_id":"0c49a7b7-2a7e-48f6-a193-0cdfba83bd33","resolution":{"observed_at":"2026-08-11T18:04:01.578851Z","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":"2026-08-11T18:04:01.583236Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.583236Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:79661ffcc7897cb218b79df3f551179351692d0113576cc90e183b39f7119e11","observation_id":"08deec09-5367-4a87-a9e8-2f67a013026c","resolution":{"observed_at":"2026-08-11T18:04:01.583236Z","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":"2026-08-11T18:04:01.587151Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.587151Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:9625432eb257adfc4ebaabc76dd7cc3a4e03712fcec2f4ecb15b91e6c38de9f7","observation_id":"0751dd0c-7ee3-48f9-93eb-2d58c40686fd","resolution":{"observed_at":"2026-08-11T18:04:01.587151Z","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":"2026-08-11T18:04:01.591094Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.591094Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:fc8337f393e1d1e5275481138da93221587e48942db40e4985153ac04b90287f","observation_id":"682b602a-9ec1-4347-a73c-ae711cc2156d","resolution":{"observed_at":"2026-08-11T18:04:01.591094Z","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":"2026-08-11T18:04:01.594975Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.594975Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:9428f41425f34c39d747957d1e169d492db25dffb6218994053ab9db8a62d5c2","observation_id":"57d45726-0b64-45a1-b94a-4862ddc2b6d3","resolution":{"observed_at":"2026-08-11T18:04:01.594975Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.11939","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:03.921587Z","title":null,"venue":null,"work_id":"9628f8a7-a382-4694-924c-664310d05d39","year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.598788Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:9e6253cbc10b54af86c725335d4c63330782b9b615f8a1a2d11d9f06d86c78b0","observation_id":"a7111a96-74c8-48dd-a333-f28ba056a220","resolution":{"observed_at":"2026-08-11T18:04:03.929211Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.603067Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.603067Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:704a59e98cf6084d3cea34911f89dff5410c7351a162a9d2ff37d3c7a4ee23ca","observation_id":"cd74a12f-06a1-43e6-b00b-8033a6954470","resolution":{"observed_at":"2026-08-11T18:04:01.603067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05711","last_updated":"2023-08-10T17:20:02Z","snapshot_observed_at":"2026-08-13T10:37:04.525800Z","submitted_at":"2023-08-10T17:20:02Z","title":"A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control","version":1},"cited_work":{"arxiv_id":"2308.05711","doi":"10.48550/arxiv.2308.05711","metadata_source":"pith","pith_arxiv_id":"2308.05711","snapshot_observed_at":"2026-08-11T18:16:15.004306Z","title":"A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control","venue":"cs.LG","work_id":"14bcc509-9075-4e06-a9f3-cbd2f07d84ca","year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.606900Z"},"links":{"cited_paper":"/paper/2308.05711","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:57fc2f483200ea4b2285f444a7060a9e31d1359f2e64cc5459789b051405a9a0","observation_id":"0969362c-fbd1-4564-b745-73f851e60a17","resolution":{"observed_at":"2026-08-11T18:04:01.938813Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.687158Z","title":"Manjavacas, A","venue":null,"work_id":"159784f1-9838-4647-895c-49217f67e075","year":2024},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.611657Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:e36c8da07eef7e04e95138f79db2ddd966bfaec16853198ee3dcc4c6927405c5","observation_id":"245cef8f-a57d-48c4-a3d3-347f7ee0fe82","resolution":{"observed_at":"2026-08-11T18:04:05.691881Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.616123Z","title":"Dmitrewski, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.616123Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:6bfdcfff2f3179223a857ee95233fb4e33c86fa86a6c07bb2bd2a16156ea2dbe","observation_id":"6d337f37-4007-43af-a537-8bcf92f4c9e8","resolution":{"observed_at":"2026-08-11T18:04:01.616123Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T18:04:01.625174Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.625174Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:8f20bf6528f1f613ec5ebc1501dc9a2f7b2857c5bf8edd3cc5d5dae91d0c6e35","observation_id":"d0232b0b-8d6a-42d3-bf04-6f98b9ce8b31","resolution":{"observed_at":"2026-08-11T18:04:01.625174Z","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":"0100.36237","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:03.451615Z","title":null,"venue":null,"work_id":"89b111f3-ef40-4b46-856c-45482fffcf32","year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.629923Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:6601acc7c7d1e33c0c9f75ecaf8def075d179a5be06def91f06221ca78f63c9e","observation_id":"2bd6413f-a815-49ed-a811-5a8b49462182","resolution":{"observed_at":"2026-08-11T18:04:03.458922Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.673911Z","title":"Jim´ enez-Raboso, A","venue":null,"work_id":"94c49ca5-53e1-41be-b83b-223fa554ba8a","year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.634584Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:c8f90eeb372a55151d5575dc91bb7c8aff7cebb9141029deba8f475f089ad406","observation_id":"49cb7bbe-43af-4b46-9f86-ba3be438abd1","resolution":{"observed_at":"2026-08-11T18:04:05.677937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00389","last_updated":"2025-05-02T07:21:03Z","snapshot_observed_at":"2026-08-13T00:17:47.535517Z","submitted_at":"2024-05-01T08:42:22Z","title":"Employing Federated Learning for Training Autonomous HVAC Systems","version":2},"cited_work":{"arxiv_id":"2405.00389","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.00389","snapshot_observed_at":"2026-08-11T18:04:03.293213Z","title":"Employing Federated Learning for Training Autonomous HVAC Systems","venue":"math.OC","work_id":"63001fca-4f10-4bc6-901e-ddef1a40f303","year":2024},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.639476Z"},"links":{"cited_paper":"/paper/2405.00389","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:d5bd2df148ea1038e37377b7c26160f3c96f15adfbd8ea9042a7084d9788638e","observation_id":"db317e0b-96d8-47ba-857b-9ff4ff3c4566","resolution":{"observed_at":"2026-08-11T18:04:03.298640Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0100.36256","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:03.270000Z","title":"W¨ olfle, S","venue":null,"work_id":"985a2f4e-6d5f-4954-b07d-9434e2414c08","year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.644628Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:89b41f98d1a767a0797f5c371928db8c1c2c691f02e0f3246ab6d1164625c562","observation_id":"db0b6b47-16d5-4ddb-bf3a-5f795d586b65","resolution":{"observed_at":"2026-08-11T18:04:03.277524Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.659349Z","title":"Jang, Active reinforcement learning for robust building control, Master’s thesis, EECS Department, University of California, Berkeley (May 2023)","venue":null,"work_id":"3ae75212-0f6d-4d7b-a181-81c2b99e7427","year":2023},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.649304Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:cd263fe28311e22e5ebba44e3395403060ff1f3e2cd2b498b0f3aaa3775df8f9","observation_id":"a912ee43-0f96-4d6e-8f74-899a292b0f14","resolution":{"observed_at":"2026-08-11T18:04:05.663866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17032","last_updated":"2025-11-02T13:42:19Z","snapshot_observed_at":"2026-08-13T22:24:37.672685Z","submitted_at":"2024-07-24T06:35:05Z","title":"Gymnasium: A Standard Interface for Reinforcement Learning Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17032","snapshot_observed_at":"2026-08-11T18:04:01.653922Z","title":"Kwiatkowski, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.653922Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:a9480ef37aa272340edbe587ed85bfc4dd2fe0e5f60ffed4c0664896b0a04b74","observation_id":"c225549e-fe2d-48a8-b73b-bc5cffc5601c","resolution":{"observed_at":"2026-08-11T18:04:01.653922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-11T18:04:01.658843Z","title":"Brockman, V","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.658843Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:7ae6f9cbf5289d09eba49752f355e9daffbdff2e8c3e2e891c9db58d95b2c72e","observation_id":"02823433-983e-4d77-9076-215567d1b3be","resolution":{"observed_at":"2026-08-11T18:04:01.658843Z","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":"2026-08-11T18:04:05.645096Z","title":"Raffin, A","venue":null,"work_id":"2967c43e-95b5-4fb2-8f9e-4ffd1c1cdde8","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.663937Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:4d8dc930f1097c57487fa4275bdf8b9a5c65078c2c7a83a1fedaf2c30b3b68eb","observation_id":"a42ed3be-8056-4181-bc4d-83a7a3687ddb","resolution":{"observed_at":"2026-08-11T18:04:05.649372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.629833Z","title":"Liang, R","venue":null,"work_id":"a1c4af7e-5410-406c-a6ea-7f50808be307","year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.668482Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:2d5f24a7ce9b7e0ebae0b65e7ef77f0a75541569979c4c2235ac4fda46138f23","observation_id":"928da74c-b05d-45ef-ad32-5ca2b7fa6151","resolution":{"observed_at":"2026-08-11T18:04:05.634191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.11503","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:03.170006Z","title":null,"venue":null,"work_id":"f8dd71d2-8194-4fa2-a8c3-1ed1973b367c","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.672815Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:29323209bf44b56794791e618397d75c9df23e7969b0d83eb6c799630aebfba5","observation_id":"78711e9d-73bb-4430-bcc2-b47d35215805","resolution":{"observed_at":"2026-08-11T18:04:03.177123Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.677375Z","title":"Biemann, F","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.677375Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:f5e63fcd0ac4454a91dd3e8cba530881642face687f538df04252d64f16f5270","observation_id":"c81f1f4c-3844-460e-8974-f0e729061957","resolution":{"observed_at":"2026-08-11T18:04:01.677375Z","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":"10.1007/978-981-13-2853-4_4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:08:50.044864Z","title":"Moriyama, G","venue":"Communications in computer and information science","work_id":"b5084b86-eae2-4eef-9829-041621995edd","year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.681840Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:0cb7eb976a094915197833bbfde2d527072da489079450ce182c6078ce444d0a","observation_id":"f6d89433-f6c1-416a-b4a3-eb80fab622e5","resolution":{"observed_at":"2026-08-11T18:04:01.887258Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.614862Z","title":"Arroyo, C","venue":null,"work_id":"1b84d36a-5f2a-4628-b2e1-70750c4b388a","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.686504Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:cc2ca4399d02cbbb4cb15b688c065d249ae6854d65316e732c797c3defc76b6e","observation_id":"e644cb9b-fc37-4190-a394-1ddf7f27ff2a","resolution":{"observed_at":"2026-08-11T18:04:05.619980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/app11083518","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:01.867569Z","title":"Scharnhorst, B","venue":null,"work_id":"8a94bf31-33cb-447e-b21a-61701b484799","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.690916Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:cb8f2a21976dfa7566e1d0e0a107f17588834395b4d2599c0617916effa61bcf","observation_id":"c896784a-ac1d-4a5d-ae69-b221989a6de9","resolution":{"observed_at":"2026-08-11T18:04:01.872343Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5984.33659","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:03.001951Z","title":"Lukianykhin, T","venue":null,"work_id":"d74f25b7-8d4e-4512-a7a4-1c77d084cae2","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.695650Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:9c14e5bf4ba727737ec7c6f5db89e1d9afe28345bb678595d8b333e3abc4cb34","observation_id":"2589eb9c-08c3-4e9e-9caf-a498de4d2df6","resolution":{"observed_at":"2026-08-11T18:04:03.009281Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.699897Z","title":"W¨ olfle, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.699897Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:ffeeeae07e249809cf59d23bf79a44c5f8c831d076c02453bbe0e87d1b0d7cbe","observation_id":"d18f7af8-0d89-48ed-9bc3-24de3fdd8246","resolution":{"observed_at":"2026-08-11T18:04:01.699897Z","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":"8308.34311","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:02.841010Z","title":"Zhang, O","venue":null,"work_id":"6ce8886e-cee0-40ad-b525-2b5eca05c64f","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.704235Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:a3b8a50724e7dd2126b17f6fa6f16e11db01b4f7550c5279b7c9a1ae4183f04b","observation_id":"91b1fab5-d641-4799-abb3-971c8b21e433","resolution":{"observed_at":"2026-08-11T18:04:02.848422Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.708247Z","title":"Nweye, K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.708247Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:424647f69413fbbfb70629dd369a5d75ff21de3102cc5bb5e86015d66c7378a2","observation_id":"f1c67c70-8de7-426c-a85e-8ca3523c3cbf","resolution":{"observed_at":"2026-08-11T18:04:01.708247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06396","last_updated":"2021-10-12T23:19:29Z","snapshot_observed_at":"2026-08-13T17:54:15.830206Z","submitted_at":"2021-10-12T23:19:29Z","title":"GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management","version":1},"cited_work":{"arxiv_id":"2110.06396","doi":"10.48550/arxiv.2110.06396","metadata_source":"pith","pith_arxiv_id":"2110.06396","snapshot_observed_at":"2026-08-11T18:16:15.004306Z","title":"GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management","venue":"cs.MA","work_id":"a88599d5-c0b3-4796-9f4b-8bbe303208ea","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.712709Z"},"links":{"cited_paper":"/paper/2110.06396","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:434b8bae26af4809c47199a03cb6f17c5107c5175ecee74fba4139556a50bada","observation_id":"e9a99f6c-3320-45d8-9f63-1ee0e2d27350","resolution":{"observed_at":"2026-08-11T18:04:01.856911Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.598673Z","title":"Marot, B","venue":null,"work_id":"50fc8b2a-5d8c-4d0f-acfc-e7836168962e","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.717723Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:a8cf55ec356c6f4ed7d826c9a450ebb3014d048a98aea4b21c35be24c6fd4f58","observation_id":"9fa68357-7d23-4bc8-88f4-f06b381c3a31","resolution":{"observed_at":"2026-08-11T18:04:05.604068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06893","last_updated":"2018-04-20T16:49:52Z","snapshot_observed_at":"2026-08-04T06:10:10.723883Z","submitted_at":"2018-04-18T19:49:13Z","title":"A Study on Overfitting in Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.06893","snapshot_observed_at":"2026-08-11T18:04:01.722304Z","title":"Zhang, O","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.722304Z"},"links":{"cited_paper":"/paper/1804.06893","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:24f81ce5e84be877a012db423123f28dc8e6c4368df565effdd9f96d5da517a6","observation_id":"85b2353c-e5a8-4ff3-921d-2baec7c76d53","resolution":{"observed_at":"2026-08-11T18:04:01.722304Z","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":"2021.11764","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:02.679529Z","title":"Pinto, D","venue":null,"work_id":"7f1df555-33e8-4fed-9211-b721cca8a7ab","year":2021},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.726905Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:e9216c02ce34fd5e36f11ddfd8adccfbc5298c48cd1a8a936b572eda2203a7ce","observation_id":"044db07b-e2c3-4bba-94f6-8452e31932e1","resolution":{"observed_at":"2026-08-11T18:04:02.688595Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8308.34279","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:02.278458Z","title":null,"venue":null,"work_id":"766ef4d6-8fa8-4d73-a3a3-595142aa4450","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.730892Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:6bfd76b79869046865e32549f1e966f2d84aa69843b8dd1f3ba7ef4588e18428","observation_id":"9ee5d5a5-5457-4072-97c3-33dfcc8e8642","resolution":{"observed_at":"2026-08-11T18:04:02.285253Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:01.735023Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.735023Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:9ad1d61b15efe0531958dc87e2c320126a0f7a614a0a655855a1092a99e8c248","observation_id":"dde2db94-e37e-4259-8845-6abab6da531e","resolution":{"observed_at":"2026-08-11T18:04:01.735023Z","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":"2026-08-11T18:04:05.582642Z","title":null,"venue":null,"work_id":"99da20b2-1aa3-4207-b23a-8ffc6f3205a4","year":2010},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.738820Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:f303735a4da0741901be99f9c1255d34e5a5f8d88dd2cd11d2d6ec8d1b08aea0","observation_id":"6c676749-30e0-4388-a47a-c64cce816119","resolution":{"observed_at":"2026-08-11T18:04:05.587350Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.apenergy.2012.10.064","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:04:01.813617Z","title":"Z´ arate-Mi˜ nano, M","venue":null,"work_id":"5b9e7ab4-572b-408f-8c5a-ff28aeb17365","year":2013},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.742796Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:bbcccf86f1dd098cd1e961c83d449062c27495e41a6db0f9ac37ef7df7df1d44","observation_id":"10cc34f3-427f-464b-aab8-2774b06b6d19","resolution":{"observed_at":"2026-08-11T18:04:01.819369Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.568047Z","title":"Biewald, Experiment tracking with weights and biases, software available from wandb.com (2020)","venue":null,"work_id":"30929302-32c2-4a6b-bf1a-2bf1a017f400","year":2020},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.747221Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:91f66818eee3670b7471205479812c5fecd9d7fad71e8df2e279c53459506588","observation_id":"0d7e418e-c7bf-4031-bfae-5e46ce36ca32","resolution":{"observed_at":"2026-08-11T18:04:05.572673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.552848Z","title":"Haarnoja, A","venue":null,"work_id":"448327a6-a1fa-420c-a613-6d56870c8e6d","year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.751175Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:b18632f25c0b26a09a8eb766097ef0fcbb56b5ead9ca0f554dfe9d12dc92f9e2","observation_id":"49e9f151-aaf5-463a-9716-c07863277da4","resolution":{"observed_at":"2026-08-11T18:04:05.557125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T18:04:05.537514Z","title":"Fujimoto, H","venue":null,"work_id":"e09af307-64b2-4da6-ba03-3e0a2f7f6834","year":2018},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.755577Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:3c61b4813b4b091e10403a1be1ffa6de10608819740acdc5e49d3374d83ec30a","observation_id":"6e3f95c6-d845-49af-8f89-67f1c8a09b7f","resolution":{"observed_at":"2026-08-11T18:04:05.542314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-11T18:04:01.760236Z","title":"Schulman, F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.760236Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:e1b47a847c0780d4681e14938896843cc30ff3898fd598c16b640e2efd5caf23","observation_id":"19ac0793-615c-439c-84b9-b0ddfd5a1039","resolution":{"observed_at":"2026-08-11T18:04:01.760236Z","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":"2026-08-11T18:04:05.521429Z","title":null,"venue":null,"work_id":"947ccbee-0d99-444c-b139-55ec8f069d05","year":2016},"citing_paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T18:04:01.765007Z"},"links":{"citing_paper":"/paper/2412.08293"},"observation_digest":"sha256:52c6de60bc742d30f657b3dee6a29bbf32f7761d8a21cf5cf804c8709d4d8008","observation_id":"5d33b49d-8adf-48e3-9b5f-c7194f4067c5","resolution":{"observed_at":"2026-08-11T18:04:05.526840Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.08293","last_updated":"2024-12-11T11:09:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T14:12:14.965337Z","submitted_at":"2024-12-11T11:09:13Z","title":"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":4,"metadata_mismatch":10,"parse_uncertain":0,"unresolved":25,"verified_exact":7,"verified_fuzzy":10},"total_outbound_references":56},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2412.08293."}