{"as_of":"2026-08-11T01:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b66509e7207957fdd909c204c7fe098e4bb117aebd0217c7dd2b4072385d3b21","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:22:27.432252Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"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/2507.16867/citation-record","integrity":"/paper/2507.16867/integrity","json":"/paper/2507.16867/citation-record.json","paper":"/paper/2507.16867"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:31.140225Z","title":"Research and prospect of multi- microgrid control strategies,","venue":null,"work_id":"1e5304f1-8267-43f6-a783-816c75ddd0e6","year":2016},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.416429Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:14a3893141e02f5e88da13c3280ddfe248b3fada98102f51c59f9cc323ba9c70","observation_id":"994a6657-37fa-4f15-adfd-8bb01e382028","resolution":{"observed_at":"2026-08-06T15:22:31.166179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:31.014040Z","title":"Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes,","venue":null,"work_id":"43d0f9aa-267e-4b96-9d16-c235ba546ff7","year":2018},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.476663Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:bf578c55e3d61d94168fe977a5017b8267b3c594e9bcf663fc512521c4ffee65","observation_id":"3ed4389e-e33f-4b4d-8d4d-12900cf058aa","resolution":{"observed_at":"2026-08-06T15:22:31.054739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.859221Z","title":"Minlp probabilistic scheduling model for demand response programs integrated energy hubs,","venue":null,"work_id":"a19e22c6-ccd1-42bf-b75f-c84dc8ff2007","year":2017},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.595895Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:2de6fd07c2c004dd5834d389f56efdd1d32852b72a1c941c0c2835e9a27d32e6","observation_id":"fef19839-1792-4e7b-8c70-03ffe3cf849a","resolution":{"observed_at":"2026-08-06T15:22:30.947277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.738428Z","title":"Robust optimal power management system for a hybrid ac/dc micro-grid,","venue":null,"work_id":"9173afdc-0625-40ba-9a4e-8c4a40aa3abb","year":2015},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.642145Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:1dd57cc10166b630222839fcddfb1eac13de289e0f1d051a88869928271fc707","observation_id":"9a8f4d65-6c61-4a2c-b8dd-7800e5f09854","resolution":{"observed_at":"2026-08-06T15:22:30.815332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.616749Z","title":"Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule,","venue":null,"work_id":"fd4cc73f-5645-4f1f-a14d-dd729db285e6","year":2018},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.728433Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:2933b93c853b7bfb6a0dffd1e603c5e9edfa1c46dc18f2db26d011724dda777a","observation_id":"ad0bd589-71da-4ab5-987e-1a9c2cfda558","resolution":{"observed_at":"2026-08-06T15:22:30.671510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.422159Z","title":"A model predictive control approach to microgrid operation optimization,","venue":null,"work_id":"b28e9097-911f-44cc-87f4-711590c8a186","year":2014},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.801333Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:ef72184dce84606414144a910ca29d551c1b8a83a95aa41d254f272e0e11a54f","observation_id":"af31605a-9401-46b2-97d3-db83cfe67d39","resolution":{"observed_at":"2026-08-06T15:22:30.494997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.261254Z","title":"Real- time integration of optimal generation scheduling with mpc for the energy management of a renewable hydrogen-based microgrid,","venue":null,"work_id":"b3bcd2dc-f048-467b-8b5b-688df7e4e3f8","year":2016},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.897300Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:bf84626a0a50a8abf99dbbef43e5a704454fc39fa24e951e9c08fe8e964728e1","observation_id":"230f0173-6174-49a6-b8e5-20dc69940ebb","resolution":{"observed_at":"2026-08-06T15:22:30.326486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:30.088789Z","title":"A hierarchical energy management system based on hierarchical optimization for microgrid community economic operation,","venue":null,"work_id":"4bf07a61-ea3b-45f5-afb7-c7d93ee91633","year":2015},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:25.994454Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:fb8e111ac50a3b6efecb3f1bca045d837ad7092968d56573e69f69e970f118f0","observation_id":"e5a97bd5-92d8-41bb-be34-220c34b95002","resolution":{"observed_at":"2026-08-06T15:22:30.167396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.922254Z","title":"Distributed optimal energy management of a microgrid community,","venue":null,"work_id":"c0a7478d-58c2-4fc9-ba88-2bc46e96af40","year":2022},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.054263Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:06ca4fcfd3d7a9b1c5de98ca58e75968b4fa79e5f7288d57e1d5b5dc0d55c7b5","observation_id":"74af1f6a-8510-4dc5-a6db-348c65de867c","resolution":{"observed_at":"2026-08-06T15:22:30.018491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.771480Z","title":"Double deep q-learning- based distributed operation of battery energy storage system considering uncertainties,","venue":null,"work_id":"282cc19b-8fc3-49f2-8a78-05f50bf9a25c","year":2019},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.160894Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:eea8a13cc5d2c2ec3521af40ed9e876bfef89dd45f888757c88df4061ed2ba92","observation_id":"54905c59-8885-45d9-8c47-41569317418d","resolution":{"observed_at":"2026-08-06T15:22:29.828681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.611647Z","title":"Drl-hems: Deep reinforcement learning agent for demand response in home energy management systems considering customers and operators perspectives,","venue":null,"work_id":"bc8da8d4-cf02-46ac-926c-64ef9905cb0f","year":2022},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.203968Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:e102c33471a97fb2c92fa726609eea41bcf454b3ab819e613a6d62c596cd9034","observation_id":"df3fa3d0-19ef-4f8d-aba1-de79c259778e","resolution":{"observed_at":"2026-08-06T15:22:29.691797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.472431Z","title":"Online optimal power scheduling of a microgrid via imitation learning,","venue":null,"work_id":"408c7644-c839-411c-ad39-1e6c302c893f","year":2021},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.275857Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:d541938462c613483227aebb8502b463de17dfada7db92b79a9068885e41ef3d","observation_id":"e5d2d7d3-10ab-4398-bfc3-5bae5306141f","resolution":{"observed_at":"2026-08-06T15:22:29.546990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.330589Z","title":"Online cooper- ative optimal power scheduling of multiple microgrids via hierarchical imitation learning,","venue":null,"work_id":"2227fc89-be25-4f84-9228-0e3c407f1f0b","year":2024},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.351784Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:03e160ab0f2d3700a0875aa53787ad9dc8722a94f3cd1d873fb2e814747efb3f","observation_id":"21ec8dc9-5dc0-4365-ba76-0c25806d3fa0","resolution":{"observed_at":"2026-08-06T15:22:29.371003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:29.183585Z","title":"Multi-agent imitation learning based energy management of a microgrid with hybrid energy storage and real-time pricing,","venue":null,"work_id":"8a3e3181-4754-431b-b5f6-49f0de460e9e","year":2025},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.423993Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:2cb9a7c9519028bc1f056e3de1f273dcbe72e18c82a95d53ec8f8e51bab1a6ff","observation_id":"2002eca2-1d3c-4c54-b94c-dd5796deaf50","resolution":{"observed_at":"2026-08-06T15:22:29.240924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"1912.05032","last_updated":"2019-12-10T22:31:09Z","snapshot_observed_at":"2026-08-09T18:56:44.864560Z","submitted_at":"2019-12-10T22:31:09Z","title":"Imitation Learning via Off-Policy Distribution Matching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.05032","snapshot_observed_at":"2026-08-06T15:22:26.488059Z","title":"Imitation learning via off- policy distribution matching,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.488059Z"},"links":{"cited_paper":"/paper/1912.05032","citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:5ce57303a9f907be3d9c7141bd18a9d6837a57c46d443b41f11b984f2748603f","observation_id":"d040edbd-238b-4cf2-a3a2-2d88181b49fc","resolution":{"observed_at":"2026-08-06T15:22:26.488059Z","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":"2026-08-06T15:22:29.052368Z","title":"A novel model-free deep reinforcement learning framework for energy manage- ment of a pv integrated energy hub,","venue":null,"work_id":"20eb79e1-2237-473d-bbf5-af0da564abb1","year":2022},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.544759Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:104eb1a01d0818d7eae6f3588b4f282a3fcfc20c36fec6c80cd010e3328cd4be","observation_id":"e2bf6d35-215d-46c2-8cd4-2ea72414223d","resolution":{"observed_at":"2026-08-06T15:22:29.115010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:28.908586Z","title":"A data-driven drl-based home energy management system optimization framework considering uncertain household parameters,","venue":null,"work_id":"8cdb2ef1-6575-4caf-89c1-189a161aa067","year":2024},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.623458Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:77d57412b3cffc31032f8b1021293686226a1eb04ea63e1baa6cd0eddb880f9e","observation_id":"64ff1518-b2e2-41dc-872e-ccb89719d470","resolution":{"observed_at":"2026-08-06T15:22:28.964327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:28.764982Z","title":"Coordinated operation of active distribution network, networked microgrids, and electric vehicle: A multi-agent ppo optimization method,","venue":null,"work_id":"5ef2a740-3d2c-4641-ae3e-a83aabfea94e","year":2023},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.679470Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:9cebbe1c869fc66867e4e48e6930be7f9c0f78dd6259421761666e5e7a204a9f","observation_id":"e727a43f-8152-4381-9f69-7bf40bd0b115","resolution":{"observed_at":"2026-08-06T15:22:28.818911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:28.583489Z","title":"Scalable and privacy-preserving distributed energy management for multimicrogrid,","venue":null,"work_id":"12984ae8-d40f-4e80-8705-2a6136781de6","year":2024},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.789895Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:fab5e4078c22f9928fc9c15523762a947528b83d49e31fdf871c6c7484531b16","observation_id":"da75931d-5b64-45fe-9040-b831fe262817","resolution":{"observed_at":"2026-08-06T15:22:28.671400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2303.13052","last_updated":"2023-11-21T08:49:09Z","snapshot_observed_at":"2026-08-09T08:01:54.030303Z","submitted_at":"2023-03-23T05:54:45Z","title":"Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13052","snapshot_observed_at":"2026-08-06T15:22:26.844673Z","title":"Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.844673Z"},"links":{"cited_paper":"/paper/2303.13052","citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:ef5524ba7cb0b696789f89202fda3ca8c407c3a2d3d0b4e2ce6d243e0d35ecc5","observation_id":"8a4b8e99-d638-41ab-b494-e43ff22a4df6","resolution":{"observed_at":"2026-08-06T15:22:26.844673Z","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":"document/1052922","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:27.938103Z","title":"Two-Step Diffusion Policy Deep Reinforcement Learning Method for Low-Carbon Multi-Energy Microgrid Energy Management,","venue":null,"work_id":"e83d761d-22a8-42d9-9559-b0ad8b66ef74","year":2024},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.942752Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:ca7ef007b36bc884731021360dafae7f82248131c288ad59492daf2096eab420","observation_id":"0d8fd8c9-b7d2-42b1-b5ae-91e67585cc09","resolution":{"observed_at":"2026-08-06T15:22:28.008065Z","resolver_source":"raw_fallback","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":"2026-08-06T15:22:27.015924Z","title":"Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.015924Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:6b7a1799a3d7f0fdf5ff1dfaf51387973175437dd265164bb11416912dc79f8c","observation_id":"d33ad700-b898-46dc-83f6-f46140e46b8a","resolution":{"observed_at":"2026-08-06T15:22:27.015924Z","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":"2026-08-06T15:22:28.450537Z","title":"Risk-sensitive reinforcement learning,","venue":null,"work_id":"a8003b66-cdb2-4e30-85c4-8a6f8fa1cbc8","year":2014},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.107859Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:28a7442db399553d4cec03065688ec277649cf428e7da3963efd67ffd98f3707","observation_id":"6f96736e-e684-4537-802d-cd7c6c66fdc9","resolution":{"observed_at":"2026-08-06T15:22:28.498725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2402.09992","last_updated":"2024-02-15T14:55:38Z","snapshot_observed_at":"2026-08-05T21:55:31.852425Z","submitted_at":"2024-02-15T14:55:38Z","title":"Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09992","snapshot_observed_at":"2026-08-06T15:22:27.163902Z","title":"Risk-sensitive soft actor-critic for robust deep reinforcement learning under distribution shifts,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.163902Z"},"links":{"cited_paper":"/paper/2402.09992","citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:1cb5129233b2c09feebb70696dac21191ba0260a2349a9bc4cdcfaf2bbb8bb9c","observation_id":"25be73d3-0a7c-486e-8318-8bd1ebf69cb2","resolution":{"observed_at":"2026-08-06T15:22:27.163902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06273","last_updated":"2019-06-14T16:25:20Z","snapshot_observed_at":"2026-07-06T08:00:25.359981Z","submitted_at":"2019-06-14T16:25:20Z","title":"Epistemic Risk-Sensitive Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"1906.06273","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.06273","snapshot_observed_at":"2026-08-06T15:22:27.602247Z","title":"Epistemic Risk-Sensitive Reinforcement Learning","venue":"cs.LG","work_id":"25cc1b80-bf80-4044-9b11-b26823c2b442","year":2019},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.240451Z"},"links":{"cited_paper":"/paper/1906.06273","citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:891f6c65b61a2513197c6d86b7af3d5a74ae67bb593904b255cc78f6b992ae17","observation_id":"61dd92d9-8ef5-420f-ba6b-e718e5da3009","resolution":{"observed_at":"2026-08-06T15:22:27.694001Z","resolver_source":"local_arxiv","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:22:28.323125Z","title":"Dsac: Distributional soft actor-critic for risk-sensitive reinforcement learning,","venue":null,"work_id":"4cdc5ac5-4157-4c0f-9723-5a4e2e04d548","year":2025},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.309854Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:729eb8d6a14582b5fbb716e47e579167561b6f25b853011b90c935efdb0a4ee9","observation_id":"deb174fb-94ae-4109-bdb2-0197d2f0d040","resolution":{"observed_at":"2026-08-06T15:22:28.362243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-06T15:22:27.392348Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.392348Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:e267d76667521c396130ca8f0d2fbef1c649e0ba030bd8bc2d7da50ec1156c10","observation_id":"f96a7d9f-67d6-4347-b3f7-eacf203f019b","resolution":{"observed_at":"2026-08-06T15:22:27.392348Z","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":"2026-08-06T15:22:28.159173Z","title":"Deep-reinforcement-learning-based capacity scheduling for pv-battery storage system,","venue":null,"work_id":"13cb7f1c-30b2-4d23-85bf-b7d1dd9468ef","year":2020},"citing_paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:27.432252Z"},"links":{"citing_paper":"/paper/2507.16867"},"observation_digest":"sha256:d597a2f74c287dc37e605400dbfe16ca991abf3a17241cf82d2aebe4701884cd","observation_id":"6d2434d9-56cf-498a-b35a-4b5eb737317e","resolution":{"observed_at":"2026-08-06T15:22:28.234133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2507.16867","last_updated":"2025-07-22T03:27:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:13:17.595742Z","submitted_at":"2025-07-22T03:27:07Z","title":"Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":2,"verified_fuzzy":21},"total_outbound_references":28},"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 11 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.16867."}