{"as_of":"2026-08-08T02:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffafc31a97b1a3559b2a50ac0a66acdb5123ba9a4dbe117eaf2593e842ba17e5","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-25T19:36:51.509710Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2606.25599/citation-record","integrity":"/paper/2606.25599/integrity","json":"/paper/2606.25599/citation-record.json","paper":"/paper/2606.25599"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-25T19:36:51.509710Z","title":"Toward sustainable and low-carbon industrial transformation: Insights from industrial green microgrids","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:2afdbd1ca2e1a4ed75d6cc2a6a5e164b5444d92348311436abf7a970b5f4ec1c","observation_id":"7e33cb38-5e72-47cc-b2ca-c0660341f67d","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A review of industrial load ﬂexibility enhancement for demand-response interaction","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:8d2ea27a2411d16b00c943f8203b705291cd377c258bd2663e151418b8290a5e","observation_id":"69c37497-7f6c-4b00-a7f4-ec77d636616f","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:ab795d37d3eff4f6d4a76180d011424b720a975cccad5a31f5ab8d14ba6862b9","observation_id":"55c83a7f-f1e1-4768-9b53-43f01770c1d1","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:434e61cac71c7678b2a6409b732cc32e1f447069fb5a83d51d79970090b85995","observation_id":"22731a68-a410-4935-9a68-5271483c5832","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A demand response energy management scheme for industrial facilities in smart grid","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:463b285490f0df4d9d730eb2162de1a58df2ab5e5e55d173334298be7520325a","observation_id":"02facbc9-8eef-4e13-9f49-4e0c3f5e36d7","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Optimal industrial load control in smart grid","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:85055928b72a61080a51d4b671fb3c5d390b4f085aa7af742cde54e7c714f190","observation_id":"81919fce-6cfc-452e-95b0-29edfc4596ec","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A real-time decision model for industrial load management in a smart grid","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:bd8818de5c0e10ef8487cd0f856fe1f16058c24136c98c7be7b591be3d0426ea","observation_id":"477f444a-dcb6-48bc-b31c-41a05480bc67","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Demand response of ancillary service from industrial loads coordinated with energy storage","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:948ef829ba2e0b16069a8a9552d64e274cfc1d2e486483c09cf43e63abab35ec","observation_id":"fa670e42-91f7-43f6-b835-ec47f5f0e0ab","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:c0b8d1c1514662808483cc51ae49ff05ba78690e0885c99b6d1f1e07b63c1a5c","observation_id":"f8384408-ac9a-4e8c-858e-a043ed3880c5","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Robust self-scheduling of operational processes for industrial demand response aggregators","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:ba7e196012b8c65a2af348ffb99f45a34756fde6bd5ef28ed005b15f77b08cd1","observation_id":"d484b827-4aab-40b8-bbb2-14780baa9ebd","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Cost-eﬀective scheduling of steel plants with ﬂexible eafs","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:127b803248e3f9b7e614dcfcfdc35de8ea5514183888828747bd39e4b1295946","observation_id":"57a1b87b-831d-4f85-bb1c-9ae7320bab32","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Quantifying ﬂexibility provisions of the ladle furnace reﬁning process as cuttable loads in the iron and steel industry","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:d96a468801d1902f9f258bc9f27ee3c542b32fe55af1a569c7ced3ea77e73524","observation_id":"13958372-4b81-44f9-94c6-eb7bb082fc49","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Cost-eﬀective scheduling of a hydrogen-based iron and steel plant powered by a grid-assisted renewable energy system","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:1addb126eacf9aaf0357283204c793a49fe1cb06bffa492127271cde52e2ff79","observation_id":"52a83e24-da57-40b0-b9a5-087349efca00","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Distributionally robust chance-constrained energy management of steel industrial microgrid with energy storage in distribution market","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:a43d2b9fb2b226473a3c72dd1eac2f70db5c8730da53af343b829a9afa0c6fcf","observation_id":"79a27f74-fa88-42d5-b6aa-89b48d06e19c","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Eﬃcient scheduling of discrete industrial processes through continuous modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:93d82845554e8ba24a62f5efe4c258c6341d5ebfb226d5c39088c07c8e095e1b","observation_id":"6731ee10-c793-412a-b61c-c203c6aea5a8","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Smoothing tie-line power ﬂuctuations for industrial microgrids by demand side control: An output regulation approach","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:372a488f9f11f98dedff8efca3db6aa8f1b0d6ed55e41cc332f549069b089d93","observation_id":"bf832129-ce3a-4054-90b5-ea54959d5052","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Suppressing active power ﬂuctuations at pcc in grid-connection microgrids via multiple besss: A collaborative multi-agent reinforcement learning approach","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:844a997d7bb896166d401f27531e771301f2276e5445d0021e8995a953f9ddb0","observation_id":"69be4b9e-db03-46ac-9311-8343211c764a","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-time-scale energy management of renewable microgrids considering grid-friendly interaction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:107c47d330a267d9b3da769fd9fb7d51fee2f4f8696606db9116ee166f508c94","observation_id":"c82039dd-4a22-4a4f-87ac-5f6d63667f64","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Flat tie-line power scheduling control of grid-connected hybrid microgrids","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:8728968b1aa170ab8fa7ed232be96a42fa3885aac5bc4e0026c1fedde5a2c6b7","observation_id":"e5a94b0e-877f-444d-96b1-824559590bb0","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:df135eabe72f1252071999ef940ab675d804da104cd0fde6a3e347cdfafb1344","observation_id":"9862a939-5432-4ad1-a6ed-42b0ec7c7684","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Data center holistic demand response algorithm to smooth microgrid tie-line power ﬂuctuation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:9b5fd2cc3fc3a720e7a56ebd00ac30ce7afcd105c906e2c253fc6d10034cf561","observation_id":"87cfd4a8-acd2-4dc8-ad66-926a4b9707f4","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-data center tie-line power smoothing method based on demand response","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:9f30b5051c2b8b8f96d6692ebf3847a74bb1d0fa05c03aafb621060841f90ae7","observation_id":"d5794086-2e0b-401a-9a4c-a93f3448c6ff","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A novel rolling optimization strategy considering grid-connected power ﬂuctuations smoothing for renewable energy microgrids","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:c3fd01a7a13c8b9975a225b3e7aebd5568d5fd87f560170d1b67cf7778e1edac","observation_id":"f7e867f8-261b-45ce-9d29-d1be8330ad5c","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"A coordinated multitimescale model predictive control for output power smoothing in hybrid microgrid incorporating hydrogen energy storage","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:20ec6435f7c3e946499374dd47c6f7bfc2627ce27dd0c3f876dd41723a8b1341","observation_id":"6c57abae-8bae-41f8-ae0f-9140251a516b","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:2942fe30f3eac40fc4239e91b760e809b4e41ceed1dd6e0d179614b48979628e","observation_id":"2e186190-0ae4-4441-8f95-3c80cd670e04","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:3e81653b0ab6a30f465855546cdc431482a9be3e83d3dd1f0811dd36297df4e1","observation_id":"2178bb2f-4546-43f7-ba86-336648fbfea3","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:88d96cae676492986bfdd96ac2c86527f1f9b27b41887a75462a2f49acf8bf8c","observation_id":"1d365505-2497-4bac-bad2-acfe53044fdc","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Coordination for multienergy microgrids using multiagent reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:e61307fab3845ec449b741f2bf8f7c46e1cae0f429894b607026c43ebf418994","observation_id":"b2b8f6b9-f6cd-49b5-ba1f-b3c6a6dfcdad","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:dd3a643905982b12fff2c119cd5eb14b814d1e7d09c07400a0b29814182e9b13","observation_id":"4829e3ec-87a3-4848-82bc-e2f9af4bf668","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:e1834789d5f362b4f1d0854975e16be27ac3dfdbbf40bc93c6d43fd151069498","observation_id":"1a552c4f-df7f-4768-9bd3-fa42344fbd31","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:7f8424cf88a1881dcfd0fb4f4e5fc15dca83fec30e8388ffa2b8f195afc19be0","observation_id":"9da6aa9f-dccb-45a1-94fe-47af58a0a22f","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Multi-agent hierarchical reinforcement learning for energy management","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:9b9904268fee751ba58e0cc6f847d2238f51a512765e69ef23b4d49850f4647f","observation_id":"03399fa6-a294-44ef-ad24-0a727cc344b1","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:66ce59be51a3d8f41b21027252e551aaea81455c4d73478aef9cd69cb810a0eb","observation_id":"0908b4fc-a7dd-4b47-9d7d-6219bf5d09d2","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11251","last_updated":"2022-04-04T06:39:20Z","snapshot_observed_at":"2026-08-06T03:41:51.704789Z","submitted_at":"2021-09-23T09:44:35Z","title":"Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2109.11251","doi":"10.48550/arxiv.2109.11251","metadata_source":"arxiv_reference","pith_arxiv_id":"2109.11251","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"G., Chen, R., Wen, M., Wen, Y ., Sun, F., Wang, J., and Yang, Y","venue":"arXiv (Cornell University)","work_id":"d8162a53-3a6d-4ae0-af31-4f93824499bd","year":2021},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"cited_paper":"/paper/2109.11251","citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:534fa58b6343c043d0e41a1cf22ff69c68be23fcb9d978d2fdd2f508da095d88","observation_id":"bbbcca0b-11cc-4c9c-93b3-d697a1a39799","resolution":{"observed_at":"2026-07-04T20:50:11.104720Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-25T19:36:51.509710Z","title":"Heterogeneous-agent reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:2d1c9f7f66a9ca1dda94b38d8e4eb903c075b19cde941449c43dcc19e8d9cffc","observation_id":"6fb4bc0f-9e52-47f2-a422-9be46313878c","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Guidelines for the Construction and Application of Industrial Green Microgrids (2026–2030)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:994001d23431f50bd0dec1edea92a1141a9cdc22f409f25235ed0a1b031648cc","observation_id":"aa72be2b-05fa-4850-ad97-05da2b2b180a","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Long-term energy management for microgrid with hybrid hydrogen-battery energy storage: A prediction-free coordinated optimization framework","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:d040154760284b662bed3adf1d2a553f61826505dd33141375ce5472d497068e","observation_id":"0a2eca1f-7895-4557-a6d9-a2f0669759c9","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","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":"2026-06-25T19:36:51.509710Z","title":"Data Miner 2: Real-Time Five Minute LMPs","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-25T19:36:51.509710Z"},"links":{"citing_paper":"/paper/2606.25599"},"observation_digest":"sha256:1f37524e37c96edaffb44bbd99cbba7bb9f35aca3ec8fbc30c6d34a5fdfbd727","observation_id":"b39750fe-c7e2-4062-8c00-f75a3b71a682","resolution":{"observed_at":"2026-06-25T19:36:51.509710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.25599","last_updated":"2026-06-24T09:06:12Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-07T15:59:18.310592Z","submitted_at":"2026-06-24T09:06:12Z","title":"Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":37,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":38},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.25599."}