{"as_of":"2026-08-16T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffdf396da1dd7c57fd7761dff89f660b1534d1626c3b0ac258d37e9d7cf81b97","coverage":[{"denominator":211,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:51:57.531457Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:46:02.910773Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T23:46:43.449626Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18525","snapshot_observed_at":"2026-08-15T16:46:02.910773Z","title":"Federated learning from molecules to processes: A perspective","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20527","last_updated":"2025-08-29T08:38:56Z","snapshot_observed_at":"2026-08-16T14:13:11.604628Z","submitted_at":"2025-08-28T08:14:33Z","title":"Molecular Machine Learning in Chemical Process Design","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T16:46:02.910773Z"},"links":{"cited_paper":"/paper/2506.18525","citing_paper":"/paper/2508.20527"},"observation_digest":"sha256:f8d0965a74d1789eed24e8036313aa22c0b60d28a6e90b7204234f88bad63413","observation_id":"50ccc5d6-2ad7-4e3d-9a6f-1cace251da08","resolution":{"observed_at":"2026-08-15T16:46:02.910773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"cited_work":{"arxiv_id":"2506.18525","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.18525","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated learning from molecules to processes: A perspective","venue":null,"work_id":"29c5d4d1-f06f-4654-adf7-940e5e4b18f9","year":2025},"citing_paper":{"arxiv_id":"2604.26073","last_updated":"2026-04-28T19:26:43Z","snapshot_observed_at":"2026-07-06T23:11:48.524898Z","submitted_at":"2026-04-28T19:26:43Z","title":"Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-07T16:19:25.127008Z"},"links":{"cited_paper":"/paper/2506.18525","citing_paper":"/paper/2604.26073"},"observation_digest":"sha256:78b2adf120ae12f562a372ec1fe846039531412777f0e44763e90e393c8ba56d","observation_id":"31fe0f84-77dd-4301-9b22-c78d2a4185e7","resolution":{"observed_at":"2026-05-11T23:46:43.563484Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.18525/citation-record","integrity":"/paper/2506.18525/integrity","json":"/paper/2506.18525/citation-record.json","paper":"/paper/2506.18525"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:51:56.993485Z","title":"Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft, Jens-Uwe Repke, Sebastian Sager, and Alexander Mitsos","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:56.993485Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:0a0f10c173b6a5b287ff611029def1543ee9999c181c29b5015f119f34b662ac","observation_id":"562278f7-ed0e-423b-849a-a5767250a01d","resolution":{"observed_at":"2026-08-15T18:51:56.993485Z","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":"2026-08-15T18:51:56.999789Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:56.999789Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:0cbab314dc95c8357ecb7916747c24dc9c88a9016d943e6dfcb95dd40c0bd462","observation_id":"b27c931b-938c-47d1-91f8-48b27e77c8a6","resolution":{"observed_at":"2026-08-15T18:51:56.999789Z","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":"2026-08-15T18:51:57.004719Z","title":"Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.004719Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2dc20d3af6e9387b101d8c80dc76068f468ed088ad8fc7cba48cddaaa271e513","observation_id":"79098267-3abb-451a-b59f-7257d33f43ce","resolution":{"observed_at":"2026-08-15T18:51:57.004719Z","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":"2026-08-15T18:51:57.010079Z","title":"How to do impactful research in artificial intelligence for chemistry and materials science","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.010079Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:aceca4f4cb3dbc0211a58c92fc579bf107349bada025f6a24c23ea90e283bf2d","observation_id":"9e67806c-c59f-44ad-b289-fb3c5f7b8351","resolution":{"observed_at":"2026-08-15T18:51:57.010079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00409","last_updated":"2017-12-01T17:13:14Z","snapshot_observed_at":"2026-08-14T02:46:56.838057Z","submitted_at":"2017-12-01T17:13:14Z","title":"Deep Learning Scaling is Predictable, Empirically","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00409","snapshot_observed_at":"2026-08-15T18:51:57.015197Z","title":"Deep learning scaling is predictable, empirically","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.015197Z"},"links":{"cited_paper":"/paper/1712.00409","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7a2e855a87b5631dc675af65c6d3ccf8d806a3dfb9cc848e95c894101f5e7fce","observation_id":"5f4bc769-e887-4881-acb2-0d75ff95f0de","resolution":{"observed_at":"2026-08-15T18:51:57.015197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-15T18:51:57.020470Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.020470Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:4a1dc9fb25c8b8c62651158aef90001a3fef00f16fae67e612fc23af238e17f3","observation_id":"7f0749dd-cbdc-4652-a25d-b4f805539a5c","resolution":{"observed_at":"2026-08-15T18:51:57.020470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-09T19:52:33.533277Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-15T18:51:57.026624Z","title":"Training compute-optimal large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.026624Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:174292c76392fbc903260a5202e826c144036fda2f48b95774f338aaafe7fc7b","observation_id":"4bc1c649-780e-4f92-bb9d-c9af0fe8a1a2","resolution":{"observed_at":"2026-08-15T18:51:57.026624Z","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":"2026-08-15T18:51:57.031892Z","title":"Scaling vision transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.031892Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:83df50d431d9addac02d796687042683e80572f49a72b90aba9b3720a514b4fc","observation_id":"3197aa68-2769-4a45-a68d-f838f2bee213","resolution":{"observed_at":"2026-08-15T18:51:57.031892Z","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":"2026-08-15T18:51:57.037145Z","title":"Advances and opportunities in machine learning for process data analytics","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.037145Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:81ae377607b47e7fc4a5d5b95baf25b4ee4db7ef5f0dd8e7104b7a3aa2423777","observation_id":"f7257970-bbdd-4548-a883-64ff7b0e18e9","resolution":{"observed_at":"2026-08-15T18:51:57.037145Z","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":"2026-08-15T18:51:57.042505Z","title":"Maximizing informa- tion from chemical engineering data sets: Applications to machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.042505Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:16570412d0fe200148fbb6e6df666b91776baff2ca197098160325b2336954df","observation_id":"f339627d-5b3a-429f-ba66-0a88c8b99849","resolution":{"observed_at":"2026-08-15T18:51:57.042505Z","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":"2026-08-15T18:51:57.047971Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.047971Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:f26cbf7ed6dfd3f81405cdc0626880c15c16a1d628c7e16fccab9770220ec019","observation_id":"091fb33a-ad00-4856-a452-a80eadb9b38a","resolution":{"observed_at":"2026-08-15T18:51:57.047971Z","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":"2026-08-15T18:51:57.053077Z","title":"Federated learning in chemical engineering: A tutorial on a framework for privacy-preserving collaboration across distributed data sources","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.053077Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:116761734b5cb170c581428103d9a1c672c51e6f68bb974b59e6888b46b2e513","observation_id":"6937aa21-ddcd-40a3-bff3-190887485e79","resolution":{"observed_at":"2026-08-15T18:51:57.053077Z","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":"2026-08-15T18:51:57.059285Z","title":"The sampl2 blind prediction challenge: introduction and overview","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.059285Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b37c8238ca56a7ee562dda61b4e4dd484ea5ab9fb12d0ac9d065b9969f132e5e","observation_id":"8b0aa0a9-1337-4db9-9c8e-0e74ca600b07","resolution":{"observed_at":"2026-08-15T18:51:57.059285Z","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":"2026-08-15T18:51:57.064892Z","title":"Freesolv: a database of experimental and calculated hydration free energies, with input files","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.064892Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b3c17e4bfb931cb1fcbe2cf208aa7a06faae01b4f4e1cd5522a323e25a14b06d","observation_id":"46980896-e917-44a5-bddd-95de5e1e66bc","resolution":{"observed_at":"2026-08-15T18:51:57.064892Z","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":"2026-08-15T18:51:57.070044Z","title":"Dral, Matthias Rupp, and O","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.070044Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:30a754faa607b1ab2746e96cce66522bdcb29164b0bc6218c96972af40d3d54d","observation_id":"c2698eb4-75cf-414c-b10e-70208163566a","resolution":{"observed_at":"2026-08-15T18:51:57.070044Z","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":"2026-08-15T18:51:57.075332Z","title":"Summit: benchmarking machine learning methods for reaction optimisation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.075332Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:4763fe566f01fda1c0c35cc59ab2e3cb0cde31f5f57891e61d5d5814db064480","observation_id":"e19ae195-dd44-4ad0-b35b-a1a83170be46","resolution":{"observed_at":"2026-08-15T18:51:57.075332Z","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":"2026-08-15T18:51:57.079998Z","title":"Orderly: data sets and benchmarks for chemical reaction data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.079998Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:13b05bf2345798541efb0edaa958f9fd600d469f91dbf8145454659a2141daf7","observation_id":"54634abf-29a7-47b0-98cc-97f3a524f850","resolution":{"observed_at":"2026-08-15T18:51:57.079998Z","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":"2026-08-15T18:51:57.086304Z","title":"Fault detection and diagnosis in industrial systems","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.086304Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:91e6e665d2a2b11173e1761267d12919b17a456ba9276cf3cef49d6b72c4eefd","observation_id":"0a36de57-af2c-49fc-ba53-5752c6d7d059","resolution":{"observed_at":"2026-08-15T18:51:57.086304Z","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":"2026-08-15T18:51:57.092047Z","title":"Perspectives on the integration between first-principles and data-driven modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.092047Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:a77f85ce42ce86d7718fa1a46ac416e46553a6f890ab0101e9d0128e7effaae1","observation_id":"4e839f70-a543-470f-814b-82823d35eb25","resolution":{"observed_at":"2026-08-15T18:51:57.092047Z","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":"2026-08-15T18:51:57.097951Z","title":"A review and perspective on hybrid modeling methodologies","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.097951Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:aa9f7b74c5b70a19f5673cdc37a6e71ffd69e8b104b0ee8f9317280021e0d5c5","observation_id":"eebd11ed-69f7-41d9-bde9-2f4c7bd40ca4","resolution":{"observed_at":"2026-08-15T18:51:57.097951Z","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":"2026-08-15T18:51:57.102645Z","title":"Physics- informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.102645Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2dfd6e8e978bbecab0d056b844057cd4f0b3ca07446a2930c31d27a64c02f6fc","observation_id":"b1e495f8-3e32-4695-a8a3-af8d72228f04","resolution":{"observed_at":"2026-08-15T18:51:57.102645Z","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":"2026-08-15T18:51:57.108199Z","title":"Autonomous chemical research with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.108199Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b5624cd3ea242712fae4e6f631875d0fd1a6abd1f6067ca1b6e4195ba3251336","observation_id":"4e015180-b1ed-40ad-a0aa-7fa56e692435","resolution":{"observed_at":"2026-08-15T18:51:57.108199Z","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":"2026-08-15T18:51:57.112865Z","title":"Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.112865Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7f12a115318bae32f017533b3c32aebbd8eb624f10120923eebad8e19d0550cd","observation_id":"f39030f0-a7a1-4571-9a70-c679b3137dda","resolution":{"observed_at":"2026-08-15T18:51:57.112865Z","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":"2026-08-15T18:51:57.118199Z","title":"Transfer learning for solvation free energies: From quantum chemistry to experiments","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.118199Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2f1c976eb5379957b54f59328c0ebdab61e25b980f1570712e32b2b25bf62f8b","observation_id":"66e5d4c4-0ed7-43ba-adc2-50ccbe397295","resolution":{"observed_at":"2026-08-15T18:51:57.118199Z","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":"2026-08-15T18:51:57.122889Z","title":"Fault detection and diagnosis based on transfer learning for multimode chemical processes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.122889Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:640f7275c02ab47b942d01e37e2f74bc8403e42eab7d7d49338062acc21a8ced","observation_id":"9d175380-654b-4aa9-8784-14490069b165","resolution":{"observed_at":"2026-08-15T18:51:57.122889Z","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":"2026-08-15T18:51:57.128258Z","title":"Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.128258Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:1e9ec417797e4f9dc76ef4976adee37f586c14c3ffb49158390bea8049e77857","observation_id":"72731e9b-c56a-48b1-98b7-1ae03aed1c69","resolution":{"observed_at":"2026-08-15T18:51:57.128258Z","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":"2026-08-15T18:51:57.133214Z","title":"Multi-fidelity data-driven design and analysis of reactor and tube simulations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.133214Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:80c6a49ecbd5de4d278330ec42dd9c1cf9c92b883fa5aae2302b42b4e8efd734","observation_id":"333161fe-b43d-49db-ad98-e580dd832bb8","resolution":{"observed_at":"2026-08-15T18:51:57.133214Z","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":"10.26434/chemrxiv-2024-3t818","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:51:58.181434Z","title":"Multi-fidelity graph neural networks for predicting toluene/water partition coefficients","venue":null,"work_id":"7bbc60d1-e0a3-4f39-a944-88829ede4508","year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.138963Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7827e81abd8779b750f243b217dd0c04d7869b92899aadde9ebeeda593c5bb5b","observation_id":"09c5c846-85fd-4fec-b435-fe38001a161d","resolution":{"observed_at":"2026-08-15T18:51:58.190283Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T18:51:57.144731Z","title":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.144731Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:22e65091c4b741ac8fbc889611b467eb521a193ab356810dbee931cb815a2459","observation_id":"191ee797-a08a-489a-a184-063d69ddde09","resolution":{"observed_at":"2026-08-15T18:51:57.144731Z","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":"2026-08-15T18:51:57.149774Z","title":"Brendan McMahan, Brendan Avent, Aurelien Bellet, and Sen Zhao","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.149774Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:d75177fc6318d9db7de88cf714f037d832e2569718e4c86b28a9c04897039d25","observation_id":"f1fcfd72-745b-4043-8215-de00ddb673d3","resolution":{"observed_at":"2026-08-15T18:51:57.149774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08892","last_updated":"2025-03-03T04:14:17Z","snapshot_observed_at":"2026-08-16T13:10:17.639540Z","submitted_at":"2024-10-11T15:10:38Z","title":"Federated Learning in Practice: Reflections and Projections","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08892","snapshot_observed_at":"2026-08-15T18:51:57.154936Z","title":"Brendan McMahan, Daniel Ramage, and Zheng Xu","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.154936Z"},"links":{"cited_paper":"/paper/2410.08892","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:8598fceb50c07d5eb174523563723d16c6d875f5ca5575d9cefa9f55c4d043db","observation_id":"e062d992-ab5a-4374-a0ba-aaae45c44ac3","resolution":{"observed_at":"2026-08-15T18:51:57.154936Z","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":"2026-08-15T18:51:57.160095Z","title":"Recent advances on federated learning: A systematic survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.160095Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:76f8406635e57a778cf4c27b9aa02a937b6d0add459c4543eb88fd312020636a","observation_id":"31cd28e8-8aea-4cc5-8ba0-96110ccb2c3f","resolution":{"observed_at":"2026-08-15T18:51:57.160095Z","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":"2026-08-15T18:51:57.165011Z","title":"A survey on federated learning: challenges and applications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.165011Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:accd431b123cdf76382dc2bbbb478bb8c0ea5a9868cfdc536c13bca9b4529cd8","observation_id":"91506800-ef06-4e5b-ad27-67d4ba2f9319","resolution":{"observed_at":"2026-08-15T18:51:57.165011Z","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":"2026-08-15T18:51:57.169978Z","title":"H Ngai, and Thiemo V oigt","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.169978Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:f72f0d37f0644c3bd87b8513d9199d991d2a9e633cc3bf9c869cfd53fe373d17","observation_id":"da75d912-a499-4577-abd4-6eae3aaf5b02","resolution":{"observed_at":"2026-08-15T18:51:57.169978Z","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":"2026-08-15T18:51:57.174774Z","title":"The future of digital health with federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.174774Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:67d573d69ffe3cbd7ddf806aa3ef16535970f133410be7c34a49666a1ec53d58","observation_id":"b706ec0a-26ce-45bb-b51f-c53b757738b1","resolution":{"observed_at":"2026-08-15T18:51:57.174774Z","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":"2026-08-15T18:51:57.179863Z","title":"Nguyen, Quoc-Viet Pham, Pubudu N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.179863Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:c49a2a1c50a087a0fcf4bae6b59d8df9fd82c81e9b63ca47b90fa345266ea6ed","observation_id":"2f87a6bb-ca55-42f7-a0af-fc5b609efab0","resolution":{"observed_at":"2026-08-15T18:51:57.179863Z","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":"2026-08-15T18:51:57.184936Z","title":"Rawat, and Vladimir Vlassov","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.184936Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:ce4d264d15e3493c86cf4f6f1f21325f044d2d70448e3fcadae00d44e4f200f2","observation_id":"800c5034-d5b1-49d6-b4ab-24d3b9bf03c8","resolution":{"observed_at":"2026-08-15T18:51:57.184936Z","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":"2026-08-15T18:51:57.189929Z","title":"Göller, Yves Moreau, Mathieu N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.189929Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:d0db0f85b8247a076e5fbb0635de510a6e938159fa8133fcc09bc6b533e0ee8c","observation_id":"f13c39f3-2464-40b2-a6d8-63d6404c734f","resolution":{"observed_at":"2026-08-15T18:51:57.189929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.02903","last_updated":"2018-12-07T04:18:12Z","snapshot_observed_at":"2026-08-14T17:47:08.657480Z","submitted_at":"2018-12-07T04:18:12Z","title":"Applied Federated Learning: Improving Google Keyboard Query Suggestions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.02903","snapshot_observed_at":"2026-08-15T18:51:57.194913Z","title":"Applied federated learning: Improving google keyboard query suggestions","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.194913Z"},"links":{"cited_paper":"/paper/1812.02903","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:728913c2058e1e49b8dfebd423cafe33d96c1a62da301a169af25211c96c4e6c","observation_id":"49878795-b5d7-47f5-a5d5-e8ccc5a306dd","resolution":{"observed_at":"2026-08-15T18:51:57.194913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10853","last_updated":"2024-10-14T16:37:29Z","snapshot_observed_at":"2026-08-16T13:51:50.149221Z","submitted_at":"2024-05-17T15:27:52Z","title":"The Future of Large Language Model Pre-training is Federated","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10853","snapshot_observed_at":"2026-08-15T18:51:57.200477Z","title":"Shen, Preslav Aleksandrov, Xinchi Qiu, and Nicholas D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.200477Z"},"links":{"cited_paper":"/paper/2405.10853","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:9090149e1530d5df1f1c6804be4c755e49f2f561360880685410bc7ee1369bd5","observation_id":"b7160d00-9b89-4c6b-8d35-3f13801b31d9","resolution":{"observed_at":"2026-08-15T18:51:57.200477Z","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":"2026-08-15T18:51:57.205695Z","title":"Federated learning for computational pathology on gigapixel whole slide images","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.205695Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:e67d16d8f56e23ec724c24dbed7a6e351060d473e5ca22e7e073274e75008543","observation_id":"a1a0cb69-bfc1-43fc-812a-5550127a7963","resolution":{"observed_at":"2026-08-15T18:51:57.205695Z","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":"2026-08-15T18:51:57.210871Z","title":"Federated learning for medical image analysis: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.210871Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:e8bbf2c09262e7f40083bbfb2030dace54d8effdc925ab87e178cf20c31b13f9","observation_id":"e99e5322-b9ee-414e-80bb-559cb4ed7a40","resolution":{"observed_at":"2026-08-15T18:51:57.210871Z","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":"2026-08-15T18:51:57.215754Z","title":"Conformal efficiency as a metric for comparative model assessment befitting federated learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.215754Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:8af809349e37620d064e0e2e5dca801bb4b47403f4ad8323f281ebf607766527","observation_id":"8310c22f-67a7-49d2-8d78-3e4673f64214","resolution":{"observed_at":"2026-08-15T18:51:57.215754Z","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":"2026-08-15T18:51:57.220692Z","title":"Industry-scale orchestrated federated learning for drug discovery","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.220692Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5d4c472c461b57a9366a71ef753b3ad7aafbc2014eefd375af0b5e32f95225ac","observation_id":"2de27719-7f4b-4439-9d7b-e275817995bc","resolution":{"observed_at":"2026-08-15T18:51:57.220692Z","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":"2026-08-15T18:51:57.225494Z","title":"Communication-efficient federated learning via knowledge distillation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.225494Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2ba5483f9e7fb6745b4f91696165e5daa2ad63e9368ab2917af7b24a500ce608","observation_id":"6211b270-fb97-4ae1-bd54-2cc7064c561a","resolution":{"observed_at":"2026-08-15T18:51:57.225494Z","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":"2026-08-15T18:51:57.230311Z","title":"Anger, Chris Barber, Richard J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.230311Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5a46ca14519081b164c0734c826db919defa24f70c80350267810a7031fdb640","observation_id":"198854d0-4602-4d61-89d5-e3d9238c7cc9","resolution":{"observed_at":"2026-08-15T18:51:57.230311Z","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":"2026-08-15T18:51:57.235112Z","title":"Decentralized and incentivized federated learning for the chemical engineering domain","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.235112Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:1a1d63470e3efd94ddfbf8fb989e056fb48b18af1d2040cb13e4675f3390dc41","observation_id":"a36eb324-5efc-48c4-a34b-ad588667ff9b","resolution":{"observed_at":"2026-08-15T18:51:57.235112Z","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":"2026-08-15T18:51:57.239992Z","title":"Privacy-preserving federated machine learning modeling and predictive control of heterogeneous nonlinear systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.239992Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:232c6b992a51e41ca7d448d1e2f8529593a5d9321e69e0e5acb62536eaa575f9","observation_id":"3b71d09c-c5da-4b51-b957-d860d1354bda","resolution":{"observed_at":"2026-08-15T18:51:57.239992Z","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":"2026-08-15T18:51:57.244964Z","title":"A review of federated learning in energy systems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.244964Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5026f9bc1e6084f7635c439f876bbc7193c78eb354309b5a31c1379847cf7b3e","observation_id":"8bc00515-ee0f-4cb1-908c-d3d6006809ca","resolution":{"observed_at":"2026-08-15T18:51:57.244964Z","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":"2026-08-15T18:51:57.251277Z","title":"A review of federated learning in renewable energy applica- tions: Potential, challenges, and future directions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.251277Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:6a7fe5e56c1337dc2e0090266e136e1ed193c64e312519bb58f9e2fb4e2b69d9","observation_id":"690052d6-55ef-4e4c-9166-b3ec50572a1e","resolution":{"observed_at":"2026-08-15T18:51:57.251277Z","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":"2026-08-15T18:51:57.264086Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.264086Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:eb27e16c07b75694e331f5dfc5f183786de6894838f48bca8e2cbe62965ffc5f","observation_id":"af26b841-d48c-4550-bcea-fe0080b60832","resolution":{"observed_at":"2026-08-15T18:51:57.264086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01630","last_updated":"2024-12-02T15:45:46Z","snapshot_observed_at":"2026-08-16T13:00:39.783687Z","submitted_at":"2024-12-02T15:45:46Z","title":"Review of Mathematical Optimization in Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01630","snapshot_observed_at":"2026-08-15T18:51:57.270639Z","title":"Review of mathematical optimization in federated learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.270639Z"},"links":{"cited_paper":"/paper/2412.01630","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:aa09244404fe5a0c0549526bffd3876488dc4479b34c50a6fc2f290fe6ede337","observation_id":"1e61194e-8744-405f-ad35-105b80c5fa4f","resolution":{"observed_at":"2026-08-15T18:51:57.270639Z","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":"2026-08-15T18:51:57.276038Z","title":"Talwalkar","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.276038Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2b9b6fde294314aa5251b758d1a5051478d6a4e6a344236dd698b771428c7319","observation_id":"c9f1611a-08e8-469e-99c6-7076f440beb9","resolution":{"observed_at":"2026-08-15T18:51:57.276038Z","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":"2026-08-15T18:51:57.281275Z","title":"Model aggregation techniques in federated learning: A comprehensive survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.281275Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2a600f1dd51ab6560b7689e4cce2f3310f00f5c4f026bb7fc525b2129614772a","observation_id":"d2a77dd0-a99d-4502-8a24-13f6a3e0977e","resolution":{"observed_at":"2026-08-15T18:51:57.281275Z","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":"2026-08-15T18:51:57.286261Z","title":"Dinh, Tung T","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.286261Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:96041136a2f2b75e79f92d4d801d6b2c8426179ce916cd406918c7371f3d2a29","observation_id":"9618fdfd-cd9e-4531-8921-43647a28171c","resolution":{"observed_at":"2026-08-15T18:51:57.286261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10655","last_updated":"2024-02-18T06:16:41Z","snapshot_observed_at":"2026-08-13T10:52:05.148653Z","submitted_at":"2023-07-20T07:35:42Z","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10655","snapshot_observed_at":"2026-08-15T18:51:57.291941Z","title":"A survey of what to share in federated learning: Perspectives on model utility, privacy leakage, and communication efficiency","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.291941Z"},"links":{"cited_paper":"/paper/2307.10655","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:a78a64a17d8c53de070ec49b9982bad87e7d7847ecb9f8e88db618b0b25b5ab6","observation_id":"2ae71d60-1c20-49ea-a49b-fbf99995dc68","resolution":{"observed_at":"2026-08-15T18:51:57.291941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04723","last_updated":"2022-06-09T18:25:25Z","snapshot_observed_at":"2026-08-13T15:27:06.844982Z","submitted_at":"2022-06-09T18:25:25Z","title":"On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04723","snapshot_observed_at":"2026-08-15T18:51:57.297519Z","title":"On the unreasonable effectiveness of federated averaging with heterogeneous data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.297519Z"},"links":{"cited_paper":"/paper/2206.04723","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:8407848513d6914fde97d4ad1908325047b3d7abec9cb22be8e1c6e4f7a6e3aa","observation_id":"1da8a5ff-ba91-402d-8162-47af99de69ac","resolution":{"observed_at":"2026-08-15T18:51:57.297519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18372","last_updated":"2024-07-08T06:58:56Z","snapshot_observed_at":"2026-08-16T13:36:17.602293Z","submitted_at":"2024-07-08T06:58:56Z","title":"Thermodynamics-Consistent Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2407.18372","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.18372","snapshot_observed_at":"2026-08-15T18:51:58.482084Z","title":"Thermodynamics-Consistent Graph Neural Networks","venue":"cond-mat.dis-nn","work_id":"60f26e1c-0f6a-438c-aebd-ce4ee3fc3a2b","year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.302892Z"},"links":{"cited_paper":"/paper/2407.18372","citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b3fa9efb6b0993edd10d29e8f9dfb68e4dd06da20f2c14f0b4ed5b362745910c","observation_id":"2745a2c5-a94c-4e14-b766-98ec6b14d8c2","resolution":{"observed_at":"2026-08-15T18:51:58.487971Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T18:51:57.307802Z","title":"Digitization of chemical process flow diagrams using deep convolutional neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.307802Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:9c76b6c549350d5244d0407226dce0184546d33ac1972291be21c4e208eff284","observation_id":"42df5d36-7560-41c1-a50f-f0a856370d04","resolution":{"observed_at":"2026-08-15T18:51:57.307802Z","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":"2026-08-15T18:51:57.312630Z","title":"Flowsheet generation through hierarchi- cal reinforcement learning and graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.312630Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:45a53ceff3808cc6dca86748211b4566d0beb9290f712dd8141a9678aec56ca5","observation_id":"32aa0c8d-fa98-47b0-95f5-8a1d3562ddcb","resolution":{"observed_at":"2026-08-15T18:51:57.312630Z","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":"2026-08-15T18:51:57.319910Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.319910Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:9e88978ad5174b008d57494a2465ed55a6c8cf0bce718bc1158d1e8a35b1c7dc","observation_id":"db1c0a09-3a25-4f99-867f-cfa793041dd5","resolution":{"observed_at":"2026-08-15T18:51:57.319910Z","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":"2026-08-15T18:51:57.325076Z","title":"A digitization and conversion tool for imaged drawings to intelligent piping and instrumentation diagrams (P&ID)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.325076Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:de55588d3ad316589d07670477d8ad240157a45581d54a6183a5da48ca5c56e0","observation_id":"8342c341-b9ba-4df5-a907-822ffa6fb26b","resolution":{"observed_at":"2026-08-15T18:51:57.325076Z","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":"2026-08-15T18:51:57.330515Z","title":"Transforming engineering diagrams: A novel approach for P&ID digitization using transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.330515Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:89c9af0cd35722aee1ac0536e11cf95ec8cf1822bc5388a63f5218c334181c7b","observation_id":"6406cd26-0f0f-4222-831e-6fff4d924e41","resolution":{"observed_at":"2026-08-15T18:51:57.330515Z","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":"2026-08-15T18:51:57.335856Z","title":"Schweidtmann","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.335856Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:94ab4a4dab08e8d901eed8fb8583e60fc0d0fab97097d67186761df0fa146b39","observation_id":"39676c7f-da53-4756-b01f-55f78f8ddf25","resolution":{"observed_at":"2026-08-15T18:51:57.335856Z","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":"2026-08-15T18:51:57.342150Z","title":"Advances in surrogate based modeling, feasibility analysis, and optimization: A review","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.342150Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:531913d49a27a2a4fb276e0642303f5dfbcc0196ada7d37ff45612c58eb33b8c","observation_id":"b8d207e2-4bdb-4f23-8b4e-0ff663459099","resolution":{"observed_at":"2026-08-15T18:51:57.342150Z","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":"2026-08-15T18:51:57.349917Z","title":"Deterministic global optimization with artificial neural networks embedded","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.349917Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:132ec97decdd3b38fd1dc7a91184bb7264c964f2bf8b203c323289eaec1d6b6d","observation_id":"6266f42d-0d15-44b9-9d02-b8bf97304114","resolution":{"observed_at":"2026-08-15T18:51:57.349917Z","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":"2026-08-15T18:51:57.357930Z","title":"Overview of surrogate modeling in chemical process engineering","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.357930Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7529e8b30e3d7157a39d00b5167820a63a86fc017c270ce1536c66b8eb4224dd","observation_id":"153efb78-b7aa-430d-ab86-522f8b89354f","resolution":{"observed_at":"2026-08-15T18:51:57.357930Z","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":"2026-08-15T18:51:57.363770Z","title":"Architectures for neural networks as surrogates for dynamic systems in chemical engineering","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.363770Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:3d8ded1da37f378e5f7ddf3627bcc3f64cc98a1eb542ee47beba7d7599985c16","observation_id":"8a260908-ebbd-4ff2-8ba0-a14a6f9bc146","resolution":{"observed_at":"2026-08-15T18:51:57.363770Z","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":"2026-08-15T18:51:57.369122Z","title":"Formulating data-driven surrogate models for process optimization.Computers & Chemical Engineering, 179:108411, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.369122Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:4ab9bef059e8eef2f4c3b99463824f33c01433cd094f3670efa211bc465a64d5","observation_id":"de8cc320-31ad-4a1d-9068-07a63d4a7866","resolution":{"observed_at":"2026-08-15T18:51:57.369122Z","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":"2026-08-15T18:51:57.374315Z","title":"Recent trends on hybrid modeling for industry 4.0","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.374315Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:59cd7077aa10adc49442a4033b7c34b3ad16a1e1ccc857e64af6ba2e5db15041","observation_id":"53146ac5-ffb7-4eaf-bf16-a7ce7b6f5408","resolution":{"observed_at":"2026-08-15T18:51:57.374315Z","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":"2026-08-15T18:51:57.379317Z","title":"Machine learning for chemical reactions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.379317Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5f249069351e2759f5ed506a96e4c3dbbf761297b869161ef07811d8068554b7","observation_id":"2f985d0d-e25a-4deb-ad44-6cf324d834ea","resolution":{"observed_at":"2026-08-15T18:51:57.379317Z","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":"2026-08-15T18:51:57.384573Z","title":"Exploring catalytic reaction networks with machine learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.384573Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b771f7917bcf61dec6d3e5ac443e183c7c1a26431c32100aa7beccd1e880e1c6","observation_id":"c0315b97-3e71-4418-b3ff-32af6c10a14c","resolution":{"observed_at":"2026-08-15T18:51:57.384573Z","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":"2026-08-15T18:51:57.390065Z","title":"Chemical data intelligence for sustainable chemistry","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.390065Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:51f4dcf754e08e6be6832c0e9ae426b9ae5205e85dd35c9200af861875f3210f","observation_id":"eaea6b25-2a99-4d0b-9eee-18b41b8f3be2","resolution":{"observed_at":"2026-08-15T18:51:57.390065Z","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":"2026-08-15T18:51:57.395093Z","title":"Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.395093Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:3c19fcab67e7c9fd76b16b63d4f46b499652406abfd2eea5b69a13de2417d94a","observation_id":"d1e936bc-2587-4df7-aff6-834d5c617806","resolution":{"observed_at":"2026-08-15T18:51:57.395093Z","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":"2026-08-15T18:51:57.400027Z","title":"Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.400027Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:ec3b970595c95eb660760a573ce58285386307670dfc1a07ea13a56e64e00eb2","observation_id":"4017c88f-3daf-4122-bca1-f779a13880b4","resolution":{"observed_at":"2026-08-15T18:51:57.400027Z","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":"2026-08-15T18:51:57.404726Z","title":"An artificial neural network approach to recognise kinetic models from experimental data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.404726Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:d8542ae5ebcc6028b59f0c4211e7c72787fd1915ca6678c01feecfb4f7e780cb","observation_id":"d985cf55-20da-4279-89db-b126daaed89d","resolution":{"observed_at":"2026-08-15T18:51:57.404726Z","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":"2026-08-15T18:51:57.409403Z","title":"Generative artificial intelligence in chemical engineering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.409403Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5b388373b03036ede28813135c83fc4d6e3f28dee31e4ef90884f6401511e409","observation_id":"ffa70615-8bef-4665-9592-51392ecd6583","resolution":{"observed_at":"2026-08-15T18:51:57.409403Z","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":"2026-08-15T18:51:57.414003Z","title":"Automated synthesis of steady-state continuous processes using reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.414003Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:e3e27d4c3c76d098b54df9ede06bbe9c11a6eb675cf8518a1be2ed73e96daf1f","observation_id":"93c16490-91c1-4e18-a265-52fd7116b67d","resolution":{"observed_at":"2026-08-15T18:51:57.414003Z","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":"2026-08-15T18:51:57.418648Z","title":"Schweidtmann","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.418648Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:278f1af6c0f4261e53f8f870f21dbd05ee668115afd26b990c03ee899e83f6af","observation_id":"ac201b78-4e31-434a-9199-eebe6915999e","resolution":{"observed_at":"2026-08-15T18:51:57.418648Z","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":"2026-08-15T18:51:57.423509Z","title":"esfiles: Intelligent process flowsheet synthesis using process knowledge, symbolic ai, and machine learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.423509Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:20aba3e2c303145905951463520caea8b334948e0e70ab465fe37db904c5372b","observation_id":"9854729a-e5ee-4951-8987-a6ce89db764c","resolution":{"observed_at":"2026-08-15T18:51:57.423509Z","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":"2026-08-15T18:51:57.428731Z","title":"Schweidtmann","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.428731Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:f61ae8e47f1cb8f35709fbed9fb58b78906934f1c474449f31227e074a5aa676","observation_id":"f91c362a-6b66-43fc-bac0-7d9b3c544dab","resolution":{"observed_at":"2026-08-15T18:51:57.428731Z","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":"2026-08-15T18:51:57.433815Z","title":"Graph-to-sfiles: Control structure prediction from process topologies using generative artificial intelligence","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.433815Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:5012d6a5c26fd2c5e6bbdd8941210f7f56427f515d1bf5d3e5609fb984d2f2f1","observation_id":"4bb549f7-5d83-48fb-8223-1a755c9817c4","resolution":{"observed_at":"2026-08-15T18:51:57.433815Z","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":"2026-08-15T18:51:57.438959Z","title":"Deep reinforcement learning for process design: Review and perspective","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.438959Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:54d25331fa62fadf5158a2e745b95a06e3f282904b24151592b4d2b9b0e48a38","observation_id":"7867bb46-340d-4c5c-a184-fbe100982a9f","resolution":{"observed_at":"2026-08-15T18:51:57.438959Z","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":"2026-08-15T18:51:57.444109Z","title":"Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.444109Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:8c93a8a867ada1ab2d0d9e5765ade6f2fecf87184c5e0ea061ff415be41b995e","observation_id":"739ff514-2180-4635-9315-8bce3f7761f0","resolution":{"observed_at":"2026-08-15T18:51:57.444109Z","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":"2026-08-15T18:51:57.449413Z","title":"Data-driven control: Overview and perspectives","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.449413Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:1f2475f89a793ba98b6ed11a9213dc95c3e56a2c10718324b09abded35d81cc2","observation_id":"2dc26ffb-888f-4a54-8a47-5e02690c5f90","resolution":{"observed_at":"2026-08-15T18:51:57.449413Z","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":"2026-08-15T18:51:57.455723Z","title":"System identification: A machine learning perspective","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.455723Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:9e7c23fa8f077820def690e598948233f8df6ec63b37ecc61820620e762f25f8","observation_id":"5c66ae6c-5e30-49e4-b487-3f102a5e11f9","resolution":{"observed_at":"2026-08-15T18:51:57.455723Z","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":"2026-08-15T18:51:57.461143Z","title":"Comparative study of machine learning and system identification for process systems engineering dynamics.Industrial & Engineering Chemistry Research, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.461143Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:4330eaf9d8c38cc618bfc8d4d0667f0acc0946cde86cd0f53bca38d9242f19da","observation_id":"e1103335-5415-4ac1-92ad-801bde597f8f","resolution":{"observed_at":"2026-08-15T18:51:57.461143Z","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":"2026-08-15T18:51:57.466902Z","title":"Process control via artificial neural networks and reinforcement learning","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.466902Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:d7a07b6b6b30429e25c61100faba91604b5dbfa2b1155707d1b51d2159ce01a3","observation_id":"d308097f-b85c-403e-9850-93a82069efa7","resolution":{"observed_at":"2026-08-15T18:51:57.466902Z","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":"2026-08-15T18:51:57.472554Z","title":"Reinforcement learning–overview of recent progress and implications for process control","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.472554Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:2b1e0a70f0bd5810941691b4dea88eb6734c21575ea329f29d2ce75a4a29c575","observation_id":"42da0cbb-6243-4b21-b793-0b4ab7ad1421","resolution":{"observed_at":"2026-08-15T18:51:57.472554Z","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":"2026-08-15T18:51:57.478667Z","title":"A review on reinforcement learning: Introduction and applications in industrial process control","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.478667Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:0d61f8076d595f2f45e86dc83a538e5ea167d138ee66f42a11d19b36154438ef","observation_id":"f92da903-7ae6-4d1d-b29c-fcf69b7cd320","resolution":{"observed_at":"2026-08-15T18:51:57.478667Z","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":"2026-08-15T18:51:57.485530Z","title":"Recent advances in reinforcement learning for chemical process control","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.485530Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:4e7a26aa826ab283f7c2416da86601d1c59463fe5d4db010aa148d70070845ca","observation_id":"a51825a4-c1ff-4fbc-a297-3a62e854689a","resolution":{"observed_at":"2026-08-15T18:51:57.485530Z","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":"2026-08-15T18:51:57.490666Z","title":"Modeling and predictive control of nonlinear processes using transfer learning method","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.490666Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7986c675488c590a7d7550e26bd0bea3b7ba9a900c5bb1014569d4f3c285a548","observation_id":"6b175bb9-ad42-4d15-ba10-a43d7c948148","resolution":{"observed_at":"2026-08-15T18:51:57.490666Z","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":"2026-08-15T18:51:57.495792Z","title":"Hedengren","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.495792Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:06b30c56d1f9d8621c404fb1710a1f559107cb21d5cd5ab209db154fd1b5b333","observation_id":"71f9b1cd-94ab-4616-a514-79b62f4bc0cc","resolution":{"observed_at":"2026-08-15T18:51:57.495792Z","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":"2026-08-15T18:51:57.500805Z","title":"Optimization-based multi-source transfer learning for modeling of nonlinear processes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.500805Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:bb37334262c53b683dfdb2d80181da63ed003f2ce52c9f27981215ff43bd213f","observation_id":"bde9fb51-858a-4e8c-aa63-41f12e60efef","resolution":{"observed_at":"2026-08-15T18:51:57.500805Z","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":"2026-08-15T18:51:57.506350Z","title":"Control strategies for microgrids with distributed energy storage systems: An overview","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.506350Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:6e2fb18dac337b44d5a561ff399fe6aaba6ceb9f4d71cf2c89879e6581ad20b2","observation_id":"8b7bf70d-c881-4af0-95f4-3d0cfe72fdfa","resolution":{"observed_at":"2026-08-15T18:51:57.506350Z","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":"2026-08-15T18:51:57.510905Z","title":"Cooperative optimal power flow with flexible chemical process loads","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.510905Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:a600a6762552008ece1801e92a000a0410275a2087e74b44ef564431815300a6","observation_id":"834c2014-773a-470e-827b-d3730cdc9dec","resolution":{"observed_at":"2026-08-15T18:51:57.510905Z","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":"2026-08-15T18:51:57.516234Z","title":"Toward distributed energy services: Decentralizing optimal power flow with machine learning.IEEE Transactions on Smart Grid, 11(2):1296–1306, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.516234Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:a40bcb9cb01fe8c306d76dab85d8cb2f64ef566cca6c330c55b698c521507e72","observation_id":"0f360032-18a1-43fe-9ab8-8ad7c4db8dd9","resolution":{"observed_at":"2026-08-15T18:51:57.516234Z","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":"2026-08-15T18:51:57.521382Z","title":"Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.521382Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:9ea6d958e868ee0683154e78a9ce877a40840bb9e03938cba05d66075037293a","observation_id":"e4deb51b-6689-48c8-9164-815bd70b886a","resolution":{"observed_at":"2026-08-15T18:51:57.521382Z","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":"2026-08-15T18:51:57.526396Z","title":"Federated multiagent deep reinforcement learning approach via physics-informed reward for multimicrogrid energy management","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.526396Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:7187d37ee299aaf27a10163c1bbb5e69b5f62bd7e1f0d1bb27f3f93a5b5bbdfc","observation_id":"23d2f18b-8ed4-4dd4-9b2f-3ff8ca80f22b","resolution":{"observed_at":"2026-08-15T18:51:57.526396Z","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":"2026-08-15T18:51:57.531457Z","title":"Survey on ai and machine learning techniques for microgrid energy management systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-15T18:51:57.531457Z"},"links":{"citing_paper":"/paper/2506.18525"},"observation_digest":"sha256:b4dbcd70f1e09289be90880411aed18b54fc8952da8069f770635f59c09817d7","observation_id":"93c7cf81-282c-4c5b-ab65-abd01f26dbdf","resolution":{"observed_at":"2026-08-15T18:51:57.531457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18525","last_updated":"2025-06-23T11:27:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T12:13:45.777416Z","submitted_at":"2025-06-23T11:27:34Z","title":"Federated Learning from Molecules to Processes: A Perspective"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":98,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":211},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 100 of 211 outbound references and 2 inbound Pith citation observations for arXiv:2506.18525."}