{"as_of":"2026-08-07T04:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e113f8fd64a32feeb7e4e14c6c92399e235e0c4f2a155ccc34f9998b6a1c8ec4","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-08T01:03:05.626076Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.05179/citation-record","integrity":"/paper/2607.05179/integrity","json":"/paper/2607.05179/citation-record.json","paper":"/paper/2607.05179"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.775401Z","title":"Jiang, C","venue":null,"work_id":"39b9e567-1f61-4c37-afa1-6a58ff8e2cbc","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:72cf2ea76adc3bd2b0647944eeffbe2f324eb1767f92cc6236bef8ba6686062f","observation_id":"f8752f7d-6e01-436a-97ce-88d3722960d3","resolution":{"observed_at":"2026-07-08T01:04:24.776733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.748205Z","title":"Goeckner, Y","venue":null,"work_id":"8edcb8f0-830b-44b0-b510-bbb9cb5d6248","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:00f204d7200c8959f9744d8eb3121050f0d56b4b901ef83c640c29dee8593353","observation_id":"0279920d-e801-4a04-8a09-61821be8053b","resolution":{"observed_at":"2026-07-08T01:04:24.749955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.750547Z","title":null,"venue":null,"work_id":"4753bfbb-e794-4e30-b75d-f413e31ef2f4","year":2025},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:b4a6f7d314ecc81e1482876b01d783a67589cd007de4f64c0993d8551f986111","observation_id":"a0d42610-e54e-4c0e-9cac-62e74e71175b","resolution":{"observed_at":"2026-07-08T01:04:24.751839Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.114056","doi":"10.1016/j.asoc.2025.114056","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ardjmand, E","venue":"Applied Soft Computing","work_id":"eaf90f75-2313-405e-bd21-10078e78351d","year":2026},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:060e4d267395df218cf66ab00ca843576fcfc22e92717aa9ec72e24c8b1036b9","observation_id":"be08aa79-0e55-4963-a085-d5ff32d03835","resolution":{"observed_at":"2026-07-08T01:04:24.529477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:49:58.743807+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:49:58.743807+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.113163","doi":"10.1016/j.asoc.2025.113163","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Applied Soft Computing","work_id":"d83f86ca-7d49-4b9a-ac3c-e533207790bb","year":2025},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:2988d652f6b9b2aa2b23de78aee756fb88b855323d366d4639e018bbc8335636","observation_id":"0ae70a8d-8779-48f7-93d6-946f4925492c","resolution":{"observed_at":"2026-07-08T01:04:24.514759Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:49:59.034927+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:49:59.034927+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.104338","doi":"10.1016/j.trd.2024.104338","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Transportation Research Part D Transport and Environment","work_id":"03cdacc5-bc06-47a5-957a-c1f385f2999f","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:1a3dfae3bd350bfee16c5b188ab472fe404fc88688ccd2840ea2c499152c2063","observation_id":"f1313072-f9d3-4595-852b-4a00849b793d","resolution":{"observed_at":"2026-07-08T01:04:24.571213Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:49:59.321427+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:49:59.321427+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.trb.2022.08.004","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gutiérrez-Hita, O","venue":"Transportation Research Part B Methodological","work_id":"85f8ecf0-f9d8-4a8d-b8a9-770fdf8582c9","year":2022},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:ab31705e3d309d1b154d6a81e5836449b461b859aafe980be0afa425e967260f","observation_id":"c28e70da-1fa4-459e-8c1a-f95621187444","resolution":{"observed_at":"2026-07-08T01:04:24.537381Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:49:59.601051+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:49:59.601051+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.65109/edcb3795","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Agarwal, S","venue":null,"work_id":"22969179-9bab-45a9-8ca5-3ec32c1274f1","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:6c609911cce5014cc8b8585472964d0ed739b85899123dd18e5dc74cdd0e2167","observation_id":"b56be995-eb1e-4675-808d-5530adf0bda4","resolution":{"observed_at":"2026-07-08T01:04:24.564632Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:49:59.837715+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:49:59.837715+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.740152Z","title":"Nayak, K","venue":null,"work_id":"6cd274cc-c66e-4bb5-aa7a-900222caa3a5","year":2023},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:0fbd97875e4615a37d4198c9df6f58326553fb00df431db4a5b3961a7cc9d39d","observation_id":"0fa139a3-3166-4fef-bbc9-eaddc5d00465","resolution":{"observed_at":"2026-07-08T01:04:24.741487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v32i1.11794","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"9c0bc699-e55c-4d9c-99ec-7eefb98fa35f","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:951611a650cb929ce8005873fb9145ca92284403a24315246ba960b57eb7b8aa","observation_id":"94e1cadf-8ae3-4c45-b71d-f9f1c5ea0564","resolution":{"observed_at":"2026-07-08T01:04:24.548730Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:00.086821+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:00.086821+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.771367Z","title":"2017-December, 2017","venue":null,"work_id":"8ce0cf82-5288-4532-87e9-20e8790e421c","year":2017},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:de45f90f158ae719295be79a00b8b3ab6101799b774b327ce9a38758280b9fd8","observation_id":"f4808e07-5058-4fc5-9d11-991074a4ace6","resolution":{"observed_at":"2026-07-08T01:04:24.772701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01465","last_updated":"2019-12-02T16:00:20Z","snapshot_observed_at":"2026-08-01T19:44:03.464325Z","submitted_at":"2019-10-03T13:40:46Z","title":"Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics","version":2},"cited_work":{"arxiv_id":"1910.01465","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.01465","snapshot_observed_at":"2026-07-08T01:04:24.711710Z","title":"arXiv preprint arXiv:1910.01465 , year=","venue":"cs.LG","work_id":"dff193eb-9a3f-49a4-9cec-d39d10c7a8ce","year":2019},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"cited_paper":"/paper/1910.01465","citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:38cd8ce1868c828e788ea42aea6764c888ee9f0babdafeb37880a2512cf2ee79","observation_id":"66d62411-90ce-4672-a94e-a83febe3a60f","resolution":{"observed_at":"2026-07-08T01:04:24.713311Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.754386Z","title":"Zambaldi, D","venue":null,"work_id":"6f5157c8-9713-4fe5-a957-1aef2eaa6cb7","year":2019},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:2c12a8c1c597e0448563daa53f4ce0d513ba38bca2e28e00b9bc554d9da302c2","observation_id":"c3cfa6a5-05f2-4c2d-9bfe-d9ed6b3e5be6","resolution":{"observed_at":"2026-07-08T01:04:24.755830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2501.08234","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.708300Z","title":"Dynamic Pricing in High-Speed Railways Using Multi- Agent Reinforcement Learning","venue":null,"work_id":"50a2f2e1-0d2e-482a-881c-ec3744f8b5f7","year":2025},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:549c361b5185252bb42740ded71684634b1a51118e98803828d6923e2a5e27ca","observation_id":"e470f6cb-bd07-49b0-a5f9-e218c329e869","resolution":{"observed_at":"2026-07-08T01:04:24.710321Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.752452Z","title":null,"venue":null,"work_id":"d079da83-844d-49bc-816b-955ce91b471a","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:1901b950ba414123bdec754c4a8f06d33f10bdda1ca76da7cc088b3023e7489d","observation_id":"f8d449e7-bb7d-4efa-be6e-13160a11630f","resolution":{"observed_at":"2026-07-08T01:04:24.753777Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.108057","doi":"10.1016/j.jfranklin.2025.108057","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Journal of the Franklin Institute","work_id":"c8cfb5bb-a3c4-4fb7-ae52-c09c43ba52b1","year":2025},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:ca6948a874068528b10aa42db0f7679a629eee8fe3132a89891682db3df31453","observation_id":"ed8bf371-6b45-4a27-8169-825028ec4aab","resolution":{"observed_at":"2026-07-08T01:04:24.590071Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:00.406171+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:00.406171+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.769330Z","title":null,"venue":null,"work_id":"a1c47758-c6f4-4f54-a9ab-fb82485a4c1f","year":2022},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:b68b0251d4f239634e6a19de4db8249b33ed1f130e8ba20de92d398674908908","observation_id":"7ea3b303-de3a-485e-938b-d37c6e14480c","resolution":{"observed_at":"2026-07-08T01:04:24.770699Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.764135Z","title":"Iqbal, F","venue":null,"work_id":"41f317c6-23b3-43d4-93b9-f2956ea8fcc7","year":2019},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:9c38b4224644de4fb77124b14df0558538e5ceea70b5032ccd176bf5b88492f0","observation_id":"63edb5ba-bac3-415b-9aea-839b9cfa23c1","resolution":{"observed_at":"2026-07-08T01:04:24.765833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.108843","doi":"10.1016/j.ijepes.2022.108843","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MADIA - Meteorological variables for agriculture: A dataset for the Italian area , journal =","venue":"International Journal of Electrical Power & Energy Systems","work_id":"d3cdd0d3-5a27-4706-af1d-9118f2d8cdad","year":2023},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:7b6b09c28cf82c8bca43c3077cb8cfe1e68afc2bbf5dc3c20b2eb635e2ef407b","observation_id":"8fbb813c-69e1-4b0c-b163-b5b5fcba3f55","resolution":{"observed_at":"2026-07-08T01:04:24.520570Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:00.72868+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:00.72868+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.100139","doi":"10.1016/j.segy.2024.100139","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fraija, N","venue":"Smart Energy","work_id":"a7bbc09c-e976-466e-bb42-3a62610d7edf","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:dd697c505a914ff3c8305df3d7a481e9e8d3f35d788092bb60a3f4d68fbfc9bb","observation_id":"5e6c8466-e184-462e-8b8a-70df71791754","resolution":{"observed_at":"2026-07-08T01:04:24.534656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:01.04873+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:01.04873+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.328120","doi":"10.1109/tmc.2023.3281203","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"IEEE Transactions on Mobile Computing","work_id":"8f1f07ed-c449-4ffc-849d-0f9d52625dd1","year":2023},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:d4e1721a59d40e8f952614956aba679c96b93c2cdaac7f7719f352e0d3038398","observation_id":"edacc877-6153-4a54-9c2e-cfb4da8d1922","resolution":{"observed_at":"2026-07-08T01:04:24.586693Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:01.282502+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:01.282502+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.108012","doi":"10.1016/j.engappai.2024.108012","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Engineering Applications of Artificial Intelligence","work_id":"129527f3-25c7-42e7-ac8f-38220c7bd888","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:09e78d0188ac22b7a50ab04c17c5b5e765693c7c292fc731f73e228a81c8353c","observation_id":"8b710912-d74b-44ea-bd6f-13c1dffada07","resolution":{"observed_at":"2026-07-08T01:04:24.599968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:01.596786+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:01.596786+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.742072Z","title":null,"venue":null,"work_id":"4e86117a-b681-47f3-8a84-e9186f512211","year":2017},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:187b9b14fc71cf2ab8ca251fe34d54499f2680683d30b0a49b4b9f37a3ef79b7","observation_id":"e502e792-99f6-4f37-836f-d1805a78c556","resolution":{"observed_at":"2026-07-08T01:04:24.743408Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v34i05.6211","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"91cf9390-4fec-4d5c-b5f1-196222786392","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:1f3d6aa2a47edd95ee22f3bf59e7d58c595ccd7f05f2ca24899c6f6af116e563","observation_id":"3e010fdb-519f-40be-86f2-f53801349f93","resolution":{"observed_at":"2026-07-08T01:04:24.524533Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:01.837933+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:01.837933+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.738119Z","title":"Hamilton, Z","venue":null,"work_id":"6c3e12e8-cf1a-4ab6-8ff0-f18116015a84","year":2017},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:b69fea5ffea14f460c59aa0d10dba6be4176f9aa49f90416c82058d1788adb67","observation_id":"c9f79f2d-49e0-453f-b6dc-d015d7504e7c","resolution":{"observed_at":"2026-07-08T01:04:24.739554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.103321","doi":"10.1016/j.trc.2021.103321","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Transportation Research Part C Emerging Technologies","work_id":"bb056a06-7308-4e36-b8ee-53a70283f6c7","year":2021},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:23d79c5db241dc303837f1b8a2f68feb70fc726a2cd434be859de508ba5d9bae","observation_id":"95b61768-22ad-4e75-9315-b5629103d864","resolution":{"observed_at":"2026-07-08T01:04:24.596638Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:02.082675+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:02.082675+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.766613Z","title":null,"venue":null,"work_id":"55912ea7-8e46-493d-a7e2-8a1dfca5d88d","year":2023},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:c7fcceedc627f2f51bae29066ebcc14b56bd3b9d7009cafe1404e645264a21bd","observation_id":"27bc0d7d-83b3-4a4a-979e-4993e9cdc4f5","resolution":{"observed_at":"2026-07-08T01:04:24.768330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2026.114587","doi":"10.1016/j.asoc.2026.114587","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Applied Soft Computing","work_id":"47a6be46-84b9-4466-bcce-66a24b6e51c7","year":2026},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:0969eff937c3ca5d8ddb4cfde1c5c289b4da1553ac0acdc07292163c4e5d8dc1","observation_id":"9b5f2628-352e-47b4-a223-1b41d2426b6a","resolution":{"observed_at":"2026-07-08T01:04:24.605803Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:02.393084+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:02.393084+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.310538","doi":"10.1109/tits.2021.3105380","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi,","venue":"IEEE Transactions on Intelligent Transportation Systems","work_id":"42c0cc69-c23a-461c-a1b1-9d4908f238e8","year":2022},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:348544092b4d7bf3d0ee1d967d3b7c69a5d49e0266eb6b9a162b5fb96205d9da","observation_id":"3cf46c37-af56-4717-9d43-b321fc6152d5","resolution":{"observed_at":"2026-07-08T01:04:24.552560Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:02.618269+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:02.618269+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.334446","doi":"10.1109/tits.2023.3344468","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"IEEE Transactions on Intelligent Transportation Systems","work_id":"8bac99eb-0eae-4640-843f-481b838fce86","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:de810a3b8d9d8c6fb59d2c661bdaea87a35a92ae1b628c6074fbde4e880c46a2","observation_id":"9aefb4bd-ca06-41ce-8e57-8d65bcba060f","resolution":{"observed_at":"2026-07-08T01:04:24.545477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:02.846367+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:02.846367+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.05893","last_updated":"2020-12-11T14:51:22Z","snapshot_observed_at":"2026-08-05T01:38:43.322014Z","submitted_at":"2020-12-10T18:54:27Z","title":"Flatland-RL : Multi-Agent Reinforcement Learning on Trains","version":2},"cited_work":{"arxiv_id":"2012.05893","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.05893","snapshot_observed_at":"2026-07-08T01:04:24.702041Z","title":"P., Nygren, E., Laurent, F., Schneider, M., Scheller, C","venue":"cs.AI","work_id":"a05972db-4960-45d3-b555-c952f5b3da2c","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"cited_paper":"/paper/2012.05893","citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:c1f50704311d8aa27679d16dc56bf95b7033990c5e2a0bb1872f1d0ae3a1c989","observation_id":"0db13c2d-0330-440c-9512-782bcc53818a","resolution":{"observed_at":"2026-07-08T01:04:24.703682Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.108615","doi":"10.1016/j.ress.2022.108615","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mohammadi, Q","venue":"Reliability Engineering & System Safety","work_id":"2a0d55b7-488e-4cb9-ad0e-0f8e210d9031","year":2022},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:5af02b4f9ce44c5f68fb840cdecb4baae1246430175b754651c663418766d036","observation_id":"0d04868a-7c6d-44d5-bc25-c195c4c93cd8","resolution":{"observed_at":"2026-07-08T01:04:24.561928Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:03.146281+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:03.146281+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10994-024-06559-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Arcieri, C","venue":"Machine Learning","work_id":"71f7e6e7-3eb6-48d0-b6b0-c8767bde9af2","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:281b02928f0018d4ec925e28a58b9835660cc0cc4384a2578dfb51e689c140ce","observation_id":"e7083640-b94e-4f63-80f7-034ed1b038a7","resolution":{"observed_at":"2026-07-08T01:04:24.608126Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:03.494499+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:03.494499+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8891.346889","doi":"10.1145/3468891.3468898","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"c2c01e72-7acc-4b1d-8e14-cc47768e8244","year":2021},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:be0e2f6868a84079247f030d0c848c94f21bbdbd839358417b3c31dec60aa93f","observation_id":"2364f106-ced7-4b87-a567-99d0fc56acd4","resolution":{"observed_at":"2026-07-08T01:04:24.574719Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:03.725487+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:03.725487+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6826.342683","doi":"10.1145/3426826.3426833","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"20c4dab1-76de-4594-93a4-ad16aa8dddce","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:2ddba7e74c9c64e92a7432e00524ccf61860d422d9c31863cd5e2d52eaf6f843","observation_id":"9a4f03db-49fb-416b-abbd-742101085cfa","resolution":{"observed_at":"2026-07-08T01:04:24.555979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:03.967536+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:03.967536+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.109226","doi":"10.1016/j.engappai.2024.109226","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi:10.1016/j.engappai.2024.109226","venue":"Engineering Applications of Artificial Intelligence","work_id":"a70566bc-f62e-499e-8df3-18112f768fe6","year":2024},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:f06978e4525853e6c077a3704183e9a379474ec589cac6096ce789ca84e3155e","observation_id":"5b10a326-83fc-43ac-9ee8-72cc08fcad10","resolution":{"observed_at":"2026-07-08T01:04:24.614077Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:04.265339+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:04.265339+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/socs.v12i1.18576","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Proceedings of the International Symposium on Combinatorial Search","work_id":"1bc6afe2-2ce4-48f9-a070-2ba3fee8cc9d","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:efbe36109aff825d7051537f47fea79b87bb144bdd37bdf21244d12d840f96af","observation_id":"4d079007-0dd0-4f99-932d-2511b24ef707","resolution":{"observed_at":"2026-07-08T01:04:24.577033Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:04.491343+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:04.491343+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/b978-1-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.600648Z","title":"Littman, Markov games as a framework for multi-agent reinforce- ment learning, in: Proceedings of the 11th International Conference on Machine Learning, ICML 1994, 1994, pp","venue":null,"work_id":"fb9817b0-d785-4116-a8ed-c6e745344944","year":1994},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:6ab75cc9e743714505c302e138651dd3043ddefe5e8e6cc905852a67da82ed7b","observation_id":"9423e5d2-249c-4cea-a10d-fceb7af6481d","resolution":{"observed_at":"2026-07-08T01:04:24.602658Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.758477Z","title":null,"venue":null,"work_id":"34d24ca6-cd6a-45d1-a5be-e78a4c539dd6","year":2016},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:a88cbde8aa775d6ca65b3f924ea9e0ae5c2350cc68eb1c70af5dcefd7219c6b1","observation_id":"596a770d-5b08-499e-8e66-677752108723","resolution":{"observed_at":"2026-07-08T01:04:24.759736Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01261","last_updated":"2018-10-17T17:51:36Z","snapshot_observed_at":"2026-07-06T06:42:54.610341Z","submitted_at":"2018-06-04T17:58:18Z","title":"Relational inductive biases, deep learning, and graph networks","version":3},"cited_work":{"arxiv_id":"1806.01261","doi":"10.48550/arxiv.1806.01261","metadata_source":"pith","pith_arxiv_id":"1806.01261","snapshot_observed_at":"2026-07-10T16:47:24.357908Z","title":"Relational inductive biases, deep learning, and graph networks","venue":"cs.LG","work_id":"858410c0-7a66-4b27-b4e5-49aee9725be0","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"cited_paper":"/paper/1806.01261","citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:89469c11c9b3cafeb8df5b63add353ea02f2b3441c3d42b2f6340120e3754604","observation_id":"60a68ce5-8d70-438e-9829-9172b83d78a8","resolution":{"observed_at":"2026-07-08T01:04:24.719215Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-03T17:38:15.281147+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:15.281147+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.777381Z","title":"Ugadiarov, V","venue":null,"work_id":"aa391e92-b38d-4220-a21f-7a6ebd74fab2","year":2025},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:d69e75817d7cc702223f2ecfb0e1529208d56ec3be2f74e867256b79c0b05afd","observation_id":"838cd0d8-4c08-4acd-a9a1-04dd705b87a2","resolution":{"observed_at":"2026-07-08T01:04:24.778621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.760414Z","title":"Schlichtkrull, T","venue":null,"work_id":"ffdd769d-1089-4c59-b680-2338ed4b4774","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:42689db8c0473fcfd0284821aaa9f89857365f095c2ff2a1aa8277374c86ab4b","observation_id":"e0abeb85-c7ca-4918-94f1-a90160831b3d","resolution":{"observed_at":"2026-07-08T01:04:24.761673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/cvpr.2016.90","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T09:16:59.350888Z","title":"Deep residual learning for image recognition","venue":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","work_id":"b353bda2-591d-479a-9c8b-22dfcba12431","year":2016},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:b37b87fec19dc06dbcde5e9afdcc02cc44689f1b61da0aecf6f50199899dac75","observation_id":"ab02f9f6-41dc-464c-af1e-ebc3399f61c8","resolution":{"observed_at":"2026-07-08T01:04:24.568085Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-03T17:08:01.374485+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:08:01.374485+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":"1607.06450","doi":"10.1007/978-3-319-32025-0","metadata_source":"pith","pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Layer Normalization","venue":"stat.ML","work_id":"20a2d720-0046-4c7c-bcd6-327ec8143f69","year":2016},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:8a528e9309010438b8c0fe94985420a6843b6558995999485683ad39e6e5e949","observation_id":"a52761e0-baf3-4a67-bd4a-866191c6cb5a","resolution":{"observed_at":"2026-07-08T01:04:24.716233Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.746122Z","title":"Zaheer, S","venue":null,"work_id":"4b203b5b-9e4f-43a5-b3b5-2ac8f3aa3daf","year":2017},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:87e835815488a7159eff34cdedf30b7de88613c47c6f53bb9585dcafd70675ef","observation_id":"453ae9e5-8510-4c4e-af7d-edb09abe1dd3","resolution":{"observed_at":"2026-07-08T01:04:24.747610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.773324Z","title":"Fujimoto, H","venue":null,"work_id":"94e0badb-cc1b-41af-9a8a-170d33c4dc5a","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:b81c439887474ec5ccff45674775b8aa820139c96c14c6954dda2f1c98f4edce","observation_id":"5ab54ab9-db66-4dac-9fe1-eb022ec539bd","resolution":{"observed_at":"2026-07-08T01:04:24.774630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.743972Z","title":null,"venue":null,"work_id":"025786f4-8d3d-487b-916b-9186f2013941","year":2016},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:64b2d3de90d862a83ee5bd9fd960f0d5c0ec3a2729f683f21f27b7bbb6ca1f05","observation_id":"86561f7d-9f9d-4466-9ef2-27322cabd3e2","resolution":{"observed_at":"2026-07-08T01:04:24.745415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.756396Z","title":"Haarnoja, A","venue":null,"work_id":"4c8e575a-97d2-4906-a341-85605adeaed3","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:ed803a7f85a6d80e3f491d53029b24bf7c7ad424c98f93c031050d4c02685e7d","observation_id":"68bec9da-da28-472f-a52d-4032e44c314e","resolution":{"observed_at":"2026-07-08T01:04:24.757749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.735889Z","title":"Agarwal, M","venue":null,"work_id":"ab7d35fc-eef8-4f5f-a574-81884ce2a93f","year":2021},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:23f6f76e7036042d55bcbd89a5a9e37224f710ae1e3786b55b81537bcca2fdbe","observation_id":"cabe981e-b530-44cb-8963-eac3b6af0a7c","resolution":{"observed_at":"2026-07-08T01:04:24.737512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v32i1.11604","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deeper insights into graph convolutional net- works for semi-supervised learning","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"7a76b93a-1a42-4265-81f0-fdc98e7cb510","year":2018},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:ab3943bd403cc3d927bc457d0e04c1c81dcc2d62ca4adb4a5b58e86368874c83","observation_id":"ab1e6c8c-d861-44c7-8ac2-70fed99c98bf","resolution":{"observed_at":"2026-07-08T01:04:24.610572Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:04.97375+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:04.97375+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.779282Z","title":"Van Der Maaten, G","venue":null,"work_id":"a126eee9-52af-4924-baa1-27d5e043853f","year":2008},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:944e981747a56c8fabdbad5033a5471b3a40b4af1441be50399398fdccab51c8","observation_id":"dbbcc4ae-d280-43ba-b124-c5dc9a164db7","resolution":{"observed_at":"2026-07-08T01:04:24.780461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10637","last_updated":"2020-10-09T11:39:32Z","snapshot_observed_at":"2026-08-02T05:44:45.513979Z","submitted_at":"2020-06-18T16:06:18Z","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","version":3},"cited_work":{"arxiv_id":"2006.10637","doi":"10.48550/arxiv.2006.10637","metadata_source":"pith","pith_arxiv_id":"2006.10637","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","venue":"cs.LG","work_id":"148caacb-5e39-4b83-8a6b-07b844a00d64","year":2020},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"cited_paper":"/paper/2006.10637","citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:6529c5f5b1426a67fb9fe959512f9c0ec0b2d341eb7a3ff1ae7cff9e3f970e8d","observation_id":"74c1a712-1a62-4ff2-9a54-94fd9196064c","resolution":{"observed_at":"2026-07-08T01:04:24.706890Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T01:04:24.762288Z","title":"Kingma, J","venue":null,"work_id":"416a033c-0965-4f57-86bc-e3014cdca039","year":2015},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:2fac9fdaa40279dd722142643bc9d39e1df2b45766759e5285957d807615d198","observation_id":"948d69ba-20ce-4553-b2fb-447fab6d0d5a","resolution":{"observed_at":"2026-07-08T01:04:24.763477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrev.36.823","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Physical Review , author =","venue":"Physical Review","work_id":"07512de4-6f9d-4548-931f-0a0826d8a660","year":1930},"citing_paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-08T01:03:05.626076Z"},"links":{"citing_paper":"/paper/2607.05179"},"observation_digest":"sha256:dfe3e42561a614781ab38382df3e5657319b7fbcd0f3adfd3ad65b90065c480c","observation_id":"8bfd6d0c-c254-4345-8cfa-a1aaf50f19dc","resolution":{"observed_at":"2026-07-08T01:04:24.583467Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:05.204108+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:05.204108+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.05179","last_updated":"2026-07-06T14:58:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T13:40:27.639870Z","submitted_at":"2026-07-06T14:58:26Z","title":"Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":31,"verified_fuzzy":15},"total_outbound_references":54},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.05179."}