{"as_of":"2026-08-21T08:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ea812cac8bec27d162a26fe6d24b26233a4723a4023191794e68432a8bca2662","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:11:23.507432Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.23080/citation-record","integrity":"/paper/2507.23080/integrity","json":"/paper/2507.23080/citation-record.json","paper":"/paper/2507.23080"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.356893Z","title":"Graph neural networks and reinforcement learning: A survey,","venue":null,"work_id":"db4d1dbe-9ee6-4cf1-88dd-eb5d74e56612","year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:20.534762Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:1fcf709de0b9607501948963855fe2ae1b2cb171fcecfd90e8153f52894608af","observation_id":"ad457e09-c5a4-440e-b6ff-efff7afd454d","resolution":{"observed_at":"2026-08-06T11:11:24.361485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-06T11:11:20.708706Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:20.708706Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:e458c652f8432045292834a91fa791b0917e0f540f063b967a7d11b1b21f03c1","observation_id":"354d8a4f-748a-46c5-9ca1-4db8bc5d8f7f","resolution":{"observed_at":"2026-08-06T11:11:20.708706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-06T11:11:20.806452Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:20.806452Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:1f6bc39eba6b8a608434178c2b966ff6a4ab206dbf0f375961624c34944edfef","observation_id":"d63e07c8-7ad5-4fc6-a198-f0b054bbd65f","resolution":{"observed_at":"2026-08-06T11:11:20.806452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14994","last_updated":"2023-11-25T10:46:06Z","snapshot_observed_at":"2026-08-16T14:40:28.270032Z","submitted_at":"2023-11-25T10:46:06Z","title":"Exploring Causal Learning through Graph Neural Networks: An In-depth Review","version":1},"cited_work":{"arxiv_id":"2311.14994","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.14994","snapshot_observed_at":"2026-08-06T11:11:23.855106Z","title":"Exploring Causal Learning through Graph Neural Networks: An In-depth Review","venue":"cs.LG","work_id":"36277daf-025a-45e0-a92e-8eda38a603ab","year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:20.867761Z"},"links":{"cited_paper":"/paper/2311.14994","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:6ed56ee2e1fe1f8459ab067983465a48367319c8576e2c2112dbd80c10357339","observation_id":"6fc07676-abde-457a-9610-57143cdf45a9","resolution":{"observed_at":"2026-08-06T11:11:23.860099Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.325755Z","title":"When graph neural network meets causality: Opportunities, methodologies and an outlook,","venue":null,"work_id":"51c919ac-3489-4744-835a-3456a77281b1","year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:20.945391Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:6bfecbe7ec342d64acbaf65e5c0287f3d43e3a324a38a2f0889ee3cd497ed35f","observation_id":"ccd58124-d35e-40b0-95c7-b4b2b9b6af11","resolution":{"observed_at":"2026-08-06T11:11:24.330391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05209","last_updated":"2023-06-01T13:43:50Z","snapshot_observed_at":"2026-08-16T15:56:17.001208Z","submitted_at":"2023-02-10T12:25:08Z","title":"A Survey on Causal Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05209","snapshot_observed_at":"2026-08-06T11:11:21.045341Z","title":"A survey on causal reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.045341Z"},"links":{"cited_paper":"/paper/2302.05209","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:20de0119b6fdd8fc273efe645d86c0fa8c7ded74df076008e9dad9dceed90613","observation_id":"b3e0e376-8afd-4175-a93c-d226c41af4aa","resolution":{"observed_at":"2026-08-06T11:11:21.045341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.309687Z","title":"Causal reinforcement learning: A survey,","venue":null,"work_id":"77134687-c49b-473e-b35e-684db3689b3c","year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.124012Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:bbb5cc1ada4b7fc935bc62a736bf7fd5b0270f56efb2ec3e7fb6d02f4663833d","observation_id":"f51342fe-2911-43e7-b070-95d6b9ba6313","resolution":{"observed_at":"2026-08-06T11:11:24.314644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.06721","last_updated":"2021-12-01T18:27:49Z","snapshot_observed_at":"2026-08-18T11:10:29.048329Z","submitted_at":"2021-11-12T13:44:31Z","title":"Causal Multi-Agent Reinforcement Learning: Review and Open Problems","version":2},"cited_work":{"arxiv_id":"2111.06721","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.06721","snapshot_observed_at":"2026-08-06T11:11:23.813105Z","title":"Causal Multi-Agent Reinforcement Learning: Review and Open Problems","venue":"cs.LG","work_id":"55c6b8df-5787-46b8-8fdb-ff14a88e9214","year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.202207Z"},"links":{"cited_paper":"/paper/2111.06721","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:88b7f7210dc764891aa279d55f283d3bdd62233814063d457d807559f0d07317","observation_id":"7284a51e-0154-458b-b601-6095577422c3","resolution":{"observed_at":"2026-08-06T11:11:23.818123Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.07308","last_updated":"2016-11-21T11:37:17Z","snapshot_observed_at":"2026-08-14T21:29:27.927932Z","submitted_at":"2016-11-21T11:37:17Z","title":"Variational Graph Auto-Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.07308","snapshot_observed_at":"2026-08-06T11:11:21.267811Z","title":"Variational graph auto-encoders,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.267811Z"},"links":{"cited_paper":"/paper/1611.07308","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:6f828ac6564310c516f07e3eca976c77e9af3ac2054be14d6b1de573f552af41","observation_id":"0d89bd45-5a7c-4e1f-b1b1-deb6a49edde9","resolution":{"observed_at":"2026-08-06T11:11:21.267811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.340355Z","title":"Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications,","venue":null,"work_id":"effcb07d-73a0-4f2b-ba37-d96eebfdfee9","year":2024},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.344932Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:5db7e8cc2c71e0dc87d11e722acc4d4a59d8b85acfe88a06f4693ca100107052","observation_id":"80f512bc-2b8b-4a60-a1f6-9f55b64704ca","resolution":{"observed_at":"2026-08-06T11:11:24.346212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12776","last_updated":"2022-01-30T10:09:43Z","snapshot_observed_at":"2026-08-16T17:24:55.522989Z","submitted_at":"2022-01-30T10:09:43Z","title":"Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments","version":1},"cited_work":{"arxiv_id":"2201.12776","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.12776","snapshot_observed_at":"2026-08-06T11:11:23.772773Z","title":"Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments","venue":"cs.RO","work_id":"f7cbf0ed-ec05-4747-9ff8-dc16092d0fec","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.413120Z"},"links":{"cited_paper":"/paper/2201.12776","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:7035f1286fe43433c664b543941630de9e2d0a23a3a431dd368c21ec93b6ea0a","observation_id":"35286224-a290-4b1d-9131-54cd363e9fc2","resolution":{"observed_at":"2026-08-06T11:11:23.778039Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.293806Z","title":"Generalized single-vehicle- based graph reinforcement learning for decision-making in autonomous driving,","venue":null,"work_id":"1cc3a824-08af-426c-baaa-9bdb27ad8b1f","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.498031Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:e2912880acf82b92f8e079d6e6f7a83bde3a9962c76b125afcd0c6ba6719e3df","observation_id":"43622470-8e60-453d-8e64-1d2109eedfcb","resolution":{"observed_at":"2026-08-06T11:11:24.298663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.271856Z","title":"Multi-agent decision-making modes in uncertain interactive traffic scenarios via graph convolution-based deep reinforcement learning,","venue":null,"work_id":"8829dd06-5b01-4cc2-b915-bdca429e329e","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.574745Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:3ebc9a980423e0735b0de54130b12d7ad182fe62a1d034753d591766ea1747be","observation_id":"cfed2446-e679-49f3-873a-80ceb7336051","resolution":{"observed_at":"2026-08-06T11:11:24.281506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.252788Z","title":"Graph neural network and reinforcement learning for multi-agent cooperative control of con- nected autonomous vehicles,","venue":null,"work_id":"29051e9d-65b6-45e3-b5e6-9026dc7dc549","year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.646573Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:85ad3a4c20e498d7149c6b10f3366f6def6d562869d1ba76eeb06b6b1257171b","observation_id":"3549bbb8-6cc8-46e4-8b50-deeb3605e55a","resolution":{"observed_at":"2026-08-06T11:11:24.257740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11376","last_updated":"2022-07-26T06:28:15Z","snapshot_observed_at":"2026-08-16T17:19:18.154156Z","submitted_at":"2022-02-23T09:36:15Z","title":"Cooperative Behavior Planning for Automated Driving using Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2202.11376","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.11376","snapshot_observed_at":"2026-08-06T11:11:23.744200Z","title":"Cooperative Behavior Planning for Automated Driving using Graph Neural Networks","venue":"cs.RO","work_id":"3ebbfcf8-142f-4c64-bfee-50b3a09cc467","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.737216Z"},"links":{"cited_paper":"/paper/2202.11376","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:26f8ba1c29b84e87f129f5e0a110d8a4b5e14b55b2054748f50edb9390f8d997","observation_id":"bc3471ab-e7d6-46de-ba7f-a583e0eeb755","resolution":{"observed_at":"2026-08-06T11:11:23.749357Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.238157Z","title":"Efficient connected and automated driving system with multi-agent graph rein- forcement learning,","venue":null,"work_id":"a347c486-b976-45fc-a0e9-f46c0a75f051","year":2020},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.796855Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:ecc7af0b02fcba74579c5be233a6fce84e7ef4df1ea53828b72dcbe78409f3b9","observation_id":"7fc1afc7-6a91-4024-977a-ff7eb60d4db2","resolution":{"observed_at":"2026-08-06T11:11:24.242579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.222057Z","title":"Dq-gat: Towards safe and efficient autonomous driving with deep q-learning and graph attention networks,","venue":null,"work_id":"bd941cc9-8cd1-4caa-b17b-bdad33da1c20","year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:21.907457Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:644b17befe03d755d071042ea7faaa6a0187641792965bbc559343afc5f96d0d","observation_id":"6d3093bf-9ea3-469c-a872-15bc405d0858","resolution":{"observed_at":"2026-08-06T11:11:24.226633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.206259Z","title":"Drl-gat-sa: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture,","venue":null,"work_id":"b6957c75-17c1-4b35-a146-1c8ea57fd105","year":null},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.016864Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:44ab7bfc8e229f15652bf8f4cdccd9553915cf62afe1baa27908fe1eb20c39b9","observation_id":"290b616a-9ab9-49c5-be34-de4de1adc91e","resolution":{"observed_at":"2026-08-06T11:11:24.211074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.190060Z","title":"Causal based q-learning,","venue":null,"work_id":"fc6989b9-2a84-4d49-889d-c13ffce447a1","year":2020},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.284054Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:fde747afea281f1978b0749e50937dccbd2ff61a45e2e11da82a075ce6ee4e21","observation_id":"36d9d36b-1d62-4bb8-bbc5-57a60aa3a2c6","resolution":{"observed_at":"2026-08-06T11:11:24.195028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.172983Z","title":"Efficient reinforcement learning with prior causal knowledge,","venue":null,"work_id":"59a3aaaf-7929-4c19-ba1e-ea1def3d355e","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.407525Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:e0915c282af3b889574b8b7619d70131d87d87c0589b272b3255e5b86be440fa","observation_id":"fb78d273-0694-43af-a281-010f340b74e6","resolution":{"observed_at":"2026-08-06T11:11:24.177460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.154934Z","title":"Counterfactual policy evaluation for decision- making in autonomous driving,","venue":null,"work_id":"b149b58e-f56b-4453-967b-e55f35a749d8","year":2020},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.567875Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:8d0f9f891cb8910c7fdfc560d7d55a599898ae847de947d9195b1deede96246a","observation_id":"13b510a5-f631-41f2-9b14-236d281eebbf","resolution":{"observed_at":"2026-08-06T11:11:24.160124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.06964","last_updated":"2022-10-13T12:42:48Z","snapshot_observed_at":"2026-08-16T16:24:30.822277Z","submitted_at":"2022-10-13T12:42:48Z","title":"Causality-driven Hierarchical Structure Discovery for Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2210.06964","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.06964","snapshot_observed_at":"2026-08-06T11:11:23.622883Z","title":"Causality-driven Hierarchical Structure Discovery for Reinforcement Learning","venue":"cs.LG","work_id":"d746f40a-9bc5-46d5-bbec-d0a558bdd49d","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.781680Z"},"links":{"cited_paper":"/paper/2210.06964","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:c3f662b233b43b815936f59e1c4c448b384876a1daaf260e89fadc1e96da5042","observation_id":"baca878d-f849-4dda-8117-6e986dec2079","resolution":{"observed_at":"2026-08-06T11:11:23.631107Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.138177Z","title":"Constructing bayesian network models of gene expression networks from microarray data,","venue":null,"work_id":"03bc8f15-0b9c-441f-9f8d-ec4be222cac2","year":2000},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.023977Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:50d58ce99c8b3ef2ffc7e1f1684f2b7e404ee73a8989b4bfac3015604670012c","observation_id":"a44d024a-3ad4-4df6-af19-54f5f1839d50","resolution":{"observed_at":"2026-08-06T11:11:24.143153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.119517Z","title":"Multi-Channel Causal Variational Autoencoder,","venue":null,"work_id":"461ddace-789a-4589-8912-f894f20be806","year":2024},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.201393Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:6c00f3ce2ad92e77a242ef72d895010a43e5005ab1a0edd3e8e85f4bb0bfb7da","observation_id":"184d9306-6d72-4662-a1a4-32de301b7d19","resolution":{"observed_at":"2026-08-06T11:11:24.124989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.102910Z","title":"On causally disen- tangled representations,","venue":null,"work_id":"e43658fb-4221-4a27-8063-76bcba7643f5","year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.365386Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:598e3ee29f4117f93e5e6ba924615f1e1bdd6a4e9092c195bb6b03807e481bb3","observation_id":"1d5dc35c-3dba-4e7d-a357-8374ed87c23a","resolution":{"observed_at":"2026-08-06T11:11:24.107774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.086152Z","title":"Weakly supervised disentangled generative causal representation learning,","venue":null,"work_id":"868c75e7-457e-471b-a2b1-3cec7e719a56","year":2020},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.432805Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:dd03844ba634a4f1b6fe541413d198f3d2b6991dce1214ccf0f98cab7f0c5e0b","observation_id":"8c4bd703-3c83-498d-aa8e-c36d74d10be8","resolution":{"observed_at":"2026-08-06T11:11:24.090988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.068300Z","title":"Causal- vae: Disentangled representation learning via neural structural causal models,","venue":null,"work_id":"efb0adc7-8b4a-429c-82bc-eed03333edb9","year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.438640Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:82b50690f49e97ebacdd8b2c850972e0a4c28f596c1428e5a1649af9c575ed4d","observation_id":"16dff7c4-45a7-4547-8558-dd44ab5f53b0","resolution":{"observed_at":"2026-08-06T11:11:24.073217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10638","last_updated":"2023-11-17T16:50:00Z","snapshot_observed_at":"2026-08-17T23:40:37.615785Z","submitted_at":"2023-11-17T16:50:00Z","title":"Concept-free Causal Disentanglement with Variational Graph Auto-Encoder","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.10638","snapshot_observed_at":"2026-08-06T11:11:23.443722Z","title":"Concept-free causal disentanglement with variational graph auto-encoder,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.443722Z"},"links":{"cited_paper":"/paper/2311.10638","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:fef9103c48ae4705bc4266dcec7f0c11551fa4e20d99738a29036151247acacf","observation_id":"99cde973-fd9c-4893-8929-b3448ea134b7","resolution":{"observed_at":"2026-08-06T11:11:23.443722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.049336Z","title":"Cadet: A causal dis- entanglement approach for robust trajectory prediction in autonomous driving,","venue":null,"work_id":"b32aa9ba-9b86-4d3f-b751-8a11fce6bb52","year":2024},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.449307Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:70614ed42997662c1c8b24e7f0ceda0ed5a0344f7ff8fb1bd25b4ecc116d9e06","observation_id":"65539ae0-e98a-4a17-a2e8-883bf6f9ccd0","resolution":{"observed_at":"2026-08-06T11:11:24.055073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14491","last_updated":"2022-01-31T07:20:20Z","snapshot_observed_at":"2026-07-06T11:14:04.454155Z","submitted_at":"2021-05-30T10:17:58Z","title":"How Attentive are Graph Attention Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14491","snapshot_observed_at":"2026-08-06T11:11:23.454955Z","title":"How attentive are graph attention networks?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.454955Z"},"links":{"cited_paper":"/paper/2105.14491","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:1b5b4eb47db7d8ccdbb3bf78792ccadceb5a609d33502574e23b7a1269b90fc1","observation_id":"1ff5fb6f-6ae7-48fd-a4b8-ed02b567d766","resolution":{"observed_at":"2026-08-06T11:11:23.454955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.031642Z","title":"Dueling network architectures for deep reinforcement learning,","venue":null,"work_id":"b05e0c4b-0274-4873-a4ec-7af9ed13ae3c","year":2016},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.460201Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:f997cdc734b88bcff834073185de6b8c9151786c46b005b0d6e22d3d4f254213","observation_id":"745418b9-fb2e-4bec-b0e0-0f3c861d6113","resolution":{"observed_at":"2026-08-06T11:11:24.037226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:24.014689Z","title":"Information flows in causal networks,","venue":null,"work_id":"679b0ff1-f5f9-467b-a232-cb70d1a504c1","year":2008},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.465062Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:c7cd5b94874bd7f30deb2e2b72bd8d78c934909917d873630d50beda478c566a","observation_id":"48ea7a4c-ac15-4c69-b978-e27c5cd12840","resolution":{"observed_at":"2026-08-06T11:11:24.019318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.997540Z","title":"Ci-gnn: A granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis,","venue":null,"work_id":"d99c8483-5140-4f62-9ebd-9f1121506ca7","year":2023},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.470740Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:8c690e5ba033012e4a6040bac8943e9c9d05ffb5b22086c99e3218f23b6b9229","observation_id":"40d7d295-f90f-4b86-abc3-ee65d2098275","resolution":{"observed_at":"2026-08-06T11:11:24.002812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.981344Z","title":"Orphicx: A causality-inspired latent variable model for interpreting graph neural networks,","venue":null,"work_id":"5cd17a52-4c92-43ea-abde-ba558fccd6bb","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.476259Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:ed86b05f174cd640b853b39a568d23bcd66410bce4b0ef7aa11b88ef7918cf25","observation_id":"a433f333-d392-4200-8982-b19f06862cc7","resolution":{"observed_at":"2026-08-06T11:11:23.986174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.965462Z","title":"Estimation of renyi entropy and mutual information based on generalized nearest-neighbor graphs,","venue":null,"work_id":"bea3d0cc-2167-43fa-9388-ea7cd9549c15","year":2010},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.481466Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:d4542e5744ca14c2f98e6b10d48b4fb781f51151b0372b891a5d1644d65e6f11","observation_id":"89fe2347-1f95-44d9-b411-c17b1bbe2144","resolution":{"observed_at":"2026-08-06T11:11:23.970205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.04062","last_updated":"2021-08-14T18:35:05Z","snapshot_observed_at":"2026-08-20T05:41:39.601452Z","submitted_at":"2018-01-12T05:42:58Z","title":"MINE: Mutual Information Neural Estimation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.04062","snapshot_observed_at":"2026-08-06T11:11:23.486523Z","title":"Mine: Mutual information neural estimation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.486523Z"},"links":{"cited_paper":"/paper/1801.04062","citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:d66b7df0b4019e80e681e71b54742f7f53e16c5df94b0f8285cb0fe1a6ea98d5","observation_id":"79afd849-7b65-4952-b73b-ddb33d0de4ae","resolution":{"observed_at":"2026-08-06T11:11:23.486523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.948558Z","title":"Measures of entropy from data using infinitely divisible kernels,","venue":null,"work_id":"73358fc7-c3b9-436f-b1c5-81d46f1e163f","year":2012},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.491687Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:1187ff7698f9bdac74449e81a3a5680853d6b3cc3a33b409dfe418c541e39645","observation_id":"9db99de6-cd19-4003-bccc-5b75e6f0da37","resolution":{"observed_at":"2026-08-06T11:11:23.954049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T11:11:23.496250Z","title":"An environment for autonomous driving decision-making,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.496250Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:a0010c8e22d4e6da1bc76abf07caa1f1e94597f2206637a91e73f2d377f49998","observation_id":"0daa11d8-7aac-4522-9f1b-c813cfc0408c","resolution":{"observed_at":"2026-08-06T11:11:23.496250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.921037Z","title":"Congested traffic states in empirical observations and microscopic simulations,","venue":null,"work_id":"aabf42ea-10f6-450c-bbca-944f3e9fc262","year":2000},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.502491Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:ff60313f28df176a4ef892770a70318b12896189a230de0c59485a7735162b53","observation_id":"9d09b79f-e9b5-4a9e-9e0c-22c01be5a6cd","resolution":{"observed_at":"2026-08-06T11:11:23.926393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.905628Z","title":"Reasoning graph-based reinforce- ment learning to cooperate mixed connected and autonomous traffic at unsignalized intersections,","venue":null,"work_id":"484940c4-3810-4b28-b392-b12781a23df5","year":2024},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:23.507432Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:1f946532b58555dcb9a1b65a5b10205935d073793b57d28bb11553ca65ad146d","observation_id":"0c6d37d1-7a0b-4002-b9f4-6c51b2bcbc78","resolution":{"observed_at":"2026-08-06T11:11:23.910678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.10250","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:11:23.717596Z","title":"Available: https://doi.org/10.1016/j.sysarc.2022.102505","venue":null,"work_id":"daaf9309-84a0-42d8-975a-0afe4c91ed9c","year":2022},"citing_paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T11:11:22.139542Z"},"links":{"citing_paper":"/paper/2507.23080"},"observation_digest":"sha256:e5cb8740be77bbec7997d81cb8df3bb59690f78838db022c4474d168d9438a65","observation_id":"0b1e0def-916f-4f67-a27a-c59d761fdb52","resolution":{"observed_at":"2026-08-06T11:11:23.727562Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.23080","last_updated":"2025-07-30T20:26:02Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-16T22:50:04.477788Z","submitted_at":"2025-07-30T20:26:02Z","title":"Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":5,"verified_fuzzy":27},"total_outbound_references":41},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.23080."}