{"as_of":"2026-08-21T22:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a6b201e4fd14f47f105f284b15b4e82716448326e5c7c2984f9fd6007d9350be","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:41:05.156238Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2412.02316/citation-record","integrity":"/paper/2412.02316/integrity","json":"/paper/2412.02316/citation-record.json","paper":"/paper/2412.02316"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:41:05.652401Z","title":"The New Plastics Economy: Rethinking the future of plastics,","venue":null,"work_id":"44ed6da8-8528-49c6-9d51-7d0014a78e0f","year":2016},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.019350Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:02bee780a440984a21fb995bc4fbc6c17410773c1c223bb8b9414850dbf02f63","observation_id":"ef848971-afc7-4360-a390-d3ef77df7475","resolution":{"observed_at":"2026-08-11T23:41:05.657403Z","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":"2026-08-11T23:41:05.635748Z","title":"A survey on multi-robot systems,","venue":null,"work_id":"ba5f152d-350a-4b39-8387-646302a84836","year":2012},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.024917Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:df7f1207f1bbec0a042965acd89eca04b825db6f77bc0fada7c9f917541213f1","observation_id":"596e9926-cda3-494c-b00c-3e436b0aa676","resolution":{"observed_at":"2026-08-11T23:41:05.641593Z","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":"2026-08-11T23:41:05.616549Z","title":"Cooperative heterogeneous multi- robot systems: A survey,","venue":null,"work_id":"aa9ece33-97b9-4c7d-9b5d-5f585b6dfdf6","year":2019},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.030870Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:f3ef44e9caf99e1aa148f8d193d6858d3009ea87855d72a8dbe573db0fbd4534","observation_id":"6f5b98fb-af9e-48be-b468-42d9272dff92","resolution":{"observed_at":"2026-08-11T23:41:05.622513Z","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":"2026-08-11T23:41:05.598422Z","title":"A survey and critique of multiagent deep reinforcement learning,","venue":null,"work_id":"f7e6f2ca-2759-444d-9b2a-89fad6cbd6f9","year":2019},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.036189Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:5c03b9077d8018b8f96075db5f4a835f36c791697abc1950cfa5f5204a53445c","observation_id":"2aae8f83-321b-45bd-86d9-396850e1f94e","resolution":{"observed_at":"2026-08-11T23:41:05.604757Z","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":"2026-08-11T23:41:05.579662Z","title":"Learning- based methods for adaptive informative path planning,","venue":null,"work_id":"abff9384-ca86-4537-97d0-3aeb2e9654bc","year":2024},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.041677Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:691bd714b5bad7a70a0708ef87c279bf4ece1e381a78ad724aa7dbd44e1ce33e","observation_id":"e47ab2eb-137a-4609-9d18-491da3386357","resolution":{"observed_at":"2026-08-11T23:41:05.585692Z","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":"2026-08-11T23:41:05.562896Z","title":"Aquafel-pso: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal pso and federated learning,","venue":null,"work_id":"13805ef3-6632-4631-893b-108915471698","year":2024},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.046631Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:498f75d8d935688c2fe72e7aae85fcc71f4c619c5e916aa5347cc68525ee8713","observation_id":"661cab09-5159-4aff-8ad3-16242301d405","resolution":{"observed_at":"2026-08-11T23:41:05.568370Z","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":"2026-08-11T23:41:05.547162Z","title":"Water quality online modeling using multi-objective and multi-agent bayesian optimization with region partitioning,","venue":null,"work_id":"9a7e0195-7f29-4d7d-9037-c00640be70a1","year":2023},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.051832Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:e3ff3a1521fa62d04c69e956debace4c08f8e7cd93bc2986dd2228d91dfeb99c","observation_id":"e1aea49d-0592-4287-91c9-2c74427168ab","resolution":{"observed_at":"2026-08-11T23:41:05.552211Z","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":"2026-08-11T23:41:05.531328Z","title":"Deep reinforcement learning algorithms for path planning domain in grid-like environment,","venue":null,"work_id":"33896508-7146-433d-96f6-9544cdaeb3ff","year":2021},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.056852Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:d3405a1846a797e2908db50497dbee743fa367443c58b54815b5963d6170151f","observation_id":"8b070143-376e-4456-8db1-def44ce5bd04","resolution":{"observed_at":"2026-08-11T23:41:05.536554Z","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":"2026-08-11T23:41:05.515444Z","title":"Deep reinforcement learning with dynamic graphs for adaptive informative path planning,","venue":null,"work_id":"3c3e6c2e-3ee3-4a82-8f4c-9cc70b3a9cba","year":2024},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.061621Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:317b8ce36ddf4b8da7230af5e3576514706f1411bcb6fde60fd560a682a3d54d","observation_id":"f4422bdf-475b-414f-a731-79f691917d43","resolution":{"observed_at":"2026-08-11T23:41:05.520473Z","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":"2026-08-11T23:41:05.498925Z","title":"Dynamic path planning of unknown environment based on deep reinforcement learning,","venue":null,"work_id":"8e3ed824-3b68-45b7-9310-5c8cff4741e6","year":2018},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.066192Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:ef9a63dee0eaecd85fb4ea2f8d36a57f52f928cd5a1934a2ecfa67132827a07b","observation_id":"e684d138-b0b8-4e6a-bded-981ca3ef37cf","resolution":{"observed_at":"2026-08-11T23:41:05.504194Z","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":"2026-08-11T23:41:05.483095Z","title":"Deep reinforcement multiagent learning framework for infor- mation gathering with local gaussian processes for water monitoring,","venue":null,"work_id":"16c9422e-c925-4106-b83d-e4ace8e1f72b","year":2024},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.071048Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:6cc29698aa2ef313828165677ffb674c8ef104471388a862b9641dfb63f0bc86","observation_id":"d94856fb-b8e8-4495-9b24-0078f55dab5c","resolution":{"observed_at":"2026-08-11T23:41:05.488297Z","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":"2026-08-11T23:41:05.467453Z","title":"Multi-robot path planning based on a deep reinforcement learning dqn algorithm,","venue":null,"work_id":"75b48041-db4c-4ae6-8496-7f0948dd84e8","year":2020},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.075864Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:b2424dfa43c9895cb99bf1b4b077c1db78c5ed33694a41c001a9f50a65d5559b","observation_id":"6d6e4376-79a2-408a-8881-72128fa3c992","resolution":{"observed_at":"2026-08-11T23:41:05.472358Z","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":"2026-08-11T23:41:05.451876Z","title":"Collision avoidance for an unmanned surface ve- hicle using deep reinforcement learning,","venue":null,"work_id":"f74bde6c-8354-4c5e-b0d5-e0d0ed235b21","year":2020},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.080776Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:80a421a0a5d3861d4d028d59f9f3ceeb20ddf380c465528f6fab182529202c6f","observation_id":"746cb41a-69b1-4339-bdfb-15f3089d24ef","resolution":{"observed_at":"2026-08-11T23:41:05.457140Z","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":"2026-08-11T23:41:05.434671Z","title":"Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,","venue":null,"work_id":"a9897fc1-03b0-470d-8198-ac816c438224","year":2017},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.085634Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:fcdc9affb4d9d29d7da85b9ef39acaf01873e6b9bf8725612eadc601f1eebd6b","observation_id":"0701920c-1340-4e7c-8dd1-40ab25f87236","resolution":{"observed_at":"2026-08-11T23:41:05.440256Z","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":"2026-08-11T23:41:05.416197Z","title":"Informative deep reinforcement path planning for heterogeneous au- tonomous surface vehicles in large water resources,","venue":null,"work_id":"089a9080-6128-4188-ac00-c00306143286","year":2024},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.090394Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:87d55d93bf4a19eefdd8fc0991a1461ebb7c19a944cc0243a9f8547ffaea0083","observation_id":"53e5576e-4b69-4d34-8b81-a9830b202d1d","resolution":{"observed_at":"2026-08-11T23:41:05.422319Z","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":"2026-08-11T23:41:05.398510Z","title":"Heterogeneous multi-agent deep reinforce- ment learning for traffic lights control,","venue":null,"work_id":"b2cbecc4-358e-4ce4-bb0a-98a0d2d4a527","year":2018},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.094838Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:fa02b31736a4070a174e4d5b0302a17bc070ff379f5277d9701c0486b28a7f36","observation_id":"c5bfd7d4-3a64-440c-b73b-91c0793dcf5a","resolution":{"observed_at":"2026-08-11T23:41:05.403897Z","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":"2026-08-11T23:41:05.380094Z","title":"Asymmetric self-play-enabled intelligent heterogeneous multi- robot catching system using deep multiagent reinforcement learning,","venue":null,"work_id":"4d24b2ba-6356-4e02-82b3-89937aa9bea4","year":2023},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.099339Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:479aa725d09533ccddf644e2f89b8257842b072a56d99735dde12cd10c32e46f","observation_id":"07825623-e2cc-4011-9409-d168024760d1","resolution":{"observed_at":"2026-08-11T23:41:05.386381Z","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":"2026-08-11T23:41:05.363439Z","title":"Unmanned floating waste collecting robot,","venue":null,"work_id":"0b79ec90-9f1b-4ecb-9bfb-347a8353be63","year":2019},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.105091Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:f4346309d0fbacc34e3e4f3bf1ed5bd3b72983582b0189a6f4c1ca48e125b0f0","observation_id":"b313fd77-5ba2-4fc1-b74e-6afe5c6be266","resolution":{"observed_at":"2026-08-11T23:41:05.368521Z","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":"2026-08-11T23:41:05.347322Z","title":"Development of water surface mobile garbage collector robot,","venue":null,"work_id":"36e382ec-ae99-471e-b501-5b11bb4dcd53","year":2021},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.110095Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:81ff6dffe1afc0f39bc7131a366a066b8b863a303bc52591bfa9a5c4b50007a9","observation_id":"a5d24ac2-1be5-4872-9ff4-9bb94637b261","resolution":{"observed_at":"2026-08-11T23:41:05.352364Z","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":"10.1016/j.dcan.2022.12.014","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:41:05.192380Z","title":"Automatic collaborative water surface coverage and cleaning strategy of UA V and USVs,","venue":null,"work_id":"b7a59381-00d8-4f49-bfa0-81c3c0323856","year":2022},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.115020Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:4905a1b66fd49ebab04eefea194de6da5e06628c81208fd5ea2835fc4728703b","observation_id":"2a42da2d-d9e4-4509-bbab-6828b4fab80f","resolution":{"observed_at":"2026-08-11T23:41:05.199229Z","resolver_source":"doi","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":"2026-08-11T23:41:05.331556Z","title":"Flow: A dataset and bench- mark for floating waste detection in inland waters,","venue":null,"work_id":"a9053b02-2868-4e9e-876e-978f126f5419","year":2021},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.119904Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:d07793f7b75d679714ac4ee94ddd83e32357e0cd5d7199d9e9ae3ad0a7704693","observation_id":"74310d48-6258-41b1-965a-820af949ff3d","resolution":{"observed_at":"2026-08-11T23:41:05.336570Z","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":"2026-08-11T23:41:05.315009Z","title":"Deep reinforcement learning with double q-learning,","venue":null,"work_id":"cff6caec-cae4-4617-9d51-a926f7f9c988","year":2016},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.124397Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:38b73a2bb1d04a1121fdbcd80ee03a452f1b6c45ca2c6a98b463d673e21cde56","observation_id":"c6025813-f8f4-4730-8087-ba1caefedea9","resolution":{"observed_at":"2026-08-11T23:41:05.320498Z","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":"2026-08-11T23:41:05.297603Z","title":"Bellman, Dynamic Programming","venue":null,"work_id":"5925980f-a22d-48c1-a8d5-9d65bcb0a16d","year":1957},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.129124Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:348bc55320ca052c19f4564c9ac33f221bcd6bd6cf39d870ff9c1916708ce29a","observation_id":"2bcaf1e7-25ea-40f5-b7fc-7a9368427262","resolution":{"observed_at":"2026-08-11T23:41:05.302757Z","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":"2026-08-11T23:41:05.279844Z","title":"Prioritized experience replay,","venue":null,"work_id":"1e224b86-bd96-4673-af70-1fbf93f34f26","year":2016},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.134163Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:4bc5d7feeaa120a86b7f662b8baef936e0cca615c26b7f379014fa9134f886c7","observation_id":"14845e2f-2c5f-40f1-90fd-16f5d2e94d27","resolution":{"observed_at":"2026-08-11T23:41:05.285505Z","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":"2026-08-11T23:41:05.262490Z","title":"Dueling network architectures for deep reinforcement learning,","venue":null,"work_id":"f3346349-a691-4d24-908e-cdcd0c04b9e7","year":1995},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.139512Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:cdb1ce3281e20ace3dd3b3f83583f53aaad21ff6ee7b0fbc17aed4ab75aa1760","observation_id":"e9b97b96-221e-4c3d-8051-9418e4049e5b","resolution":{"observed_at":"2026-08-11T23:41:05.268108Z","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":"2026-08-11T23:41:05.246291Z","title":"Reward (mis)design for autonomous driving,","venue":null,"work_id":"36331fee-6a8e-49a8-824f-f15b6bec6c8f","year":2023},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.144907Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:41e5bc1480c78f834fa734b2f89782f4f2670e42dd0fc0eed453b40ea1a36fb5","observation_id":"1dfd2af1-e2cc-4e16-bd54-4f9701fc44fb","resolution":{"observed_at":"2026-08-11T23:41:05.251295Z","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":"2205.15400","last_updated":"2022-05-30T19:48:52Z","snapshot_observed_at":"2026-08-20T08:34:22.127282Z","submitted_at":"2022-05-30T19:48:52Z","title":"Designing Rewards for Fast Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.15400","snapshot_observed_at":"2026-08-11T23:41:05.150853Z","title":"Designing rewards for fast learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.150853Z"},"links":{"cited_paper":"/paper/2205.15400","citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:7f1675a66c197e2404ceeb7a8acfc1b051bf02530ddda91da4c6b89d4251003e","observation_id":"acdb7612-10c1-4f0b-b002-a87c6acd77e5","resolution":{"observed_at":"2026-08-11T23:41:05.150853Z","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":"2026-08-11T23:41:05.230142Z","title":"Greed is good: Near-optimal submodular maximization via greedy optimization,","venue":null,"work_id":"9fa912b3-72b8-4afc-ae96-9def7a3b4aa5","year":2017},"citing_paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:05.156238Z"},"links":{"citing_paper":"/paper/2412.02316"},"observation_digest":"sha256:42b788113ce807a3af681ad58927f8f94d50e7b076026bbf6b1adfc8903b0cf9","observation_id":"ac82a55e-0760-4ffd-8d15-3f544ebad055","resolution":{"observed_at":"2026-08-11T23:41:05.235397Z","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"}}],"paper":{"arxiv_id":"2412.02316","last_updated":"2024-12-03T09:32:02Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-19T18:19:25.072232Z","submitted_at":"2024-12-03T09:32:02Z","title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+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 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.02316."}