{"as_of":"2026-08-09T15:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c1e158f696fa28f5be5a3f5a95ffff08919fad401893ce0f994a293ea47f4ae3","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:03:05.612017Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T04:53:10.425398Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T04:55:54.390079Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2502.05537","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.05537","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sequential stochastic combinatorial optimization using hierarchal reinforcement learning","venue":null,"work_id":"d99f7839-90cf-4cc4-857d-6f672e422e0c","year":2025},"citing_paper":{"arxiv_id":"2510.19544","last_updated":"2026-04-19T17:25:37Z","snapshot_observed_at":"2026-07-06T22:33:48.680749Z","submitted_at":"2025-10-22T12:50:27Z","title":"Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-18T04:53:10.425398Z"},"links":{"cited_paper":"/paper/2502.05537","citing_paper":"/paper/2510.19544"},"observation_digest":"sha256:f1346aeec012ebe9dea8d632dbfd4b2ea070f89ca4a5465d61454c25d3f6e3da","observation_id":"2a5b9ce3-0b75-478c-b9e1-ec527baf48a4","resolution":{"observed_at":"2026-05-18T04:55:54.392627Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.05537/citation-record","integrity":"/paper/2502.05537/integrity","json":"/paper/2502.05537/citation-record.json","paper":"/paper/2502.05537"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:03:06.536756Z","title":"Hindsight experience replay","venue":null,"work_id":"e4da1e98-be53-4848-8d64-dfffcf174f87","year":2017},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.324922Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:44aa9ebaa130c092d18e9b40dc0fd519c66a5ba291e99b6ed6aea61adf776277","observation_id":"c6c77021-e3ee-4112-b584-715da3499ddf","resolution":{"observed_at":"2026-08-08T19:03:06.542013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.329854Z","title":"Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.329854Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:4673aef55ba764edcc5568a956817dac0f58af706aeb63e7029b5c9b0d4e276c","observation_id":"80bc2e2b-6d14-497d-bdfc-a089e43c1f27","resolution":{"observed_at":"2026-08-08T19:03:05.329854Z","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":"2026-08-08T19:03:06.510848Z","title":"The option-critic architecture","venue":null,"work_id":"2dadf7df-f508-4d48-bc05-a3b4b483ada2","year":2017},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.335054Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:0dd7ae250d4d2ffb40b98d7892016da110632ff2dcf2cb3b481a434ea05c9f14","observation_id":"a3983750-a7ef-4efb-baee-21a3f9c5dd32","resolution":{"observed_at":"2026-08-08T19:03:06.515774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.09940","last_updated":"2017-01-12T23:55:36Z","snapshot_observed_at":"2026-07-06T05:20:39.825511Z","submitted_at":"2016-11-29T23:22:39Z","title":"Neural Combinatorial Optimization with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.09940","snapshot_observed_at":"2026-08-08T19:03:05.340426Z","title":"Neural combina- torial optimization with reinforcement learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.340426Z"},"links":{"cited_paper":"/paper/1611.09940","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:0793eddbc3e0f4c80506d4979f3e35867229cb9d2c4f5960d9c884b3502069fe","observation_id":"1a98aa2a-c9a5-4ac4-81ab-4cc5cad97071","resolution":{"observed_at":"2026-08-08T19:03:05.340426Z","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":"2026-08-08T19:03:06.494445Z","title":"Machine learning for combinatorial optimization: A methodological tour d’horizon","venue":null,"work_id":"58680cd9-bdb0-44f8-bc30-6a207c1c6dd9","year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.345801Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:215646c4c7cf60caa2c678259c6ece2344ce86a01b877095e2b35f0f936892da","observation_id":"346bc664-227d-4ce3-a8de-a8a07546b3b2","resolution":{"observed_at":"2026-08-08T19:03:06.499738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.17100","last_updated":"2025-07-21T08:23:56Z","snapshot_observed_at":"2026-07-06T15:48:25.059865Z","submitted_at":"2023-06-29T16:57:22Z","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.17100","snapshot_observed_at":"2026-08-08T19:03:05.351089Z","title":"Rl4co: an extensive reinforcement learning for combina- torial optimization benchmark","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.351089Z"},"links":{"cited_paper":"/paper/2306.17100","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:bc590cc893d3401d4a4d3c4637f9678d4cf57cb38695a670a044af830a99ad5f","observation_id":"fc86a519-a3bf-44ae-8b0b-2c73b28e54d1","resolution":{"observed_at":"2026-08-08T19:03:05.351089Z","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":"2026-08-08T19:03:06.478620Z","title":"Models and algorithms for combinatorial optimization problems arising in railway applications","venue":null,"work_id":"aff63873-e309-4bbf-b363-a1714188c9e2","year":2009},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.356856Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:48e993eca854ecc57550e4955c5d1a121cc779845aaa14f8ae6f7dd083809bb7","observation_id":"fb027c3d-feae-4b3b-9fa6-fc9845017877","resolution":{"observed_at":"2026-08-08T19:03:06.483727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.464134Z","title":"Applying gis and combinatorial optimization to fiber deployment plans","venue":null,"work_id":"36364fb7-13a6-47e8-a79f-2630778eab30","year":1999},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.361449Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:6b577697e2d0ec50ff2d6ae6b5ac4023bf49795f9ae54b6a867ebcbbc19e6f93","observation_id":"ee40282e-4239-4fe5-9e56-a68597b703dc","resolution":{"observed_at":"2026-08-08T19:03:06.468790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.449527Z","title":"Improving optimization bounds using machine learning: Decision diagrams meet deep reinforcement learning","venue":null,"work_id":"2ffffb07-b2be-4aeb-a6f5-6b3f04859c25","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.366486Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:f43ec4f13c1e126ee27c35b03d23a5aa0681618f93cabc9b77932b463e3eab6b","observation_id":"521f4a51-8597-44ec-81a6-908180e21947","resolution":{"observed_at":"2026-08-08T19:03:06.454089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.435114Z","title":"Contingency-aware influence maximization: A reinforcement learning approach","venue":null,"work_id":"0a78150c-bd09-4c9d-82e1-b336da5377f0","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.371644Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:57df9a5e31fbaf1f49cb0c1e3df2dbc436cdd3819706e77eddbae812f6357608","observation_id":"3954d364-95aa-4a94-964d-af507c0c7b21","resolution":{"observed_at":"2026-08-08T19:03:06.439691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.419549Z","title":"Learning to perform local rewriting for combinatorial optimization","venue":null,"work_id":"5ae396e7-6602-44a8-8f3b-c2a270017f98","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.376450Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:e1d3403baf4f0ec2ea7702c3cb89949a9680434423761621930c85152d4b96ac","observation_id":"6be00938-74af-433d-9b9f-c98995bc0d09","resolution":{"observed_at":"2026-08-08T19:03:06.424938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.404146Z","title":"Discriminative embeddings of latent variable models for structured data","venue":null,"work_id":"bdbba4e8-cb31-4911-b7b3-f0f5e36e0190","year":2016},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.381049Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:61c0d33c5401abe913d288d330cdde20391294b4ed658e6ac241612d3572fd7b","observation_id":"405cfe16-e1c0-4ae7-a095-5ad040e51962","resolution":{"observed_at":"2026-08-08T19:03:06.409130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.389015Z","title":"Feudal reinforcement learning","venue":null,"work_id":"26c8c988-aec8-4366-b7e8-6f6219146988","year":1992},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.385410Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:39930ddb5f2ca472bff7724a44fddfd6a617e06e1a40803631423f881840a1e4","observation_id":"41e48dd5-391d-42c7-af87-dc38750570eb","resolution":{"observed_at":"2026-08-08T19:03:06.394011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.372582Z","title":"Learning heuristics for the tsp by policy gradient","venue":null,"work_id":"e1c49930-faae-4ff9-bfab-c0fb6b8424a6","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.389827Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:2eb89cf08bf582f6533b96d9ce4a852ad33e4e770e92b9d3d2f8d765bef3498f","observation_id":"44c5ad8f-c132-4ef7-a2f7-b547bc87892f","resolution":{"observed_at":"2026-08-08T19:03:06.377570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.07010","last_updated":"2018-05-18T01:10:09Z","snapshot_observed_at":"2026-07-06T06:39:50.447759Z","submitted_at":"2018-05-18T01:10:09Z","title":"Learning Permutations with Sinkhorn Policy Gradient","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.07010","snapshot_observed_at":"2026-08-08T19:03:05.394095Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.394095Z"},"links":{"cited_paper":"/paper/1805.07010","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:102b981cfc048c1df7edc378bd17ca09d1ba47e340d4409de9c5bc7c254159a7","observation_id":"6fe79b74-02fa-480e-89a8-c4f60958210f","resolution":{"observed_at":"2026-08-08T19:03:05.394095Z","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":"2026-08-08T19:03:06.357420Z","title":"Generalize a small pre-trained model to arbitrarily large tsp instances","venue":null,"work_id":"14833f1d-7eac-4474-a595-b59322c41265","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.399133Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:78deeec4662ca26667d6f221d71f514f1056eb43a882617c8e1a402f68e2f4e1","observation_id":"74691e8f-3ad9-44be-a04c-ad897a778d18","resolution":{"observed_at":"2026-08-08T19:03:06.361992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00016","last_updated":"2024-02-28T02:15:47Z","snapshot_observed_at":"2026-07-06T17:37:39.610989Z","submitted_at":"2024-02-28T02:15:47Z","title":"Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization","version":1},"cited_work":{"arxiv_id":"2403.00016","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.00016","snapshot_observed_at":"2026-08-08T19:03:05.702657Z","title":"Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization","venue":"cs.LG","work_id":"f3295dec-742f-4b96-960b-86997b5f25d4","year":2024},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.403352Z"},"links":{"cited_paper":"/paper/2403.00016","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:3f93d61acc1e9810f5588f45eec8c63fa10b64829fabd5f0fc38f3b779813919","observation_id":"aa9ad8b4-5f2d-45dc-88e7-2bfdba7dc047","resolution":{"observed_at":"2026-08-08T19:03:05.709732Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.342138Z","title":"node2vec: Scalable feature learning for networks","venue":null,"work_id":"4fdab9cc-7ede-48a0-b7d5-ca08692f62fb","year":2016},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.407984Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:141615e36f8a6b4507166707b169dc43912afa567a828eeb9206fd6315b26a73","observation_id":"bbf9f8ec-f50d-4c7f-94d2-e164ca2ec384","resolution":{"observed_at":"2026-08-08T19:03:06.346542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.327981Z","title":"Double q-learning","venue":null,"work_id":"3ce6398e-9271-4c65-aab9-23d7dc2a807e","year":2010},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.412203Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:852ce9879793eac10d38472d65a020295e2695c6d699530f3a1ba00c7c4c1494","observation_id":"92a868bb-bc63-406c-80e1-5ec618317c80","resolution":{"observed_at":"2026-08-08T19:03:06.332009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.313410Z","title":"Efficient active search for combinatorial optimization problems","venue":null,"work_id":"eadc7680-35e2-4e91-ba1f-5bb31485e928","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.417071Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:1736f942973ede89b7bf22e4620addad841f180e66e0d7d282ae77ae0fca5393","observation_id":"e64f3668-ab28-4724-a84d-f348a2ae5b93","resolution":{"observed_at":"2026-08-08T19:03:06.317716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.297446Z","title":"Convergence of stochastic iterative dynamic programming algorithms","venue":null,"work_id":"fe5d21d4-e0d4-409f-a524-6c8ff4b4614d","year":1993},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.422052Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:f3c9bcc656f848ae740b593affe662c9580e90ab15a03ffd62595f7f5a6e4fe5","observation_id":"40361bc2-72a8-449d-8805-1839b7c52e35","resolution":{"observed_at":"2026-08-08T19:03:06.302762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.280456Z","title":"Deep reinforcement learning approach to solve dynamic vehi- cle routing problem with stochastic customers","venue":null,"work_id":"b893144c-3108-4cc6-917a-cbe25171d8fb","year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.427074Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:ac3150b0cf0575df86cfe8beb681c6d445fd0430833ae4d0a6287a7ba0422a04","observation_id":"0a24db4d-80a0-4aab-965b-bac1f1209523","resolution":{"observed_at":"2026-08-08T19:03:06.285983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.263348Z","title":"Maximizing the spread of influence through a social network","venue":null,"work_id":"dd64c4d0-4b6a-4942-9461-52d148afe822","year":2003},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.432052Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:ea807e599296364cc34ee70a8112eed5cf01d4a757ae0328bcd3f2d4e14fcf52","observation_id":"9a21f23e-ea64-4fd5-b9ad-2ed62956359d","resolution":{"observed_at":"2026-08-08T19:03:06.268148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.246499Z","title":"Learning combinatorial optimization algorithms over graphs","venue":null,"work_id":"1a44f9fa-33a6-47e4-a3bc-9f5bb9cd7980","year":2017},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.436959Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:985529239b18bd0999ba063facd53ba1ab33325e436f4049b9171a4c304f827e","observation_id":"2785ac3a-62da-4da8-b57b-e8fa6f42d1f1","resolution":{"observed_at":"2026-08-08T19:03:06.252054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.231064Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":"300b4211-41e1-46ae-a1b5-592d260936db","year":2016},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.441860Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:79c2cf6c846096d65c3a79828473178cceb47d6eba18cb30f462daa8740bfef5","observation_id":"a2bb0535-2d7a-4016-9f72-c87ce1aecf1d","resolution":{"observed_at":"2026-08-08T19:03:06.235822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.214962Z","title":"Attention, learn to solve routing problems! In International Conference on Learning Representations, 2018","venue":null,"work_id":"cae13b91-06e5-47fc-a23b-a77f0fb68feb","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.447001Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:a548e963d0991f0c3b21efa373dbbad059cffb7dc69272e22ac61d014cb01aaa","observation_id":"1b5389a6-f425-482d-9f06-05742a76a4ca","resolution":{"observed_at":"2026-08-08T19:03:06.219988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.198505Z","title":"Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation","venue":null,"work_id":"baa40b44-3b9f-4ee8-8291-76509efc777a","year":2016},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.451936Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:d58668e63b003724c22da67550de062c77e6fbe05cd3397101b3390ffba3144e","observation_id":"4d7c55bb-7df6-4aab-82dc-e25f51a54107","resolution":{"observed_at":"2026-08-08T19:03:06.203778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.457088Z","title":"Pomo: Policy optimization with multiple optima for reinforcement learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.457088Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:8960fd19c777c33fca3733f421a6bf020154a8094149602893f0bf378456f0c5","observation_id":"2cef4d40-8a12-49c3-9d15-bc00610ca9e1","resolution":{"observed_at":"2026-08-08T19:03:05.457088Z","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":"2026-08-08T19:03:06.170863Z","title":"Mind dataset for diet planning and dietary healthcare with machine learning: dataset creation using combinatorial optimization and controllable generation with domain experts","venue":null,"work_id":"388c8262-c408-4968-8bf5-d1bbf1e7e004","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.461851Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:5e34cb99b5087d301c64b86f20db936765c13fdca23fcde3060b3ccbb7365c59","observation_id":"80205747-990b-40b6-84b7-8ba9815b5e4e","resolution":{"observed_at":"2026-08-08T19:03:06.176470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.466597Z","title":"Learning multi-level hierar- chies with hindsight","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.466597Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:29002d2e394bd865a3a7edc743d71a016b337e315c2929d5d8d7e6b71d0266c7","observation_id":"a2ac4060-43e0-48b6-8bd0-2fb646b53ad6","resolution":{"observed_at":"2026-08-08T19:03:05.466597Z","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":"2026-08-08T19:03:06.144320Z","title":"Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning","venue":null,"work_id":"5edc1b73-5b95-40ff-95f7-e3884e201671","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.471258Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:a7887fce768e619af916b2e8ad178f35bc62ca341c622467dc54afda040e765f","observation_id":"eec2e7cf-b65b-412b-a3d1-67f2ee418624","resolution":{"observed_at":"2026-08-08T19:03:06.149253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.129849Z","title":"Deep-learning-based wireless resource allocation with application to vehicular networks","venue":null,"work_id":"a19910b7-6858-4847-bfd5-3a11dfe99c89","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.475492Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:b7c2f6b076ba750882ee175d1e4f346a98b843e8b007a47fecb0287dab3bc9f8","observation_id":"53b0f20a-d133-472e-95af-d7c7763a832c","resolution":{"observed_at":"2026-08-08T19:03:06.134149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11829","last_updated":"2025-03-10T16:14:30Z","snapshot_observed_at":"2026-07-06T16:34:54.717394Z","submitted_at":"2023-10-18T09:31:21Z","title":"Graph Foundation Models: Concepts, Opportunities and Challenges","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.11829","snapshot_observed_at":"2026-08-08T19:03:05.479770Z","title":"Towards graph foundation models: A survey and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.479770Z"},"links":{"cited_paper":"/paper/2310.11829","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:b8e9294196eeddbb8ad379b0e109a5b11e96d2ce76d117ad12bd48bba2ae34d0","observation_id":"bb161e73-031d-4ca3-97ce-e66c00828c30","resolution":{"observed_at":"2026-08-08T19:03:05.479770Z","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":"2026-08-08T19:03:06.115723Z","title":"A learning-based iterative method for solving vehicle routing problems","venue":null,"work_id":"c09a7ce5-4993-4c3d-8b2c-85849f8e3f98","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.484808Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:13fb02efff326016ebfe198f3e5e5131b2c6de0e41b6c84487f5a96c001ccaed","observation_id":"30db095e-8246-41c7-bd14-7d30d5f6072c","resolution":{"observed_at":"2026-08-08T19:03:06.119961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.101518Z","title":"Reinforcement learning for combinatorial optimization: A survey","venue":null,"work_id":"cd7b0e00-183c-451f-bfa9-748bb4e7d6e9","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.488997Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:bf1c150c99d304115eba611605bf84c4476d7898c1a9f9ab2e33abed3f5bbae1","observation_id":"9a4472fc-995c-4090-96df-e06f811c33ba","resolution":{"observed_at":"2026-08-08T19:03:06.105831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-07-06T03:31:23.521122Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-08T19:03:05.494362Z","title":"Playing atari with deep reinforcement learning","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.494362Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:85c676248e486574f55d2153fe1d2d53c8bf54a1744d516a9beb00c552ee0447","observation_id":"967d589c-0932-4ad7-811d-7b08b4d7210d","resolution":{"observed_at":"2026-08-08T19:03:05.494362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:03:05.499797Z","title":"Human-level control through deep reinforcement learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.499797Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:690ebf29e9ef472bbadd55c00401b148836d7ffb8cfe3e263c48ab3e51a71999","observation_id":"207edfba-cd35-45a7-9863-a94c18bb49a8","resolution":{"observed_at":"2026-08-08T19:03:05.499797Z","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":"2026-08-08T19:03:06.076264Z","title":"Data-efficient hierarchical reinforcement learning","venue":null,"work_id":"76ecec98-9c9a-427e-8877-86564c9af4ae","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.504359Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:39c4d4437ab9a1d844d63c59ab60836555daddf341f0d8417b4b75adfeac4366","observation_id":"cf0b18ab-bd0d-429a-9660-4e91e7b6b0d7","resolution":{"observed_at":"2026-08-08T19:03:06.081050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.060143Z","title":"Reinforcement learning for solving the vehicle routing problem","venue":null,"work_id":"33c4cb39-9ca8-4eb8-9c8f-d889610347fa","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.509865Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:acc893b36f489d21c519dc113f8ef5adb0c2227e8b743817fe87645fa4c26753","observation_id":"2ff9a79b-80bf-4460-8447-9033028d33bb","resolution":{"observed_at":"2026-08-08T19:03:06.065490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.043861Z","title":"Solo: search online, learn offline for combinatorial optimization problems","venue":null,"work_id":"ab9d140a-fa94-4a31-901c-c4f195454f44","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.514510Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:7397a868e0e49dc225962a5bd66221d1436e367773cef52f16ed8c0fc8048583","observation_id":"246a2440-7e7b-43bc-af6b-ae762afb04a0","resolution":{"observed_at":"2026-08-08T19:03:06.048989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.028072Z","title":"Active screening for recurrent diseases: A reinforcement learning approach","venue":null,"work_id":"8b0ffca6-4a3a-462f-ba45-869dec3d95ec","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.526265Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:7b2ce2d8bb7259b9551fe32e387c968b17617431c80f2f08f61f3a19588836b8","observation_id":"83b47402-5b46-4d48-ad9d-f6127206dc96","resolution":{"observed_at":"2026-08-08T19:03:06.033330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:06.012695Z","title":"Adaptive influence maximization with myopic feedback.Advances in Neural Information Processing Systems, 32, 2019","venue":null,"work_id":"31e322c9-bfd3-4eff-bc8c-0ce16d403173","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.531358Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:f131400ce98b3708d5b0659bcde4abfe3454acb246c3cb2b65d6ab6860cf71ae","observation_id":"8b6c6c81-7f71-49d5-af77-7c80f94e8b0f","resolution":{"observed_at":"2026-08-08T19:03:06.017390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.997593Z","title":"Deepwalk: Online learning of social repre- sentations","venue":null,"work_id":"fdde3aaf-246d-4e8a-a13d-1a4205dd3545","year":2014},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.536450Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:14601dbba00fa721e0403ab5886f4872a348c4add27f6dbe927bedfa60d47278","observation_id":"828aafdf-ef8f-4fef-89bf-07e24994a913","resolution":{"observed_at":"2026-08-08T19:03:06.002672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.981749Z","title":"Scarselli, M","venue":null,"work_id":"b3281f22-89a2-4bc1-8cd8-4f2cf03a5f1f","year":2009},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.542776Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:988b88913cb1d084ced458361a87fca2380cf4a6a56e0ce51eb5c311c6eb321d","observation_id":"9a8c371d-4573-41fe-abc6-965b6d67feb0","resolution":{"observed_at":"2026-08-08T19:03:05.986934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.548651Z","title":"Combinatorial optimization with physics-inspired graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.548651Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:3312877800c8c21e3f523b9286866c1b5e7aa3f3de01d8c5ae7b24ddf0b2def4","observation_id":"3b84c475-f100-4b82-aca3-724f22386111","resolution":{"observed_at":"2026-08-08T19:03:05.548651Z","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":"2026-08-08T19:03:05.954325Z","title":"Learning to predict by the methods of temporal differences","venue":null,"work_id":"e2f65601-9e7b-45b1-905d-1acc440e6c63","year":1988},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.553555Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:33bd63e417f72f3674de2fa3b254b568ea593f392474952d9573339081a9e4f3","observation_id":"d58d75d6-008c-4983-9f35-cf0c142f8d09","resolution":{"observed_at":"2026-08-08T19:03:05.958959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.938419Z","title":"Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning","venue":null,"work_id":"61927259-7112-4d2d-9aea-429acc58ac3c","year":1999},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.558800Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:8ce0fd9182ba56248966cf6214e8437eb9492b854502453307313541c0c4460d","observation_id":"87d164f2-5c43-4c6c-854f-37a091e712aa","resolution":{"observed_at":"2026-08-08T19:03:05.943619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.921407Z","title":"Time-constrained adaptive influence maximization","venue":null,"work_id":"7047245e-a1c0-4fc6-99ad-48dd346bfcd9","year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.562939Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:8ac2958a1948bedd66c1135db1ed2b4c237c36cce5537d4b24fefade27217550","observation_id":"f83cec7f-51d8-46ce-bb53-7d15a57cd815","resolution":{"observed_at":"2026-08-08T19:03:05.926788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.903496Z","title":"Graph attention networks","venue":null,"work_id":"fa4753f5-7f36-4e97-9c21-f5a67efae3d4","year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.567015Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:f00335778ac5ffff0798a98f8d2bdceef996836ae2c93305193242c796e24d5b","observation_id":"279574b4-c7a4-4f92-85b6-13b4b0a75da0","resolution":{"observed_at":"2026-08-08T19:03:05.908402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.884726Z","title":"Feudal networks for hierarchical reinforcement learning","venue":null,"work_id":"6490a1aa-db20-49fc-b6b9-5e70cfa1c82e","year":2017},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.571246Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:33d830c11f356c273a5cc289dbc3c3701c5d3611f30897b9db2f7502a6f679b1","observation_id":"d64edccd-f62f-4dcc-be02-b3ef8e8cd0e6","resolution":{"observed_at":"2026-08-08T19:03:05.890658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.868690Z","title":"Q-learning","venue":null,"work_id":"48138f66-a506-4a7e-b71b-31a0d5ca26d1","year":1992},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.576211Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:41d905ad6cb2f1008f20ddb7d11bc39a4162a3f76b8d058b59383a0e6b8685da","observation_id":"761b4a4b-41cc-4472-836b-2eae7c724760","resolution":{"observed_at":"2026-08-08T19:03:05.873513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.851190Z","title":"On efficiency in hierarchical reinforcement learning","venue":null,"work_id":"e2c13e3c-9d05-4706-baa7-2e19854d6c8f","year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.581755Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:50ad5f03d3288b26060da6a3683b25410f31656f385f331d965f767edb8a1232","observation_id":"94f9e66f-b35b-41b9-8587-bfd7d718a5b7","resolution":{"observed_at":"2026-08-08T19:03:05.857250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.833274Z","title":null,"venue":null,"work_id":"817a3113-8293-4de5-8437-77756c445be1","year":2021},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.587774Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:eec7c1b60fcc293a78fa5800fd61c5e1a6a2612db5b9517cc0552263d111b9a8","observation_id":"dcb0eca0-bb02-48f1-8da4-797527bdb221","resolution":{"observed_at":"2026-08-08T19:03:05.838649Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-07-06T07:05:24.565760Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-08T19:03:05.592590Z","title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.592590Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:67a89e86378b0641d6f61be9503e058db276be03bcce306ddbf365532ae7f326","observation_id":"49ba5402-4cf3-4dd3-8839-9d705c867b04","resolution":{"observed_at":"2026-08-08T19:03:05.592590Z","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":"2026-08-08T19:03:05.816271Z","title":"Accelerating Exact Combinatorial Optimization via RL-based Ini- tialization - A Case Study in Scheduling","venue":null,"work_id":"f3ed94a3-d5a3-4504-a0f6-1c540ba8fa11","year":2023},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.597802Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:93fcc9354904e17263977a95e7d3e9d3ea968b0498260232985850f3084c1e8b","observation_id":"23ad6750-cbdc-4758-b9ac-cac85fe42033","resolution":{"observed_at":"2026-08-08T19:03:05.821556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.799742Z","title":"Gnnexplainer: Generating explanations for graph neural networks","venue":null,"work_id":"92423284-c3fb-41c8-a054-e871a40879aa","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.602875Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:d65f1b0f91cfdd4f28e5a738fca2d136210de22fde9a7299af83e0142a909f1e","observation_id":"e364317d-dd16-435b-b6a0-fb87109b78ec","resolution":{"observed_at":"2026-08-08T19:03:05.805142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.784173Z","title":"Graph transformer networks","venue":null,"work_id":"9f9c04f9-1690-495c-a21b-0d43956404a1","year":2019},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.607378Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:ea11a39b25187bfe6eb5149191755990c3109461159b144ecf1181138f91ace8","observation_id":"d38485cb-03f0-466e-b2e2-d4abaa383722","resolution":{"observed_at":"2026-08-08T19:03:05.788657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:03:05.769419Z","title":"Graph neural networks: A review of methods and applications","venue":null,"work_id":"65ff13cd-4f66-4455-aa69-c539ef883034","year":2020},"citing_paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-08T19:03:05.612017Z"},"links":{"citing_paper":"/paper/2502.05537"},"observation_digest":"sha256:6408ed512af773efba93fad65435c2985da37b15a61475b87f89375b26bc672b","observation_id":"b8b130ab-4d76-4cf8-bdee-1637f0c75ed7","resolution":{"observed_at":"2026-08-08T19:03:05.773978Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.05537","last_updated":"2025-02-08T12:00:30Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T13:04:16.332693Z","submitted_at":"2025-02-08T12:00:30Z","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":44},"total_outbound_references":58},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2502.05537."}