{"as_of":"2026-08-22T05:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:494e88cb63cf500be95fbbe942d92c029a3c8e60363aa73e0dfd58b71a910509","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:51:01.284638Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"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/2412.08565/citation-record","integrity":"/paper/2412.08565/integrity","json":"/paper/2412.08565/citation-record.json","paper":"/paper/2412.08565"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:51:01.105277Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.105277Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:5e847850d007da13164a8c3ce1adbcf92b9f01759957dd5255c6d046dac310bc","observation_id":"6cddaacd-779c-4905-8edf-5d6a90f0d760","resolution":{"observed_at":"2026-08-11T17:51:01.105277Z","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":"2026-08-11T17:51:01.111316Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.111316Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:de6b2f0e105b46a589a72025dd9fdbb284fd87aede8ccd2c13d17e58de377abc","observation_id":"984e48e5-e91e-4e31-a13a-d225adf08445","resolution":{"observed_at":"2026-08-11T17:51:01.111316Z","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":"2026-08-11T17:51:01.829477Z","title":null,"venue":null,"work_id":"0e6a82a4-50d7-4dad-8133-30eb144d741b","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.117675Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:c121fae4e09f964f4b1c3108507e8e5e11be60ba63b916d6feeec120077648c7","observation_id":"d9423a47-8dad-4945-87ed-21d0fc78fd6a","resolution":{"observed_at":"2026-08-11T17:51:01.834578Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2402.04997","last_updated":"2024-06-05T20:31:17Z","snapshot_observed_at":"2026-08-18T16:01:16.556024Z","submitted_at":"2024-02-07T16:15:36Z","title":"Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04997","snapshot_observed_at":"2026-08-11T17:51:01.125389Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.125389Z"},"links":{"cited_paper":"/paper/2402.04997","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:be5f4620aa382541b80f34a8d8e6453fe7cfcc338c46beaa00332226e5904686","observation_id":"030912b2-97ef-4b1a-8afe-b47c236c8ec8","resolution":{"observed_at":"2026-08-11T17:51:01.125389Z","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":"2026-08-11T17:51:01.812913Z","title":"B.; and Vela, P","venue":null,"work_id":"6b53eaf6-6787-499f-af47-107fe85407d0","year":2023},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.131335Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:ab646e00126ebf355e599ad5c8a23c3088694585d1e0104829707494494c2ce3","observation_id":"c09106f6-fdfe-4ae6-83f7-0b137e41d94b","resolution":{"observed_at":"2026-08-11T17:51:01.817821Z","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":"2026-08-11T17:51:01.794323Z","title":null,"venue":null,"work_id":"eaf46101-9b81-42af-8f39-0f83d5ed0de1","year":2021},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.136641Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:eb29eba29ebf10b5fb0480e6e6b1b07e3b36c154cd51adba8180379242c7a9b8","observation_id":"59ba18a2-8b6d-48bd-a208-e809a2962796","resolution":{"observed_at":"2026-08-11T17:51:01.800112Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:51:01.141926Z","title":"H.; and Bengio, Y","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.141926Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:b15d524e5755cb8087fa3ab97a1a073e8747b196d0e94ecc0164211650d9ffe8","observation_id":"5864aba3-2e64-470d-b065-7aa2c6ac02f3","resolution":{"observed_at":"2026-08-11T17:51:01.141926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.04137","last_updated":"2024-03-14T04:36:31Z","snapshot_observed_at":"2026-08-08T17:26:38.829859Z","submitted_at":"2023-03-07T18:50:03Z","title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.04137","snapshot_observed_at":"2026-08-11T17:51:01.146929Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.146929Z"},"links":{"cited_paper":"/paper/2303.04137","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:98893c26688af8da6bc74a9a87f26271f4a5e81c303fc04872d59b8883adf856","observation_id":"335bddcd-16c8-4adb-aa25-3dd1eea70858","resolution":{"observed_at":"2026-08-11T17:51:01.146929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-11T17:51:01.152902Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.152902Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:a51cda95047b5bda4353526eceeb2fcba7bea570b82bc13234e8a5f3b3c6dea4","observation_id":"313833ea-707b-46f7-8395-3935da4aefac","resolution":{"observed_at":"2026-08-11T17:51:01.152902Z","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":"2026-08-11T17:51:01.766005Z","title":null,"venue":null,"work_id":"38139421-8416-479a-bdfd-eb4417847863","year":2024},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.157867Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:306a38643d92dc8fdb174fd0709de2e70756bbc3960818d4bf41ba4e4ffa66a4","observation_id":"60191ffa-75ce-4cf9-ae2b-e11319fbfa75","resolution":{"observed_at":"2026-08-11T17:51:01.771645Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-11T17:51:01.749213Z","title":null,"venue":null,"work_id":"390257ea-bc0e-46a3-9873-8603a9bb5e9a","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.162821Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:447cb9bce63ad024e7e8cf9b22f2dce4f82f1bff8a8066e23801f3aef476648c","observation_id":"d759b893-d01a-4ad0-bbcf-d9e136c48534","resolution":{"observed_at":"2026-08-11T17:51:01.754586Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-11T17:51:01.732509Z","title":null,"venue":null,"work_id":"9fe20fc9-2b1a-4bb1-929c-1b1fae38226b","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.168677Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:e01623602e57da26b3f166b821b1cb167b032292d4aafa31b02fecdf6a69b34e","observation_id":"c27cb086-97ec-4c46-938a-56b9e3548657","resolution":{"observed_at":"2026-08-11T17:51:01.737796Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-11T17:51:01.715071Z","title":null,"venue":null,"work_id":"28e70ee8-777e-477a-9897-24ac8d8cd7fe","year":2021},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.173962Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:eac84c39325a4916d12179e8a9f53ec7321c1e48a256e5557a9380a03a933041","observation_id":"6a7c0059-a4d2-48df-ae67-21c73453a45e","resolution":{"observed_at":"2026-08-11T17:51:01.720415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:51:01.179272Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.179272Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:5b3282035093b55fe5fb96285ba4cbf785322b027f592ef3fa31471efdb20a61","observation_id":"cb819eda-54b3-42ee-8521-12ea4848d58c","resolution":{"observed_at":"2026-08-11T17:51:01.179272Z","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":"2026-08-11T17:51:01.684095Z","title":null,"venue":null,"work_id":"d7cb3a03-98ba-4716-ab24-ebb86beec3c8","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.184099Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:c7dd92a11f5da769e35000924858c0e33d39d912eed8a9f7cb95b933e6a8c655","observation_id":"ac9b9cec-f2f6-4f4a-87ac-538c553396b1","resolution":{"observed_at":"2026-08-11T17:51:01.689700Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2106.02736","last_updated":"2022-03-15T07:11:00Z","snapshot_observed_at":"2026-08-16T18:19:31.823480Z","submitted_at":"2021-06-04T22:04:30Z","title":"Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.02736","snapshot_observed_at":"2026-08-11T17:51:01.189310Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.189310Z"},"links":{"cited_paper":"/paper/2106.02736","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:e662b0a192aba6a4defb545245a18fb554f5ef87675a068966c5ec984c84ffb7","observation_id":"59ad7f52-848d-400c-87bc-a75173319e6d","resolution":{"observed_at":"2026-08-11T17:51:01.189310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.11956","last_updated":"2019-10-25T23:01:43Z","snapshot_observed_at":"2026-08-18T06:32:11.847614Z","submitted_at":"2019-10-25T23:01:43Z","title":"Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.11956","snapshot_observed_at":"2026-08-11T17:51:01.195551Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.195551Z"},"links":{"cited_paper":"/paper/1910.11956","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:b7db61b35fe43c0f117235886f40f4fc7acfa299a9ac75d0de244b72bad07d4c","observation_id":"5420df7a-9483-4c9b-85bf-775f86375ff4","resolution":{"observed_at":"2026-08-11T17:51:01.195551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08165","last_updated":"2017-07-21T20:25:54Z","snapshot_observed_at":"2026-08-14T21:14:39.482415Z","submitted_at":"2017-02-27T07:16:41Z","title":"Reinforcement Learning with Deep Energy-Based Policies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08165","snapshot_observed_at":"2026-08-11T17:51:01.200728Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.200728Z"},"links":{"cited_paper":"/paper/1702.08165","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:d1154ca38f2e88d4a347b046c97fb573a6b30cebb6e245dbe8f8e08cf382b3d9","observation_id":"20f7e92c-744a-41dc-ad7b-e5d5e28a1895","resolution":{"observed_at":"2026-08-11T17:51:01.200728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-11T17:51:01.207301Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.207301Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:87509091c7adcc747ed6b5190a36b379c2c3f90d42b11de5954f4f37e18042eb","observation_id":"c94faeb0-bdf0-48b7-bfcc-b05ec0dc0e19","resolution":{"observed_at":"2026-08-11T17:51:01.207301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.09991","last_updated":"2022-12-21T01:06:18Z","snapshot_observed_at":"2026-08-17T05:09:53.121073Z","submitted_at":"2022-05-20T07:02:03Z","title":"Planning with Diffusion for Flexible Behavior Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.09991","snapshot_observed_at":"2026-08-11T17:51:01.214329Z","title":"B.; and Levine, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.214329Z"},"links":{"cited_paper":"/paper/2205.09991","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:359d6e885113ccd5ebf1750a2b6ed308f9f8d133bc8f170a48ac23655586e3b5","observation_id":"84b01d54-6785-4e8f-a983-9791c2b3b2d9","resolution":{"observed_at":"2026-08-11T17:51:01.214329Z","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":"2026-08-11T17:51:01.665243Z","title":null,"venue":null,"work_id":"914058f7-e74b-4e0a-9d59-238db2e095a2","year":2021},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.221161Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:9c689727f7e1360e6edb508b047becf544ef3cffe450042f54860a9c1016f6b7","observation_id":"df8c7c42-968e-4e02-97ef-555344d3859d","resolution":{"observed_at":"2026-08-11T17:51:01.670860Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2110.06169","last_updated":"2021-10-12T17:05:05Z","snapshot_observed_at":"2026-08-13T07:57:56.087944Z","submitted_at":"2021-10-12T17:05:05Z","title":"Offline Reinforcement Learning with Implicit Q-Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06169","snapshot_observed_at":"2026-08-11T17:51:01.226545Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.226545Z"},"links":{"cited_paper":"/paper/2110.06169","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:dc143c2420cc9e353bea0269e6b3c2cd710ee292053680d042e0985fd3be2038","observation_id":"00097875-f3b3-4a9b-85ae-96597a627ad1","resolution":{"observed_at":"2026-08-11T17:51:01.226545Z","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":"2026-08-11T17:51:01.647474Z","title":null,"venue":null,"work_id":"92a1e490-9b85-42b6-b84a-8a805018dc9c","year":2019},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.231643Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:2950fb4ece833d0d47fcc93a9bbbe2f52350d95f98f1d861d153bdfecfc0201c","observation_id":"5e064230-2c65-44ed-93e4-2c105f375e70","resolution":{"observed_at":"2026-08-11T17:51:01.652507Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2203.09637","last_updated":"2022-03-17T22:24:38Z","snapshot_observed_at":"2026-08-16T23:54:14.915630Z","submitted_at":"2022-03-17T22:24:38Z","title":"Investigating Compounding Prediction Errors in Learned Dynamics Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.09637","snapshot_observed_at":"2026-08-11T17:51:01.236890Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.236890Z"},"links":{"cited_paper":"/paper/2203.09637","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:061bbe78f1e8573960f8ffa546a1cf200e76536d2cf7f90824e39b9daa6f830c","observation_id":"5e8d5b20-5332-441f-ae3d-5214a563ee5f","resolution":{"observed_at":"2026-08-11T17:51:01.236890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03181","last_updated":"2024-06-28T04:15:33Z","snapshot_observed_at":"2026-08-16T14:12:33.322650Z","submitted_at":"2024-03-05T18:19:29Z","title":"Behavior Generation with Latent Actions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03181","snapshot_observed_at":"2026-08-11T17:51:01.242418Z","title":"J.; Shafiullah, N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.242418Z"},"links":{"cited_paper":"/paper/2403.03181","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:d43c0c9aa22def1bce8f001836120909b6ede76a6bcb5f20a129000d5330e842","observation_id":"0fba7ff2-aa82-4f8b-9a00-91d5c707ca0c","resolution":{"observed_at":"2026-08-11T17:51:01.242418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01643","last_updated":"2020-11-01T23:50:25Z","snapshot_observed_at":"2026-08-21T15:57:22.153221Z","submitted_at":"2020-05-04T17:00:15Z","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.01643","snapshot_observed_at":"2026-08-11T17:51:01.247534Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.247534Z"},"links":{"cited_paper":"/paper/2005.01643","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:be8e407387d810e2ea938938dfb36f5b7caa7f23def1de93c36d11fb2e6cab7b","observation_id":"7318cd86-8702-4fe8-8d3f-c64c0c17e69a","resolution":{"observed_at":"2026-08-11T17:51:01.247534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02845","last_updated":"2022-06-10T17:05:55Z","snapshot_observed_at":"2026-08-16T17:36:50.372453Z","submitted_at":"2021-12-06T08:11:05Z","title":"Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02845","snapshot_observed_at":"2026-08-11T17:51:01.252978Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.252978Z"},"links":{"cited_paper":"/paper/2112.02845","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:54c9ad532dc58bc563e4c69c7b576f6133879e6d3cc300f7dfcd402bc22ed49b","observation_id":"8348593a-04b3-42b6-9d2e-cf91a489c6b1","resolution":{"observed_at":"2026-08-11T17:51:01.252978Z","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":"2026-08-11T17:51:01.631728Z","title":null,"venue":null,"work_id":"ae51cde8-925e-4e5e-9c49-b81e58cbfc66","year":2018},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.258443Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:6d297b6e32a454331769fb215e246cf56a6edc1d366111fc11df453d2cb7c0b5","observation_id":"5d939f4a-dd7b-4f27-b190-5be118226f89","resolution":{"observed_at":"2026-08-11T17:51:01.636393Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1912.02875","last_updated":"2020-06-23T15:55:05Z","snapshot_observed_at":"2026-08-17T05:48:50.270608Z","submitted_at":"2019-12-05T21:10:08Z","title":"Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02875","snapshot_observed_at":"2026-08-11T17:51:01.263833Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.263833Z"},"links":{"cited_paper":"/paper/1912.02875","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:248b1b10b4c89f589317f4135df22fbf452c6865f239d94d467c00e46bd7a6cf","observation_id":"697d7ca2-eb1d-4c4d-a695-be48221c09c2","resolution":{"observed_at":"2026-08-11T17:51:01.263833Z","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":"2026-08-11T17:51:01.615122Z","title":null,"venue":null,"work_id":"48efe99d-762b-4e6b-a815-aea2675d76aa","year":2023},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.269209Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:4b2986c1bc1b874d5d51828290dcceb71cbf08daf17cf69f09085f5be9c5d783","observation_id":"dfb71f17-22cb-491d-86e9-82424a35efab","resolution":{"observed_at":"2026-08-11T17:51:01.619816Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-11T17:51:01.599047Z","title":null,"venue":null,"work_id":"599172d6-8154-416b-a79c-0b29276d8a3c","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.274249Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:db51096e5ede85a4f4ee0a833fb181f7e7e54ab3f8d64d50e996077500c52634","observation_id":"be3b6b6f-218d-413d-aa5d-1bcbb7db222a","resolution":{"observed_at":"2026-08-11T17:51:01.603684Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2005.13239","last_updated":"2020-11-22T07:04:17Z","snapshot_observed_at":"2026-08-06T03:32:02.156770Z","submitted_at":"2020-05-27T08:46:41Z","title":"MOPO: Model-based Offline Policy Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.13239","snapshot_observed_at":"2026-08-11T17:51:01.279515Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.279515Z"},"links":{"cited_paper":"/paper/2005.13239","citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:6a3d107c5537dad3c9705ae348607b2b3bca76b790219fd836f6d55cd510a473","observation_id":"501acdbe-9c6e-4593-830b-559ad8c36396","resolution":{"observed_at":"2026-08-11T17:51:01.279515Z","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":"2026-08-11T17:51:01.580636Z","title":null,"venue":null,"work_id":"e4547039-8723-40a7-adde-ab6105e171f3","year":2022},"citing_paper":{"arxiv_id":"2412.08565","last_updated":"2024-12-25T19:45:43Z","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T17:51:01.284638Z"},"links":{"citing_paper":"/paper/2412.08565"},"observation_digest":"sha256:02b77a1f4ee5e942a42b4f2b128bad0cc072585b7d5f14c31a1c538ca552a84e","observation_id":"f5b2742c-452f-47fd-ba74-759bbb6244ed","resolution":{"observed_at":"2026-08-11T17:51:01.586965Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2412.08565","last_updated":"2024-12-25T19:45:43Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T14:31:54.890104Z","submitted_at":"2024-12-11T17:32:33Z","title":"GenPlan: Generative Sequence Models as Adaptive Planners"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":33},"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 22 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2412.08565."}