{"as_of":"2026-08-22T22:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cb88e645c9d3960fce18e1d80be9b46e57d03e816fbad1a1ae98540e4a2ab97d","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:36:16.229866Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2412.00293/citation-record","integrity":"/paper/2412.00293/integrity","json":"/paper/2412.00293/citation-record.json","paper":"/paper/2412.00293"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:36:16.451768Z","title":null,"venue":null,"work_id":"e1c87d6a-5b0f-4ffc-be5a-3aba603db664","year":2018},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.136257Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:f6792994167708d91df1a78160674ea52f08950fa928bfff70a61ae9fc3af570","observation_id":"1a841a92-0092-4849-8a57-167f196f0bfa","resolution":{"observed_at":"2026-08-12T05:36:16.455141Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.443622Z","title":"Deadly triad matters for offline reinforcement learning,","venue":null,"work_id":"6380ef11-b985-4159-80d6-a87ca7925b59","year":2024},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.141961Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:ad916b0a858104d33d82b9014e8c63349ad6c96d2c1255934c679bbc514674dc","observation_id":"14206156-1303-4bfb-a22a-bfd061c289e5","resolution":{"observed_at":"2026-08-12T05:36:16.447397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.145903Z","title":"Goal-conditioned reinforcement learning: Problems and solutions,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.145903Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:47a36532e84fc1723a2f3d1633884634140872fce28e50d3c7b60dff745ea455","observation_id":"babb5244-e6f4-4352-bf14-325d76be6df2","resolution":{"observed_at":"2026-08-12T05:36:16.145903Z","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":"2026-08-12T05:36:16.434103Z","title":"Decision transformer: Re- inforcement learning via sequence modeling,","venue":null,"work_id":"905beab9-f8ee-4f6a-955b-10d31c3654b4","year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.149959Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:e615ed6ecf7d5ccc8a8e73e161903f4659b686a57dccf841f2c7bb69e39fa382","observation_id":"867a0529-e38f-461d-aa49-803b2f950d53","resolution":{"observed_at":"2026-08-12T05:36:16.437549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.424483Z","title":"Planning with sequence models through iterative energy minimization,","venue":null,"work_id":"91a6408d-8fcd-4c9f-b8f6-fb0014bddba1","year":2023},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.153959Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:d1608490ad9fe86828a69cc3dbf523f31a7ee329521f051650ddec4b48c13c92","observation_id":"395d768a-8b06-4c71-a8c0-8fadabdf8cd0","resolution":{"observed_at":"2026-08-12T05:36:16.428111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.415519Z","title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems,","venue":null,"work_id":"f74857f3-8d59-47f9-86c2-d103b98cb409","year":2020},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.157337Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:09d364a333cf6cfe253ad7ef40c1a5cc66bb6bbc6dc6ed1baae147d6f1971ee0","observation_id":"abaab2fc-3c9e-4561-ac07-50e56d9206f2","resolution":{"observed_at":"2026-08-12T05:36:16.418668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.405257Z","title":"Off-policy deep reinforcement learning without exploration,","venue":null,"work_id":"5570b6de-69b6-44f7-954c-b47f46cdcba4","year":2019},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.160501Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:bc1a65f7549c3b916ff0e9dbf2748f4e7b7c2f6a5e32b3a0f36c0ee47f50c6d8","observation_id":"3c1e5fd2-2b3f-4e8f-85cb-6b53c82e1224","resolution":{"observed_at":"2026-08-12T05:36:16.408854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.396514Z","title":"Stabilizing off-policy q-learning via bootstrapping error reduction,","venue":null,"work_id":"2b434426-6a40-4777-92b3-e6b76cea966f","year":2019},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.163230Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:73c2f54f9837bfb2bbdd32dd0c1e51d861f1e7959a425f02d84674624eae0271","observation_id":"831f8042-8e24-4aa3-b37f-1346828104c6","resolution":{"observed_at":"2026-08-12T05:36:16.399591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.387858Z","title":"A minimalist approach to offline reinforce- ment learning,","venue":null,"work_id":"38f6becf-a09d-4f4a-a926-25e6850448d0","year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.167702Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:f9986d2896852e0b403cfdfb985e0252b9f6718260c5a9353a29e5afb47a02de","observation_id":"f834e5fd-779f-46c8-95a0-a87e7d9500e7","resolution":{"observed_at":"2026-08-12T05:36:16.391938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.171378Z","title":"Conservative q- learning for offline reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.171378Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:df70ee01fa26128e1693d2e3827689c5b9d7459ab48f09890fe7c7311ec7a823","observation_id":"88a17ffe-7ff9-4592-af59-2683e4a50269","resolution":{"observed_at":"2026-08-12T05:36:16.171378Z","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":"2026-08-12T05:36:16.174139Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.174139Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:03bcbd1c449274eb2ca14b737a7920d923768fe437a586eb41d4560de0d1696d","observation_id":"a7629272-33af-4a83-ab59-bdc045fef931","resolution":{"observed_at":"2026-08-12T05:36:16.174139Z","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":"2026-08-12T05:36:16.178536Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.178536Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:837c7fb292bec174c1ca9ff3f11f66a0cf92acdd12eb88705c16d6c02f2e2e18","observation_id":"c47dc2fd-b396-4919-869e-7617c90d56b2","resolution":{"observed_at":"2026-08-12T05:36:16.178536Z","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":"2026-08-12T05:36:16.367126Z","title":"Offline Reinforcement Learning as One Big Sequence Modeling Problem,","venue":null,"work_id":"a86f64a4-8788-4600-9caf-2873ebeb0341","year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.181698Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:cb6bd36d9c567b3b270c82827df45a3eb5435fcbdbbac54c4bccccb61332ae61","observation_id":"5470bd73-5cdd-4a79-b87b-f630bce3e5b6","resolution":{"observed_at":"2026-08-12T05:36:16.370130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.359065Z","title":"Generalized decision transformer for offline hindsight information matching,","venue":null,"work_id":"07293769-a8f2-458e-a4d5-d34daa21fe70","year":2022},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.184302Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:bcf821c69eaa128e55910e90bc0e4e9108c21648331ae53780cfa0a135b080ae","observation_id":"33a7dd12-ac4c-4654-aecb-d55803c52435","resolution":{"observed_at":"2026-08-12T05:36:16.362002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.351840Z","title":"You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments,","venue":null,"work_id":"1f6d6a91-5dec-4bdd-ac10-32762d657fb8","year":2022},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.187890Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:f6660ead749fa87956a4adcd8de49d34e645ee00a2d970a38ffd3e856cbabf78","observation_id":"f2ad8743-cefc-406e-9681-e36aa73ecd5f","resolution":{"observed_at":"2026-08-12T05:36:16.354597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.343383Z","title":"Maximum entropy gain exploration for long horizon multi-goal reinforcement learning,","venue":null,"work_id":"e76c2593-4bf1-4d9f-8a02-7f4522e294bf","year":2020},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.190812Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:1b72abe99f13f0311b937c4ffb08cde9edb5dca062c93cdb746811dfac78320c","observation_id":"8756ef34-fc24-428b-87a5-41369d78d884","resolution":{"observed_at":"2026-08-12T05:36:16.346931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10751","last_updated":"2022-05-11T03:17:44Z","snapshot_observed_at":"2026-08-18T16:15:48.653369Z","submitted_at":"2021-12-20T18:55:16Z","title":"RvS: What is Essential for Offline RL via Supervised Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10751","snapshot_observed_at":"2026-08-12T05:36:16.193382Z","title":"Rvs: What is essential for offline rl via supervised learning?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.193382Z"},"links":{"cited_paper":"/paper/2112.10751","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:2d930ba71636f4a73623dc20ac5ba07e5e93ef3978195a3c71ec079df0a84899","observation_id":"8c520d41-c0a7-43c9-accf-38eef9cf9e44","resolution":{"observed_at":"2026-08-12T05:36:16.193382Z","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":"2026-08-12T05:36:16.333889Z","title":"Waypoint transformer: Reinforcement learning via supervised learning with intermediate targets,","venue":null,"work_id":"de62d6bd-60c4-4104-b2ad-5676ce5bf2c6","year":2023},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.197218Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:1cb7cc59c4a9da4995bd537e7d72f80b5a212ca1f2d2191b6d6a4a36719e3643","observation_id":"3079d82d-ef69-4cec-bbbe-ec449c981298","resolution":{"observed_at":"2026-08-12T05:36:16.338013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.201177Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.201177Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:db59275715761b001ad2005ccfedb9fe991ebeed1d62579d43ed62e3b563f97a","observation_id":"74006f79-7056-4c43-b4ce-acda921b7622","resolution":{"observed_at":"2026-08-12T05:36:16.201177Z","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":"2026-08-12T05:36:16.203894Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.203894Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:79d96660b849087bd22a36fbea359e106b4b5d303809c8107d97885fbbad04ec","observation_id":"7098f9b6-eeb6-4b85-98c8-075fc1db1142","resolution":{"observed_at":"2026-08-12T05:36:16.203894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02157","last_updated":"2023-09-05T11:49:33Z","snapshot_observed_at":"2026-08-16T15:03:13.596435Z","submitted_at":"2023-09-05T11:49:33Z","title":"Model-based Offline Policy Optimization with Adversarial Network","version":1},"cited_work":{"arxiv_id":"2309.02157","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.02157","snapshot_observed_at":"2026-08-12T05:36:16.274771Z","title":"Model-based Offline Policy Optimization with Adversarial Network","venue":"cs.LG","work_id":"dc98a8da-6fcf-44f2-8929-144834d63598","year":2023},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.207284Z"},"links":{"cited_paper":"/paper/2309.02157","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:4978165f4455c3812251b28f97952af1586f6493b4a13ad0b567fd93610fbe48","observation_id":"b24cbb3c-b067-4702-a125-f17dda8bce3c","resolution":{"observed_at":"2026-08-12T05:36:16.279851Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.210285Z","title":"Exposing the implicit energy networks behind masked language models via metropolis– hastings,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.210285Z"},"links":{"cited_paper":"/paper/2106.02736","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:87dfb90ca0a31dfcefbfa020fc2cb817e5bfbfd89eb5680bf6b8b1382959e8c1","observation_id":"e32e11db-68ad-4fff-ba8b-f146d5c38f41","resolution":{"observed_at":"2026-08-12T05:36:16.210285Z","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":"2026-08-12T05:36:16.317387Z","title":"Online decision transformer,","venue":null,"work_id":"8c65c3ea-624b-419b-82f0-aebceeec69d7","year":2022},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.213221Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:69e93ed7509958d8817c8c566d5330689fc11aa6dc150573d0ac94acb26e9cd3","observation_id":"3f94996e-d1bf-45f0-897a-d03d56f81188","resolution":{"observed_at":"2026-08-12T05:36:16.320244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-12T05:36:16.216323Z","title":"Soft actor- critic algorithms and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.216323Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:ef4220feabe66c50983738db725fc00aafd16a73e1b57d732cc5e5e30b6228b9","observation_id":"a7108bc3-00af-4821-a46f-d0b82ed103bf","resolution":{"observed_at":"2026-08-12T05:36:16.216323Z","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":"2026-08-12T05:36:16.220196Z","title":"Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.220196Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:a7f08619d9f8c78daa5ffa1f6c8d66a57187831f408493e55621e8d124e7db29","observation_id":"8e9b4d6d-d7ec-42a3-b24f-a4307975895a","resolution":{"observed_at":"2026-08-12T05:36:16.220196Z","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":"2026-08-12T05:36:16.305028Z","title":"BabyAI: First steps towards grounded language learning with a human in the loop,","venue":null,"work_id":"8ecf8f9c-f0d2-471f-93bf-4ad0b2b3c306","year":2019},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.222898Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:7936c944553b8c42bb8d5139bc1e53216d3c0e97578e6f3c05b5057418bd81e4","observation_id":"03574109-7735-4e78-b7aa-13b7a09b83cf","resolution":{"observed_at":"2026-08-12T05:36:16.308221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.297229Z","title":"Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks,","venue":null,"work_id":"b5fdf566-c9cd-4d9a-b23f-31290589a002","year":2023},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.226355Z"},"links":{"citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:ec37b6495e8d6f9666fe8db206945bf22a6abcdd4e27133e14475d22196ed5d1","observation_id":"86953cec-eda8-44f7-964c-639de92aeb9e","resolution":{"observed_at":"2026-08-12T05:36:16.300275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T05:36:16.229866Z","title":"Offline reinforcement learning with implicit q-learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T05:36:16.229866Z"},"links":{"cited_paper":"/paper/2110.06169","citing_paper":"/paper/2412.00293"},"observation_digest":"sha256:f6ea58c94b61bf10d579e7c3c9155f6caf9c540319fd4e9a4f7d80836ceb9031","observation_id":"3280859f-1aaa-42f6-b1d4-65f354a0c9ba","resolution":{"observed_at":"2026-08-12T05:36:16.229866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.00293","last_updated":"2024-11-30T00:34:41Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-20T08:49:45.976988Z","submitted_at":"2024-11-30T00:34:41Z","title":"Adaptformer: Sequence models as adaptive iterative planners"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.00293."}