{"as_of":"2026-08-18T13:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:535d1470ec40f828574a20c4b2ff2a583fac756ccc718df18b3e749238e6a7d4","coverage":[{"denominator":92,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":92,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:25:09.689761Z","state":"measured"},{"denominator":93,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":93,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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":"2026-05-10T16:25:25.739019Z","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":"2026-05-11T08:56:00.573367Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2505.13144","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13144","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.13144 , year=","venue":null,"work_id":"4b647377-cb44-497b-8a1d-d29c2bdf3572","year":null},"citing_paper":{"arxiv_id":"2605.01663","last_updated":"2026-05-28T02:21:27Z","snapshot_observed_at":"2026-08-15T17:39:17.142597Z","submitted_at":"2026-05-03T01:32:11Z","title":"Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-10T16:25:25.739019Z"},"links":{"cited_paper":"/paper/2505.13144","citing_paper":"/paper/2605.01663"},"observation_digest":"sha256:b71b28de45a2f71771b8eb62b0f2b139ed91796d325fc2546264f53d9776b1ac","observation_id":"1fec2f2a-66aa-4ca9-bcf3-9f64390f4523","resolution":{"observed_at":"2026-05-11T08:56:00.575502Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.13144/citation-record","integrity":"/paper/2505.13144/integrity","json":"/paper/2505.13144/citation-record.json","paper":"/paper/2505.13144"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:25:09.207105Z","title":"Deep reinforcement learning at the edge of the statistical precipice","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.207105Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:cd68d7c22b027f1b480e9bb698b40be29b35e38afdbc90cb1c34176a4e40fb76","observation_id":"e1cd538c-c209-40fe-be5a-228d999db8a0","resolution":{"observed_at":"2026-08-15T20:25:09.207105Z","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":"2026-08-15T20:25:09.214646Z","title":"OPAL : Offline primitive discovery for accelerating offline reinforcement learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.214646Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:dca12349fe762c328f1e6f18a12a59939fa887dc5733fcdbd6137dcbc734705b","observation_id":"b79eac3d-6c2b-4f5d-ba00-1a3c7534befc","resolution":{"observed_at":"2026-08-15T20:25:09.214646Z","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":"2026-08-15T20:25:09.220642Z","title":"Learning M arkov state abstractions for deep reinforcement learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.220642Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:f0e28e5ddda5fdbc0728b0305e34299d51949e032f1db77d8f94c435a4ff39e8","observation_id":"3dad8488-e787-4b74-9043-d92174afae52","resolution":{"observed_at":"2026-08-15T20:25:09.220642Z","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":"2026-08-15T20:25:09.228809Z","title":"Uncertainty-based offline reinforcement learning with diversified Q -ensemble","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.228809Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:96d29020c7e712b10bb0e99bf40b45a9b537d0bc7c3d1e67adeccc80ad496f03","observation_id":"5304bbd2-c51b-440b-85ec-9c549d361fc5","resolution":{"observed_at":"2026-08-15T20:25:09.228809Z","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":"2026-08-15T20:25:09.233815Z","title":"Hindsight experience replay","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.233815Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9b1b12d400850d1331b983285d738a79a024d5bdd63268f3d1b2682c030c83fd","observation_id":"08c05610-018a-4a1a-baab-68c7091a5829","resolution":{"observed_at":"2026-08-15T20:25:09.233815Z","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":"2026-08-15T20:25:09.238908Z","title":"and Arnold, G","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.238908Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:0344234b34c4c877f3fdcd5ebeef67f6df35c7069f75299f69e58341ec5b8768","observation_id":"84f33a4b-dd7e-489e-848d-9c26c8ce5226","resolution":{"observed_at":"2026-08-15T20:25:09.238908Z","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":"2026-08-15T20:25:09.244693Z","title":"Autoencoders","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.244693Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:35c3a1f94675e44584618f78410e2ac7944d4895ff8f7a828ede1c2a3cf362b1","observation_id":"4328227d-61a6-456e-83dc-e3caa7bee374","resolution":{"observed_at":"2026-08-15T20:25:09.244693Z","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":"2026-08-15T20:25:09.249490Z","title":"Successor features for transfer in reinforcement learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.249490Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:c42f5f6c06202817aa211ac0a7bfe37a18838452e6ee3868f0f249c2d35e59e8","observation_id":"08880532-99c2-43f8-abec-6a61ee1ae7a6","resolution":{"observed_at":"2026-08-15T20:25:09.249490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-15T20:25:09.254310Z","title":"OpenAI gym","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.254310Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:d0ea9f68557018d0801204222e7cfb33d74595202a0d9f6143f25c4f7bfa68dc","observation_id":"5a4ad987-9bec-41f6-be37-c0f4d2084723","resolution":{"observed_at":"2026-08-15T20:25:09.254310Z","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":"2026-08-15T20:25:09.261198Z","title":"Improving generalization for temporal difference learning: The successor representation","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.261198Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:4032163aae2b1701c7c63c51c0d07596baac6865602361dc879dd83f239842cd","observation_id":"10ecf7ad-651b-4486-9f70-b888bef0d872","resolution":{"observed_at":"2026-08-15T20:25:09.261198Z","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":"2026-08-15T20:25:09.266476Z","title":"and Hazan, E","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.266476Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:76a6d563a8ba7a86fcc0dba15aedfae7ccab30249365bc0f5f9f1fadbf3705bb","observation_id":"f155b088-cc16-470e-947c-26f32ca6bb14","resolution":{"observed_at":"2026-08-15T20:25:09.266476Z","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":"2026-08-15T20:25:09.273044Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.273044Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:ed57349f374047274deb9fdf70d1ce5fd27c2c77fe27182e1e765f7fd1bbd4b1","observation_id":"29ce66af-dc7e-4063-899b-2b840df8c1cd","resolution":{"observed_at":"2026-08-15T20:25:09.273044Z","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":"2026-08-15T20:25:09.278593Z","title":"The impact of dataset on offline reinforcement learning performance in uav-based emergency network recovery tasks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.278593Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9eb7bdfa7eb720b7de676d1e4ddd48cb0af517ddcc1bcdbd97cc88eba2f42891","observation_id":"64b310e2-6840-47f6-b5b7-5828a548c520","resolution":{"observed_at":"2026-08-15T20:25:09.278593Z","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":"2026-08-15T20:25:11.284561Z","title":"IMPALA : Scalable distributed deep- RL with importance weighted actor-learner architectures","venue":null,"work_id":"7a4788d9-33bf-4408-b217-22adde91fcf1","year":2018},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.283840Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:0f271f9a6ceee929af458c8660188ffd6df6f3825097a353adf980a7de7207ad","observation_id":"0798deed-8bfe-4336-b17c-d3d141f73833","resolution":{"observed_at":"2026-08-15T20:25:11.290043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.267776Z","title":"Bisimulation makes analogies in goal-conditioned reinforcement learning","venue":null,"work_id":"a0f4add5-7d53-448b-80f7-4480c3368cd0","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.289412Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:93bae7bc9f73ccc699a05775f8f68f32ac627806b543c1ecb3938f5b4fb0d9f6","observation_id":"7bd5089f-00f2-4d96-8777-847416a00f43","resolution":{"observed_at":"2026-08-15T20:25:11.272971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.250431Z","title":"C-learning: Learning to achieve goals via recursive classification","venue":null,"work_id":"3a27d96e-b580-4214-bd71-2e9b9e19a9d9","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.294951Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:cfa2f6d1f3f96c1b4578f55f485d26e93a2c364596520f891077c39636ae0ee7","observation_id":"f3286861-4c10-446b-8946-620e10a000e9","resolution":{"observed_at":"2026-08-15T20:25:11.256448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.234377Z","title":"Contrastive learning as goal-conditioned reinforcement learning","venue":null,"work_id":"143912cd-72e8-4f9d-a14e-19a1a31d7e6f","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.300375Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:5937b7f825f924590a347d9df11ff5608348e65275b5481271eee9b9c2919ce0","observation_id":"f6f3133c-2e10-4142-bae9-eea29404cf6a","resolution":{"observed_at":"2026-08-15T20:25:11.239523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07219","last_updated":"2021-02-06T01:57:28Z","snapshot_observed_at":"2026-08-16T08:32:46.407746Z","submitted_at":"2020-04-15T17:18:19Z","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.07219","snapshot_observed_at":"2026-08-15T20:25:09.305951Z","title":"D4RL : Datasets for deep data-driven reinforcement learning","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.305951Z"},"links":{"cited_paper":"/paper/2004.07219","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:7232fe2b99a1b0b36574fe26af6257e8b01f07aa38b3ed7a521047ca66df50e5","observation_id":"d05173ae-a940-4c91-896b-cc4dfb9172d3","resolution":{"observed_at":"2026-08-15T20:25:09.305951Z","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":"2026-08-15T20:25:11.218311Z","title":"and Gu, S","venue":null,"work_id":"de3d235a-4119-423f-ba93-1bc00b79a706","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.311626Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:618eabeb4d0de1529f12638ba9e39bf7f372d5829ef9d73ed351a4f701468bb9","observation_id":"1c224caa-e7e8-4c81-9241-cd413759f639","resolution":{"observed_at":"2026-08-15T20:25:11.223110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.200056Z","title":"For SALE : State-action representation learning for deep reinforcement learning","venue":null,"work_id":"60adf1bc-3baf-48e1-9d3e-ce456c63911d","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.316672Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:1e826a2f70e34a0bccde973810dd696d93e6c31461ae4831a5fd19d1c259ca23","observation_id":"4ba86f66-6857-4c9f-8b92-c3f4f2617922","resolution":{"observed_at":"2026-08-15T20:25:11.205589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.182431Z","title":"Learning to reach goals via iterated supervised learning","venue":null,"work_id":"32cb1272-4c50-48e8-948f-c76866d41f3e","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.321656Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:7c67f5349a08c61b3d1005edbd1434f9921ea1b0a96d5fe54e7c1f08ff2bcfc7","observation_id":"c1401b15-946f-4fa8-98d3-6d0b73b0a7e9","resolution":{"observed_at":"2026-08-15T20:25:11.188393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.165597Z","title":"Reinforcement learning from passive data via latent intentions","venue":null,"work_id":"36b97342-98aa-4273-acd5-4e11f68e5d7b","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.326311Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:d37cec4abeb7a709cd758365f35ddb61105273c3b5296b79024d7ea19f13ce66","observation_id":"ba634ecf-1b41-4b7a-96c2-0b67ced5571a","resolution":{"observed_at":"2026-08-15T20:25:11.170987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.145159Z","title":"Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning","venue":null,"work_id":"18cbd6c1-fac4-4802-9c6f-f584f340366d","year":2019},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.331080Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:1f0b961ac41dc28a65d35ed5a36a28a4d3618f58e4e480bb84ff5120b85770d2","observation_id":"3d12a7d5-f62c-49cf-a560-d5661cc6dc39","resolution":{"observed_at":"2026-08-15T20:25:11.151011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.128497Z","title":"Learning latent dynamics for planning from pixels","venue":null,"work_id":"9eb570a2-5171-44ed-92f0-8d80063da3b5","year":2019},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.336160Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:a2e19db043812d66723dfa14d3d46b629b8784648ae0cc969206964876b71957","observation_id":"8797393f-891c-4bca-96e3-34915fd994c4","resolution":{"observed_at":"2026-08-15T20:25:11.133992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.112202Z","title":"Distance weighted supervised learning for offline interaction data","venue":null,"work_id":"2d0a1551-6703-4ea4-bdf8-6510e6851506","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.340972Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:80043651482e242e153c052d2dffb090a02ec56b8a06248a89a3754e80674796","observation_id":"76f80694-f9f3-47b7-88de-4fb614c190c2","resolution":{"observed_at":"2026-08-15T20:25:11.117782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.096053Z","title":"Efficient planning in a compact latent action space","venue":null,"work_id":"7f3706eb-b47c-496f-a6b2-d052e9bb3c29","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.346494Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:e1c68d4c73613e3d93477dbc8fb65623520f583a919ecbe1e26ee3ed42d691df","observation_id":"61f2304e-9073-463c-b304-9c429192e187","resolution":{"observed_at":"2026-08-15T20:25:11.101452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.078356Z","title":"Learning to achieve goals","venue":null,"work_id":"13b94bcb-6a43-42ed-9e0d-8cfa8e5cce5f","year":1993},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.351267Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b608c4e5a9acce2e8acc1d71821d6d280ea3a83447da2ad028e5783685611aa4","observation_id":"5669dc45-8d5f-4b47-9024-3a891a0d83ab","resolution":{"observed_at":"2026-08-15T20:25:11.083605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.058140Z","title":"MOReL : Model-based offline reinforcement learning","venue":null,"work_id":"c5a0baf2-fa52-4baa-a5d4-8a48b7ff5f96","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.357567Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:0b55e163a302b8d9f39acca38e6e099ea6870a4556cc3088b49f3c3bc11b93de","observation_id":"f925b495-dafd-4217-80bc-71eb9d601531","resolution":{"observed_at":"2026-08-15T20:25:11.063662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-15T20:25:09.362820Z","title":"and Welling, M","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.362820Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:2ec7e5ead778a4bd717977c17a569a218e368adff9fa6337c5968870d4dfc5e2","observation_id":"63044ad9-abcf-453c-84df-cb066564d8f9","resolution":{"observed_at":"2026-08-15T20:25:09.362820Z","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":"2026-08-15T20:25:11.038359Z","title":"Offline reinforcement learning with implicit q -learning","venue":null,"work_id":"94f09c49-04f6-4497-b048-3d5a03a110ed","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.368787Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:c5eeaac9175e090641d4ee06499abd18af5dd8d1e4efdc2a31d4f45635765b5f","observation_id":"e3f3aacd-faf8-4a77-9d15-98b5c0490be4","resolution":{"observed_at":"2026-08-15T20:25:11.043843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.019594Z","title":"Stabilizing off-policy Q -learning via bootstrapping error reduction","venue":null,"work_id":"b209dab2-d8f0-4e93-9cf2-e24c038af477","year":2019},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.374051Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:66b16d9031e2d232954323842fc35c3860216b2365147532f173d6b06ac4bd47","observation_id":"b769f48f-2056-4a2d-bc0a-89ba07505f00","resolution":{"observed_at":"2026-08-15T20:25:11.026947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:11.002304Z","title":"Conservative Q -learning for offline reinforcement learning","venue":null,"work_id":"31bdcd65-ece9-4097-8229-01363ecab1c7","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.379740Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:0eeaf93fc2095284b071930aba884fec27c7cf6b7f1005ba68667796785003ef","observation_id":"2badb7e8-344e-4a30-913e-f3decc7c16e6","resolution":{"observed_at":"2026-08-15T20:25:11.007839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.985406Z","title":"CURL : Contrastive unsupervised representations for reinforcement learning","venue":null,"work_id":"c0716306-a308-4fe2-b66c-ff3569f99fe7","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.384613Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:3b92f2a0cc86ff8c85f34da1b906cf70d1e0093b4acb996ebc55e83e7d80d328","observation_id":"9775d383-aeb1-4236-b82c-cfc2e62bf589","resolution":{"observed_at":"2026-08-15T20:25:10.991386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.967518Z","title":"Lipschitz lifelong reinforcement learning","venue":null,"work_id":"99f3a77e-69ad-45ce-880d-d742ac8d1470","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.389920Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:add9064a4ea6a9d295efbd9d948d58ba3c5304b0b495ad73bde936143e9a6e9f","observation_id":"1b5aba79-32c6-42c8-b796-f408218bfe0d","resolution":{"observed_at":"2026-08-15T20:25:10.973917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.947626Z","title":"Representation balancing offline model-based reinforcement learning","venue":null,"work_id":"f7db6cd7-666b-470e-8df0-bc651eca6fc9","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.395094Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:fd1fb83581661641a76bb34660316e403a6022d44e50da19e6c1e1f3e98c04a2","observation_id":"3f77e42c-9241-4dff-8790-5e94b02f47ed","resolution":{"observed_at":"2026-08-15T20:25:10.953244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.928866Z","title":"and Kwon, M","venue":null,"work_id":"871eb9d2-a581-47c2-992e-a5f090c06919","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.400321Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9adf7d93fb4f7be51ae00072bbdc7bf8ae40c369ccd24316bc9203d7fa271407","observation_id":"166f8952-892a-4551-8df9-769fb8291620","resolution":{"observed_at":"2026-08-15T20:25:10.934775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.911584Z","title":"and Kwon, M","venue":null,"work_id":"4a5763b2-5ce3-4a92-b020-12c8398a87d7","year":2025},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.405947Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:5e313002f85885291ec2595fcda2381c3929c172d8680d2a45923e0ce021e03d","observation_id":"85585392-8a0c-4e56-af50-4b7fe8d034f8","resolution":{"observed_at":"2026-08-15T20:25:10.916390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.892755Z","title":"AD4RL : Autonomous driving benchmarks for offline reinforcement learning with value-based dataset","venue":null,"work_id":"c22a3cc4-6f03-466e-8e8a-09c05916ccdf","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.411955Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:d7e08fcb8262ca0bfcbc31b13a7be842b593905dfb994829d13e3f2deabac182","observation_id":"49797fa3-f996-4b15-a343-271f9087ddba","resolution":{"observed_at":"2026-08-15T20:25:10.899118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.870523Z","title":"K., Choi, W., and Woo, H","venue":null,"work_id":"2195fa76-225c-4890-85ce-de229bb09824","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.416818Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:df47b996ca0d8d3408f8658522cf54c2cdef46bf250a27cc8e562f37e4bd631a","observation_id":"8ffa6316-3475-42b9-8c05-c52d5089d772","resolution":{"observed_at":"2026-08-15T20:25:10.878724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.851675Z","title":"GTA : Generative trajectory augmentation with guidance for offline reinforcement learning","venue":null,"work_id":"c7d27764-8ceb-433b-9cf9-a0b85d4d5b63","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.421852Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:815d7304b57d2f7aebc7d3eb940c00b735f315d895b2370555b37940c8022d59","observation_id":"c67fac9f-3df7-42c5-8d0f-c0215ce0a01a","resolution":{"observed_at":"2026-08-15T20:25:10.857609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.833535Z","title":"Metric residual network for sample efficient goal-conditioned reinforcement learning","venue":null,"work_id":"920a1477-d577-4ec3-8952-e7e586b5297a","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.427089Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b1e29ef5e57d9dcd47420f45581ba31cdba7e9e1832399c999dc5001a5e2a028","observation_id":"1987829c-3d00-416e-9349-b6144803d731","resolution":{"observed_at":"2026-08-15T20:25:10.839673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.811370Z","title":"Synthetic experience replay","venue":null,"work_id":"33a5f72e-ecf1-45e7-8db3-1a37e1ee1cb5","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.431842Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:87254b1bb3363d8922ee626c237c186a36fc7076e688fd0a51aa0ab4b69551d6","observation_id":"b52a406f-a2b8-4d15-bc66-eadd68548f75","resolution":{"observed_at":"2026-08-15T20:25:10.816339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.795501Z","title":"Conservative offline distributional reinforcement learning","venue":null,"work_id":"244fa0f9-cbe8-42a3-bf57-5547a72f3929","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.436373Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:7bead9d32ec320c5e21c31241f3757f07ba091a724746c4d8eb8df58ac34350c","observation_id":"48346471-2cce-4c8e-8b53-aa72fb5b9f71","resolution":{"observed_at":"2026-08-15T20:25:10.800647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.778325Z","title":"VIP : Towards universal visual reward and representation via value-implicit pre-training","venue":null,"work_id":"4dd98225-dd1e-445e-824e-893f340f4e52","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.441535Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9459cab0af43e157292ce0344674ae617f7b9d6bbbe7010232986b21c4a8c550","observation_id":"93fbf303-4e4d-4c3f-b341-64f181ff34db","resolution":{"observed_at":"2026-08-15T20:25:10.783509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.761950Z","title":"Contrastive value learning: Implicit models for simple offline RL","venue":null,"work_id":"df680a1f-46c0-432a-a2dd-25ff1fc1597b","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.446491Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:c903f9b90392f12241ce34ed471461859cb5da681416b03fb54bb2b78c869e0a","observation_id":"1b143d75-7add-48a6-bbc7-c0c9bf54e49d","resolution":{"observed_at":"2026-08-15T20:25:10.767068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.744879Z","title":"CALVIN : A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks","venue":null,"work_id":"1cf61aef-e5f1-47ed-aa90-bb5b59830f34","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.451310Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:4958897f50c577b24ead4368ae5754286d7c948012710158e0be4739ad622d46","observation_id":"e3843d57-d0c0-492a-a723-ac6b2d368148","resolution":{"observed_at":"2026-08-15T20:25:10.750532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.728430Z","title":"Discovering and achieving goals via world models","venue":null,"work_id":"4252d7e6-cf67-44b5-8e9a-9afaa24bdc8d","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.456421Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:2e81ad48ed0c0ce029fd13734dbd537a32261a87af980223a9692ed9e1d3a5b4","observation_id":"98b2a25e-e1a5-4d2d-b190-cfab05ad6540","resolution":{"observed_at":"2026-08-15T20:25:10.733769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.711753Z","title":"Offline meta-reinforcement learning with advantage weighting","venue":null,"work_id":"ac191f47-eed3-4bfb-9f1e-4108b1782753","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.461662Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:74ad9c95fed6b199ab78b355fcebacaf09f4053d55f43ed051b33037dd8ef8cd","observation_id":"b0db7f68-7d60-4093-9226-bdb23bfc887a","resolution":{"observed_at":"2026-08-15T20:25:10.717358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.694749Z","title":"Human-level control through deep reinforcement learning","venue":null,"work_id":"90ee5c62-93b2-422e-853a-263540d13939","year":2015},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.467043Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:2a0c5088575a0e68b927424245e7491b9bf0fc26decb27e943a93187105684c0","observation_id":"3dd4bf2b-2620-45bb-945c-bd4216d3347f","resolution":{"observed_at":"2026-08-15T20:25:10.700490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.677466Z","title":"Learning temporal distances: Contrastive successor features can provide a metric structure for decision-making","venue":null,"work_id":"8a9216ae-aa52-4004-9a9b-c8bf5babbdbe","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.472047Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:56baefbc992b4723e5d7e9a42fcc1f9671ee58b7aea7cbbf81fbc52f85b75d44","observation_id":"65c45e28-d60c-4698-92dc-4c740200a872","resolution":{"observed_at":"2026-08-15T20:25:10.682967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09359","last_updated":"2021-04-24T22:39:30Z","snapshot_observed_at":"2026-08-15T03:52:49.245753Z","submitted_at":"2020-06-16T17:54:41Z","title":"AWAC: Accelerating Online Reinforcement Learning with Offline Datasets","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09359","snapshot_observed_at":"2026-08-15T20:25:09.477942Z","title":"AWAC : Accelerating online reinforcement learning with offline datasets","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.477942Z"},"links":{"cited_paper":"/paper/2006.09359","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:1f0fd89cd07f5b3edc9a350d3cd234787207c352105fc640082c6d83d8a62b35","observation_id":"545ca6e0-e0cf-456e-8d4c-1a5982a732a7","resolution":{"observed_at":"2026-08-15T20:25:09.477942Z","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":"2026-08-15T20:25:10.660943Z","title":"Planning with goal-conditioned policies","venue":null,"work_id":"49ffd79e-8e4a-4919-8eda-0e70644d78be","year":2019},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.483589Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:deafdc50b883645ad4dee5d8fbabcb891235f51fb92aff6f1e76a0370c277823","observation_id":"379f56dd-9a02-46b9-948b-838d6f41623a","resolution":{"observed_at":"2026-08-15T20:25:10.666063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.643829Z","title":"Geometric autoencoders--what you see is what you decode","venue":null,"work_id":"1bbbd4ef-8595-46e7-948d-acf78eb9146f","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.488674Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:82722ae09f19d79a51dbc9a7eb19328c6f71dc731338ba44c0dd5df32fca2381","observation_id":"3fee0249-592f-401e-aaa1-967076e9736c","resolution":{"observed_at":"2026-08-15T20:25:10.649145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.627293Z","title":"and Powell, J","venue":null,"work_id":"a9ddeb1e-fc53-42b5-b97e-6bd3488719d7","year":1987},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.493642Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:bc33210c17d98d4904feea102eeeec9356981416929379ce962215afbce84062","observation_id":"d61df6c0-38a8-46db-a6b2-7af3428c78a3","resolution":{"observed_at":"2026-08-15T20:25:10.632279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.610544Z","title":"HIQL : Offline goal-conditioned RL with latent states as actions","venue":null,"work_id":"65b4c026-07ac-4469-b346-05e6c4899304","year":null},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.498842Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:4ef69659a307a6d037e0b1b2ce08f2193a41879d117fc4667a6eb45d6a901001","observation_id":"eaf3475c-ff04-4794-9805-c9aa42404d85","resolution":{"observed_at":"2026-08-15T20:25:10.616096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.593785Z","title":"Foundation policies with H ilbert representations","venue":null,"work_id":"1c8800f5-f1ae-43c9-86bd-170da9d3798c","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.504073Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:eafe984c879946a276c0d806c8cc7cf72b6b5bc1c1d75156ec611625e48fd126","observation_id":"8ed43098-98aa-4ce3-8e5f-fb4538020964","resolution":{"observed_at":"2026-08-15T20:25:10.599071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.576134Z","title":"Long-horizon visual planning with goal-conditioned hierarchical predictors","venue":null,"work_id":"2ab50045-e421-4f3c-90f2-9e854eafeed8","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.509257Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:fdba3a85a059176668d6634160ad2578cae0130c9b5da15c462e515cecc0049d","observation_id":"07489cba-ed33-49e7-819e-d26c40eff11b","resolution":{"observed_at":"2026-08-15T20:25:10.581846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.557722Z","title":"and Juditsky, A","venue":null,"work_id":"5db01cc9-ff9a-4579-b3a7-7da6d6dd4fd8","year":1992},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.514130Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:d9b612c5640efa0fcfb0062c478c2b90d6a7abcad64045b012cb117e0571fcdf","observation_id":"e257e055-6e27-4205-9fde-48a1b24847a9","resolution":{"observed_at":"2026-08-15T20:25:10.563193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.540274Z","title":"Temporal difference models: Model-free deep RL for model-based control","venue":null,"work_id":"e3894703-d146-43a2-ba23-d3230fc85a2b","year":2018},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.518915Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:54a97fcf34e3dde96563af8dbce21c78db472714ebc488677bc3f2de006dc658","observation_id":"09968429-d669-4a51-a08e-57f550de8483","resolution":{"observed_at":"2026-08-15T20:25:10.545500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.523654Z","title":"MOTO : Offline pre-training to online fine-tuning for model-based robot learning","venue":null,"work_id":"c890e9a1-2e66-4fd5-baa9-3030fc9bc6fa","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.523633Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:a2d27ea119a36a59bf2f5f740f9cb68f2557c192ceb609c9c60a59c06804e2e7","observation_id":"501f3a5a-301f-42b2-b265-bb6bd2ce85aa","resolution":{"observed_at":"2026-08-15T20:25:10.528766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:09.528322Z","title":"Goal-conditioned offline reinforcement learning via metric learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.528322Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:0913420daec76ea02c2093eb7053ab4a55c0923cbfbfd68e229915b5a1a07976","observation_id":"75e608e3-de1b-40d5-8a98-3f3ef04757d2","resolution":{"observed_at":"2026-08-15T20:25:09.528322Z","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":"2026-08-15T20:25:10.506790Z","title":"RAMBO-RL : Robust adversarial model-based offline reinforcement learning","venue":null,"work_id":"d95a158b-1674-4781-8c84-d3efa5837ad9","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.533157Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:8b35b0c8b35e069d49f97676741ddefd4f85bc80dda3df6c2654220fb62f1a62","observation_id":"11a93f55-1063-4b51-b018-0147d8c7307f","resolution":{"observed_at":"2026-08-15T20:25:10.512174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.04747","last_updated":"2017-06-15T13:21:04Z","snapshot_observed_at":"2026-08-15T03:49:17.013617Z","submitted_at":"2016-09-15T17:32:34Z","title":"An overview of gradient descent optimization algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.04747","snapshot_observed_at":"2026-08-15T20:25:09.537802Z","title":"An overview of gradient descent optimization algorithms","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.537802Z"},"links":{"cited_paper":"/paper/1609.04747","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:39901e14dfdf6603caaac4e8f26b7d0805a9c9259c8f58064af64d264b142c66","observation_id":"f6e61a0a-aea5-4b8e-ac32-5eb4289fc6ad","resolution":{"observed_at":"2026-08-15T20:25:09.537802Z","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":"2026-08-15T20:25:10.489363Z","title":"Universal value function approximators","venue":null,"work_id":"ea7d79f9-81b6-4a29-8304-b3e79b14a65f","year":2015},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.542684Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:7cf0c4299a459e500516d799b09afc845b9afd4a845ffb751fca77f5b79a00e4","observation_id":"7695a798-a148-4099-930f-95a27f05bffd","resolution":{"observed_at":"2026-08-15T20:25:10.495276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.469700Z","title":"Reinforcement learning with action-free pre-training from videos","venue":null,"work_id":"6db882e3-d03f-4b2c-9467-7b89b3500f34","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.547429Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:5ffaea36b9145294c162c4ddd194c09e38ee9b0eef5b5f8c0d9afb561e25c792","observation_id":"4ebb8f12-af3b-4ec8-b530-eb1872c3b133","resolution":{"observed_at":"2026-08-15T20:25:10.476581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.452735Z","title":"Skill-based model-based reinforcement learning","venue":null,"work_id":"71059859-ea1a-47b0-8fa4-f170cc586ce3","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.552038Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9b2a7eca82b8bee7111717e2995ff9ab4866381d915b6e08924823bd876f43b5","observation_id":"d3ea083e-aabd-4351-806e-c57a9085a037","resolution":{"observed_at":"2026-08-15T20:25:10.458026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.434728Z","title":"K., and Woo, H","venue":null,"work_id":"3fcf6937-58b9-48df-ad24-abfc82e5f0e4","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.557425Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:4a594d8a627c5e9d3e51726d51a4552cc8604ca23ab9ce202548f7e6f453513c","observation_id":"d1bfc6ce-35c8-4136-8805-abbc65109a81","resolution":{"observed_at":"2026-08-15T20:25:10.440697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.415798Z","title":"S4RL : Surprisingly simple self-supervision for offline reinforcement learning in robotics","venue":null,"work_id":"38d343d6-7d15-49e1-82b8-bc17174b6e84","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.562414Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:75ff70191801237e40ef592a61c1f28cb91a86326325a184d930a2ad46ebf56a","observation_id":"9bf83f89-1d6d-4ae1-8d8f-e57049288fa8","resolution":{"observed_at":"2026-08-15T20:25:10.421547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.394195Z","title":"Offline RL for natural language generation with implicit language Q learning","venue":null,"work_id":"4d03301b-3c90-49e0-a583-e1b4a0dcd2bf","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.568039Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:9e14d3e28429a875880902d5fa4abd38104047add0148945345d0c8382af8ce3","observation_id":"04bf1025-d401-4cdc-bdea-3af053d7825b","resolution":{"observed_at":"2026-08-15T20:25:10.400577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.375270Z","title":"Intrinsic motivation and automatic curricula via asymmetric self-play","venue":null,"work_id":"e7368746-6f22-45d7-88c6-6c1d0b868fae","year":2018},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.573333Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:f27570dbea939957b979e79385bc0250e44de3ef1fe9500a6aeb487b07162d15","observation_id":"c065a14c-3941-4103-893d-eecd266b00f6","resolution":{"observed_at":"2026-08-15T20:25:10.381987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.353497Z","title":"Model- B ellman inconsistency for model-based offline reinforcement learning","venue":null,"work_id":"429b8969-6fb6-4f8f-a55e-84922af0c8da","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.578993Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:02322a3a11538c426ed112b3e2256300006739d416c5a2948834b1ee4099de93","observation_id":"e6a33efc-f50e-4827-bd30-771b9b32f575","resolution":{"observed_at":"2026-08-15T20:25:10.359352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.335504Z","title":"Leveraging factored action spaces for efficient offline reinforcement learning in healthcare","venue":null,"work_id":"cb1afcad-2e4b-455a-bffa-c3193e33496f","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.584308Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:970fe2a63d3f644fa9dedcbd13e1e3fdb00c0ae05d46a05fa196453d03622409","observation_id":"60834f7b-27a5-489c-8e87-fe12b6448648","resolution":{"observed_at":"2026-08-15T20:25:10.341074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.317461Z","title":"Revisiting the minimalist approach to offline reinforcement learning","venue":null,"work_id":"f0d0bac0-a91a-4f4c-8211-8394ff3807d3","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.590352Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:416d9c8417303c5d1ae4bdda4be9e6ec145b8982129f7db06715718579721013","observation_id":"9c3bf189-2b1e-498e-a47a-a193da26113c","resolution":{"observed_at":"2026-08-15T20:25:10.323486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.296525Z","title":"CORL : Research-oriented deep offline reinforcement learning library","venue":null,"work_id":"d96a07e9-8957-4a2f-a0d0-65598657906f","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.595661Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:7007c00c3546e615e0a2d3038f2904e5a6853455063a75dece16f8263b1e4903","observation_id":"e4dd58b2-71ad-426d-a282-a339a3f5cc27","resolution":{"observed_at":"2026-08-15T20:25:10.302213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.278182Z","title":"and Mannor, S","venue":null,"work_id":"78f0e65b-0c8c-4575-bcb9-d25ddee54f10","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.600941Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:a75a31e1f4a8bbc6044d660d8f40bc65a6990e1e79f3dca58d346631a8d84b4e","observation_id":"e34ed147-2a30-4712-9fd6-4f6723d47e53","resolution":{"observed_at":"2026-08-15T20:25:10.284608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.256129Z","title":"Offline reinforcement learning with reverse model-based imagination","venue":null,"work_id":"343c0d2d-9efb-4045-b54d-b48110322063","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.606971Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:e9f772fe9125a1c90f1fd936b2346e6570c55605ecac6ec297d107d8a1a72200","observation_id":"064133d3-37be-42b8-ba84-4e50f3938e4e","resolution":{"observed_at":"2026-08-15T20:25:10.263319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.234433Z","title":"and Isola, P","venue":null,"work_id":"cacc72ce-d42d-4bd2-a73c-73560c7eab72","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.612981Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:771fe225f529402802f9de60de37a874577c1dca875d3067e527ab93b3577dee","observation_id":"389f57b2-b4ce-43d4-9a2f-ae5149ce1488","resolution":{"observed_at":"2026-08-15T20:25:10.240000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.216531Z","title":"Optimal goal-reaching reinforcement learning via quasimetric learning","venue":null,"work_id":"8aab49d9-0269-48e7-a11e-36c6bb8f5818","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.618021Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:23946d308d2956b0d43f77cc2586c945d81884bc659f687984c3db711fa88d3e","observation_id":"f3a0d81e-7b9c-4061-a12a-e24211017145","resolution":{"observed_at":"2026-08-15T20:25:10.222515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.196769Z","title":"Critic regularized regression","venue":null,"work_id":"2fbebf7a-89d3-4938-917b-37a37c798352","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.622779Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b4c76eda972e01c5b56c04ebad332e25c84a1b33b87f8b008c9a9dc3540ff494","observation_id":"498aac16-3c47-4786-8817-530c5f367e61","resolution":{"observed_at":"2026-08-15T20:25:10.203046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.178720Z","title":"OCEAN-MBRL : Offline conservative exploration for model-based offline reinforcement learning","venue":null,"work_id":"11c81ff2-90d5-4b37-857b-88c98334245d","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.627535Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b58ca755b9301e5f303fcec7d53bffa6c2a99324f02b70d203652214263cf6a3","observation_id":"a9e55d8a-34d5-476f-9bd4-ef7926ae8098","resolution":{"observed_at":"2026-08-15T20:25:10.184568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11361","last_updated":"2019-11-26T06:11:34Z","snapshot_observed_at":"2026-08-12T13:17:36.653110Z","submitted_at":"2019-11-26T06:11:34Z","title":"Behavior Regularized Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11361","snapshot_observed_at":"2026-08-15T20:25:09.632253Z","title":"Behavior regularized offline reinforcement learning","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.632253Z"},"links":{"cited_paper":"/paper/1911.11361","citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:622282f5add7cc457c96330d592aa23c330dc7bfde92ccbb7e81850fb01dc5a2","observation_id":"ad7d5074-944c-424e-bc95-982aa4363e5c","resolution":{"observed_at":"2026-08-15T20:25:09.632253Z","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":"2026-08-15T20:25:10.160840Z","title":"A policy-guided imitation approach for offline reinforcement learning","venue":null,"work_id":"dce4ad16-fa39-49e5-8669-ad422bf2c0a6","year":2022},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.637365Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:f9ea88439f448ec4c8de8545526abaf2afb29a000f6819c21663ff6925381d73","observation_id":"a62dea00-cbf1-45b7-874b-be5bf8aef162","resolution":{"observed_at":"2026-08-15T20:25:10.166948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.142821Z","title":null,"venue":null,"work_id":"941d0133-5173-4d51-b79a-06fc29c72364","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.642253Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:2751e4b6b808763a2a308f590010c4b76a0180046d50079a45f4a89d1e494b70","observation_id":"900ecb0c-a6c7-43db-8919-70a9f11f0e77","resolution":{"observed_at":"2026-08-15T20:25:10.148776Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.123868Z","title":"Image augmentation is all you need: Regularizing deep reinforcement learning from pixels","venue":null,"work_id":"617e466b-ec2b-4afa-9b52-63adef4e75c6","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.647705Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:61fbeb051f72bdc42ea5302e821c9e4c29eddde4f675ef663d9ec8506f9bdd35","observation_id":"478de198-5e9d-49f3-8fb8-2f882a3fc592","resolution":{"observed_at":"2026-08-15T20:25:10.129625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.099286Z","title":"MOPO : Model-based offline policy optimization","venue":null,"work_id":"2b33e0ca-aa33-48f8-9eb1-216e5f31ae10","year":2020},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.652509Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:2bcc272c270de32e4673d651a1d13ace9948282075921dfa1a4045e7acd32c69","observation_id":"00f9f802-f9ba-4e02-8452-8db4eec90fee","resolution":{"observed_at":"2026-08-15T20:25:10.105493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.072718Z","title":"COMBO : Conservative offline model-based policy optimization","venue":null,"work_id":"3f40243e-b557-4843-b351-d0d3c0d5db97","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.658143Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b0473de4eaac1ba2b1e65c96754655b78624a897a920aa463f3216bcacbcb7d1","observation_id":"a6832146-84fb-412d-9529-9dca4c43c8dd","resolution":{"observed_at":"2026-08-15T20:25:10.083276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.046744Z","title":"BRAC+ : Improved behavior regularized actor critic for offline reinforcement learning","venue":null,"work_id":"489ac5ec-3015-4a0f-ad0d-1476fb53936e","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.663112Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:869e76bacc51e4722b8d405acf2bf9d2385e69740af1b19afecdfa88a7532f66","observation_id":"374e67d8-7dd6-457a-af84-fb5095ac550d","resolution":{"observed_at":"2026-08-15T20:25:10.052484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.029806Z","title":"Discriminator-guided model-based offline imitation learning","venue":null,"work_id":"118d086b-96da-4bdf-b005-6590fad3ba41","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.667972Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:650fd50ed53b60002a8fb46b3bb772a17169bd7b54aa4825fb243aa136b3271a","observation_id":"b31c4ee2-01e6-49d9-8348-a85cf8f357ef","resolution":{"observed_at":"2026-08-15T20:25:10.035057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:10.011757Z","title":"Contrastive difference predictive coding","venue":null,"work_id":"c82a3131-6934-4811-b84f-722c99d98445","year":2024},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.673237Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:d294ed6b1478d83c9b6d400f9cd469558d1ad4be99f3190c6524ff6934762cfa","observation_id":"b4b97e3c-5988-4b6f-8794-185d72226a91","resolution":{"observed_at":"2026-08-15T20:25:10.017605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:09.993204Z","title":"TACO : Temporal latent action-driven contrastive loss for visual reinforcement learning","venue":null,"work_id":"a3ae2584-3806-475d-b27e-acce5f9c473f","year":2023},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.678721Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b37e84d835a8b4022932004b890eead1e3902eaa7c4af02d7d368fbd2280da0b","observation_id":"35bf770d-1907-4a23-9dc6-6dba20c99c4a","resolution":{"observed_at":"2026-08-15T20:25:09.999200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:09.970814Z","title":"PLAS : Latent action space for offline reinforcement learning","venue":null,"work_id":"07d4ff44-a9ef-414a-ae28-280f7e72c2df","year":2021},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.683966Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:b947c2e4f6dc483876b89cffee6ec94b9531a324f84db86099e588f5a8b94e81","observation_id":"b5471c6f-52fa-432a-b3b6-64483d3709a4","resolution":{"observed_at":"2026-08-15T20:25:09.978520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T20:25:09.689761Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-15T20:25:09.689761Z"},"links":{"citing_paper":"/paper/2505.13144"},"observation_digest":"sha256:742962211f1f0062dcbc213b1f8473031912759c6cdb73f805068e1ca0d4fed2","observation_id":"28f766fc-8c4f-40f2-907a-9b22376fc385","resolution":{"observed_at":"2026-08-15T20:25:09.689761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.13144","last_updated":"2025-05-19T14:11:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T01:31:00.705521Z","submitted_at":"2025-05-19T14:11:14Z","title":"Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning"},"reference_resolution":{"displayed":92,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":71},"total_outbound_references":92},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 1 inbound Pith citation observation for arXiv:2505.13144."}