{"as_of":"2026-08-10T03:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:96d21229502b8ffdcb491543573ceaf8e2f5a4cf85301f3582d092a28c88aced","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:49:12.947806Z","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":"2026-07-03T02:07:34.156817Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"1907.00664","last_updated":"2019-07-01T11:22:52Z","snapshot_observed_at":"2026-07-06T08:04:02.269421Z","submitted_at":"2019-07-01T11:22:52Z","title":"Learning World Graphs to Accelerate Hierarchical Reinforcement Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-25T12:31:38.848720Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/1907.00664"},"observation_digest":"sha256:d26c42c427dd517b63ab4343d22ab3fcf947e1681544c4af69e1a72cd907b5f2","observation_id":"81199ecb-c522-41f6-b07a-18363b25401f","resolution":{"observed_at":"2026-05-25T12:35:49.133772Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2409.12917","last_updated":"2024-10-04T17:28:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-19T17:16:21Z","title":"Training Language Models to Self-Correct via Reinforcement Learning","version":2},"reference_index":217,"source":"arxiv_source","source_observed_at":"2026-05-17T12:04:10.210508Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2409.12917"},"observation_digest":"sha256:819451159896d9ee8e4b43b505148262f06e1f5b1e75bcdd34846dd05e9ae063","observation_id":"257f28c5-d6ed-4b06-bfeb-5d0b6e4cb1e6","resolution":{"observed_at":"2026-05-17T12:04:10.833730Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-06T23:49:12.947806Z","title":"Learning multi-level hierarchies with hindsight,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.16336","last_updated":"2025-06-19T14:14:55Z","snapshot_observed_at":"2026-08-09T21:09:22.863034Z","submitted_at":"2025-06-19T14:14:55Z","title":"Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:49:12.947806Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2506.16336"},"observation_digest":"sha256:70438113936278344246e7805ce3e65fd73479dafe981177a8d0c5369265de12","observation_id":"77b7febc-4a27-4da2-bba3-9c65c30fe74c","resolution":{"observed_at":"2026-08-06T23:49:12.947806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2506.21039","last_updated":"2026-05-20T06:53:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-26T06:35:42Z","title":"Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T00:53:46.002945Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2506.21039"},"observation_digest":"sha256:371d6e8cde9ed97529f0ed0e50ab81556c9e0d2ba9c68a9564c16d4ad3289b06","observation_id":"55873d67-a843-40b5-a060-7d878673b2f0","resolution":{"observed_at":"2026-05-22T00:54:31.206433Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2509.00338","last_updated":"2026-05-08T15:17:41Z","snapshot_observed_at":"2026-08-02T17:36:08.274992Z","submitted_at":"2025-08-30T03:42:10Z","title":"Scalable Option Learning in High-Throughput Environments","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-18T20:04:58.064472Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2509.00338"},"observation_digest":"sha256:292c77e63441d90536b013c59f40062e9a4bb9e5012813477708416dc5cb8ea3","observation_id":"c4a50341-8965-468d-98c0-0d740f3f11d1","resolution":{"observed_at":"2026-05-18T20:06:49.738978Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-03T03:18:43.091575Z","title":"Hier- archical actor-critic.arXiv:1712.00948, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.08557","last_updated":"2026-06-30T09:31:26Z","snapshot_observed_at":"2026-08-09T14:57:06.127218Z","submitted_at":"2026-02-09T11:54:45Z","title":"Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T03:18:43.091575Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2602.08557"},"observation_digest":"sha256:b670db4839d6a5658b5bd465ba96e51f3bef634fe2a7d3b2ec2416b61f908dbb","observation_id":"5e35741b-8585-4856-96b7-a9ebb544b128","resolution":{"observed_at":"2026-08-03T03:18:43.091575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2605.01862","last_updated":"2026-05-08T03:23:44Z","snapshot_observed_at":"2026-07-06T23:15:01.968194Z","submitted_at":"2026-05-03T13:11:28Z","title":"QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL","version":2},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-05-11T01:17:48.643521Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2605.01862"},"observation_digest":"sha256:0fe88e8e28822e71b91dcc277c530b4b3a49391b817465e4e52f66f3ac1b04bf","observation_id":"a0c15070-9519-4b6b-864c-67ed56e00bd4","resolution":{"observed_at":"2026-05-11T04:30:58.065014Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2605.12261","last_updated":"2026-05-12T15:28:32Z","snapshot_observed_at":"2026-08-04T14:43:17.447713Z","submitted_at":"2026-05-12T15:28:32Z","title":"Delay-Empowered Causal Hierarchical Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T05:46:51.659283Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2605.12261"},"observation_digest":"sha256:9a8de59ad118d8ad8cad7abbe97464d1d208d38f5941e982702865a149eca83b","observation_id":"10a79b59-f989-4d6f-9a5f-ff99fc4abf32","resolution":{"observed_at":"2026-05-13T05:47:21.233473Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2605.22711","last_updated":"2026-05-21T16:50:26Z","snapshot_observed_at":"2026-07-06T23:32:59.663926Z","submitted_at":"2026-05-21T16:50:26Z","title":"Abstraction for Offline Goal-Conditioned Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-22T07:46:20.289421Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2605.22711"},"observation_digest":"sha256:1f178df2ca0ace003b6b44f3813a6d1b55962e98598a51c78a023f83a552ef4c","observation_id":"11ae49c6-7149-4f27-829e-509333980c88","resolution":{"observed_at":"2026-05-22T07:51:16.522569Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":"1712.00948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-03T02:07:34.156817Z","title":"Learning Multi-Level Hi- erarchies with Hindsight, September 2019","venue":null,"work_id":"610e4e55-7fea-4113-bbed-a6a8ca756e07","year":2017},"citing_paper":{"arxiv_id":"2606.09476","last_updated":"2026-06-08T13:35:22Z","snapshot_observed_at":"2026-08-01T13:38:00.934917Z","submitted_at":"2026-06-08T13:35:22Z","title":"Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T16:07:28.361043Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2606.09476"},"observation_digest":"sha256:1298dbba62125a8971cfbbba227a2b9df5bbe421e1ef66fd2aecef99f9ff966d","observation_id":"4bd6a974-0da4-402a-b89c-7116527be498","resolution":{"observed_at":"2026-07-03T02:07:34.158320Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-02T03:42:47.680209Z","title":"Learning multi-level hierarchies with hindsight.arXiv preprint arXiv:1712.00948, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.13799","last_updated":"2026-07-15T13:07:25Z","snapshot_observed_at":"2026-08-07T09:30:39.356505Z","submitted_at":"2026-07-15T13:07:25Z","title":"Vision-Based Obstacle Separation for Strawberry Harvesting in Clusters Using Hierarchical Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T03:42:47.680209Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2607.13799"},"observation_digest":"sha256:8db5794863948fa94f7034508fee26ea78d98bbc68307e4f95e0971428eefa17","observation_id":"99c8ec5e-840c-437a-9ce2-1e0ccfab4028","resolution":{"observed_at":"2026-08-02T03:42:47.680209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-01T13:10:39.713561Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.19232","last_updated":"2026-07-21T16:03:34Z","snapshot_observed_at":"2026-08-06T14:25:31.245866Z","submitted_at":"2026-07-21T16:03:34Z","title":"S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T13:10:39.713561Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2607.19232"},"observation_digest":"sha256:4c4e9075eecef78c8810af7e89e04007c85189f4fdd621c3dd87cc17febfab0b","observation_id":"fcd44bf2-ed5b-4e57-bc47-5d60f92935d3","resolution":{"observed_at":"2026-08-01T13:10:39.713561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-07-30T14:49:29.163862Z","title":"Chenghao Liu, Fei Zhu, Quan Liu, and Yuchen Fu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23726","last_updated":"2026-07-26T15:41:52Z","snapshot_observed_at":"2026-08-08T06:25:45.831329Z","submitted_at":"2026-07-26T15:41:52Z","title":"Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-30T14:49:29.163862Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2607.23726"},"observation_digest":"sha256:0b29924d0361e2037852984f5bb28040ed0d5db78d22abef2daf5b7ff9ac6a33","observation_id":"e82ab8b3-acef-4e7a-a1df-96add91289d3","resolution":{"observed_at":"2026-07-30T14:49:29.163862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-05T00:28:21.497424Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.00750","last_updated":"2026-08-01T16:33:15Z","snapshot_observed_at":"2026-08-06T23:19:16.536876Z","submitted_at":"2026-08-01T16:33:15Z","title":"Hierarchical Residual Policy Optimization for Generative Recommendations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T00:28:21.497424Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2608.00750"},"observation_digest":"sha256:98ae3bbaf918c1401583f3a6298752fd475210c00434c6b79cd408e7676a52c9","observation_id":"460194cc-9184-4098-9d02-d495822f57c1","resolution":{"observed_at":"2026-08-05T00:28:21.497424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00948","snapshot_observed_at":"2026-08-04T19:45:34.875368Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.01851","last_updated":"2026-08-03T07:58:35Z","snapshot_observed_at":"2026-08-07T23:22:57.641630Z","submitted_at":"2026-08-03T07:58:35Z","title":"Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills","version":1},"reference_index":132,"source":"pdf_text","source_observed_at":"2026-08-04T19:45:34.875368Z"},"links":{"cited_paper":"/paper/1712.00948","citing_paper":"/paper/2608.01851"},"observation_digest":"sha256:707bd1f20b22ee3422cf3673b03ce8401b2b394fa31fd6aa33b3d5abcf5493a5","observation_id":"72a1f002-9694-4f28-b2f1-e02318a63af0","resolution":{"observed_at":"2026-08-04T19:45:34.875368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1712.00948/citation-record","integrity":"/paper/1712.00948/integrity","json":"/paper/1712.00948/citation-record.json","paper":"/paper/1712.00948"},"outbound":[],"paper":{"arxiv_id":"1712.00948","last_updated":"2019-09-03T21:05:21Z","latest_version":5,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T00:36:08.175723Z","submitted_at":"2017-12-04T08:18:08Z","title":"Learning Multi-Level Hierarchies with Hindsight"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:1712.00948."}