{"as_of":"2026-08-08T06:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a9cedf3e124cee5c8614692b6e3b20c76e56cb0be93b248181ce58db0b1aa01","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:17:07.704599Z","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":"2026-05-16T12:55:40.303269Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":"2408.03539","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2408.03539 (2024) 2","venue":null,"work_id":"e60b7581-8fb7-4759-9580-285bffc4c4fd","year":2024},"citing_paper":{"arxiv_id":"2505.18719","last_updated":"2025-05-24T14:42:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-24T14:42:51Z","title":"VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-16T12:55:40.245908Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2505.18719"},"observation_digest":"sha256:994450f7cd625521eb36e3426884eabf7cc476223945175cdbe647399f71ae1e","observation_id":"f71efbaf-08a7-48ce-99b5-42145d35d2c2","resolution":{"observed_at":"2026-05-16T12:55:40.305488Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-07T13:17:07.704599Z","title":"Deep reinforce- ment learning for robotics: A survey of real-world successes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00043","last_updated":"2025-05-28T11:21:33Z","snapshot_observed_at":"2026-08-07T13:09:45.267667Z","submitted_at":"2025-05-28T11:21:33Z","title":"From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T13:17:07.704599Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2506.00043"},"observation_digest":"sha256:159f76668beaa9d720e0a830717383ad7ac01f5b3eded803ce026a8848785b3e","observation_id":"d2d5d553-adf2-4344-aef8-99a91d835eb0","resolution":{"observed_at":"2026-08-07T13:17:07.704599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-07T05:10:10.450532Z","title":"Deep reinforcement learning for robotics: A survey of real- world successes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08856","last_updated":"2025-06-10T14:44:40Z","snapshot_observed_at":"2026-08-07T04:57:52.807086Z","submitted_at":"2025-06-10T14:44:40Z","title":"Fast Estimation of Globally Optimal Independent Contact Regions for Robust Grasping and Manipulation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:10.450532Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2506.08856"},"observation_digest":"sha256:5cf170bb5d68880039528b2025b35848ab258a28691560797498486498ebffb0","observation_id":"c9346b67-0e30-4054-b925-b411996995bb","resolution":{"observed_at":"2026-08-07T05:10:10.450532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-07T00:55:17.013563Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12312","last_updated":"2025-06-14T02:22:28Z","snapshot_observed_at":"2026-08-07T12:23:07.508214Z","submitted_at":"2025-06-14T02:22:28Z","title":"Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:17.013563Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2506.12312"},"observation_digest":"sha256:8fb31ab57d605c130d47202b691d140d3edba52f5e9ac49c7fdd67e269dbccea","observation_id":"133df37f-1da7-4a14-8587-0b0219ea0ed7","resolution":{"observed_at":"2026-08-07T00:55:17.013563Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-06T10:22:39.899194Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.00174","last_updated":"2025-07-31T21:39:24Z","snapshot_observed_at":"2026-08-06T10:22:38.801716Z","submitted_at":"2025-07-31T21:39:24Z","title":"RL as Regressor: A Reinforcement Learning Approach for Function Approximation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T10:22:39.899194Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2508.00174"},"observation_digest":"sha256:1b8b929d6feec511223cb9019e7101fe5a6476b4b13f6a23e3bf117421b96184","observation_id":"71c9dd8f-fd74-41b4-beb6-56d24296c8b6","resolution":{"observed_at":"2026-08-06T10:22:39.899194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-04T15:18:02.500174Z","title":"Deep reinforcement learning for robotics: A survey of real- world successes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.20623","last_updated":"2026-06-01T22:34:49Z","snapshot_observed_at":"2026-08-04T15:18:01.516373Z","submitted_at":"2025-09-24T23:58:23Z","title":"Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T15:18:02.500174Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2509.20623"},"observation_digest":"sha256:315be4d9412bacfe9686437741f3aa095a3ec7b338b5f4743de440e873e27cf1","observation_id":"2f9521a9-f171-4ad0-bdd4-1165b78b3942","resolution":{"observed_at":"2026-08-04T15:18:02.500174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":"2408.03539","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2408.03539 (2024) 2","venue":null,"work_id":"e60b7581-8fb7-4759-9580-285bffc4c4fd","year":2024},"citing_paper":{"arxiv_id":"2605.11697","last_updated":"2026-05-12T07:53:28Z","snapshot_observed_at":"2026-08-07T05:04:29.706221Z","submitted_at":"2026-05-12T07:53:28Z","title":"Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-13T06:15:01.077535Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2605.11697"},"observation_digest":"sha256:ccc7b26b2657f40b3d37b9f0b7b7d28b9e05a2e2510d2f4af6ff6fd07f89503d","observation_id":"066f57d9-e8a0-41f9-9e8f-50480fb642a2","resolution":{"observed_at":"2026-05-13T06:17:22.981802Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03539","snapshot_observed_at":"2026-08-05T15:25:40.543647Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03644","last_updated":"2026-08-04T13:29:00Z","snapshot_observed_at":"2026-08-07T23:12:14.215199Z","submitted_at":"2026-08-04T13:29:00Z","title":"Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details","version":1},"reference_index":256,"source":"arxiv_source","source_observed_at":"2026-08-05T15:25:40.543647Z"},"links":{"cited_paper":"/paper/2408.03539","citing_paper":"/paper/2608.03644"},"observation_digest":"sha256:fd40c2904c6c7db0d43b9b8b633bbca847359717a087dea1600f84a56029fcc3","observation_id":"3ae82f45-9016-431e-800c-995033ea0cae","resolution":{"observed_at":"2026-08-05T15:25:40.543647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.03539/citation-record","integrity":"/paper/2408.03539/integrity","json":"/paper/2408.03539/citation-record.json","paper":"/paper/2408.03539"},"outbound":[],"paper":{"arxiv_id":"2408.03539","last_updated":"2024-09-16T15:41:05Z","latest_version":3,"primary_category":"cs.RO","snapshot_observed_at":"2026-07-06T18:57:43.764663Z","submitted_at":"2024-08-07T04:35:38Z","title":"Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.03539."}