{"as_of":"2026-08-07T17:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:518e5406802a4fb7324309a74a5f8f7997087b847a0de75cd50afe787f3c8fbf","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:02:23.080991Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.01243/citation-record","integrity":"/paper/2507.01243/integrity","json":"/paper/2507.01243/citation-record.json","paper":"/paper/2507.01243"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:02:22.976670Z","title":"Advances in real-world applications for legged robots,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.976670Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:23ad5c086e369de2d092d38396e06b0cdd7697394378111d3790ce1a42a760a1","observation_id":"207c1f1e-2712-47c9-9e46-ec7fae8c15de","resolution":{"observed_at":"2026-08-06T21:02:22.976670Z","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":"2026-08-06T21:02:23.361051Z","title":"Mul- timodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue,","venue":null,"work_id":"13e86160-3351-4aea-92c8-c93380557339","year":2022},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.981709Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:9102b197c595cf9f123be83dba3d51263ba4e423f6da453cd598aad0352290d2","observation_id":"1e7fa7dc-77fb-4202-955b-d5efbf3f30d6","resolution":{"observed_at":"2026-08-06T21:02:23.364797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:22.985354Z","title":"Extreme parkour with legged robots,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.985354Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:29b1e77d1e5273e53e0419bb11d8857613f84848f5260c7e03952db43fc75ea0","observation_id":"17ff5c4a-b7cc-4821-9c1a-3a5a8d7e3d94","resolution":{"observed_at":"2026-08-06T21:02:22.985354Z","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":"2026-08-06T21:02:22.989795Z","title":"Learning agile loco- motion on risky terrains,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.989795Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:f420e7bb0a48d25052188d20caaa1ef10beecb03f2f494edca923cfbeebe841f","observation_id":"e9f1078d-e6b1-448b-9c67-2171d6b2e2f1","resolution":{"observed_at":"2026-08-06T21:02:22.989795Z","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":"2026-08-06T21:02:22.994720Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.994720Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:205230472eafe62bfc6e13638bbc76ad71b8eba6a92727f081260078f48a1838","observation_id":"bed6b29f-3083-4e9b-958b-20281d7114dc","resolution":{"observed_at":"2026-08-06T21:02:22.994720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06662","last_updated":"2025-08-05T20:44:52Z","snapshot_observed_at":"2026-08-07T16:07:17.078968Z","submitted_at":"2025-04-09T07:53:09Z","title":"RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06662","snapshot_observed_at":"2026-08-06T21:02:22.998300Z","title":"Rambo: Rl-augmented model-based optimal control for whole-body loco- manipulation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:22.998300Z"},"links":{"cited_paper":"/paper/2504.06662","citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:367578242f8754a563ad2ecd7e34d364e3b9096d5099a7e3103d1c572ab1e6a6","observation_id":"6a290992-2881-4971-b111-2b0b59fe03c5","resolution":{"observed_at":"2026-08-06T21:02:22.998300Z","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":"2026-08-06T21:02:23.332192Z","title":"Enhance generality by model-based reinforcement learning and domain ran- domization,","venue":null,"work_id":"6a5ba92c-f7fa-432f-b292-975e1a33a38f","year":2023},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.002142Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:8ce1c009ee1d8a9d71acedca0be57dc90f27be508117b89ce1d51f76cbfed43e","observation_id":"667ec2e9-c92b-4066-abdf-6f38513a0ee1","resolution":{"observed_at":"2026-08-06T21:02:23.336118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.321750Z","title":"Transferable latent-to-latent locomotion policy for efficient and versatile motion control of diverse legged robots,","venue":null,"work_id":"6dc06d32-a33e-486c-a11f-ca1a2783f806","year":2025},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.005711Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:0eb28c1198116f3e7fdec6218787642f8116a09e4865c08ace6a690519b8dfef","observation_id":"e7f409b2-635d-4cb1-84c1-300bc95dee64","resolution":{"observed_at":"2026-08-06T21:02:23.325185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15692","last_updated":"2025-03-03T08:59:56Z","snapshot_observed_at":"2026-07-06T19:20:55.427960Z","submitted_at":"2024-09-24T03:11:02Z","title":"Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15692","snapshot_observed_at":"2026-08-06T21:02:23.009017Z","title":"Walking with terrain reconstruction: Learning to traverse risky sparse footholds,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.009017Z"},"links":{"cited_paper":"/paper/2409.15692","citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:0dcbf92397e5d6ff53815365fdd29571cbb8b9ddaa02a712ada3574759f4bbf8","observation_id":"2a1231c0-d1a6-458d-ab58-4e0de58ff4db","resolution":{"observed_at":"2026-08-06T21:02:23.009017Z","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":"2026-08-06T21:02:23.013186Z","title":"Learning robust perceptive locomotion for quadrupedal robots in the wild,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.013186Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:5bd97d0b5aeda78db484a381a4809cf42f2329d629f698a07471fbc141eede41","observation_id":"738e41a1-d545-49d5-82b4-14f42f0fb23e","resolution":{"observed_at":"2026-08-06T21:02:23.013186Z","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":"2026-08-06T21:02:23.016577Z","title":"Learning quadrupedal locomotion over challenging terrain,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.016577Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:cc1368bef51e4302c29603c1b9d5d80abfbdd13bc108af447c4e46374b53a1cf","observation_id":"cf82c1ac-f8d7-4402-88f2-1bb5ead01272","resolution":{"observed_at":"2026-08-06T21:02:23.016577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16784","last_updated":"2024-09-25T09:47:31Z","snapshot_observed_at":"2026-07-06T19:21:41.819005Z","submitted_at":"2024-09-25T09:47:31Z","title":"World Model-based Perception for Visual Legged Locomotion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.16784","snapshot_observed_at":"2026-08-06T21:02:23.019808Z","title":"World model-based perception for visual legged locomotion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.019808Z"},"links":{"cited_paper":"/paper/2409.16784","citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:0e5b405dc6d55feec7d0f03e93f3e3f4cb93bd3d48ca3f2707729ef21de4b024","observation_id":"b159802d-1622-4ad1-8f48-a5ec6ae7bccd","resolution":{"observed_at":"2026-08-06T21:02:23.019808Z","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":"2026-08-06T21:02:23.299334Z","title":"Underactuated robotics: Learning, planning, and control for efficient and agile machines course notes for mit 6.832,","venue":null,"work_id":"a8a533a7-8f09-42e9-88d5-4bb1abe09366","year":2009},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.024009Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:8ccb782ff9b43c51b4eb216a9181fd504740dc258923b7aa53551a7b82728c3c","observation_id":"9a5ebd54-e6e5-4941-94b1-7e6855224a26","resolution":{"observed_at":"2026-08-06T21:02:23.303699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.288180Z","title":"Task- space riccati feedback based whole body control for underactuated legged locomotion,","venue":null,"work_id":"a6789700-eafd-4a7b-82e9-cbb37ee0712f","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.027451Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:a4728d5c9ccf3df8ce123d336209cdb66a7565a50c49d6421ee0d75902845c0f","observation_id":"2e6c2e7c-9598-46aa-87ca-b1649748a032","resolution":{"observed_at":"2026-08-06T21:02:23.292221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.277207Z","title":"Meta-learning for fast adaptive locomotion with uncertainties in environments and robot dynamics,","venue":null,"work_id":"b4514dd6-54ae-4fa7-8c0c-3616d4d8f3de","year":2021},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.030471Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:50e61094efacb6ba2f9559a05ec7142cf782ce20e9ce25155aec3cd6b1f4b71f","observation_id":"8bb46dcd-0f65-4358-910f-ab8a7f1c2034","resolution":{"observed_at":"2026-08-06T21:02:23.281508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.265805Z","title":"Multi-task learning of active fault-tolerant controller for leg failures in quadruped robots,","venue":null,"work_id":"e37464ef-f764-452e-a2dd-e15de65bd6e3","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.034461Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:a353dd68ee2b9bfc9f482e79e73df6ad62e181401327b58e86ce20262f03c0dd","observation_id":"5e38a46e-3ad0-481f-9ce3-8248d8eeb74a","resolution":{"observed_at":"2026-08-06T21:02:23.270172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.255845Z","title":"Towards fault-tolerant quadruped loco- motion with reinforcement learning,","venue":null,"work_id":"d761c1de-64df-40ea-86e1-5e5e8b3e88dc","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.037688Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:73d4aebc65394295031db4ee7cbe48bd00f16238414bcc1cf217c6d147f8bbe6","observation_id":"686fa5c2-0143-49e5-b990-cfc021141c22","resolution":{"observed_at":"2026-08-06T21:02:23.259387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21401","last_updated":"2025-03-28T06:15:56Z","snapshot_observed_at":"2026-08-07T16:33:08.190685Z","submitted_at":"2025-03-27T11:47:20Z","title":"AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control","version":2},"cited_work":{"arxiv_id":"2503.21401","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.21401","snapshot_observed_at":"2026-08-06T21:02:23.121438Z","title":"AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control","venue":"cs.RO","work_id":"04335908-d9d6-4a1d-aff9-c57ce4673ef0","year":2025},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.040718Z"},"links":{"cited_paper":"/paper/2503.21401","citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:8f64520183a857b43f09af4ff333065363332208e71c77fa698f49464101248f","observation_id":"eb4bccf0-ddf5-41a6-b841-abdbca8e357e","resolution":{"observed_at":"2026-08-06T21:02:23.127445Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.246009Z","title":"Learning agile bipedal motions on a quadrupedal robot,","venue":null,"work_id":"4a081a35-5699-491e-8283-dd7d36a0bbd6","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.044104Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:04f4d8973c6954f486b3390699d0451a9378fd661690ce1ec5e784f9a16cba39","observation_id":"baa69d99-e1ef-4b4a-9731-a0494ebeeb67","resolution":{"observed_at":"2026-08-06T21:02:23.249484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.236632Z","title":"Jump-start reinforcement learning,","venue":null,"work_id":"cfb0023e-aeb6-4be8-bdda-6c276282b576","year":2023},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.048074Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:1ecefabb2295879cd0e48f902fe5058421feffa959298fff236ee232fcfc67e4","observation_id":"5b65ffd7-531f-4db3-ad33-0a3324c63738","resolution":{"observed_at":"2026-08-06T21:02:23.240135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.227275Z","title":"Rocket landing control with random annealing jump start reinforcement learning,","venue":null,"work_id":"5a5b7698-92f1-41c2-8725-da141a26df07","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.051097Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:afa025b715def41e0d89050f1da3df882ddcec69ddc88bd4753581c2f06d6179","observation_id":"c87caecf-8fc2-452c-91c0-994b55a6ac82","resolution":{"observed_at":"2026-08-06T21:02:23.230664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.217917Z","title":"A transformation-aggregation framework for state representation of au- tonomous driving systems,","venue":null,"work_id":"5e440c35-b967-4985-8d15-195a440489de","year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.054123Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:602de6cabdcbc4d7f0499fd6a926080b4546a187fdbe5ba0591bb73cd65ae362","observation_id":"13f8c0da-abad-4a74-b4eb-239cb619301b","resolution":{"observed_at":"2026-08-06T21:02:23.221470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T21:02:23.057092Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.057092Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:998ad8daa7623d822c7fd3ecba05a36281155cf05410677a39cc32dc58fb0391","observation_id":"8c8cfcea-1aa9-49d7-b01f-65d0c2271982","resolution":{"observed_at":"2026-08-06T21:02:23.057092Z","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":"2026-08-06T21:02:23.207908Z","title":"High- dimensional continuous control using generalized advantage estima- tion,","venue":null,"work_id":"869a5c55-ae59-489e-a5d3-1a6675dc5c01","year":2016},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.061537Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:830de83a5e86d657325f0278fe6ef935fc890f56aecfdd87c6c63e5fa31675a0","observation_id":"1bbec26f-7f33-43af-89ea-c631f63a1b6e","resolution":{"observed_at":"2026-08-06T21:02:23.210997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.198503Z","title":"Isaac gym: High performance gpu based physics simulation for robot learning,","venue":null,"work_id":"a14f172e-2e00-4038-9356-fe37c5c5fc9f","year":2021},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.065533Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:d3d8ded745c22328364e23804fcacd998b828eea00de2e14c8b0909fa4882486","observation_id":"560a2264-f893-4870-ad32-c0098e70c677","resolution":{"observed_at":"2026-08-06T21:02:23.202266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.069606Z","title":"Orbit: A unified simulation framework for interactive robot learning environments,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.069606Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:c1cd4492963464ba464a2b4efd263ed271fa89205af92c8a804b27f74f74b1f9","observation_id":"9e525617-bb5d-4429-8e91-080d52d0f5e2","resolution":{"observed_at":"2026-08-06T21:02:23.069606Z","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":"2026-08-06T21:02:23.073719Z","title":"Learning to walk in minutes using massively parallel deep reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.073719Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:720660b2724aea994dff5ac9478ddfe69bc78905801617f4c1c7a388c703c9e2","observation_id":"0256d215-b7f8-48e0-a852-8507255aa5b9","resolution":{"observed_at":"2026-08-06T21:02:23.073719Z","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":"2026-08-06T21:02:23.176926Z","title":"Conformal symplectic optimization for stable reinforcement learn- ing,","venue":null,"work_id":"d8c99509-4cd7-4006-888a-5cbf02ab0ef9","year":2025},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.077200Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:b2731f9f68650427e031b4ab4d6184c1995c894f299b30df7048604b6f78b318","observation_id":"d45a85db-94f9-4a42-87c8-444dd2a25df7","resolution":{"observed_at":"2026-08-06T21:02:23.180721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:02:23.080991Z","title":"Curriculum-based reinforcement learning for quadrupedal jumping: A reference-free design,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:23.080991Z"},"links":{"citing_paper":"/paper/2507.01243"},"observation_digest":"sha256:fdf68a57a3dbc52a2d9afb07afff7bfac0fd5745f879d6dd1da0c0db6fd338a5","observation_id":"dbb77371-d61c-4121-ae43-2484292816f1","resolution":{"observed_at":"2026-08-06T21:02:23.080991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.01243","last_updated":"2025-07-01T23:31:36Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-06T20:54:12.434351Z","submitted_at":"2025-07-01T23:31:36Z","title":"Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":29},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.01243."}