{"as_of":"2026-08-06T18:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8a938d744c044cc568d22c5631e83d71171c5442a5004474e781a4c77d23496","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T04:39:32.224984Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-04T03:59:33.473334Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1812.11103","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-07-04T03:59:33.473334Z","title":"Learning to Walk via Deep Reinforcement Learning","venue":"cs.LG","work_id":"dc5409c9-eb98-4d56-b7d8-9e2d89497d82","year":2018},"citing_paper":{"arxiv_id":"2603.15759","last_updated":"2026-05-12T15:04:33Z","snapshot_observed_at":"2026-07-06T22:49:19.323519Z","submitted_at":"2026-03-16T18:00:23Z","title":"Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T09:49:03.333757Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2603.15759"},"observation_digest":"sha256:8565062f83a10b3e7130830fed1438fa90a8fbf24a61b0165dc3e8cd011fa219","observation_id":"5344e850-54f4-431e-8aac-6bef68d897ad","resolution":{"observed_at":"2026-05-15T09:49:54.532049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1812.11103","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-07-04T03:59:33.473334Z","title":"Learning to Walk via Deep Reinforcement Learning","venue":"cs.LG","work_id":"dc5409c9-eb98-4d56-b7d8-9e2d89497d82","year":2018},"citing_paper":{"arxiv_id":"2604.02744","last_updated":"2026-04-03T05:37:26Z","snapshot_observed_at":"2026-08-02T06:20:27.430713Z","submitted_at":"2026-04-03T05:37:26Z","title":"Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T20:34:14.475625Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2604.02744"},"observation_digest":"sha256:e0103f3d086dde5a648ab685576fab686b964121d7eba16b001551ed7d94aba6","observation_id":"dcdab800-9235-477a-b240-98a1c7b04adc","resolution":{"observed_at":"2026-05-13T20:38:15.084843Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1812.11103","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-07-04T03:59:33.473334Z","title":"Learning to Walk via Deep Reinforcement Learning","venue":"cs.LG","work_id":"dc5409c9-eb98-4d56-b7d8-9e2d89497d82","year":2018},"citing_paper":{"arxiv_id":"2605.09595","last_updated":"2026-06-09T02:45:20Z","snapshot_observed_at":"2026-08-06T01:08:43.975335Z","submitted_at":"2026-05-10T15:16:07Z","title":"Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-12T04:24:33.237352Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2605.09595"},"observation_digest":"sha256:909b204687b6be1dc04b0e823fadb398bde8a53edb62c0ba8f26941280b06611","observation_id":"7eac299d-3f11-488a-93c3-8a3232e01ae1","resolution":{"observed_at":"2026-05-12T06:16:29.215901Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1812.11103","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-07-04T03:59:33.473334Z","title":"Learning to Walk via Deep Reinforcement Learning","venue":"cs.LG","work_id":"dc5409c9-eb98-4d56-b7d8-9e2d89497d82","year":2018},"citing_paper":{"arxiv_id":"2605.09595","last_updated":"2026-06-09T02:45:20Z","snapshot_observed_at":"2026-08-06T01:08:43.975335Z","submitted_at":"2026-05-10T15:16:07Z","title":"Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-30T22:48:25.063920Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2605.09595"},"observation_digest":"sha256:5a8718940515d5804bd9cdef94d4bf485b16fbf78c130f7428812c8740c0951d","observation_id":"be9cc1ac-1a21-4896-abf0-5eb1aca2a276","resolution":{"observed_at":"2026-07-01T13:45:45.925510Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1812.11103","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-07-04T03:59:33.473334Z","title":"Learning to Walk via Deep Reinforcement Learning","venue":"cs.LG","work_id":"dc5409c9-eb98-4d56-b7d8-9e2d89497d82","year":2018},"citing_paper":{"arxiv_id":"2606.19980","last_updated":"2026-06-18T09:21:27Z","snapshot_observed_at":"2026-08-02T17:59:36.162113Z","submitted_at":"2026-06-18T09:21:27Z","title":"ENPIRE: Agentic Robot Policy Self-Improvement in the Real World","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T17:25:29.469359Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2606.19980"},"observation_digest":"sha256:de7cc8125a4692ff46272cea8f11426fc053f8294326852e90f1ffe4b85d0a40","observation_id":"c503ae92-2b8d-4891-84ca-2cca180361d9","resolution":{"observed_at":"2026-07-04T03:59:33.475797Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.11103","snapshot_observed_at":"2026-08-03T04:39:32.224984Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.29559","last_updated":"2026-07-31T15:50:29Z","snapshot_observed_at":"2026-08-06T05:11:44.921639Z","submitted_at":"2026-07-31T15:50:29Z","title":"LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback","version":1},"reference_index":278,"source":"arxiv_source","source_observed_at":"2026-08-03T04:39:32.224984Z"},"links":{"cited_paper":"/paper/1812.11103","citing_paper":"/paper/2607.29559"},"observation_digest":"sha256:a70a17cbb2df84402ffcd42766092a888072401bf7bd2760d0a9fcd144ccc94a","observation_id":"17fc5320-4f6e-46f2-a632-63646128478c","resolution":{"observed_at":"2026-08-03T04:39:32.224984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1812.11103/citation-record","integrity":"/paper/1812.11103/integrity","json":"/paper/1812.11103/citation-record.json","paper":"/paper/1812.11103"},"outbound":[],"paper":{"arxiv_id":"1812.11103","last_updated":"2019-06-19T17:40:58Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T07:23:52.401834Z","submitted_at":"2018-12-26T10:07:13Z","title":"Learning to Walk via Deep Reinforcement Learning"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1812.11103."}