{"as_of":"2026-08-19T05:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34b5219135566700ee454bcc6b0d38734764b934488380d95692a991057212b4","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:40:19.955005Z","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":"2026-07-04T19:00:06.252923Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2012.05909","last_updated":"2021-04-13T18:07:49Z","snapshot_observed_at":"2026-08-19T00:08:56.683077Z","submitted_at":"2020-12-10T11:32:01Z","title":"Blending MPC & Value Function Approximation for Efficient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.05909","snapshot_observed_at":"2026-08-12T11:40:19.955005Z","title":"Blending mpc & value function approximation for efficient reinforcement learning,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.00086","last_updated":"2024-11-27T03:33:42Z","snapshot_observed_at":"2026-08-18T13:02:04.803542Z","submitted_at":"2024-11-27T03:33:42Z","title":"Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T11:40:19.955005Z"},"links":{"cited_paper":"/paper/2012.05909","citing_paper":"/paper/2412.00086"},"observation_digest":"sha256:3862d83a3512bc5b558dcc6a936bcd13e80c9af7b9dd0ac20bd86574cde0f418","observation_id":"32a6123b-57f1-4c17-a164-c6a99acf5243","resolution":{"observed_at":"2026-08-12T11:40:19.955005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.05909","last_updated":"2021-04-13T18:07:49Z","snapshot_observed_at":"2026-08-19T00:08:56.683077Z","submitted_at":"2020-12-10T11:32:01Z","title":"Blending MPC & Value Function Approximation for Efficient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.05909","snapshot_observed_at":"2026-08-04T23:17:52.963414Z","title":"Blending mpc & value function approximation for efficient reinforcement learning,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.06714","last_updated":"2025-09-08T14:09:33Z","snapshot_observed_at":"2026-08-16T16:36:51.404187Z","submitted_at":"2025-09-08T14:09:33Z","title":"RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T23:17:52.963414Z"},"links":{"cited_paper":"/paper/2012.05909","citing_paper":"/paper/2509.06714"},"observation_digest":"sha256:e893c26c5e8812c8c155529600f43c9142c600649ab1d8f465ca267ddb719f13","observation_id":"c06e1088-12c5-45ca-98b4-f21f0ac7a269","resolution":{"observed_at":"2026-08-04T23:17:52.963414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.05909","last_updated":"2021-04-13T18:07:49Z","snapshot_observed_at":"2026-08-19T00:08:56.683077Z","submitted_at":"2020-12-10T11:32:01Z","title":"Blending MPC & Value Function Approximation for Efficient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2012.05909","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2012.05909","snapshot_observed_at":"2026-07-04T19:00:06.252923Z","title":"arXiv preprint arXiv:2012.05909 , year=","venue":null,"work_id":"4ac0ac48-bafd-4e7f-b0d4-e9469b0839a8","year":2012},"citing_paper":{"arxiv_id":"2606.10825","last_updated":"2026-06-09T13:09:21Z","snapshot_observed_at":"2026-07-06T23:49:56.404171Z","submitted_at":"2026-06-09T13:09:21Z","title":"MODIP: Efficient Model-Based Optimization for Diffusion Policies","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-27T13:44:19.708550Z"},"links":{"cited_paper":"/paper/2012.05909","citing_paper":"/paper/2606.10825"},"observation_digest":"sha256:dc3714ea5d53ef0b83aff9d970c130aec5199b1c5c6ceac211f088a523b368e6","observation_id":"b0374d4f-b61d-47d8-a87f-2ba1770a2d91","resolution":{"observed_at":"2026-07-03T04:37:37.555572Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2012.05909","last_updated":"2021-04-13T18:07:49Z","snapshot_observed_at":"2026-08-19T00:08:56.683077Z","submitted_at":"2020-12-10T11:32:01Z","title":"Blending MPC & Value Function Approximation for Efficient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2012.05909","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2012.05909","snapshot_observed_at":"2026-07-04T19:00:06.252923Z","title":"arXiv preprint arXiv:2012.05909 , year=","venue":null,"work_id":"4ac0ac48-bafd-4e7f-b0d4-e9469b0839a8","year":2012},"citing_paper":{"arxiv_id":"2606.24991","last_updated":"2026-06-23T14:51:59Z","snapshot_observed_at":"2026-08-17T15:01:33.366712Z","submitted_at":"2026-06-23T14:51:59Z","title":"Solving Markov Decision Processes with Future Information via MPC","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-06-25T22:00:02.577707Z"},"links":{"cited_paper":"/paper/2012.05909","citing_paper":"/paper/2606.24991"},"observation_digest":"sha256:2dbd35ad079be341c6916869a5bf803cdcb0650c495fbc978f473833a6f8ff7d","observation_id":"efc0843b-b95c-44d9-b03c-958abd52932b","resolution":{"observed_at":"2026-07-04T19:00:06.254484Z","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/2012.05909/citation-record","integrity":"/paper/2012.05909/integrity","json":"/paper/2012.05909/citation-record.json","paper":"/paper/2012.05909"},"outbound":[],"paper":{"arxiv_id":"2012.05909","last_updated":"2021-04-13T18:07:49Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T00:08:56.683077Z","submitted_at":"2020-12-10T11:32:01Z","title":"Blending MPC & Value Function Approximation for Efficient 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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2012.05909."}