{"as_of":"2026-08-08T17:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9cd8c3649af50b9cbab3beff67438a11c14c59ea6b28e724973d203fe76a72d0","coverage":[{"denominator":5,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T21:58:53.239986Z","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":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T12:45:19.511401Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.19357","snapshot_observed_at":"2026-07-14T12:45:19.511401Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Rein- forcement Learning on Robots,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10309","last_updated":"2026-07-11T13:28:50Z","snapshot_observed_at":"2026-08-04T14:09:35.332189Z","submitted_at":"2026-07-11T13:28:50Z","title":"Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T12:45:19.511401Z"},"links":{"cited_paper":"/paper/2606.19357","citing_paper":"/paper/2607.10309"},"observation_digest":"sha256:52d79ee9a0e33a396ed99772e42942b21e8b7613e87bf94f76765412a64fd4f0","observation_id":"7a3e1acc-c392-4b3c-adb0-14a303a17d80","resolution":{"observed_at":"2026-07-14T12:45:19.511401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2606.19357/citation-record","integrity":"/paper/2606.19357/integrity","json":"/paper/2606.19357/citation-record.json","paper":"/paper/2606.19357"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T21:58:53.239986Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T21:58:53.239986Z"},"links":{"citing_paper":"/paper/2606.19357"},"observation_digest":"sha256:00d31237deddac6ae2c060c41c8f407cd93c4edfc8643cd5fd9ac72ba1469609","observation_id":"67b4b40c-95a6-4ca4-b773-be90a98b79a3","resolution":{"observed_at":"2026-06-28T21:58:53.239986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.07113","last_updated":"2019-10-16T00:59:05Z","snapshot_observed_at":"2026-08-02T15:37:37.200292Z","submitted_at":"2019-10-16T00:59:05Z","title":"Solving Rubik's Cube with a Robot Hand","version":1},"cited_work":{"arxiv_id":"1910.07113","doi":"10.48550/arxiv.1910.07113","metadata_source":"pith","pith_arxiv_id":"1910.07113","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Solving Rubik's Cube with a Robot Hand","venue":"cs.LG","work_id":"81bf9cee-6de8-49f6-b967-3cb853e5ba67","year":2019},"citing_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T21:58:53.239986Z"},"links":{"cited_paper":"/paper/1910.07113","citing_paper":"/paper/2606.19357"},"observation_digest":"sha256:17097905d13acd23012c5c566bca7533679114c058a4fad3637b94e183677e02","observation_id":"c153920e-50d5-481e-926e-2e3bd3aed54a","resolution":{"observed_at":"2026-07-01T19:56:10.327387Z","resolver_source":"local_arxiv","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":"2026-05-22T19:53:19.266123+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T19:53:19.266123+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-06-28T21:58:53.239986Z","title":"& Silver, D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T21:58:53.239986Z"},"links":{"citing_paper":"/paper/2606.19357"},"observation_digest":"sha256:15c9e4be64e513a7a114b33f9450ac1b6c3a5e8693304f61d698866cdd94bb83","observation_id":"99dcbf54-0454-493f-9215-fbb522c757b3","resolution":{"observed_at":"2026-06-28T21:58:53.239986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01275","last_updated":"2018-04-30T04:24:26Z","snapshot_observed_at":"2026-08-03T12:09:42.690927Z","submitted_at":"2017-12-04T06:03:26Z","title":"A Deeper Look at Experience Replay","version":3},"cited_work":{"arxiv_id":"1712.01275","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.01275","snapshot_observed_at":"2026-07-10T21:47:36.357588Z","title":"A Deeper Look at Experience Replay","venue":"cs.LG","work_id":"1c2df411-b82a-476e-91ff-40bc03f4e8cd","year":2017},"citing_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T21:58:53.239986Z"},"links":{"cited_paper":"/paper/1712.01275","citing_paper":"/paper/2606.19357"},"observation_digest":"sha256:189ff5145c541458c4f891172ee7e76336fce767c185c4aeff3a5d5ebc6b6b06","observation_id":"79621fca-408d-49f6-8f10-67aa5a618826","resolution":{"observed_at":"2026-07-01T19:56:10.328824Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T21:58:53.239986Z","title":"Finally, exploration is managed via anϵ-greedy strategy with a fixedϵof2 −6 (Exploration), and new actions are selected everyPolicy Skip(2) frames","venue":null,"work_id":null,"year":2048},"citing_paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T21:58:53.239986Z"},"links":{"citing_paper":"/paper/2606.19357"},"observation_digest":"sha256:e24193881cb8c5b0799b8dc5a4e0a50cca60bdcc763c8386510bda87e04bf45f","observation_id":"c6183240-27b3-4f1b-847a-ad332256f677","resolution":{"observed_at":"2026-06-28T21:58:53.239986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.19357","last_updated":"2026-05-29T19:24:57Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-04T11:41:19.748164Z","submitted_at":"2026-05-29T19:24:57Z","title":"Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots"},"reference_resolution":{"displayed":5,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":5},"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 5 of 5 outbound references and 1 inbound Pith citation observation for arXiv:2606.19357."}