{"as_of":"2026-08-08T11:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3096e3b95219b4aec90e9b2a21e8e9ea88346f9162c6dff2382efc0c9bd2e422","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:14:44.217243Z","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":"2026-08-07T12:10:52.984604Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.05284","last_updated":"2024-11-04T18:37:07Z","snapshot_observed_at":"2026-07-06T19:12:19.677411Z","submitted_at":"2024-09-09T02:32:45Z","title":"Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.05284","snapshot_observed_at":"2026-08-07T12:14:44.217243Z","title":"Bypassing the noisy parity barrier: Learning higher-order Markov random fields from dynamics.arXiv preprint arXiv:2409.05284, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00606","last_updated":"2025-05-31T15:33:26Z","snapshot_observed_at":"2026-08-08T09:09:05.906312Z","submitted_at":"2025-05-31T15:33:26Z","title":"Heisenberg-limited Hamiltonian learning continuous variable systems via engineered dissipation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:14:44.217243Z"},"links":{"cited_paper":"/paper/2409.05284","citing_paper":"/paper/2506.00606"},"observation_digest":"sha256:3ab45c6f636c87f6ce759d3a549ef1bf90f9e389904bc1b38d8f9d878cf52cf6","observation_id":"e3dbe08c-9cea-45b4-9f1d-786fd9c43e64","resolution":{"observed_at":"2026-08-07T12:14:44.217243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05284","last_updated":"2024-11-04T18:37:07Z","snapshot_observed_at":"2026-07-06T19:12:19.677411Z","submitted_at":"2024-09-09T02:32:45Z","title":"Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics","version":2},"cited_work":{"arxiv_id":"2409.05284","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.05284","snapshot_observed_at":"2026-08-07T12:10:52.984604Z","title":"Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics","venue":"cs.LG","work_id":"c34d792e-304d-46d7-92a8-f7b7eb6cc4ec","year":2024},"citing_paper":{"arxiv_id":"2506.00764","last_updated":"2025-06-01T00:43:46Z","snapshot_observed_at":"2026-08-07T11:56:21.335564Z","submitted_at":"2025-06-01T00:43:46Z","title":"Learning Juntas under Markov Random Fields","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:10:51.207874Z"},"links":{"cited_paper":"/paper/2409.05284","citing_paper":"/paper/2506.00764"},"observation_digest":"sha256:ba4ac6ca73075621a07549f184368c981783b1b33a19e39df3294acc5905652c","observation_id":"4bbd9d23-0723-4d62-9bed-0e508dd0d6c3","resolution":{"observed_at":"2026-08-07T12:10:53.043047Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2409.05284/citation-record","integrity":"/paper/2409.05284/integrity","json":"/paper/2409.05284/citation-record.json","paper":"/paper/2409.05284"},"outbound":[],"paper":{"arxiv_id":"2409.05284","last_updated":"2024-11-04T18:37:07Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T19:12:19.677411Z","submitted_at":"2024-09-09T02:32:45Z","title":"Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics"},"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 2 inbound Pith citation observations for arXiv:2409.05284."}