{"as_of":"2026-08-21T23:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5f45a7b7f0192691711ea3df00e5e787126875129c2c947dffdfc0b9255745d","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:56:00.639379Z","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":"2026-05-18T03:22:21.033656Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-11T12:56:00.639379Z","title":"Nicoli, S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13704","last_updated":"2024-12-18T10:49:32Z","snapshot_observed_at":"2026-08-19T18:07:46.777864Z","submitted_at":"2024-12-18T10:49:32Z","title":"Diffusion models and stochastic quantisation in lattice field theory","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:56:00.639379Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2412.13704"},"observation_digest":"sha256:fde84b8ecadfc8fe9694e3526b11a814969a6880917f754f4dc13976ae0f00f2","observation_id":"e352c404-1891-4045-83b3-1cd2b973e88f","resolution":{"observed_at":"2026-08-11T12:56:00.639379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-10T20:48:33.065066Z","title":"Nicoli, S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.07371","last_updated":"2025-01-13T14:40:42Z","snapshot_observed_at":"2026-08-16T14:19:12.071510Z","submitted_at":"2025-01-13T14:40:42Z","title":"Simulating the Hubbard Model with Equivariant Normalizing Flows","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:48:33.065066Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2501.07371"},"observation_digest":"sha256:9880913f0150133db8627f5574a263590d23cc3eb38629c7417ad8c139029d0e","observation_id":"ded9d538-946f-4df0-bd3e-5205718c55d5","resolution":{"observed_at":"2026-08-10T20:48:33.065066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-09T13:19:35.152979Z","title":"Nicoli, S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02127","last_updated":"2025-02-04T09:02:41Z","snapshot_observed_at":"2026-08-16T22:02:22.851679Z","submitted_at":"2025-02-04T09:02:41Z","title":"Exploring Generative Networks for Manifolds with Non-Trivial Topology","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T13:19:35.152979Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2502.02127"},"observation_digest":"sha256:b56483bfaae4728df1e4c561430e2a7bea288398ebb5b50575d6907c7a5fadb8","observation_id":"c4103eac-99bc-4c93-b0af-da9ec7ab07c0","resolution":{"observed_at":"2026-08-09T13:19:35.152979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-09T11:38:41.588111Z","title":"Nicoli, S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02670","last_updated":"2025-02-20T21:20:33Z","snapshot_observed_at":"2026-08-17T02:36:58.132931Z","submitted_at":"2025-02-04T19:18:44Z","title":"Machine-learning approaches to accelerating lattice simulations","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T11:38:41.588111Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2502.02670"},"observation_digest":"sha256:b6481aa44c273283df862b9e63625eaa211cb67b438d83dcb4e93ae0d67060c9","observation_id":"52b565aa-d26c-42e5-b920-2789ce14232e","resolution":{"observed_at":"2026-08-09T11:38:41.588111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-05T15:02:49.484812Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.20610","last_updated":"2025-08-28T09:56:59Z","snapshot_observed_at":"2026-08-18T03:59:37.559346Z","submitted_at":"2025-08-28T09:56:59Z","title":"Studying Effective String Theory using deep generative models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T15:02:49.484812Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2508.20610"},"observation_digest":"sha256:7823592e2052bd85d99db7458e5d1545e5b64e32cb222596edd67e751c8b8522","observation_id":"a7618073-e8e5-4cdd-8d51-5ba9c380ea20","resolution":{"observed_at":"2026-08-05T15:02:49.484812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":"1910.13496","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"868da7bf-a57d-4061-b34b-4a265fe9b6c8","year":2020},"citing_paper":{"arxiv_id":"2510.25704","last_updated":"2026-04-10T12:30:09Z","snapshot_observed_at":"2026-07-06T22:34:23.736456Z","submitted_at":"2025-10-29T17:12:21Z","title":"Scaling flow-based approaches for topology sampling in $\\mathrm{SU}(3)$ gauge theory","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-18T03:21:13.881357Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2510.25704"},"observation_digest":"sha256:1abddba4cc8ac034b73bf33ad5b7f452b7d259c7336661d9147b309911bc2f54","observation_id":"f78dd0e0-27c7-4f6b-8fa5-b56888cb95c4","resolution":{"observed_at":"2026-05-18T03:22:21.036704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":"1910.13496","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"868da7bf-a57d-4061-b34b-4a265fe9b6c8","year":2020},"citing_paper":{"arxiv_id":"2604.27738","last_updated":"2026-04-30T11:29:28Z","snapshot_observed_at":"2026-08-16T20:07:25.409917Z","submitted_at":"2026-04-30T11:29:28Z","title":"Sampling two-dimensional spin systems with transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T05:59:00.370864Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2604.27738"},"observation_digest":"sha256:03c88ed116f81ce067b3baa83e80d36036555d7784b95f0ba2b21e1c4c39daf0","observation_id":"f3addb20-98e4-46d7-a76a-ed81b02349ad","resolution":{"observed_at":"2026-05-12T10:31:28.448044Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13496","snapshot_observed_at":"2026-08-11T00:34:15.952466Z","title":"Physical Review E , volume =","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2608.07648","last_updated":"2026-08-07T17:14:35Z","snapshot_observed_at":"2026-08-19T04:32:05.363664Z","submitted_at":"2026-08-07T17:14:35Z","title":"Leveraging generative models to assist Monte Carlo sampling","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T00:34:15.952466Z"},"links":{"cited_paper":"/paper/1910.13496","citing_paper":"/paper/2608.07648"},"observation_digest":"sha256:e23f442187307b028f901dab1d0cc2fbe23d967455ba26e8f5fbed8d877bf10b","observation_id":"d0393ff3-8d1e-4e71-9eab-b904d21b4539","resolution":{"observed_at":"2026-08-11T00:34:15.952466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1910.13496/citation-record","integrity":"/paper/1910.13496/integrity","json":"/paper/1910.13496/citation-record.json","paper":"/paper/1910.13496"},"outbound":[],"paper":{"arxiv_id":"1910.13496","last_updated":"2020-02-13T16:55:06Z","latest_version":2,"primary_category":"cond-mat.stat-mech","snapshot_observed_at":"2026-08-19T09:05:24.743171Z","submitted_at":"2019-10-29T19:40:08Z","title":"Asymptotically unbiased estimation of physical observables with neural samplers"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1910.13496."}