{"as_of":"2026-08-17T18:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d1f60b59b3b7ce9a89806472970733a267b56bd0fa2129b8ca576d3dcdb09bb6","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":"2026-08-17T06:30:58.91139+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":"2026-08-15T14:39:57.500053Z","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":"2026-07-03T19:58:54.723901Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.06957","last_updated":"2021-03-11T21:18:35Z","snapshot_observed_at":"2026-08-16T18:39:29.959580Z","submitted_at":"2021-03-11T21:18:35Z","title":"Efficient sampling of constrained high-dimensional theoretical spaces with machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06957","snapshot_observed_at":"2026-08-10T13:21:25.299208Z","title":"Hollingsworth, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16432","last_updated":"2025-02-06T05:08:24Z","snapshot_observed_at":"2026-08-13T18:40:34.597431Z","submitted_at":"2025-01-27T19:00:04Z","title":"Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T13:21:25.299208Z"},"links":{"cited_paper":"/paper/2103.06957","citing_paper":"/paper/2501.16432"},"observation_digest":"sha256:189f99c39e80293ad19d6edc9a45d42d9b120382f4c0d3beb9a7ce4495a60412","observation_id":"c84d95d2-426c-40b8-872f-b169e92853f3","resolution":{"observed_at":"2026-08-10T13:21:25.299208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06957","last_updated":"2021-03-11T21:18:35Z","snapshot_observed_at":"2026-08-16T18:39:29.959580Z","submitted_at":"2021-03-11T21:18:35Z","title":"Efficient sampling of constrained high-dimensional theoretical spaces with machine learning","version":1},"cited_work":{"arxiv_id":"2103.06957","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06957","snapshot_observed_at":"2026-07-03T19:58:54.723901Z","title":"Hollingsworth, M","venue":null,"work_id":"7e11faed-2de5-493a-9102-59398879c2b9","year":2021},"citing_paper":{"arxiv_id":"2509.01677","last_updated":"2026-04-24T18:02:24Z","snapshot_observed_at":"2026-08-15T09:02:45.384268Z","submitted_at":"2025-09-01T18:00:32Z","title":"Machine Learning in the 2HDM2S model for Dark Matter","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-18T19:21:02.625794Z"},"links":{"cited_paper":"/paper/2103.06957","citing_paper":"/paper/2509.01677"},"observation_digest":"sha256:6ffce742d6412448df9ca50fa07dfb1bc26e10daef8353b8819c1b4cb144baca","observation_id":"c6766226-8f8d-4e65-9e52-e38d8370dc02","resolution":{"observed_at":"2026-05-18T19:21:47.436963Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06957","last_updated":"2021-03-11T21:18:35Z","snapshot_observed_at":"2026-08-16T18:39:29.959580Z","submitted_at":"2021-03-11T21:18:35Z","title":"Efficient sampling of constrained high-dimensional theoretical spaces with machine learning","version":1},"cited_work":{"arxiv_id":"2103.06957","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06957","snapshot_observed_at":"2026-07-03T19:58:54.723901Z","title":"Hollingsworth, M","venue":null,"work_id":"7e11faed-2de5-493a-9102-59398879c2b9","year":2021},"citing_paper":{"arxiv_id":"2607.01354","last_updated":"2026-07-01T18:11:57Z","snapshot_observed_at":"2026-08-12T12:40:33.993995Z","submitted_at":"2026-07-01T18:11:57Z","title":"Local Conformal Predictions for Calibrated Surrogates","version":1},"reference_index":249,"source":"arxiv_source","source_observed_at":"2026-07-03T19:29:34.070294Z"},"links":{"cited_paper":"/paper/2103.06957","citing_paper":"/paper/2607.01354"},"observation_digest":"sha256:8c6aa81279cd9f0ead0ddf7365f460ab8755091c37c42d1d6e796b8ee4aec9bb","observation_id":"2e0ec02a-b6f6-43bc-976e-5525092e8012","resolution":{"observed_at":"2026-07-03T19:58:54.725196Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06957","last_updated":"2021-03-11T21:18:35Z","snapshot_observed_at":"2026-08-16T18:39:29.959580Z","submitted_at":"2021-03-11T21:18:35Z","title":"Efficient sampling of constrained high-dimensional theoretical spaces with machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06957","snapshot_observed_at":"2026-08-15T14:39:57.500053Z","title":"2103.06957 , archiveprefix =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06450","last_updated":"2026-08-06T18:00:00Z","snapshot_observed_at":"2026-08-16T23:48:48.625103Z","submitted_at":"2026-08-06T18:00:00Z","title":"Generative Amplification with Surrogate Monte Carlo","version":1},"reference_index":237,"source":"arxiv_source","source_observed_at":"2026-08-15T14:39:57.500053Z"},"links":{"cited_paper":"/paper/2103.06957","citing_paper":"/paper/2608.06450"},"observation_digest":"sha256:1a4df2e9de91646dc5a1c5f92a172077b8db34d48126a5db6f5eaa1797e20740","observation_id":"758eb47f-0061-411e-a4e9-5e0f669fd732","resolution":{"observed_at":"2026-08-15T14:39:57.500053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2103.06957/citation-record","integrity":"/paper/2103.06957/integrity","json":"/paper/2103.06957/citation-record.json","paper":"/paper/2103.06957"},"outbound":[],"paper":{"arxiv_id":"2103.06957","last_updated":"2021-03-11T21:18:35Z","latest_version":1,"primary_category":"hep-th","snapshot_observed_at":"2026-08-16T18:39:29.959580Z","submitted_at":"2021-03-11T21:18:35Z","title":"Efficient sampling of constrained high-dimensional theoretical spaces with machine 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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2103.06957."}