{"as_of":"2026-08-11T03:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc8737c14bbbc48846264d5d4733e57942fb6a831066a76fcc0c4929705f7a8f","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":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":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T23:37:30.047983Z","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":"2026-06-30T14:44:45.530718Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":"2305.11241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-06-30T14:44:45.530718Z","title":"Jeffrey and B","venue":null,"work_id":"b7f11f9b-7bc4-4e90-a300-b7a4fa75143b","year":2024},"citing_paper":{"arxiv_id":"2408.11065","last_updated":"2026-05-07T20:50:35Z","snapshot_observed_at":"2026-07-06T19:03:39.672411Z","submitted_at":"2024-08-12T18:34:57Z","title":"Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-23T21:53:07.688705Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2408.11065"},"observation_digest":"sha256:e43f1e415bcaeb4cb23e10f496acf99e89f6f5d542db37a44020ba60caacc33d","observation_id":"d3785442-a183-464a-bccd-349a0038e544","resolution":{"observed_at":"2026-05-23T21:53:29.620878Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-08-09T23:37:30.047983Z","title":"Jeffrey and B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18419","last_updated":"2025-03-02T02:18:54Z","snapshot_observed_at":"2026-08-10T09:44:35.106712Z","submitted_at":"2025-01-30T15:16:09Z","title":"Optimizers for Stabilizing Likelihood-free Inference","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T23:37:30.047983Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2501.18419"},"observation_digest":"sha256:ebce537229f9fc9b978121eea63c601900ac7f9e48cfb326182a4b29a3623549","observation_id":"aff14d5b-0315-443c-860c-c34cd9d6e5d0","resolution":{"observed_at":"2026-08-09T23:37:30.047983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":"2305.11241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-06-30T14:44:45.530718Z","title":"Jeffrey and B","venue":null,"work_id":"b7f11f9b-7bc4-4e90-a300-b7a4fa75143b","year":2024},"citing_paper":{"arxiv_id":"2604.15737","last_updated":"2026-04-17T06:23:04Z","snapshot_observed_at":"2026-08-05T22:43:47.435815Z","submitted_at":"2026-04-17T06:23:04Z","title":"Bayesian inference constraints on jet quenching across centrality, beam energy, and observable classes in LHC heavy-ion collisions","version":1},"reference_index":120,"source":"pdf_text","source_observed_at":"2026-05-10T08:49:49.337276Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2604.15737"},"observation_digest":"sha256:cd610f567e2b5cb9b9bb5dddfddee0298075bb6e2602d48dd8dd91e67ead2baa","observation_id":"82abd5a1-26bf-4b02-bcc7-b45f826c4d9e","resolution":{"observed_at":"2026-05-10T08:53:04.208499Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":"2305.11241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-06-30T14:44:45.530718Z","title":"Jeffrey and B","venue":null,"work_id":"b7f11f9b-7bc4-4e90-a300-b7a4fa75143b","year":2024},"citing_paper":{"arxiv_id":"2605.24082","last_updated":"2026-05-22T18:00:01Z","snapshot_observed_at":"2026-07-06T23:34:13.859813Z","submitted_at":"2026-05-22T18:00:01Z","title":"Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T14:35:45.210384Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2605.24082"},"observation_digest":"sha256:99febb9c62a97308bd7548853fd943de8cf376b7dabd4fd97972ee5dfacdd1a9","observation_id":"7c97e49a-71ba-4657-8acf-7e7cea14cce7","resolution":{"observed_at":"2026-06-30T14:44:45.532376Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-07-12T01:14:24.457057Z","title":"Machine Learning: Science and Technology , keywords =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03606","last_updated":"2026-07-03T21:31:31Z","snapshot_observed_at":"2026-08-08T05:15:13.938293Z","submitted_at":"2026-07-03T21:31:31Z","title":"Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization","version":1},"reference_index":254,"source":"arxiv_source","source_observed_at":"2026-07-12T01:14:24.457057Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2607.03606"},"observation_digest":"sha256:658f77f28623dd7feede4731bbfcb7abf8e985381a28948def14ffc20e463534","observation_id":"bac9566f-ccef-494e-9fdd-0726487103da","resolution":{"observed_at":"2026-07-12T01:14:24.457057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.11241","snapshot_observed_at":"2026-08-01T10:23:22.360458Z","title":", year = 2024, month = jan, number =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20260","last_updated":"2026-07-22T15:15:50Z","snapshot_observed_at":"2026-08-10T18:43:09.708796Z","submitted_at":"2026-07-22T15:15:50Z","title":"Microlensing Detection and Inference via Learned Bayes Factors","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T10:23:22.360458Z"},"links":{"cited_paper":"/paper/2305.11241","citing_paper":"/paper/2607.20260"},"observation_digest":"sha256:f5c42d992aeca3af16998b11c39835c234600ec13a3fdd5fec13a1d8d1a37cab","observation_id":"e97e1987-99df-4d18-b0af-ef894610ca65","resolution":{"observed_at":"2026-08-01T10:23:22.360458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.11241/citation-record","integrity":"/paper/2305.11241/integrity","json":"/paper/2305.11241/citation-record.json","paper":"/paper/2305.11241"},"outbound":[],"paper":{"arxiv_id":"2305.11241","last_updated":"2024-01-10T16:45:46Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T18:35:30.347252Z","submitted_at":"2023-05-18T18:14:53Z","title":"Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.11241."}