{"as_of":"2026-08-14T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de57bd028640a02567303a13f344c9456009c6567cf259ee9887aece15c56eb9","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":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":24,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:42:04.705647Z","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":"2026-07-03T19:58:54.792212Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-11T12:42:04.705647Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13982","last_updated":"2024-12-18T16:03:37Z","snapshot_observed_at":"2026-08-14T22:35:47.101078Z","submitted_at":"2024-12-18T16:03:37Z","title":"LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:42:04.705647Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2412.13982"},"observation_digest":"sha256:3b8ed58a66875649720123a06518e57fee6a981897085f66cf6cc9d0a33b96a7","observation_id":"9bbaebd3-ebe2-4975-a54c-25d12873e187","resolution":{"observed_at":"2026-08-11T12:42:04.705647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-10T17:18:46.079626Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12363","last_updated":"2025-07-29T08:31:51Z","snapshot_observed_at":"2026-08-11T04:25:24.319196Z","submitted_at":"2025-01-21T18:43:36Z","title":"How to Unfold Top Decays","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T17:18:46.079626Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2501.12363"},"observation_digest":"sha256:652895b3077cd0d608467d8f1a39a074289b066aa73a2fa30f1d1244aa3a1c9a","observation_id":"854caa26-5b68-4ead-ae74-2f6ad9d705b7","resolution":{"observed_at":"2026-08-10T17:18:46.079626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-08T11:07:32.926712Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08052","last_updated":"2025-02-12T01:24:49Z","snapshot_observed_at":"2026-08-14T00:33:49.020069Z","submitted_at":"2025-02-12T01:24:49Z","title":"ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T11:07:32.926712Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2502.08052"},"observation_digest":"sha256:705aa4d189f5c3e1c6478b9612a915ead971c6c0412addc12ee774509347b947","observation_id":"6e4a9dc1-e4e8-46ed-9887-81e55a23d0a5","resolution":{"observed_at":"2026-08-08T11:07:32.926712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-08T11:07:57.344086Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08053","last_updated":"2025-02-12T01:24:52Z","snapshot_observed_at":"2026-08-14T05:29:51.858832Z","submitted_at":"2025-02-12T01:24:52Z","title":"A Demonstration of ARCANE Reweighting: Reducing the Sign Problem in the MC@NLO Generation of $e^+ e^- \\rightarrow q \\bar{q} + 1\\, jet$ Events","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T11:07:57.344086Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2502.08053"},"observation_digest":"sha256:4a2150802e41626cfbe2a98445f39c328f56af466ec86c015fc34aa4d2923969","observation_id":"d209267e-6a67-4fb3-90a6-8bb59164307c","resolution":{"observed_at":"2026-08-08T11:07:57.344086Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-07T21:21:44.359928Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.09494","last_updated":"2025-02-13T17:00:11Z","snapshot_observed_at":"2026-08-08T21:55:44.943156Z","submitted_at":"2025-02-13T17:00:11Z","title":"Communicating Likelihoods with Normalising Flows","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T21:21:44.359928Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2502.09494"},"observation_digest":"sha256:0afabb7cfcf895f4233b70657a1e080f87a45b90fa4188bdc2fbdf08be34123f","observation_id":"96d334ec-a21c-41f7-81d6-782df03a884d","resolution":{"observed_at":"2026-08-07T21:21:44.359928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-06T15:48:41.956170Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.15084","last_updated":"2025-07-20T18:43:03Z","snapshot_observed_at":"2026-08-13T16:51:35.145892Z","submitted_at":"2025-07-20T18:43:03Z","title":"Simulation-Prior Independent Neural Unfolding Procedure","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:48:41.956170Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2507.15084"},"observation_digest":"sha256:72ffca443abd9d9b576747516aea57fb866589caa13fc50785a7f83f70869c9c","observation_id":"6b2d8971-660a-4368-9936-6b81038c13ed","resolution":{"observed_at":"2026-08-06T15:48:41.956170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-06T15:29:53.117259Z","title":"MadNIS - Neural multi-channel importance sampling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.15768","last_updated":"2025-07-21T16:25:41Z","snapshot_observed_at":"2026-08-11T12:22:33.913584Z","submitted_at":"2025-07-21T16:25:41Z","title":"Toward an event-level analysis of hadron structure using differential programming","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:53.117259Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2507.15768"},"observation_digest":"sha256:61365e8436a2dc6edd5318e2d1586513d16dd8578908e0596ad94b20d9dc0354","observation_id":"38c89430-e693-4257-9061-89e76b7e0246","resolution":{"observed_at":"2026-08-06T15:29:53.117259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-06T13:08:14.838274Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21039","last_updated":"2025-07-31T17:59:00Z","snapshot_observed_at":"2026-08-09T14:53:37.431367Z","submitted_at":"2025-07-28T17:56:31Z","title":"Data-parallel leading-order event generation in MadGraph5_aMC@NLO","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T13:08:14.838274Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2507.21039"},"observation_digest":"sha256:408f7e095b3d262aa7c9c120aaed8b5d66b6c44a32db95814496594f08f66702","observation_id":"acc43d76-5d43-4fd2-9322-5cac58c3d4d4","resolution":{"observed_at":"2026-08-06T13:08:14.838274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2509.00155","last_updated":"2026-03-13T15:56:11Z","snapshot_observed_at":"2026-08-14T02:59:37.703410Z","submitted_at":"2025-08-29T18:00:02Z","title":"Amplitude Uncertainties Everywhere All at Once","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-18T19:21:24.292766Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2509.00155"},"observation_digest":"sha256:bc196644e6a8d367b179c5cc32af323d377219a3b6df84ed25cc23ca1d1e5262","observation_id":"00a8285b-3e7f-4d20-9405-d6816ced3135","resolution":{"observed_at":"2026-05-18T19:21:47.197392Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-04T21:31:20.278520Z","title":"Heimel, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08048","last_updated":"2026-07-12T19:22:14Z","snapshot_observed_at":"2026-08-14T03:00:17.881677Z","submitted_at":"2025-09-09T18:00:01Z","title":"Forecasting Generative Amplification","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T21:31:20.278520Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2509.08048"},"observation_digest":"sha256:d88ff106e5580b65dce94a4da5b9e23d686be210cbbc1d5fb692808e5279c46d","observation_id":"7756e5d9-c6d6-412c-81cd-ab4771e717c6","resolution":{"observed_at":"2026-08-04T21:31:20.278520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2512.04153","last_updated":"2026-05-20T16:17:03Z","snapshot_observed_at":"2026-08-14T14:07:44.199829Z","submitted_at":"2025-12-03T19:00:00Z","title":"Data-Driven Predictions for Dark Photon and Millicharged Particle Production","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-21T18:05:20.208819Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2512.04153"},"observation_digest":"sha256:8d3a3da78d0456e533cb348d718a11b89c86172aa05420e9af81e5dee57ce0a2","observation_id":"f9387fa2-2a28-4dac-8fd0-553aa8286fff","resolution":{"observed_at":"2026-05-21T18:05:27.127630Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-03T12:08:14.344753Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.05289","last_updated":"2026-05-25T09:30:07Z","snapshot_observed_at":"2026-08-03T12:08:10.432586Z","submitted_at":"2026-01-07T19:00:04Z","title":"A universal vision transformer for fast calorimeter simulations","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T12:08:14.344753Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2601.05289"},"observation_digest":"sha256:6dd158261e255eba321359802c6094878a2334528a5d087d1706ad2c7137d1b1","observation_id":"944aba6c-ea6b-48ad-abf8-fd01d3df57f9","resolution":{"observed_at":"2026-08-03T12:08:14.344753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2604.03511","last_updated":"2026-04-03T23:18:12Z","snapshot_observed_at":"2026-08-12T12:33:37.063338Z","submitted_at":"2026-04-03T23:18:12Z","title":"Monte Carlo Event Generation with Continuous Normalizing Flows","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-13T17:54:22.245072Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2604.03511"},"observation_digest":"sha256:87397fbe85ef9677c1202e52941f20f516aac6ef642b6cc49b80c30a8140b458","observation_id":"366869f3-9674-4f8b-bc85-a5e4436bd53d","resolution":{"observed_at":"2026-05-13T17:58:04.177720Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2605.18360","last_updated":"2026-05-20T13:30:57Z","snapshot_observed_at":"2026-08-01T23:00:28.127891Z","submitted_at":"2026-05-18T13:13:28Z","title":"Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-20T09:42:31.642024Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2605.18360"},"observation_digest":"sha256:63708ab3f4708f7e3d121ad20c536515691ea752ebe1d187ed5d3efca8c29bc2","observation_id":"d500873a-7847-4992-9dec-60c90c81f367","resolution":{"observed_at":"2026-05-20T09:43:10.861690Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2605.18360","last_updated":"2026-05-20T13:30:57Z","snapshot_observed_at":"2026-08-01T23:00:28.127891Z","submitted_at":"2026-05-18T13:13:28Z","title":"Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-21T08:07:20.906466Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2605.18360"},"observation_digest":"sha256:70e5512ddb4a48a69db55d481b759cf31d7282204ab2de3c4e76b8e03fe899fe","observation_id":"5657565e-b1a9-44c2-81a0-6d55f00271ef","resolution":{"observed_at":"2026-05-21T08:09:51.602762Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2605.31221","last_updated":"2026-05-29T12:26:05Z","snapshot_observed_at":"2026-08-02T17:09:20.392822Z","submitted_at":"2026-05-29T12:26:05Z","title":"CoLoRFulNNLO for color-singlet processes: An update on NNLOCAL","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T22:02:59.005815Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2605.31221"},"observation_digest":"sha256:622c92b7a35276149caf7b5d999d34862e433f2a1e05449ea34b28b20ef2bcee","observation_id":"16145e65-60da-4a2f-812a-7b36d2d68457","resolution":{"observed_at":"2026-07-01T19:46:10.969777Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"citing_paper":{"arxiv_id":"2606.04165","last_updated":"2026-07-17T21:46:37Z","snapshot_observed_at":"2026-08-02T18:31:06.346857Z","submitted_at":"2026-06-02T19:27:19Z","title":"CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T07:41:38.548022Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2606.04165"},"observation_digest":"sha256:3878a0d17cb3a1edd81ef02f1cd680f62dfece8a1055094dd45f6e5e07fb6ae1","observation_id":"afed7f29-54da-4ee2-844c-a641d6df916b","resolution":{"observed_at":"2026-07-02T06:06:41.238410Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-02T12:30:39.031497Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.04165","last_updated":"2026-07-17T21:46:37Z","snapshot_observed_at":"2026-08-02T18:31:06.346857Z","submitted_at":"2026-06-02T19:27:19Z","title":"CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T12:30:39.031497Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2606.04165"},"observation_digest":"sha256:4bcbcc0d892fad49d20f72a57076a716f7632d73dd4811e0c06d0308cdf2521c","observation_id":"80228a13-2da6-4171-9f50-086b688e091b","resolution":{"observed_at":"2026-08-02T12:30:39.031497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-12T13:00:43.137180Z","title":"SciPost Phys.��(4), 141 (2023) https://doi.org/10.21468/SciPostPhys.15.4.141 arXiv:2212.06172 [hep-ph]","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22008","last_updated":"2026-07-06T20:28:24Z","snapshot_observed_at":"2026-08-05T16:45:42.089593Z","submitted_at":"2026-06-20T12:15:24Z","title":"An Optimal Transportation Approach for Improved Confidence Intervals","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-07-12T13:00:43.137180Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2606.22008"},"observation_digest":"sha256:86bfa117fefe7b55185d1aafff4d8abda366c812e28fc22a70145baa2d547355","observation_id":"23997b06-95a9-4363-bb78-eb3b3b224910","resolution":{"observed_at":"2026-07-12T13:00:43.137180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":"2212.06172","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-03T19:58:54.792212Z","title":"Heimel, R","venue":null,"work_id":"e5d1a686-9fb7-481b-bdc7-113f5db1591c","year":2023},"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":93,"source":"arxiv_source","source_observed_at":"2026-07-03T19:29:34.070294Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2607.01354"},"observation_digest":"sha256:d52087493b876014dc5184141b873f581776cda93f5d483c459e220f3caf9297","observation_id":"ff9e8115-f3ce-4d1a-a309-8230b9d4d70e","resolution":{"observed_at":"2026-07-03T19:58:54.793537Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-01T04:29:12.472137Z","title":"MadNIS - Neural multi-channel importance sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22813","last_updated":"2026-07-24T18:00:01Z","snapshot_observed_at":"2026-08-08T08:50:06.755567Z","submitted_at":"2026-07-24T18:00:01Z","title":"Agentic Re-Casting using Agentic Re-Simulations","version":1},"reference_index":150,"source":"arxiv_source","source_observed_at":"2026-08-01T04:29:12.472137Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2607.22813"},"observation_digest":"sha256:ecf85e08d442d7e0c69b81530dc840abd95b149a0a1e4ff6b71ceaef5c5794bc","observation_id":"3a7bb349-08bf-4f00-9107-4b82adaacbe3","resolution":{"observed_at":"2026-08-01T04:29:12.472137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-30T18:12:21.670909Z","title":"MadNIS - Neural multi-channel importance sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23591","last_updated":"2026-08-06T09:44:12Z","snapshot_observed_at":"2026-08-13T10:47:00.441759Z","submitted_at":"2026-07-26T10:40:47Z","title":"Neural Control Variates at LO and NLO","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-07-30T18:12:21.670909Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2607.23591"},"observation_digest":"sha256:878fe95c69640e5ea4105a825b6b53ad368ad099d76d4b5a32933daaec4a5504","observation_id":"9ae10614-e138-42fb-8fb6-e2b7f6e4d6e3","resolution":{"observed_at":"2026-07-30T18:12:21.670909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-07-30T22:29:53.412759Z","title":"Heimel, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26739","last_updated":"2026-07-29T10:29:46Z","snapshot_observed_at":"2026-08-02T01:10:31.416343Z","submitted_at":"2026-07-29T10:29:46Z","title":"Automated NRQCD and NRQED simulations of quarkonium and leptonium production with P-wave states and physical-mass effects","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-07-30T22:29:53.412759Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2607.26739"},"observation_digest":"sha256:3e3667f6c93b6475ed99f6169505afeb1c09c02a8a3564e57f412bb8ec36ae4a","observation_id":"efd1e18a-5396-431c-98c4-f8b09bdb225c","resolution":{"observed_at":"2026-07-30T22:29:53.412759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06172","snapshot_observed_at":"2026-08-05T00:48:47.184251Z","title":"SciPost Phys","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00529","last_updated":"2026-08-01T08:41:30Z","snapshot_observed_at":"2026-08-12T15:29:05.774634Z","submitted_at":"2026-08-01T08:41:30Z","title":"Schr\\\"{o}dinger Generator for High-Dimensional Integration and Sampling on Quantum Many-Body States","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T00:48:47.184251Z"},"links":{"cited_paper":"/paper/2212.06172","citing_paper":"/paper/2608.00529"},"observation_digest":"sha256:8ac9c805179a6827268948101f4d744c2d721318fb54324c926e758818856716","observation_id":"614cc320-f949-45db-9a4d-5f4651040b49","resolution":{"observed_at":"2026-08-05T00:48:47.184251Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2212.06172/citation-record","integrity":"/paper/2212.06172/integrity","json":"/paper/2212.06172/citation-record.json","paper":"/paper/2212.06172"},"outbound":[],"paper":{"arxiv_id":"2212.06172","last_updated":"2023-09-05T08:10:28Z","latest_version":2,"primary_category":"hep-ph","snapshot_observed_at":"2026-08-13T23:16:02.295795Z","submitted_at":"2022-12-12T19:00:05Z","title":"MadNIS -- Neural Multi-Channel Importance Sampling"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2212.06172."}