{"as_of":"2026-08-10T06:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:41f5ef921bb72f4dc4e590bff1908f07a25318d54a72414f279fa0edb9f16c0e","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:06:50.984681Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.06516/citation-record","integrity":"/paper/2507.06516/integrity","json":"/paper/2507.06516/citation-record.json","paper":"/paper/2507.06516"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:51.877746Z","title":null,"venue":null,"work_id":"e2c52ebd-48fa-4eaf-bf9a-0ef79b5f26f2","year":2019},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.984681Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:f055676800c9a5bc9f5057361f5a85e7ed0599b1697bd7ee85e2fae6bd886de2","observation_id":"2426e79a-2329-457e-a77c-7de42cd3c30f","resolution":{"observed_at":"2026-08-06T19:06:51.930108Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:50.015416Z","title":"doi: 10.1109/CVPR.2017.243","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.015416Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:cc8ff5538b3849d6379ccee345f7dd5773c1857fe705fb17d5bc877998c0f6b5","observation_id":"6f64e618-676e-4d6b-866b-838d605d42a5","resolution":{"observed_at":"2026-08-06T19:06:50.015416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:52.210207Z","title":"Rethinking the inception ar- chitecture for computer vision","venue":null,"work_id":"f8b36fb8-ef25-4f6b-aa83-20d026482893","year":2016},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.561633Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:992671a62727a07a4d67e66a29f48d0b3cb40f228ba96d3f918cbcdee4b6c804","observation_id":"6394143e-aeaa-41cc-85f1-1469e4659a65","resolution":{"observed_at":"2026-08-06T19:06:52.288269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-08T14:57:17.868613Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-06T19:06:50.806183Z","title":"Wide residual networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.806183Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:58756812a34fd2279df88ae19e5a1d4e4ecf522d3ca43028f1f3a9d693043a96","observation_id":"01b96aca-d9a0-42bb-b427-58eb5eb58b3f","resolution":{"observed_at":"2026-08-06T19:06:50.806183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20264","last_updated":"2025-03-26T06:13:41Z","snapshot_observed_at":"2026-08-09T20:35:40.981273Z","submitted_at":"2025-03-26T06:13:41Z","title":"Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification","version":1},"cited_work":{"arxiv_id":"2503.20264","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.20264","snapshot_observed_at":"2026-08-06T19:06:51.206524Z","title":"Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification","venue":"cs.LG","work_id":"6c94874f-35d2-4197-a2b1-a941a098a3a0","year":2025},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.869130Z"},"links":{"cited_paper":"/paper/2503.20264","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:e7a0353b3d3b17b60ac3d79b6212af7a6326466d7b7e7322469c018ebe34f4ef","observation_id":"a1fa4742-8fb9-4c34-8062-2dd1a81c5877","resolution":{"observed_at":"2026-08-06T19:06:51.270500Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02629","last_updated":"2020-06-10T18:18:17Z","snapshot_observed_at":"2026-07-06T07:58:30.747165Z","submitted_at":"2019-06-06T15:03:11Z","title":"When Does Label Smoothing Help?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02629","snapshot_observed_at":"2026-08-06T19:06:50.255852Z","title":"Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.255852Z"},"links":{"cited_paper":"/paper/1906.02629","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:74c04d74a1fafb686eca9e76156631b25c61ff9de1e456181ff5ef69d2caf688","observation_id":"6f3a2d52-af1c-4c75-8c59-9c8966ed4dd3","resolution":{"observed_at":"2026-08-06T19:06:50.255852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:50.163978Z","title":"Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.163978Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:7aca04dbcd88a9852233c892edf177f3d72af035381195851c3196faf8fb2b32","observation_id":"82236060-c5d1-4efb-9568-d8a82180012b","resolution":{"observed_at":"2026-08-06T19:06:50.163978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:50.476210Z","title":"doi: 10.1 162/089976602753284446","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2002,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.476210Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:0831a4255d8d94ae0b7edf1c7b31a46547ef9fb02e096d56c5462d8b95c3eb05","observation_id":"9e069d5b-146c-4df6-a209-59fc949ea5e4","resolution":{"observed_at":"2026-08-06T19:06:50.476210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:49.703952Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:49.703952Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:d3ba6bc810c26242b2e7d2e8e0d0a930c5b048f58f74e70eb7bf43402cbdc7de","observation_id":"0cf4bb2c-f822-4a21-a111-7973cfa9b56d","resolution":{"observed_at":"2026-08-06T19:06:49.703952Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:52.640938Z","title":"Weinberger","venue":null,"work_id":"362b72b9-f12a-48ac-85c2-da2ca07cb81f","year":2017},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:49.941312Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:1c06a232142042063944b7b465043a2e6f060df1e49a18b6b2273f6a1b951864","observation_id":"64ac3e57-24e6-415c-a8b6-d50fa2751bfe","resolution":{"observed_at":"2026-08-06T19:06:52.731572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:50.647938Z","title":"Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, and Wenjie Zhang","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.647938Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:1802ef5b9e6e16e43d40e0b225f90902cdb67f4d732e3f3cc10df5987ef2f990","observation_id":"da382259-6a16-4330-88bb-17c1ea2e86e2","resolution":{"observed_at":"2026-08-06T19:06:50.647938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T19:06:49.829285Z","title":"Geoffrey Hinton","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:49.829285Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:88b7aa89b26bf709d2c4e0082f03d15d5d75cbfa214616ee9224b88988c3182d","observation_id":"cc764945-5550-408d-b70d-cc2af0ca3ac7","resolution":{"observed_at":"2026-08-06T19:06:49.829285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:52.401242Z","title":null,"venue":null,"work_id":"6cf5728e-a08c-4284-99ea-9511def85fa8","year":2019},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.084850Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:ed0f73e9bb78158c9d0a537e15ac55928437ab2009d9e2dad43250eb05dc5d5d","observation_id":"529fff8f-dcac-4e95-9eea-22308268cfa7","resolution":{"observed_at":"2026-08-06T19:06:52.511202Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.01685","last_updated":"2020-08-07T18:34:21Z","snapshot_observed_at":"2026-08-04T20:23:20.720077Z","submitted_at":"2019-04-02T22:10:44Z","title":"Measuring Calibration in Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.01685","snapshot_observed_at":"2026-08-06T19:06:50.323717Z","title":"arXiv:1904.01685 [cs, stat]","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.323717Z"},"links":{"cited_paper":"/paper/1904.01685","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:6fb2058e9e236ed99246e10aa6989567a03cd5e48424e5f1df1b2b945736ff29","observation_id":"a24ca209-c0a2-4db1-bd2c-995337419308","resolution":{"observed_at":"2026-08-06T19:06:50.323717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.12800","last_updated":"2021-12-29T17:58:53Z","snapshot_observed_at":"2026-08-10T05:19:13.118448Z","submitted_at":"2020-06-23T07:18:05Z","title":"Calibration of Neural Networks using Splines","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.12800","snapshot_observed_at":"2026-08-06T19:06:49.787639Z","title":"arXiv:2006.12800 [cs, stat]","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:49.787639Z"},"links":{"cited_paper":"/paper/2006.12800","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:677e6642eb6b9ed64a467c69ff1567753d06698e30a4a8087188560160527fce","observation_id":"45cdcbb9-999c-43b9-9aa7-8d022a320198","resolution":{"observed_at":"2026-08-06T19:06:49.787639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.12807","last_updated":"2022-02-21T12:08:52Z","snapshot_observed_at":"2026-07-06T09:31:50.012114Z","submitted_at":"2020-06-23T07:55:10Z","title":"Post-hoc Calibration of Neural Networks by g-Layers","version":2},"cited_work":{"arxiv_id":"2006.12807","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.12807","snapshot_observed_at":"2026-08-06T19:06:51.428154Z","title":"Post-hoc Calibration of Neural Networks by g-Layers","venue":"cs.LG","work_id":"ba17703d-5c66-49b2-a961-5a65d67a1733","year":2020},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.397293Z"},"links":{"cited_paper":"/paper/2006.12807","citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:e57711bf3b5414e41cd489671e39c7b46156932c46094e56ce5cf03f4fe3ac56","observation_id":"6d7fa155-6154-457d-850a-11a660643eb7","resolution":{"observed_at":"2026-08-06T19:06:51.516263Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:52.046575Z","title":"Parameterized Temperature Scaling for Boosting the Ex- pressive Power in Post-Hoc Uncertainty Calibration","venue":null,"work_id":"0d9cb624-e4f4-4527-94aa-e96a5034fe32","year":2022},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:50.720657Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:6d2233756480aa49791fd31738e88133ffc8bd639c389862084eb4b23c1ee6d8","observation_id":"4e4ce5a4-b6f9-4d59-9cce-fcee9277cacd","resolution":{"observed_at":"2026-08-06T19:06:52.117962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:06:52.832616Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"bdf6ff05-729a-4780-b0ac-2ec38c14d194","year":2009},"citing_paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:49.620131Z"},"links":{"citing_paper":"/paper/2507.06516"},"observation_digest":"sha256:4b14971c7cdec1f499f496d3ecee9edd4f7cbc9b1394e3e113e8972a7a766dad","observation_id":"7f34e0b4-67f5-49d9-a3d2-daaf36058410","resolution":{"observed_at":"2026-08-06T19:06:52.920432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.06516","last_updated":"2025-07-09T03:32:49Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T20:35:05.222189Z","submitted_at":"2025-07-09T03:32:49Z","title":"Instance-Wise Monotonic Calibration by Constrained Transformation"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":4},"total_outbound_references":18},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.06516."}