{"as_of":"2026-08-05T01:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:765747e26335a50494f41f0e7c98d5c090403c77beed84c05451c50adcbc180b","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T23:53:02.118884Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-02T23:57:27.965532Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.02190","last_updated":"2026-05-04T03:37:43Z","snapshot_observed_at":"2026-08-01T01:52:21.928366Z","submitted_at":"2026-05-04T03:37:43Z","title":"KANs need curvature: penalties for compositional smoothness","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-08T18:55:16.460190Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.02190"},"observation_digest":"sha256:80b34608e1c4b11f9eab7e880d75c46d06fe1a863328b75b54020b5999d1aa53","observation_id":"3be29862-c4db-4368-9141-28567cb72fae","resolution":{"observed_at":"2026-05-09T06:05:36.394951Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.14939","last_updated":"2026-07-01T15:14:47Z","snapshot_observed_at":"2026-08-01T02:05:39.868110Z","submitted_at":"2026-05-14T15:15:43Z","title":"Real-time virtual circuits for plasma shape control via neural network emulators","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-15T14:28:09.280603Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.14939"},"observation_digest":"sha256:09195888b3943acde19fdaaa0a17f462e9c062791eda8b4e3665df15da310f37","observation_id":"1c01a776-5faf-48d7-bd81-1ccc35af79bb","resolution":{"observed_at":"2026-05-15T14:30:03.639492Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.14939","last_updated":"2026-07-01T15:14:47Z","snapshot_observed_at":"2026-08-01T02:05:39.868110Z","submitted_at":"2026-05-14T15:15:43Z","title":"Real-time virtual circuits for plasma shape control via neural network emulators","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-02T23:53:02.118884Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.14939"},"observation_digest":"sha256:cb464de292cf23249d7c88ef18ddbb4d5ee3f057b4c7c488db9822cdfd1424ac","observation_id":"f16fb640-d763-4fd9-9fe0-ae4febbbc24a","resolution":{"observed_at":"2026-07-02T23:57:27.966737Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.15463","last_updated":"2026-05-14T22:57:51Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:57:51Z","title":"Layer-wise Derivative Controlled Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T15:20:34.217113Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.15463"},"observation_digest":"sha256:3166761891f1bba7b4fc6fab9df06d52dcb38915a5fb1076afffec136f58e78a","observation_id":"5c30aa54-9cf9-40b5-a3c2-4937c36b1320","resolution":{"observed_at":"2026-05-19T15:22:37.380047Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.24340","last_updated":"2026-05-23T01:52:50Z","snapshot_observed_at":"2026-07-06T23:34:28.608412Z","submitted_at":"2026-05-23T01:52:50Z","title":"ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T15:14:34.020391Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.24340"},"observation_digest":"sha256:8cd77d794457afd456c91edb90b95a8a4b500b1beaedd4ef1ea3ea64681d0c69","observation_id":"0ea589a7-982a-42d8-902e-eddb8dacc36c","resolution":{"observed_at":"2026-06-30T15:14:46.775882Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2605.28368","last_updated":"2026-05-28T11:37:13Z","snapshot_observed_at":"2026-08-01T11:57:30.958731Z","submitted_at":"2026-05-27T12:04:15Z","title":"LEIA: Learned Environment for Interactive Architected Materials","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T13:37:38.479547Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2605.28368"},"observation_digest":"sha256:222d82983eb7e12f94dc16583e0bb6f4350b317b2158d95f8134d42e9e780697","observation_id":"4aeb3cf1-c2c4-4f8b-843e-9a5a1a0bbaf6","resolution":{"observed_at":"2026-06-29T13:43:29.070401Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks","version":3},"cited_work":{"arxiv_id":"1706.04859","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04859","snapshot_observed_at":"2026-07-02T23:57:27.965532Z","title":"Sobolev Training for Neural Networks","venue":"cs.LG","work_id":"06c95db5-4bad-4bd2-8e48-fa4bb81e9d78","year":2017},"citing_paper":{"arxiv_id":"2606.07908","last_updated":"2026-06-06T00:14:22Z","snapshot_observed_at":"2026-08-02T06:13:04.913473Z","submitted_at":"2026-06-06T00:14:22Z","title":"Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T20:32:24.292427Z"},"links":{"cited_paper":"/paper/1706.04859","citing_paper":"/paper/2606.07908"},"observation_digest":"sha256:ee1e99d25ef03df65a2bf19e051c42ded7e895c56f42a117768168cdb5963b9b","observation_id":"b9c568e0-4f09-43d9-b27c-91060410658a","resolution":{"observed_at":"2026-07-02T20:17:22.532077Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1706.04859/citation-record","integrity":"/paper/1706.04859/integrity","json":"/paper/1706.04859/citation-record.json","paper":"/paper/1706.04859"},"outbound":[],"paper":{"arxiv_id":"1706.04859","last_updated":"2017-07-26T16:18:52Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T05:47:00.003696Z","submitted_at":"2017-06-15T13:25:25Z","title":"Sobolev Training for Neural Networks"},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1706.04859."}