{"as_of":"2026-08-09T11:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1261e4224847977f3184f89b5cbf301cba79153a980b39223d10bebde40d73c3","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T00:43:08.680044Z","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":"2026-08-07T00:43:10.358066Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.06184","last_updated":"2024-02-09T04:46:48Z","snapshot_observed_at":"2026-08-04T16:19:30.614896Z","submitted_at":"2024-02-09T04:46:48Z","title":"The boundary of neural network trainability is fractal","version":1},"cited_work":{"arxiv_id":"2402.06184","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.06184","snapshot_observed_at":"2026-08-07T00:43:10.358066Z","title":"The boundary of neural network trainability is fractal","venue":"cs.LG","work_id":"86ab72df-55de-401b-abf0-2915a05f88a0","year":2024},"citing_paper":{"arxiv_id":"2506.13234","last_updated":"2025-06-16T08:35:16Z","snapshot_observed_at":"2026-08-07T00:34:22.881688Z","submitted_at":"2025-06-16T08:35:16Z","title":"The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T00:43:08.680044Z"},"links":{"cited_paper":"/paper/2402.06184","citing_paper":"/paper/2506.13234"},"observation_digest":"sha256:da067b7d3b79ba7d1c15d64d6895eb08f320d4644011b66ce2f5b23c0edea704","observation_id":"ac025386-8e78-47e3-b420-663ccce4141c","resolution":{"observed_at":"2026-08-07T00:43:10.423646Z","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":"2402.06184","last_updated":"2024-02-09T04:46:48Z","snapshot_observed_at":"2026-08-04T16:19:30.614896Z","submitted_at":"2024-02-09T04:46:48Z","title":"The boundary of neural network trainability is fractal","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06184","snapshot_observed_at":"2026-07-12T01:11:50.444218Z","title":"arXiv preprint arXiv:2402.06184 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03613","last_updated":"2026-07-03T22:03:13Z","snapshot_observed_at":"2026-07-12T01:11:45.525555Z","submitted_at":"2026-07-03T22:03:13Z","title":"Implicit Bias of SGD in Multivariate ReLU Networks: Effective Width Collapse","version":1},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-07-12T01:11:50.444218Z"},"links":{"cited_paper":"/paper/2402.06184","citing_paper":"/paper/2607.03613"},"observation_digest":"sha256:8de2746fe5aa7b2df43f2ffa61fa3f4b84f4122ce9bc5599f9c84a02bb846d40","observation_id":"93310c06-d348-4dca-b722-4524c5a892f4","resolution":{"observed_at":"2026-07-12T01:11:50.444218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06184","last_updated":"2024-02-09T04:46:48Z","snapshot_observed_at":"2026-08-04T16:19:30.614896Z","submitted_at":"2024-02-09T04:46:48Z","title":"The boundary of neural network trainability is fractal","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06184","snapshot_observed_at":"2026-07-31T07:42:46.557974Z","title":"Sohl-Dickstein, The boundary of neural network trainability is fractal, arXiv:2402.06184 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28428","last_updated":"2026-08-06T01:10:43Z","snapshot_observed_at":"2026-08-09T10:09:56.841028Z","submitted_at":"2026-07-30T16:05:51Z","title":"Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-31T07:42:46.557974Z"},"links":{"cited_paper":"/paper/2402.06184","citing_paper":"/paper/2607.28428"},"observation_digest":"sha256:ce5286ff2276e4e9297f08258269350a3572915c82d1f1cdda2210da01e62129","observation_id":"2dd9f7be-b165-4543-805f-f9c7ca376a7b","resolution":{"observed_at":"2026-07-31T07:42:46.557974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06184","last_updated":"2024-02-09T04:46:48Z","snapshot_observed_at":"2026-08-04T16:19:30.614896Z","submitted_at":"2024-02-09T04:46:48Z","title":"The boundary of neural network trainability is fractal","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06184","snapshot_observed_at":"2026-08-04T03:23:47.495003Z","title":"Sohl-Dickstein, The boundary of neural network trainability is fractal, arXiv:2402.06184 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28428","last_updated":"2026-08-06T01:10:43Z","snapshot_observed_at":"2026-08-09T10:09:56.841028Z","submitted_at":"2026-07-30T16:05:51Z","title":"Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T03:23:47.495003Z"},"links":{"cited_paper":"/paper/2402.06184","citing_paper":"/paper/2607.28428"},"observation_digest":"sha256:d903aabb5aff36b34f61831d7e6b17ab6ab41afbf652a5ff1f851b8b45596456","observation_id":"d208ca87-b1eb-4a30-a6fa-78f5ac66a57c","resolution":{"observed_at":"2026-08-04T03:23:47.495003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.06184/citation-record","integrity":"/paper/2402.06184/integrity","json":"/paper/2402.06184/citation-record.json","paper":"/paper/2402.06184"},"outbound":[],"paper":{"arxiv_id":"2402.06184","last_updated":"2024-02-09T04:46:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T16:19:30.614896Z","submitted_at":"2024-02-09T04:46:48Z","title":"The boundary of neural network trainability is fractal"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.06184."}