{"as_of":"2026-08-11T03:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:750e59055b548859115c6a44d25eef6ba7d20b171375434db8f6edf235db93a4","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T01:13:15.449012Z","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":"2026-07-09T04:35:57.532084Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-08-10T01:13:15.449012Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.449012Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:6b144a239431c0a148d1a1f6cbd3f40589b1a6f4b26ee04d3ede1fe416a6da08","observation_id":"18ab6f87-14c5-48b3-94e2-cd7596422772","resolution":{"observed_at":"2026-08-10T01:13:15.449012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-08-07T00:43:59.233421Z","title":", Merrienboer , B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12891","last_updated":"2025-06-15T15:41:44Z","snapshot_observed_at":"2026-08-10T04:20:10.441314Z","submitted_at":"2025-06-15T15:41:44Z","title":"Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T00:43:59.233421Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2506.12891"},"observation_digest":"sha256:b0637571258199a3c56e33f81fab0e579f84457a13859e06a9ba0cd1b1821b97","observation_id":"52e060a5-2a58-4eef-be44-9a53f79c9a4e","resolution":{"observed_at":"2026-08-07T00:43:59.233421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":"2201.05125","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-07-09T04:35:57.532084Z","title":"Gradmax: Growing neural networks using gradient information.arXiv preprint arXiv:2201.05125","venue":"cs.LG","work_id":"1e07daca-8608-49c6-ba9c-f86df1bbeb40","year":2022},"citing_paper":{"arxiv_id":"2510.08008","last_updated":"2026-05-15T08:53:04Z","snapshot_observed_at":"2026-08-03T22:05:31.756040Z","submitted_at":"2025-10-09T09:45:45Z","title":"Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T20:36:22.974054Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2510.08008"},"observation_digest":"sha256:830d027d87f45de86cb5ed90694a2a0d2dac0624e0e3ebc127aa477ce883f688","observation_id":"5af9cb84-4daa-4cc5-9e3b-aa3b19fc1164","resolution":{"observed_at":"2026-05-21T20:40:36.476897Z","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":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-08-03T05:27:09.807211Z","title":"Gradmax: Growing neural networks using gradient information, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.02472","last_updated":"2026-06-29T15:41:30Z","snapshot_observed_at":"2026-08-08T11:52:44.940982Z","submitted_at":"2026-02-02T18:52:52Z","title":"SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T05:27:09.807211Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2602.02472"},"observation_digest":"sha256:f0d4673f7954f81a84271d160aac1fcc9f98371d19b5b248f957e1fc43c76fe4","observation_id":"c28fc6e0-50ac-4c69-a32e-2076e3f9913b","resolution":{"observed_at":"2026-08-03T05:27:09.807211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":"2201.05125","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-07-09T04:35:57.532084Z","title":"Gradmax: Growing neural networks using gradient information.arXiv preprint arXiv:2201.05125","venue":"cs.LG","work_id":"1e07daca-8608-49c6-ba9c-f86df1bbeb40","year":2022},"citing_paper":{"arxiv_id":"2607.07637","last_updated":"2026-07-08T16:55:16Z","snapshot_observed_at":"2026-08-04T19:22:45.344291Z","submitted_at":"2026-07-08T16:55:16Z","title":"An optimal control approach for neural network architecture adaptation with a posteriori error estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-09T04:31:29.598247Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2607.07637"},"observation_digest":"sha256:14c43cae4ab1480e18d4699cf90f5c157915f4e0061d0841056501d4d1fefe66","observation_id":"b250d378-4c49-4d5f-abcf-2d975b75b51a","resolution":{"observed_at":"2026-07-09T04:35:57.533655Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2201.05125/citation-record","integrity":"/paper/2201.05125/integrity","json":"/paper/2201.05125/citation-record.json","paper":"/paper/2201.05125"},"outbound":[],"paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information"},"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 5 inbound Pith citation observations for arXiv:2201.05125."}