{"as_of":"2026-08-18T23:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d3fde232848a2f7b478f4164ae5dd2b3badc283fb33b752e403b0f2b5d6674e3","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":"2026-08-18T06:34:40.430872+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":"2026-08-11T17:34:18.089289Z","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":"2026-05-25T05:20:25.076600Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12133","snapshot_observed_at":"2026-08-11T17:34:18.089289Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08894","last_updated":"2024-12-13T04:03:14Z","snapshot_observed_at":"2026-08-17T04:10:33.897815Z","submitted_at":"2024-12-12T03:14:50Z","title":"SMMF: Square-Matricized Momentum Factorization for Memory-Efficient Optimization","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T17:34:18.089289Z"},"links":{"cited_paper":"/paper/2305.12133","citing_paper":"/paper/2412.08894"},"observation_digest":"sha256:d971228c7868e916c7238306804a9b076f519b0ff2f779a0b933e271e2f9d38e","observation_id":"86c574c8-a6ba-4c3d-9014-722472737064","resolution":{"observed_at":"2026-08-11T17:34:18.089289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12133","snapshot_observed_at":"2026-08-09T05:28:51.863442Z","title":"and Xu, Z.-Q","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04375","last_updated":"2025-05-21T09:17:46Z","snapshot_observed_at":"2026-08-18T23:06:45.608463Z","submitted_at":"2025-02-05T15:23:26Z","title":"An Analysis for Reasoning Bias of Language Models with Small Initialization","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T05:28:51.863442Z"},"links":{"cited_paper":"/paper/2305.12133","citing_paper":"/paper/2502.04375"},"observation_digest":"sha256:82de97d023a33c44d1c9d00994ab19c354fa17717bf655735d85856dd9de72e9","observation_id":"f822a794-272e-45e8-9b20-e0794a811df3","resolution":{"observed_at":"2026-08-09T05:28:51.863442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12133","snapshot_observed_at":"2026-08-07T13:01:16.145489Z","title":"Loss spike in training neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23013","last_updated":"2025-05-29T02:42:20Z","snapshot_observed_at":"2026-08-16T17:31:39.623522Z","submitted_at":"2025-05-29T02:42:20Z","title":"Scalable Complexity Control Facilitates Reasoning Ability of LLMs","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T13:01:16.145489Z"},"links":{"cited_paper":"/paper/2305.12133","citing_paper":"/paper/2505.23013"},"observation_digest":"sha256:fb68663c8b8f70fbd0772e109cf13700f4e13784652d144bdf2f2a7c28d11f75","observation_id":"3f4fd1f6-9a96-4f81-8ef5-72a172a9a297","resolution":{"observed_at":"2026-08-07T13:01:16.145489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12133","snapshot_observed_at":"2026-08-06T19:10:16.598782Z","title":"Loss spike in training neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.06464","last_updated":"2025-07-09T00:47:37Z","snapshot_observed_at":"2026-08-13T07:37:39.690725Z","submitted_at":"2025-07-09T00:47:37Z","title":"SoftSignSGD(S3): An Enhanced Optimizer for Practical DNN Training and Loss Spikes Minimization Beyond Adam","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:10:16.598782Z"},"links":{"cited_paper":"/paper/2305.12133","citing_paper":"/paper/2507.06464"},"observation_digest":"sha256:b8a68d3fae8f1d4682b59e10e4659be5b868e205e016c8628c3d5e7cd44666ac","observation_id":"ee2e7d3e-cdc5-4a94-90b3-ffc1a0050369","resolution":{"observed_at":"2026-08-06T19:10:16.598782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training Neural Networks","version":2},"cited_work":{"arxiv_id":"2305.12133","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12133","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2305.12133 , year =","venue":null,"work_id":"92c906f0-142e-4df4-b14c-dbae0dd23835","year":null},"citing_paper":{"arxiv_id":"2605.23476","last_updated":"2026-05-22T10:36:48Z","snapshot_observed_at":"2026-08-09T01:58:25.578831Z","submitted_at":"2026-05-22T10:36:48Z","title":"Non-normal spectral signatures of instability in neural network training dynamics","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-25T05:17:37.467255Z"},"links":{"cited_paper":"/paper/2305.12133","citing_paper":"/paper/2605.23476"},"observation_digest":"sha256:6c2f28cbcd06d82dd27db533d3eac2ed0a6dfed7de9550412694d5bfc717555a","observation_id":"94de0006-f7a4-48b3-818c-ab0add2e337d","resolution":{"observed_at":"2026-05-25T05:20:25.079717Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.12133/citation-record","integrity":"/paper/2305.12133/integrity","json":"/paper/2305.12133/citation-record.json","paper":"/paper/2305.12133"},"outbound":[],"paper":{"arxiv_id":"2305.12133","last_updated":"2024-10-05T05:40:02Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T15:30:15.473150Z","submitted_at":"2023-05-20T07:57:15Z","title":"Loss Spike in Training 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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2305.12133."}