{"as_of":"2026-08-23T06:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:20556e46f7cb763dbd5a3b60f3bfc8d37fc069d783117da778b3379dcb0c8d47","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":"2026-08-22T06:32:14.747728+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":"2026-08-15T17:16:59.185567Z","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":"2026-07-03T17:08:43.630762Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.07583","last_updated":"2024-06-05T14:03:57Z","snapshot_observed_at":"2026-08-21T18:31:15.147599Z","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07583","snapshot_observed_at":"2026-08-15T17:16:59.185567Z","title":"MoMo: Momentum models for adaptive learning rates","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.17288","last_updated":"2025-08-24T10:25:42Z","snapshot_observed_at":"2026-08-17T09:54:12.634968Z","submitted_at":"2025-08-24T10:25:42Z","title":"Polyak Stepsize: Estimating Optimal Functional Values Without Parameters or Prior Knowledge","version":1},"reference_index":1969,"source":"pdf_text","source_observed_at":"2026-08-15T17:16:59.185567Z"},"links":{"cited_paper":"/paper/2305.07583","citing_paper":"/paper/2508.17288"},"observation_digest":"sha256:c9b54473a88c70d07a27ac4c5a71b5501b64cfb2f04a0facb2a84af099c4ad0a","observation_id":"f8857d48-a2fb-4254-9c28-a875520e6653","resolution":{"observed_at":"2026-08-15T17:16:59.185567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07583","last_updated":"2024-06-05T14:03:57Z","snapshot_observed_at":"2026-08-21T18:31:15.147599Z","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates","version":3},"cited_work":{"arxiv_id":"2305.07583","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.07583","snapshot_observed_at":"2026-07-03T17:08:43.630762Z","title":"Schaipp, R","venue":null,"work_id":"81455f7b-c97e-413e-af90-23af6c6d639b","year":2023},"citing_paper":{"arxiv_id":"2510.04988","last_updated":"2026-05-10T14:57:23Z","snapshot_observed_at":"2026-08-14T08:23:37.106467Z","submitted_at":"2025-10-06T16:24:57Z","title":"Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-18T09:44:53.004293Z"},"links":{"cited_paper":"/paper/2305.07583","citing_paper":"/paper/2510.04988"},"observation_digest":"sha256:405a859c16b7068322db445f082a32e5f9355f5073db03866106b85e71edc946","observation_id":"e5b5d8d5-cf21-4b7d-b710-6abd3ee3f831","resolution":{"observed_at":"2026-05-18T09:46:12.541220Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07583","last_updated":"2024-06-05T14:03:57Z","snapshot_observed_at":"2026-08-21T18:31:15.147599Z","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates","version":3},"cited_work":{"arxiv_id":"2305.07583","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.07583","snapshot_observed_at":"2026-07-03T17:08:43.630762Z","title":"Schaipp, R","venue":null,"work_id":"81455f7b-c97e-413e-af90-23af6c6d639b","year":2023},"citing_paper":{"arxiv_id":"2605.11838","last_updated":"2026-05-12T09:24:59Z","snapshot_observed_at":"2026-08-15T22:36:53.700767Z","submitted_at":"2026-05-12T09:24:59Z","title":"Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-13T07:01:37.086159Z"},"links":{"cited_paper":"/paper/2305.07583","citing_paper":"/paper/2605.11838"},"observation_digest":"sha256:6c9062c9b58cf90124dc054a5e107abd5dd1a55ea3e7427daf71f6a3cc3fe702","observation_id":"6eecfa10-1506-4669-90a7-06e49abd6c73","resolution":{"observed_at":"2026-05-13T07:02:27.347009Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07583","last_updated":"2024-06-05T14:03:57Z","snapshot_observed_at":"2026-08-21T18:31:15.147599Z","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates","version":3},"cited_work":{"arxiv_id":"2305.07583","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.07583","snapshot_observed_at":"2026-07-03T17:08:43.630762Z","title":"Schaipp, R","venue":null,"work_id":"81455f7b-c97e-413e-af90-23af6c6d639b","year":2023},"citing_paper":{"arxiv_id":"2606.14970","last_updated":"2026-06-22T08:40:27Z","snapshot_observed_at":"2026-08-15T05:15:12.732458Z","submitted_at":"2026-06-12T21:46:54Z","title":"Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-27T04:33:10.554853Z"},"links":{"cited_paper":"/paper/2305.07583","citing_paper":"/paper/2606.14970"},"observation_digest":"sha256:f97d821a37966cac06bb1e5b6ed06721bffa44a29186e7a149b13892fe3b6405","observation_id":"df465041-dd4a-480a-8533-71e5089bf850","resolution":{"observed_at":"2026-07-03T17:08:43.632251Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.07583/citation-record","integrity":"/paper/2305.07583/integrity","json":"/paper/2305.07583/citation-record.json","paper":"/paper/2305.07583"},"outbound":[],"paper":{"arxiv_id":"2305.07583","last_updated":"2024-06-05T14:03:57Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T18:31:15.147599Z","submitted_at":"2023-05-12T16:25:57Z","title":"MoMo: Momentum Models for Adaptive Learning Rates"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.07583."}