{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZRAJA2OL4XWRCYNQE5BBDUIK4F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f54d0edd34eafe2875019fc5c3002201f6b0309e4bd0593e6582eb5433581e69","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T21:14:17Z","title_canon_sha256":"5893624e2587cafa8203dd2af4e8d90a20ac5ecc6affc3ca3175f01d6e911a6c"},"schema_version":"1.0","source":{"id":"2508.15071","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.15071","created_at":"2026-07-05T11:57:06Z"},{"alias_kind":"arxiv_version","alias_value":"2508.15071v1","created_at":"2026-07-05T11:57:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15071","created_at":"2026-07-05T11:57:06Z"},{"alias_kind":"pith_short_12","alias_value":"ZRAJA2OL4XWR","created_at":"2026-07-05T11:57:06Z"},{"alias_kind":"pith_short_16","alias_value":"ZRAJA2OL4XWRCYNQ","created_at":"2026-07-05T11:57:06Z"},{"alias_kind":"pith_short_8","alias_value":"ZRAJA2OL","created_at":"2026-07-05T11:57:06Z"}],"graph_snapshots":[{"event_id":"sha256:4c69b2907253d2d051978bb8256af80de60eb661e5a2706b200140ab59d23c29","target":"graph","created_at":"2026-07-05T11:57:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.15071/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern optimization algorithms that incorporate momentum and adaptive step-size offer improved performance in numerous challenging deep learning tasks. However, their effectiveness is often highly sensitive to the choice of hyperparameters, especially the step-size. Tuning these parameters is often difficult, resource-intensive, and time-consuming. Therefore, recent efforts have been directed toward enhancing the stability of optimizers across a wide range of hyperparameter choices [Schaipp et al., 2024]. In this paper, we introduce an algorithm that matches the performance of state-of-the-art","authors_text":"Antonio Orvieto, Aurelien Lucchi, Niccolo Ajroldi, Rustem Islamov","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T21:14:17Z","title":"Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15071","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:74e8b101574e892608ff37655e516ffb50e8674add3bb55b3b6ddd0da537ead4","target":"record","created_at":"2026-07-05T11:57:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f54d0edd34eafe2875019fc5c3002201f6b0309e4bd0593e6582eb5433581e69","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T21:14:17Z","title_canon_sha256":"5893624e2587cafa8203dd2af4e8d90a20ac5ecc6affc3ca3175f01d6e911a6c"},"schema_version":"1.0","source":{"id":"2508.15071","kind":"arxiv","version":1}},"canonical_sha256":"cc409069cbe5ed1161b0274211d10ae148cafd996bde8ab1a0265f72d1b073e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc409069cbe5ed1161b0274211d10ae148cafd996bde8ab1a0265f72d1b073e4","first_computed_at":"2026-07-05T11:57:06.042654Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:57:06.042654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qGfiQlTAO/AB+2bF86jg7SyzshkfFIP07NgaBVOZZWE4VOo3RqgUqVftB99N/EQUpukDdtHx8waMSnG29YigCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:57:06.043079Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.15071","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:74e8b101574e892608ff37655e516ffb50e8674add3bb55b3b6ddd0da537ead4","sha256:4c69b2907253d2d051978bb8256af80de60eb661e5a2706b200140ab59d23c29"],"state_sha256":"77fc714c7f0be624a551bfd66d22bd9692daa958a840eea083acb47766e8347c"}