{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YBI4VZK4VF2MJ3DMDAISNJH4C4","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":"d1d75d05345497d17e3f1c63bd5308928ae6d733b43fc4a250e7851abd2b2bb2","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-19T17:50:45Z","title_canon_sha256":"ebda74d26a41b945e76556a0046d3aadf42c198d01a5236ce40ab85acefcb4b7"},"schema_version":"1.0","source":{"id":"2505.13416","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13416","created_at":"2026-07-05T11:05:29Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13416v1","created_at":"2026-07-05T11:05:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13416","created_at":"2026-07-05T11:05:29Z"},{"alias_kind":"pith_short_12","alias_value":"YBI4VZK4VF2M","created_at":"2026-07-05T11:05:29Z"},{"alias_kind":"pith_short_16","alias_value":"YBI4VZK4VF2MJ3DM","created_at":"2026-07-05T11:05:29Z"},{"alias_kind":"pith_short_8","alias_value":"YBI4VZK4","created_at":"2026-07-05T11:05:29Z"}],"graph_snapshots":[{"event_id":"sha256:8c128362ef66a9900b6a2859cfc2b74b9f0be59a41484fc24a6fcd934338b3e0","target":"graph","created_at":"2026-07-05T11:05:29Z","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/2505.13416/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent developments in deep learning optimization have brought about radically new algorithms based on the Linear Minimization Oracle (LMO) framework, such as $\\sf Muon$ and $\\sf Scion$. After over a decade of $\\sf Adam$'s dominance, these LMO-based methods are emerging as viable replacements, offering several practical advantages such as improved memory efficiency, better hyperparameter transferability, and most importantly, superior empirical performance on large-scale tasks, including LLM training. However, a significant gap remains between their practical use and our current theoretical un","authors_text":"Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richt\\'arik","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-19T17:50:45Z","title":"Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13416","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":"sha256:79322748fd6fa3e70279dfd0f5eb3ce98e9dacc8d27483f3d8c0c59bd9fca1f9","target":"record","created_at":"2026-07-05T11:05:29Z","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":"d1d75d05345497d17e3f1c63bd5308928ae6d733b43fc4a250e7851abd2b2bb2","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-19T17:50:45Z","title_canon_sha256":"ebda74d26a41b945e76556a0046d3aadf42c198d01a5236ce40ab85acefcb4b7"},"schema_version":"1.0","source":{"id":"2505.13416","kind":"arxiv","version":1}},"canonical_sha256":"c051cae55ca974c4ec6c181126a4fc1710103b15acc2ba70754d0de8e2d7e55d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c051cae55ca974c4ec6c181126a4fc1710103b15acc2ba70754d0de8e2d7e55d","first_computed_at":"2026-07-05T11:05:29.709022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:29.709022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dBhN2E/r3GD65yGg23aXnlYeEoaYESyooNp8GKGjz4e+lIwJWCm74k2cBB0WqDJlSclFK3O5KjkBfaPHbz1DCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:29.709487Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.13416","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79322748fd6fa3e70279dfd0f5eb3ce98e9dacc8d27483f3d8c0c59bd9fca1f9","sha256:8c128362ef66a9900b6a2859cfc2b74b9f0be59a41484fc24a6fcd934338b3e0"],"state_sha256":"9d8aa0fcd6efd56a34b9deedd80a1fa923a3571d903251563815d2fbc57b2d5f"}