{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CIMM4MMPMW4LJLHM25E2TE4UXC","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":"5f15dd4918498bd292ac8364dc7201542049a266af826671367cebc6bca1fbe2","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-28T08:51:55Z","title_canon_sha256":"bfb4950549fe967b7ff566d5a06970944ac02bdf4f71509081ef4e43a1e83531"},"schema_version":"1.0","source":{"id":"2502.20853","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20853","created_at":"2026-07-05T11:34:05Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20853v2","created_at":"2026-07-05T11:34:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20853","created_at":"2026-07-05T11:34:05Z"},{"alias_kind":"pith_short_12","alias_value":"CIMM4MMPMW4L","created_at":"2026-07-05T11:34:05Z"},{"alias_kind":"pith_short_16","alias_value":"CIMM4MMPMW4LJLHM","created_at":"2026-07-05T11:34:05Z"},{"alias_kind":"pith_short_8","alias_value":"CIMM4MMP","created_at":"2026-07-05T11:34:05Z"}],"graph_snapshots":[{"event_id":"sha256:51793521649d9d4cc3bc14df3073a8657596c28de4fe4a8ed6e03d8ee99df476","target":"graph","created_at":"2026-07-05T11:34:05Z","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/2502.20853/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-training Transformers in FP4 precision is becoming a promising approach to gain substantial speedup, but it comes with a considerable loss of accuracy. Microscaling (MX) data format provides a fine-grained per-group quantization method to improve the representation ability of the FP4 format and is supported by the next-generation Blackwell GPU architecture. However, training with MXFP4 data format still results in significant degradation and there is a lack of systematic research on the reason.\n  In this work, we propose a novel training method TetraJet for a more accurate FP4 training. We","authors_text":"Haocheng Xi, Jianfei Chen, Jun Zhu, Yuxiang Chen","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-28T08:51:55Z","title":"Oscillation-Reduced MXFP4 Training for Vision Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20853","kind":"arxiv","version":2},"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":"sha256:47ce1ad378e2f91184f4055cf6884ff981ce6136ea5d8fc8f8bf1b5a421541d4","target":"record","created_at":"2026-07-05T11:34:05Z","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":"5f15dd4918498bd292ac8364dc7201542049a266af826671367cebc6bca1fbe2","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-28T08:51:55Z","title_canon_sha256":"bfb4950549fe967b7ff566d5a06970944ac02bdf4f71509081ef4e43a1e83531"},"schema_version":"1.0","source":{"id":"2502.20853","kind":"arxiv","version":2}},"canonical_sha256":"1218ce318f65b8b4acecd749a99394b89af8be6786311733f48421a84d52a120","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1218ce318f65b8b4acecd749a99394b89af8be6786311733f48421a84d52a120","first_computed_at":"2026-07-05T11:34:05.036281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:05.036281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C51MLDUeEjqjaExqTwbKDsV6bpmT8BTtQ6NGwhQqSLiYTt2JCE3/y4b4cPhCeOom3EqHgyAUT+t0rmSVeC+KAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:05.036747Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.20853","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47ce1ad378e2f91184f4055cf6884ff981ce6136ea5d8fc8f8bf1b5a421541d4","sha256:51793521649d9d4cc3bc14df3073a8657596c28de4fe4a8ed6e03d8ee99df476"],"state_sha256":"a2ff01213f29ef1fdcaaf704aea34edc01fdd673780df7e0050766dd28866ff9"}