{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NAOOC6B45JEBYY4P6LK6A32XLC","short_pith_number":"pith:NAOOC6B4","canonical_record":{"source":{"id":"2509.22536","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-09-26T16:16:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab8be7d0f25f03e8235ed7bde2c49b838c0516e17429904a662c77d2848709e6","abstract_canon_sha256":"e0ca84f165577d04a96c1cfcc8c6b96408925e373b59bcf215715c77844b1291"},"schema_version":"1.0"},"canonical_sha256":"681ce1783cea481c638ff2d5e06f5758a3b84184978da504d3b7d15938ebbfc4","source":{"kind":"arxiv","id":"2509.22536","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.22536","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"arxiv_version","alias_value":"2509.22536v5","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.22536","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_12","alias_value":"NAOOC6B45JEB","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_16","alias_value":"NAOOC6B45JEBYY4P","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_8","alias_value":"NAOOC6B4","created_at":"2026-08-04T01:57:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NAOOC6B45JEBYY4P6LK6A32XLC","target":"record","payload":{"canonical_record":{"source":{"id":"2509.22536","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-09-26T16:16:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab8be7d0f25f03e8235ed7bde2c49b838c0516e17429904a662c77d2848709e6","abstract_canon_sha256":"e0ca84f165577d04a96c1cfcc8c6b96408925e373b59bcf215715c77844b1291"},"schema_version":"1.0"},"canonical_sha256":"681ce1783cea481c638ff2d5e06f5758a3b84184978da504d3b7d15938ebbfc4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:57:54.917664Z","signature_b64":"0zB6mvm/GmWndZK7QE5FKBi5WHGJ3HEt7Vc/ZGJRtLyVjSQMzFG0NDKdEBRkR+JcJc2u2KyqMgAfcIoIjDSFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"681ce1783cea481c638ff2d5e06f5758a3b84184978da504d3b7d15938ebbfc4","last_reissued_at":"2026-08-04T01:57:54.916077Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:57:54.916077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.22536","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-04T01:57:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U8dA38Nik1JlJmdyLNuxxw/XQEV73fm+3VEA54nCmhvB8frTEW3iVbMnFWXXHLuu9sZvrUP0QLT5RA5ETBIEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:04:56.214002Z"},"content_sha256":"79ce74c33eb3eb29e1774094e4a3289ae5c4adf48fb3cff89652bf5608dad419","schema_version":"1.0","event_id":"sha256:79ce74c33eb3eb29e1774094e4a3289ae5c4adf48fb3cff89652bf5608dad419"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NAOOC6B45JEBYY4P6LK6A32XLC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Congkai Xie, Hongxia Yang, Jiannong Cao, Kejing Yang, Mingfa Feng, Ming Li, Shuo Cai, Wenjun Wang, Yiming Zhang, Zhen Li","submitted_at":"2025-09-26T16:16:49Z","abstract_excerpt":"The immense computational cost of training Large Language Models (LLMs) presents a major barrier to innovation. While FP8 training offers a promising solution with significant theoretical efficiency gains, its widespread adoption has been hindered by the lack of a comprehensive, open-source training recipe. To bridge this gap, we introduce an end-to-end FP8 training recipe that seamlessly integrates continual pre-training and supervised fine-tuning. Our methodology employs a fine-grained, hybrid-granularity quantization strategy to maintain numerical fidelity while maximizing computational eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.22536","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.22536/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-04T01:57:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xw9kCwKzrmxcLH4hxjtYyY6QbV/U+wCpAg2fTVHtvgRjZDfzbHD0WFp6GawdZ00TI7Wo9SOXnN4oNf1G2yjPDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:04:56.214531Z"},"content_sha256":"d35cf9465ddc9c3f3181608f385c4926198d0dfa1b1f5ffd9a43b25af2320466","schema_version":"1.0","event_id":"sha256:d35cf9465ddc9c3f3181608f385c4926198d0dfa1b1f5ffd9a43b25af2320466"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NAOOC6B45JEBYY4P6LK6A32XLC/bundle.json","state_url":"https://pith.science/pith/NAOOC6B45JEBYY4P6LK6A32XLC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NAOOC6B45JEBYY4P6LK6A32XLC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T03:04:56Z","links":{"resolver":"https://pith.science/pith/NAOOC6B45JEBYY4P6LK6A32XLC","bundle":"https://pith.science/pith/NAOOC6B45JEBYY4P6LK6A32XLC/bundle.json","state":"https://pith.science/pith/NAOOC6B45JEBYY4P6LK6A32XLC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NAOOC6B45JEBYY4P6LK6A32XLC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NAOOC6B45JEBYY4P6LK6A32XLC","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":"e0ca84f165577d04a96c1cfcc8c6b96408925e373b59bcf215715c77844b1291","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-09-26T16:16:49Z","title_canon_sha256":"ab8be7d0f25f03e8235ed7bde2c49b838c0516e17429904a662c77d2848709e6"},"schema_version":"1.0","source":{"id":"2509.22536","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.22536","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"arxiv_version","alias_value":"2509.22536v5","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.22536","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_12","alias_value":"NAOOC6B45JEB","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_16","alias_value":"NAOOC6B45JEBYY4P","created_at":"2026-08-04T01:57:54Z"},{"alias_kind":"pith_short_8","alias_value":"NAOOC6B4","created_at":"2026-08-04T01:57:54Z"}],"graph_snapshots":[{"event_id":"sha256:d35cf9465ddc9c3f3181608f385c4926198d0dfa1b1f5ffd9a43b25af2320466","target":"graph","created_at":"2026-08-04T01:57:54Z","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/2509.22536/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The immense computational cost of training Large Language Models (LLMs) presents a major barrier to innovation. While FP8 training offers a promising solution with significant theoretical efficiency gains, its widespread adoption has been hindered by the lack of a comprehensive, open-source training recipe. To bridge this gap, we introduce an end-to-end FP8 training recipe that seamlessly integrates continual pre-training and supervised fine-tuning. Our methodology employs a fine-grained, hybrid-granularity quantization strategy to maintain numerical fidelity while maximizing computational eff","authors_text":"Congkai Xie, Hongxia Yang, Jiannong Cao, Kejing Yang, Mingfa Feng, Ming Li, Shuo Cai, Wenjun Wang, Yiming Zhang, Zhen Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-09-26T16:16:49Z","title":"A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.22536","kind":"arxiv","version":5},"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":"sha256:79ce74c33eb3eb29e1774094e4a3289ae5c4adf48fb3cff89652bf5608dad419","target":"record","created_at":"2026-08-04T01:57:54Z","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":"e0ca84f165577d04a96c1cfcc8c6b96408925e373b59bcf215715c77844b1291","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-09-26T16:16:49Z","title_canon_sha256":"ab8be7d0f25f03e8235ed7bde2c49b838c0516e17429904a662c77d2848709e6"},"schema_version":"1.0","source":{"id":"2509.22536","kind":"arxiv","version":5}},"canonical_sha256":"681ce1783cea481c638ff2d5e06f5758a3b84184978da504d3b7d15938ebbfc4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"681ce1783cea481c638ff2d5e06f5758a3b84184978da504d3b7d15938ebbfc4","first_computed_at":"2026-08-04T01:57:54.916077Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-04T01:57:54.916077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0zB6mvm/GmWndZK7QE5FKBi5WHGJ3HEt7Vc/ZGJRtLyVjSQMzFG0NDKdEBRkR+JcJc2u2KyqMgAfcIoIjDSFDw==","signature_status":"signed_v1","signed_at":"2026-08-04T01:57:54.917664Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.22536","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79ce74c33eb3eb29e1774094e4a3289ae5c4adf48fb3cff89652bf5608dad419","sha256:d35cf9465ddc9c3f3181608f385c4926198d0dfa1b1f5ffd9a43b25af2320466"],"state_sha256":"f43311bcdada39686258341985db03439bbefb7f0130c7bf817e7f10169685a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Plhw2WhkGEX5kmeirtxqmFgVmP7yicOFt2yqDNq8YSf5IlnxF3mzPOAZv0OaIiDtOYiRSEctbltQDps91pLKCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:04:56.220215Z","bundle_sha256":"7e5c0b1595c201b77af1a7b1b871937b719bc4d7e56f7586756fc87aa599a704"}}