{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:I2PSZBJQORZZN2H3TE7IVLSZQ2","short_pith_number":"pith:I2PSZBJQ","canonical_record":{"source":{"id":"2312.05725","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-10T02:14:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2899810525661713d9abb45298638f047da9661f3372e717d9a85b2e946055a8","abstract_canon_sha256":"6dfe0ee4f906cb4971e2601bb19146c7a686076c7b36d23b4fb86f3143c36cb9"},"schema_version":"1.0"},"canonical_sha256":"469f2c8530747396e8fb993e8aae5986ab463fd0c8fdfa0f5715812572b8e194","source":{"kind":"arxiv","id":"2312.05725","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.05725","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"arxiv_version","alias_value":"2312.05725v2","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05725","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_12","alias_value":"I2PSZBJQORZZ","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_16","alias_value":"I2PSZBJQORZZN2H3","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_8","alias_value":"I2PSZBJQ","created_at":"2026-07-05T07:23:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:I2PSZBJQORZZN2H3TE7IVLSZQ2","target":"record","payload":{"canonical_record":{"source":{"id":"2312.05725","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-10T02:14:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2899810525661713d9abb45298638f047da9661f3372e717d9a85b2e946055a8","abstract_canon_sha256":"6dfe0ee4f906cb4971e2601bb19146c7a686076c7b36d23b4fb86f3143c36cb9"},"schema_version":"1.0"},"canonical_sha256":"469f2c8530747396e8fb993e8aae5986ab463fd0c8fdfa0f5715812572b8e194","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:10.790467Z","signature_b64":"1uSl6hzmjQuIPjHR3cmyYby1Y30nHHgiNnHfCuINlSIW0/bhjB6Z/k9L+KYMvX3IXkFnXmUCLHUMKWOA8ffxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"469f2c8530747396e8fb993e8aae5986ab463fd0c8fdfa0f5715812572b8e194","last_reissued_at":"2026-07-05T07:23:10.789855Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:10.789855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.05725","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:23:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gPhNko6ZQvA4IDlp7c7bwCC0ufb7R3/YhbvBMwDumo3/gti+qiCmNiuWmpf3N9sBaqCjD+tBPHbobNQ/6XUjBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:22:54.166065Z"},"content_sha256":"245df37b534fb9ba37173a0475e1dc86d861f683bf39330766f86ea1aa10ffe3","schema_version":"1.0","event_id":"sha256:245df37b534fb9ba37173a0475e1dc86d861f683bf39330766f86ea1aa10ffe3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:I2PSZBJQORZZN2H3TE7IVLSZQ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FP8-BERT: Post-Training Quantization for Transformer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dongkuan Xu, Ian En-Hsu Yen, Jianwei Li, Tianchi Zhang","submitted_at":"2023-12-10T02:14:34Z","abstract_excerpt":"Transformer-based models, such as BERT, have been widely applied in a wide range of natural language processing tasks. However, one inevitable side effect is that they require massive memory storage and inference cost when deployed in production. Quantization is one of the popularized ways to alleviate the cost. However, the previous 8-bit quantization strategy based on INT8 data format either suffers from the degradation of accuracy in a Post-Training Quantization (PTQ) fashion or requires an expensive Quantization-Aware Training (QAT) process. Recently, a new numeric format FP8 (i.e. floatin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05725","kind":"arxiv","version":2},"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/2312.05725/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":"2026-07-05T07:23:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9hBa41Io8CeQQMggxI2k9L/Gzfad0djvFMzgJYl2bxVAMKoPQyhVzzX8Y79kddBaPpFdv2txC+quTXdn8bS/BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:22:54.166578Z"},"content_sha256":"312a51dd96c94460b8f614d583440276a87cb7ebc9b7b18cc716e97b9a186b22","schema_version":"1.0","event_id":"sha256:312a51dd96c94460b8f614d583440276a87cb7ebc9b7b18cc716e97b9a186b22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/bundle.json","state_url":"https://pith.science/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/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":"2026-08-08T22:22:54Z","links":{"resolver":"https://pith.science/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2","bundle":"https://pith.science/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/bundle.json","state":"https://pith.science/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2PSZBJQORZZN2H3TE7IVLSZQ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:I2PSZBJQORZZN2H3TE7IVLSZQ2","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":"6dfe0ee4f906cb4971e2601bb19146c7a686076c7b36d23b4fb86f3143c36cb9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-10T02:14:34Z","title_canon_sha256":"2899810525661713d9abb45298638f047da9661f3372e717d9a85b2e946055a8"},"schema_version":"1.0","source":{"id":"2312.05725","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.05725","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"arxiv_version","alias_value":"2312.05725v2","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05725","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_12","alias_value":"I2PSZBJQORZZ","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_16","alias_value":"I2PSZBJQORZZN2H3","created_at":"2026-07-05T07:23:10Z"},{"alias_kind":"pith_short_8","alias_value":"I2PSZBJQ","created_at":"2026-07-05T07:23:10Z"}],"graph_snapshots":[{"event_id":"sha256:312a51dd96c94460b8f614d583440276a87cb7ebc9b7b18cc716e97b9a186b22","target":"graph","created_at":"2026-07-05T07:23:10Z","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/2312.05725/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer-based models, such as BERT, have been widely applied in a wide range of natural language processing tasks. However, one inevitable side effect is that they require massive memory storage and inference cost when deployed in production. Quantization is one of the popularized ways to alleviate the cost. However, the previous 8-bit quantization strategy based on INT8 data format either suffers from the degradation of accuracy in a Post-Training Quantization (PTQ) fashion or requires an expensive Quantization-Aware Training (QAT) process. Recently, a new numeric format FP8 (i.e. floatin","authors_text":"Dongkuan Xu, Ian En-Hsu Yen, Jianwei Li, Tianchi Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-10T02:14:34Z","title":"FP8-BERT: Post-Training Quantization for Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05725","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":"sha256:245df37b534fb9ba37173a0475e1dc86d861f683bf39330766f86ea1aa10ffe3","target":"record","created_at":"2026-07-05T07:23:10Z","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":"6dfe0ee4f906cb4971e2601bb19146c7a686076c7b36d23b4fb86f3143c36cb9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-10T02:14:34Z","title_canon_sha256":"2899810525661713d9abb45298638f047da9661f3372e717d9a85b2e946055a8"},"schema_version":"1.0","source":{"id":"2312.05725","kind":"arxiv","version":2}},"canonical_sha256":"469f2c8530747396e8fb993e8aae5986ab463fd0c8fdfa0f5715812572b8e194","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"469f2c8530747396e8fb993e8aae5986ab463fd0c8fdfa0f5715812572b8e194","first_computed_at":"2026-07-05T07:23:10.789855Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:23:10.789855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1uSl6hzmjQuIPjHR3cmyYby1Y30nHHgiNnHfCuINlSIW0/bhjB6Z/k9L+KYMvX3IXkFnXmUCLHUMKWOA8ffxAw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:23:10.790467Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.05725","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:245df37b534fb9ba37173a0475e1dc86d861f683bf39330766f86ea1aa10ffe3","sha256:312a51dd96c94460b8f614d583440276a87cb7ebc9b7b18cc716e97b9a186b22"],"state_sha256":"563443521cf5a5e0d33815f7db2ea6821e20dabcc8accb29ae68d3ccdf2957d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/6LKbGTAeOg+FUu3vPXhSWY58uScaywKYcEzowxbniMECrMdhhNGKUQmrc68/ZUFfsmn+xggcJrrWEwUacc7Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:22:54.170694Z","bundle_sha256":"530a4fc6f9b559dc14061c5c0bdf533a3ae725816af8e44751dbef885b9ba9af"}}