{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MUCVTCNN3JB4VJBG6XCXVDQWWR","short_pith_number":"pith:MUCVTCNN","canonical_record":{"source":{"id":"2305.15769","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T06:27:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8dffdc1fe09278b1b8d0f58475178b0aaa4538b91383928c26268d7098db4919","abstract_canon_sha256":"57c5221ecfce492b936aec20d798f4bcb721dc892d892787b06f6ed3813dd20b"},"schema_version":"1.0"},"canonical_sha256":"65055989adda43caa426f5c57a8e16b472dce290da405d79db3e83fc8a644a98","source":{"kind":"arxiv","id":"2305.15769","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15769","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15769v3","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15769","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"MUCVTCNN3JB4","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"MUCVTCNN3JB4VJBG","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"MUCVTCNN","created_at":"2026-07-05T07:22:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MUCVTCNN3JB4VJBG6XCXVDQWWR","target":"record","payload":{"canonical_record":{"source":{"id":"2305.15769","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T06:27:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8dffdc1fe09278b1b8d0f58475178b0aaa4538b91383928c26268d7098db4919","abstract_canon_sha256":"57c5221ecfce492b936aec20d798f4bcb721dc892d892787b06f6ed3813dd20b"},"schema_version":"1.0"},"canonical_sha256":"65055989adda43caa426f5c57a8e16b472dce290da405d79db3e83fc8a644a98","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:18.463405Z","signature_b64":"WMMT4AQSSX+29ukieE8kNDdQMXPhvM6bHxbNRiYlBVIccNzhNcBIzvP8rF7ymF/vbBduH7fkqTLmJMWMvxH0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65055989adda43caa426f5c57a8e16b472dce290da405d79db3e83fc8a644a98","last_reissued_at":"2026-07-05T07:22:18.462893Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:18.462893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.15769","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/dc5zYb4wtW2hqYOBP7vC/u6fuOF/cOOD5XoT55SyuOfmvnI3s/S8AcUuaHGqKvwJOmEGLd1jFYMY5YGbNA8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:42:54.084664Z"},"content_sha256":"29ac501ed49092edc38f20d3262755592360c2d1884ee7b0952f73060a6cd9c2","schema_version":"1.0","event_id":"sha256:29ac501ed49092edc38f20d3262755592360c2d1884ee7b0952f73060a6cd9c2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MUCVTCNN3JB4VJBG6XCXVDQWWR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MERGE: Fast Private Text Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Lifeng Xing, Nuo Xu, Pinghui Wang, Ruofei Zhang, Shuo Zhang, Zi Liang","submitted_at":"2023-05-25T06:27:19Z","abstract_excerpt":"The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language generation (NLG), crucial for applications like translation and code completion, remains underexplored.In addition, previous privacy-preserving techniques suffer from convergence issues during model training and exhibit poor inference speed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15769","kind":"arxiv","version":3},"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/2305.15769/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":"2026-07-05T07:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VMg7SnffScw6wzhuMpkg18vgKD0g+VpRATpX25Hm6zgsSmMKvED1Ss2XkCTHL6AeoHrho/4MRQFU6ptVrT/hBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:42:54.085231Z"},"content_sha256":"6b516c62d6a0cfedd2dbd6a1c4d82a750a2308457223bde2c1f5282326fa9cd3","schema_version":"1.0","event_id":"sha256:6b516c62d6a0cfedd2dbd6a1c4d82a750a2308457223bde2c1f5282326fa9cd3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/bundle.json","state_url":"https://pith.science/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/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":"2026-08-16T02:42:54Z","links":{"resolver":"https://pith.science/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR","bundle":"https://pith.science/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/bundle.json","state":"https://pith.science/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MUCVTCNN3JB4VJBG6XCXVDQWWR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MUCVTCNN3JB4VJBG6XCXVDQWWR","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":"57c5221ecfce492b936aec20d798f4bcb721dc892d892787b06f6ed3813dd20b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T06:27:19Z","title_canon_sha256":"8dffdc1fe09278b1b8d0f58475178b0aaa4538b91383928c26268d7098db4919"},"schema_version":"1.0","source":{"id":"2305.15769","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15769","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15769v3","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15769","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"MUCVTCNN3JB4","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"MUCVTCNN3JB4VJBG","created_at":"2026-07-05T07:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"MUCVTCNN","created_at":"2026-07-05T07:22:18Z"}],"graph_snapshots":[{"event_id":"sha256:6b516c62d6a0cfedd2dbd6a1c4d82a750a2308457223bde2c1f5282326fa9cd3","target":"graph","created_at":"2026-07-05T07:22:18Z","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/2305.15769/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language generation (NLG), crucial for applications like translation and code completion, remains underexplored.In addition, previous privacy-preserving techniques suffer from convergence issues during model training and exhibit poor inference speed","authors_text":"Lifeng Xing, Nuo Xu, Pinghui Wang, Ruofei Zhang, Shuo Zhang, Zi Liang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T06:27:19Z","title":"MERGE: Fast Private Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15769","kind":"arxiv","version":3},"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":"sha256:29ac501ed49092edc38f20d3262755592360c2d1884ee7b0952f73060a6cd9c2","target":"record","created_at":"2026-07-05T07:22:18Z","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":"57c5221ecfce492b936aec20d798f4bcb721dc892d892787b06f6ed3813dd20b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-25T06:27:19Z","title_canon_sha256":"8dffdc1fe09278b1b8d0f58475178b0aaa4538b91383928c26268d7098db4919"},"schema_version":"1.0","source":{"id":"2305.15769","kind":"arxiv","version":3}},"canonical_sha256":"65055989adda43caa426f5c57a8e16b472dce290da405d79db3e83fc8a644a98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65055989adda43caa426f5c57a8e16b472dce290da405d79db3e83fc8a644a98","first_computed_at":"2026-07-05T07:22:18.462893Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:22:18.462893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WMMT4AQSSX+29ukieE8kNDdQMXPhvM6bHxbNRiYlBVIccNzhNcBIzvP8rF7ymF/vbBduH7fkqTLmJMWMvxH0BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:22:18.463405Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.15769","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29ac501ed49092edc38f20d3262755592360c2d1884ee7b0952f73060a6cd9c2","sha256:6b516c62d6a0cfedd2dbd6a1c4d82a750a2308457223bde2c1f5282326fa9cd3"],"state_sha256":"82c530fe7b14691424af72438202f263128dc1724094b24ec15a321478bf2abd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZcsbE7wXwsyf3zQbR5O3W1LoqZLHISbKK97DsvK+28tMZGH2Ey2fr2AkXHEDJU2vOD/iXrCNB+lEqZB2C8xWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T02:42:54.088751Z","bundle_sha256":"68de680fff94c22e8aec7be838716378594e5c8578a41a9f8bfa6e52b350bdaf"}}