{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZLQBVEFR4L5UZF77PLZSQCPPFT","short_pith_number":"pith:ZLQBVEFR","canonical_record":{"source":{"id":"2605.24879","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T05:44:20Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"b87713781cf3e80088cb8cd72d21eda8e924e1919d25d54a9855b3976c13d6a3","abstract_canon_sha256":"97cda43c0a1220d30ec59a9ce29938dfb85f6349abd8091576c91419c7f8691b"},"schema_version":"1.0"},"canonical_sha256":"cae01a90b1e2fb4c97ff7af32809ef2cddd5d00b9a5efed27738091235622f79","source":{"kind":"arxiv","id":"2605.24879","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24879","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24879v1","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24879","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_12","alias_value":"ZLQBVEFR4L5U","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_16","alias_value":"ZLQBVEFR4L5UZF77","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_8","alias_value":"ZLQBVEFR","created_at":"2026-05-26T01:04:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZLQBVEFR4L5UZF77PLZSQCPPFT","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24879","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T05:44:20Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"b87713781cf3e80088cb8cd72d21eda8e924e1919d25d54a9855b3976c13d6a3","abstract_canon_sha256":"97cda43c0a1220d30ec59a9ce29938dfb85f6349abd8091576c91419c7f8691b"},"schema_version":"1.0"},"canonical_sha256":"cae01a90b1e2fb4c97ff7af32809ef2cddd5d00b9a5efed27738091235622f79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:04:03.163731Z","signature_b64":"9KgnkVmMCTIhH6W0Sd4u2qd6ijg3QZ8sLF3u9tnbRrrmw9WwYWRHkmAoQeqsHMCSGjNdjH+lPM6jDcBzmyOXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cae01a90b1e2fb4c97ff7af32809ef2cddd5d00b9a5efed27738091235622f79","last_reissued_at":"2026-05-26T01:04:03.162882Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:04:03.162882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24879","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:04:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8sftCdvakdAj50ZMWlwxRNQCBXCH1qQqXwarU/NP2M33nycjES/w0JlSuV1nY/YrGbEGFmo4EhaTyJqC3UYCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:31:29.907734Z"},"content_sha256":"08355e33d8626edc9dde8b4ef0f395dd46c9a7814a7de00e59f5325d67b1da98","schema_version":"1.0","event_id":"sha256:08355e33d8626edc9dde8b4ef0f395dd46c9a7814a7de00e59f5325d67b1da98"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZLQBVEFR4L5UZF77PLZSQCPPFT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient DP-SGD for LLMs with Randomized Clipping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Devansh Gupta, Enayat Ullah, Huanyu Zhang, Meisam Razaviyayn, Sai Aparna Aketi","submitted_at":"2026-05-24T05:44:20Z","abstract_excerpt":"Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, provides a principled framework for training LLMs with provable privacy protection. However, state-of-the-art DP training implementations rely on fast gradient clipping techniques with memory overhead $O(B \\min\\{T^2, d^2\\})$, where $B$ is the batch size, $T$ is the sequence length, and $d$ is the model width. This becomes prohibitive as both model size and context length grow. We propose DP-SGD-RC, a novel variant of D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24879","kind":"arxiv","version":1},"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/2605.24879/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":"2026-05-26T01:04:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FB9Whb6iaNtKDPbE1z4CxbUE3Z45LcOIDWLWo9Q06WKoddQP/1te/55EwyQgNttDzIV/RELtDsE4+A1ybnfdAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:31:29.908118Z"},"content_sha256":"9b29f2c8545030c858b67fa5ad7542f42910142aa1ef8bd39631344c61bbae9c","schema_version":"1.0","event_id":"sha256:9b29f2c8545030c858b67fa5ad7542f42910142aa1ef8bd39631344c61bbae9c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/bundle.json","state_url":"https://pith.science/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/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":"2026-05-30T06:31:29Z","links":{"resolver":"https://pith.science/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT","bundle":"https://pith.science/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/bundle.json","state":"https://pith.science/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZLQBVEFR4L5UZF77PLZSQCPPFT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZLQBVEFR4L5UZF77PLZSQCPPFT","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":"97cda43c0a1220d30ec59a9ce29938dfb85f6349abd8091576c91419c7f8691b","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T05:44:20Z","title_canon_sha256":"b87713781cf3e80088cb8cd72d21eda8e924e1919d25d54a9855b3976c13d6a3"},"schema_version":"1.0","source":{"id":"2605.24879","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24879","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24879v1","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24879","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_12","alias_value":"ZLQBVEFR4L5U","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_16","alias_value":"ZLQBVEFR4L5UZF77","created_at":"2026-05-26T01:04:03Z"},{"alias_kind":"pith_short_8","alias_value":"ZLQBVEFR","created_at":"2026-05-26T01:04:03Z"}],"graph_snapshots":[{"event_id":"sha256:9b29f2c8545030c858b67fa5ad7542f42910142aa1ef8bd39631344c61bbae9c","target":"graph","created_at":"2026-05-26T01:04:03Z","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/2605.24879/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, provides a principled framework for training LLMs with provable privacy protection. However, state-of-the-art DP training implementations rely on fast gradient clipping techniques with memory overhead $O(B \\min\\{T^2, d^2\\})$, where $B$ is the batch size, $T$ is the sequence length, and $d$ is the model width. This becomes prohibitive as both model size and context length grow. We propose DP-SGD-RC, a novel variant of D","authors_text":"Devansh Gupta, Enayat Ullah, Huanyu Zhang, Meisam Razaviyayn, Sai Aparna Aketi","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T05:44:20Z","title":"Efficient DP-SGD for LLMs with Randomized Clipping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24879","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":"sha256:08355e33d8626edc9dde8b4ef0f395dd46c9a7814a7de00e59f5325d67b1da98","target":"record","created_at":"2026-05-26T01:04:03Z","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":"97cda43c0a1220d30ec59a9ce29938dfb85f6349abd8091576c91419c7f8691b","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T05:44:20Z","title_canon_sha256":"b87713781cf3e80088cb8cd72d21eda8e924e1919d25d54a9855b3976c13d6a3"},"schema_version":"1.0","source":{"id":"2605.24879","kind":"arxiv","version":1}},"canonical_sha256":"cae01a90b1e2fb4c97ff7af32809ef2cddd5d00b9a5efed27738091235622f79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cae01a90b1e2fb4c97ff7af32809ef2cddd5d00b9a5efed27738091235622f79","first_computed_at":"2026-05-26T01:04:03.162882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:04:03.162882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9KgnkVmMCTIhH6W0Sd4u2qd6ijg3QZ8sLF3u9tnbRrrmw9WwYWRHkmAoQeqsHMCSGjNdjH+lPM6jDcBzmyOXCg==","signature_status":"signed_v1","signed_at":"2026-05-26T01:04:03.163731Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24879","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08355e33d8626edc9dde8b4ef0f395dd46c9a7814a7de00e59f5325d67b1da98","sha256:9b29f2c8545030c858b67fa5ad7542f42910142aa1ef8bd39631344c61bbae9c"],"state_sha256":"223bbe0eca03969dc42d3103e3c6fc9d6ffbaea977a75659f1cf21d8f8d4060e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YmPZeddCY24jYWlDCsVwjsl5qGRkQGHd7EcbUqX5Lp9KfhH5HwL5NKbWrwAJwPLCsifeCiPTyyltgO6uxuz8Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T06:31:29.910263Z","bundle_sha256":"b1e60fae2c7ef4374a3b2fa061292f2d3c81c5225e959d3e5a0aee63a43e59f5"}}