{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:P3KE6VFCZBBHO777ZF4KAE5REO","short_pith_number":"pith:P3KE6VFC","canonical_record":{"source":{"id":"2507.01752","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-02T14:29:30Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CR"],"title_canon_sha256":"3f1e95708158fc56635d41072ce6ec6e9f0c1b5bc1f90574efbbdf38c66c4185","abstract_canon_sha256":"8661f53af4380ad02ece828603519db6cf5fc0220d87271d8901b32f7f7b2a7b"},"schema_version":"1.0"},"canonical_sha256":"7ed44f54a2c842777fffc978a013b123aa37ad9167f4987cb95371923a65f34e","source":{"kind":"arxiv","id":"2507.01752","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.01752","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"arxiv_version","alias_value":"2507.01752v4","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01752","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_12","alias_value":"P3KE6VFCZBBH","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_16","alias_value":"P3KE6VFCZBBHO777","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_8","alias_value":"P3KE6VFC","created_at":"2026-06-24T01:14:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:P3KE6VFCZBBHO777ZF4KAE5REO","target":"record","payload":{"canonical_record":{"source":{"id":"2507.01752","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-02T14:29:30Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CR"],"title_canon_sha256":"3f1e95708158fc56635d41072ce6ec6e9f0c1b5bc1f90574efbbdf38c66c4185","abstract_canon_sha256":"8661f53af4380ad02ece828603519db6cf5fc0220d87271d8901b32f7f7b2a7b"},"schema_version":"1.0"},"canonical_sha256":"7ed44f54a2c842777fffc978a013b123aa37ad9167f4987cb95371923a65f34e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:57.782631Z","signature_b64":"pT0VTZr6MHORtEgYSGeIdLdjwtq5YJ9TbO2ACVng8NAi1DLJlexNFsQXBNqUQGy5u+U4LQ1b4IlBR+2ISiRdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ed44f54a2c842777fffc978a013b123aa37ad9167f4987cb95371923a65f34e","last_reissued_at":"2026-06-24T01:14:57.782155Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:57.782155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.01752","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-24T01:14:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CZxIgwTAVHocwfhN8W3REyJUJouiodDuINA0SbbJ7M+uaY8Iocpt3ngbsIEoUhLtpXGBT+VyE8NtYyuAspUTAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:52:08.683848Z"},"content_sha256":"4bcb6ed7bc6c708fb079c8f5cc955610abc88d53e84948c86fe85332bb5bd507","schema_version":"1.0","event_id":"sha256:4bcb6ed7bc6c708fb079c8f5cc955610abc88d53e84948c86fe85332bb5bd507"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:P3KE6VFCZBBHO777ZF4KAE5REO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CR"],"primary_cat":"cs.LG","authors_text":"Alessandro Leite, Ismail Labiad, Julia Kempe, Marc Schoenauer, Mathurin Videau, Matthieu Kowalski, Olivier Teytaud","submitted_at":"2025-07-02T14:29:30Z","abstract_excerpt":"Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing gradients during training can leak sensitive information about the underlying data, raising privacy and security concerns such as susceptibility to data poisoning attacks. In contrast, black-box optimization methods, which treat the model as an opaque function, relying solely on function evaluations to guide optimization, offer a promising alternative in scenarios where data access is restricted, adversarial risks are high, or overfitting is a concern. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01752","kind":"arxiv","version":4},"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/2507.01752/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":"2026-06-24T01:14:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r46ceYqyd/Yf5PFO54XaNeV+/l7rIRWxtUj8r4yQEN4hSvN4bibuoc5gB5YzRl3ffKNapIL4RMfZBZyyvA5oBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:52:08.684229Z"},"content_sha256":"f50ba2b246583806900f149c6ea82e9fcc3934c668c0b03b3f27ee9272e7abe7","schema_version":"1.0","event_id":"sha256:f50ba2b246583806900f149c6ea82e9fcc3934c668c0b03b3f27ee9272e7abe7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P3KE6VFCZBBHO777ZF4KAE5REO/bundle.json","state_url":"https://pith.science/pith/P3KE6VFCZBBHO777ZF4KAE5REO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P3KE6VFCZBBHO777ZF4KAE5REO/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":"2026-07-04T09:52:08Z","links":{"resolver":"https://pith.science/pith/P3KE6VFCZBBHO777ZF4KAE5REO","bundle":"https://pith.science/pith/P3KE6VFCZBBHO777ZF4KAE5REO/bundle.json","state":"https://pith.science/pith/P3KE6VFCZBBHO777ZF4KAE5REO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P3KE6VFCZBBHO777ZF4KAE5REO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:P3KE6VFCZBBHO777ZF4KAE5REO","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":"8661f53af4380ad02ece828603519db6cf5fc0220d87271d8901b32f7f7b2a7b","cross_cats_sorted":["cs.AI","cs.CL","cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-02T14:29:30Z","title_canon_sha256":"3f1e95708158fc56635d41072ce6ec6e9f0c1b5bc1f90574efbbdf38c66c4185"},"schema_version":"1.0","source":{"id":"2507.01752","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.01752","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"arxiv_version","alias_value":"2507.01752v4","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01752","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_12","alias_value":"P3KE6VFCZBBH","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_16","alias_value":"P3KE6VFCZBBHO777","created_at":"2026-06-24T01:14:57Z"},{"alias_kind":"pith_short_8","alias_value":"P3KE6VFC","created_at":"2026-06-24T01:14:57Z"}],"graph_snapshots":[{"event_id":"sha256:f50ba2b246583806900f149c6ea82e9fcc3934c668c0b03b3f27ee9272e7abe7","target":"graph","created_at":"2026-06-24T01:14:57Z","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/2507.01752/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing gradients during training can leak sensitive information about the underlying data, raising privacy and security concerns such as susceptibility to data poisoning attacks. In contrast, black-box optimization methods, which treat the model as an opaque function, relying solely on function evaluations to guide optimization, offer a promising alternative in scenarios where data access is restricted, adversarial risks are high, or overfitting is a concern. ","authors_text":"Alessandro Leite, Ismail Labiad, Julia Kempe, Marc Schoenauer, Mathurin Videau, Matthieu Kowalski, Olivier Teytaud","cross_cats":["cs.AI","cs.CL","cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01752","kind":"arxiv","version":4},"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":"sha256:4bcb6ed7bc6c708fb079c8f5cc955610abc88d53e84948c86fe85332bb5bd507","target":"record","created_at":"2026-06-24T01:14:57Z","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":"8661f53af4380ad02ece828603519db6cf5fc0220d87271d8901b32f7f7b2a7b","cross_cats_sorted":["cs.AI","cs.CL","cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-02T14:29:30Z","title_canon_sha256":"3f1e95708158fc56635d41072ce6ec6e9f0c1b5bc1f90574efbbdf38c66c4185"},"schema_version":"1.0","source":{"id":"2507.01752","kind":"arxiv","version":4}},"canonical_sha256":"7ed44f54a2c842777fffc978a013b123aa37ad9167f4987cb95371923a65f34e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ed44f54a2c842777fffc978a013b123aa37ad9167f4987cb95371923a65f34e","first_computed_at":"2026-06-24T01:14:57.782155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-24T01:14:57.782155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pT0VTZr6MHORtEgYSGeIdLdjwtq5YJ9TbO2ACVng8NAi1DLJlexNFsQXBNqUQGy5u+U4LQ1b4IlBR+2ISiRdAg==","signature_status":"signed_v1","signed_at":"2026-06-24T01:14:57.782631Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.01752","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4bcb6ed7bc6c708fb079c8f5cc955610abc88d53e84948c86fe85332bb5bd507","sha256:f50ba2b246583806900f149c6ea82e9fcc3934c668c0b03b3f27ee9272e7abe7"],"state_sha256":"3b967ae3da8f5ecc7612cc7bb8ec10e079b9b4d685ecfe13a0f854bfdcaa023c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"37LWq4tK0l4PJ8GWTb6Ttms+7jAjQL5lfIEbxSlyVVCPBWI/wbEzOZpGmBtHQowkwYO84imJ64uPxUs3m1MpAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T09:52:08.686270Z","bundle_sha256":"0d10b84f5c9c1f217175f4107f85d81f7f82228fef135540fdcaaf92f7852a2e"}}