{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7S3COBFVLEVOXWINZ5JXLXXABC","short_pith_number":"pith:7S3COBFV","canonical_record":{"source":{"id":"2506.07804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T14:33:28Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c71056c278c7170219423962222ac9bd447b7e94e41a9bb4e3d23e6097aff345","abstract_canon_sha256":"c4d334c962e0560739a94073c5f8306ab0897d41a771ee412058b84df3feab1b"},"schema_version":"1.0"},"canonical_sha256":"fcb62704b5592aebd90dcf5375dee00886cfbe250af618b6ad42e143e872d5b7","source":{"kind":"arxiv","id":"2506.07804","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07804","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07804v1","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07804","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_12","alias_value":"7S3COBFVLEVO","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_16","alias_value":"7S3COBFVLEVOXWIN","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_8","alias_value":"7S3COBFV","created_at":"2026-07-05T11:18:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7S3COBFVLEVOXWINZ5JXLXXABC","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T14:33:28Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"c71056c278c7170219423962222ac9bd447b7e94e41a9bb4e3d23e6097aff345","abstract_canon_sha256":"c4d334c962e0560739a94073c5f8306ab0897d41a771ee412058b84df3feab1b"},"schema_version":"1.0"},"canonical_sha256":"fcb62704b5592aebd90dcf5375dee00886cfbe250af618b6ad42e143e872d5b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:33.741923Z","signature_b64":"RJZSdyLqBopeOm6/Fm31U6mlTarXwmc5czKJFwwCEZuQbtZEZosPFpEl+Qzsh5v6pN4gj1yd+GgjggFvsHGrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcb62704b5592aebd90dcf5375dee00886cfbe250af618b6ad42e143e872d5b7","last_reissued_at":"2026-07-05T11:18:33.741030Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:33.741030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07804","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":"2026-07-05T11:18:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GkboB/rr25o+UazYygwUc6/wEP5wv4gHSuJx81CaRVyNsVekEH7WwlD30RpqDMG91x2dIQ6YJivLsOU6uGPtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:26:28.578027Z"},"content_sha256":"4d4008bc65d1d8905a54f5623c3c927849567d1ce30641c89d4679e6b5d8b4b1","schema_version":"1.0","event_id":"sha256:4d4008bc65d1d8905a54f5623c3c927849567d1ce30641c89d4679e6b5d8b4b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7S3COBFVLEVOXWINZ5JXLXXABC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chuangyin Dang, Hanwei Zhang, Jie Bao, Rui Luo, Zhixin Zhou","submitted_at":"2025-06-09T14:33:28Z","abstract_excerpt":"As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreover, accuracy alone does not provide sufficient assurance or reliable uncertainty estimates for these models. This study advances adversarial training by leveraging principles from Conformal Prediction. Specifically, we develop an adversarial attack method, termed OPSA (OPtimal Size Attack), designed to reduce the efficiency of conformal prediction at any significance level by maximizing model uncertainty without req"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07804","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/2506.07804/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":"2026-07-05T11:18:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qzuRtb/NExiUz4Ha40HC+Y1tK88D5DFcecy93s7KK3R3mSU3B4WvjmtMbR7V1CaFT8hWHxlrgDBxqYM5D4ezBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:26:28.578554Z"},"content_sha256":"dbd1c15653b00f25f4393605187981af3aba6aa19edcad55e077b624844ce869","schema_version":"1.0","event_id":"sha256:dbd1c15653b00f25f4393605187981af3aba6aa19edcad55e077b624844ce869"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7S3COBFVLEVOXWINZ5JXLXXABC/bundle.json","state_url":"https://pith.science/pith/7S3COBFVLEVOXWINZ5JXLXXABC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7S3COBFVLEVOXWINZ5JXLXXABC/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":"2026-08-08T22:26:28Z","links":{"resolver":"https://pith.science/pith/7S3COBFVLEVOXWINZ5JXLXXABC","bundle":"https://pith.science/pith/7S3COBFVLEVOXWINZ5JXLXXABC/bundle.json","state":"https://pith.science/pith/7S3COBFVLEVOXWINZ5JXLXXABC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7S3COBFVLEVOXWINZ5JXLXXABC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7S3COBFVLEVOXWINZ5JXLXXABC","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":"c4d334c962e0560739a94073c5f8306ab0897d41a771ee412058b84df3feab1b","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T14:33:28Z","title_canon_sha256":"c71056c278c7170219423962222ac9bd447b7e94e41a9bb4e3d23e6097aff345"},"schema_version":"1.0","source":{"id":"2506.07804","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07804","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07804v1","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07804","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_12","alias_value":"7S3COBFVLEVO","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_16","alias_value":"7S3COBFVLEVOXWIN","created_at":"2026-07-05T11:18:33Z"},{"alias_kind":"pith_short_8","alias_value":"7S3COBFV","created_at":"2026-07-05T11:18:33Z"}],"graph_snapshots":[{"event_id":"sha256:dbd1c15653b00f25f4393605187981af3aba6aa19edcad55e077b624844ce869","target":"graph","created_at":"2026-07-05T11:18:33Z","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/2506.07804/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreover, accuracy alone does not provide sufficient assurance or reliable uncertainty estimates for these models. This study advances adversarial training by leveraging principles from Conformal Prediction. Specifically, we develop an adversarial attack method, termed OPSA (OPtimal Size Attack), designed to reduce the efficiency of conformal prediction at any significance level by maximizing model uncertainty without req","authors_text":"Chuangyin Dang, Hanwei Zhang, Jie Bao, Rui Luo, Zhixin Zhou","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T14:33:28Z","title":"Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07804","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":"sha256:4d4008bc65d1d8905a54f5623c3c927849567d1ce30641c89d4679e6b5d8b4b1","target":"record","created_at":"2026-07-05T11:18:33Z","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":"c4d334c962e0560739a94073c5f8306ab0897d41a771ee412058b84df3feab1b","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T14:33:28Z","title_canon_sha256":"c71056c278c7170219423962222ac9bd447b7e94e41a9bb4e3d23e6097aff345"},"schema_version":"1.0","source":{"id":"2506.07804","kind":"arxiv","version":1}},"canonical_sha256":"fcb62704b5592aebd90dcf5375dee00886cfbe250af618b6ad42e143e872d5b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fcb62704b5592aebd90dcf5375dee00886cfbe250af618b6ad42e143e872d5b7","first_computed_at":"2026-07-05T11:18:33.741030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:33.741030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RJZSdyLqBopeOm6/Fm31U6mlTarXwmc5czKJFwwCEZuQbtZEZosPFpEl+Qzsh5v6pN4gj1yd+GgjggFvsHGrAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:33.741923Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07804","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d4008bc65d1d8905a54f5623c3c927849567d1ce30641c89d4679e6b5d8b4b1","sha256:dbd1c15653b00f25f4393605187981af3aba6aa19edcad55e077b624844ce869"],"state_sha256":"766a4e96fb47b7a5ddc0e1318d6e705be3dd986c4aec4ad0f2aa7bf0da653d7f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QgbA4i0WRCH2bN4yCu95xYqt3iMImwAG8XLhfs6tcQ3r/IpyA1UlVLi7aha8LTrNW0W3OtDy7PNFPi9Wldq8AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:26:28.581896Z","bundle_sha256":"deaa0b426ea7484c4bf0a9cca2aeae18210394ea5d6849d78f5206443ef275b0"}}