{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DUTEUXWU34ZNEHMG2PKV5BWURA","short_pith_number":"pith:DUTEUXWU","schema_version":"1.0","canonical_sha256":"1d264a5ed4df32d21d86d3d55e86d4882f2ae055a76f788fef42572ca48595ed","source":{"kind":"arxiv","id":"2606.23858","version":1},"attestation_state":"computed","paper":{"title":"Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Giorgos Flouris, Jo\\~ao Marques-Silva, Konstantinos Varsos, Merkouris Papamichail","submitted_at":"2026-06-22T18:50:52Z","abstract_excerpt":"A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify. To mitigate this problem, recent research focuses on the computation of robustness certifications, which, for a given input, determine the largest distortion the input may receive without breaking the network's prediction. Robustness certifications can be interpreted as an axis-aligned hyper-rectangle (multi-dimensional intervals). Most existing approaches focus on maximizing the certification's volume, but recent intractability results prohibi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.23858","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T18:50:52Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"bbd61e6e7c7292a79a1181dc9025f7e586276394260f186c4e668c69f73dc7e8","abstract_canon_sha256":"7a2a6bda4d7aff66b5494bfb73b6d1b973bfa75e293acde5bc2c5cbac40be65e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T00:14:28.721618Z","signature_b64":"lOTESwoyI40cb6/DyE3rLSCnoqVY02FfW5T+LAa4NYYqDqhHUYhaRibsXabVsIQkrS5r9mt7BTqOZpgTA4mgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d264a5ed4df32d21d86d3d55e86d4882f2ae055a76f788fef42572ca48595ed","last_reissued_at":"2026-06-24T00:14:28.721210Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T00:14:28.721210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Giorgos Flouris, Jo\\~ao Marques-Silva, Konstantinos Varsos, Merkouris Papamichail","submitted_at":"2026-06-22T18:50:52Z","abstract_excerpt":"A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify. To mitigate this problem, recent research focuses on the computation of robustness certifications, which, for a given input, determine the largest distortion the input may receive without breaking the network's prediction. Robustness certifications can be interpreted as an axis-aligned hyper-rectangle (multi-dimensional intervals). Most existing approaches focus on maximizing the certification's volume, but recent intractability results prohibi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23858","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/2606.23858/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.23858","created_at":"2026-06-24T00:14:28.721274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.23858v1","created_at":"2026-06-24T00:14:28.721274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23858","created_at":"2026-06-24T00:14:28.721274+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUTEUXWU34ZN","created_at":"2026-06-24T00:14:28.721274+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUTEUXWU34ZNEHMG","created_at":"2026-06-24T00:14:28.721274+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUTEUXWU","created_at":"2026-06-24T00:14:28.721274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA","json":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA.json","graph_json":"https://pith.science/api/pith-number/DUTEUXWU34ZNEHMG2PKV5BWURA/graph.json","events_json":"https://pith.science/api/pith-number/DUTEUXWU34ZNEHMG2PKV5BWURA/events.json","paper":"https://pith.science/paper/DUTEUXWU"},"agent_actions":{"view_html":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA","download_json":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA.json","view_paper":"https://pith.science/paper/DUTEUXWU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.23858&json=true","fetch_graph":"https://pith.science/api/pith-number/DUTEUXWU34ZNEHMG2PKV5BWURA/graph.json","fetch_events":"https://pith.science/api/pith-number/DUTEUXWU34ZNEHMG2PKV5BWURA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA/action/storage_attestation","attest_author":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA/action/author_attestation","sign_citation":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA/action/citation_signature","submit_replication":"https://pith.science/pith/DUTEUXWU34ZNEHMG2PKV5BWURA/action/replication_record"}},"created_at":"2026-06-24T00:14:28.721274+00:00","updated_at":"2026-06-24T00:14:28.721274+00:00"}