{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RY7YKZSRY3HFZ3A637BPMPX4XJ","short_pith_number":"pith:RY7YKZSR","schema_version":"1.0","canonical_sha256":"8e3f856651c6ce5cec1edfc2f63efcba4da47ddb3e17f5239d5a8631d4beb875","source":{"kind":"arxiv","id":"2508.19183","version":1},"attestation_state":"computed","paper":{"title":"Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kwan Hui Lim, Wenchuan Mu","submitted_at":"2025-08-26T16:41:04Z","abstract_excerpt":"In safety-critical deep learning applications, robustness measures the ability of neural models that handle imperceptible perturbations in input data, which may lead to potential safety hazards. Existing pre-deployment robustness assessment methods typically suffer from significant trade-offs between computational cost and measurement precision, limiting their practical utility. To address these limitations, this paper conducts a comprehensive comparative analysis of existing robustness definitions and associated assessment methodologies. We propose tower robustness to evaluate robustness, whi"},"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":"2508.19183","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T16:41:04Z","cross_cats_sorted":[],"title_canon_sha256":"a0f58a2fe176fb00d432772f8273b93feaaba079cda02e02b624ec6e0e1c0e21","abstract_canon_sha256":"f207f3ca91e225573b6e1edb48d1084d9356aaad2bb6319dd1c8c3681e1253a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:39.278850Z","signature_b64":"wJ+v8waIFenkGHxBKqVry2eZUXGJC5TsnpHT9m2OJDMzdA3C9eURMVEiMv/dqC4VwWRJQb2nfMEEVXxmAzGvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e3f856651c6ce5cec1edfc2f63efcba4da47ddb3e17f5239d5a8631d4beb875","last_reissued_at":"2026-07-05T11:59:39.278258Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:39.278258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kwan Hui Lim, Wenchuan Mu","submitted_at":"2025-08-26T16:41:04Z","abstract_excerpt":"In safety-critical deep learning applications, robustness measures the ability of neural models that handle imperceptible perturbations in input data, which may lead to potential safety hazards. Existing pre-deployment robustness assessment methods typically suffer from significant trade-offs between computational cost and measurement precision, limiting their practical utility. To address these limitations, this paper conducts a comprehensive comparative analysis of existing robustness definitions and associated assessment methodologies. We propose tower robustness to evaluate robustness, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19183","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/2508.19183/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":"2508.19183","created_at":"2026-07-05T11:59:39.278326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.19183v1","created_at":"2026-07-05T11:59:39.278326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19183","created_at":"2026-07-05T11:59:39.278326+00:00"},{"alias_kind":"pith_short_12","alias_value":"RY7YKZSRY3HF","created_at":"2026-07-05T11:59:39.278326+00:00"},{"alias_kind":"pith_short_16","alias_value":"RY7YKZSRY3HFZ3A6","created_at":"2026-07-05T11:59:39.278326+00:00"},{"alias_kind":"pith_short_8","alias_value":"RY7YKZSR","created_at":"2026-07-05T11:59:39.278326+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/RY7YKZSRY3HFZ3A637BPMPX4XJ","json":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ.json","graph_json":"https://pith.science/api/pith-number/RY7YKZSRY3HFZ3A637BPMPX4XJ/graph.json","events_json":"https://pith.science/api/pith-number/RY7YKZSRY3HFZ3A637BPMPX4XJ/events.json","paper":"https://pith.science/paper/RY7YKZSR"},"agent_actions":{"view_html":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ","download_json":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ.json","view_paper":"https://pith.science/paper/RY7YKZSR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.19183&json=true","fetch_graph":"https://pith.science/api/pith-number/RY7YKZSRY3HFZ3A637BPMPX4XJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RY7YKZSRY3HFZ3A637BPMPX4XJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ/action/storage_attestation","attest_author":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ/action/author_attestation","sign_citation":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ/action/citation_signature","submit_replication":"https://pith.science/pith/RY7YKZSRY3HFZ3A637BPMPX4XJ/action/replication_record"}},"created_at":"2026-07-05T11:59:39.278326+00:00","updated_at":"2026-07-05T11:59:39.278326+00:00"}