{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BQIV6GER33UUURRAKIPHX4FGRS","short_pith_number":"pith:BQIV6GER","schema_version":"1.0","canonical_sha256":"0c115f1891dee94a4620521e7bf0a68ca531784383ba120daeb26c8c9c8a995f","source":{"kind":"arxiv","id":"2501.12203","version":1},"attestation_state":"computed","paper":{"title":"Explainability for Vision Foundation Models: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elo\\\"ise Berthier, Gianni Franchi, Goran Frehse, R\\'emi Kazmierczak","submitted_at":"2025-01-21T15:18:55Z","abstract_excerpt":"As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and their decision-making processes. The advent of foundation models, characterized by their extensive generalization capabilities and emergent uses, has further complicated this landscape. Foundation models occupy an ambiguous position in the explainability domain: their complexity makes them inherently challenging to interpret, yet they are increasingly leveraged as tools to con"},"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":"2501.12203","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-21T15:18:55Z","cross_cats_sorted":[],"title_canon_sha256":"282d6a1da59cb3919e8a92fc6a025eb7840fd7e55c7d315690f9195085c52b41","abstract_canon_sha256":"b7066c9ff021113ec4ca7976e5e86e6b889d89e481c2296ed6312592c0730e36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:32.860878Z","signature_b64":"MKCqDAj/zC39qeHVBEnZwz+VPvtUPySWKg13XmBLTNK7grGEIGsxsXH+WK8M5ruKnqd2hnBYkzo2R8C1JgkpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c115f1891dee94a4620521e7bf0a68ca531784383ba120daeb26c8c9c8a995f","last_reissued_at":"2026-07-05T10:03:32.860487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:32.860487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainability for Vision Foundation Models: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elo\\\"ise Berthier, Gianni Franchi, Goran Frehse, R\\'emi Kazmierczak","submitted_at":"2025-01-21T15:18:55Z","abstract_excerpt":"As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and their decision-making processes. The advent of foundation models, characterized by their extensive generalization capabilities and emergent uses, has further complicated this landscape. Foundation models occupy an ambiguous position in the explainability domain: their complexity makes them inherently challenging to interpret, yet they are increasingly leveraged as tools to con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12203","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/2501.12203/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":"2501.12203","created_at":"2026-07-05T10:03:32.860545+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12203v1","created_at":"2026-07-05T10:03:32.860545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12203","created_at":"2026-07-05T10:03:32.860545+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQIV6GER33UU","created_at":"2026-07-05T10:03:32.860545+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQIV6GER33UUURRA","created_at":"2026-07-05T10:03:32.860545+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQIV6GER","created_at":"2026-07-05T10:03:32.860545+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.05810","citing_title":"Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS","json":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS.json","graph_json":"https://pith.science/api/pith-number/BQIV6GER33UUURRAKIPHX4FGRS/graph.json","events_json":"https://pith.science/api/pith-number/BQIV6GER33UUURRAKIPHX4FGRS/events.json","paper":"https://pith.science/paper/BQIV6GER"},"agent_actions":{"view_html":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS","download_json":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS.json","view_paper":"https://pith.science/paper/BQIV6GER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12203&json=true","fetch_graph":"https://pith.science/api/pith-number/BQIV6GER33UUURRAKIPHX4FGRS/graph.json","fetch_events":"https://pith.science/api/pith-number/BQIV6GER33UUURRAKIPHX4FGRS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS/action/storage_attestation","attest_author":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS/action/author_attestation","sign_citation":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS/action/citation_signature","submit_replication":"https://pith.science/pith/BQIV6GER33UUURRAKIPHX4FGRS/action/replication_record"}},"created_at":"2026-07-05T10:03:32.860545+00:00","updated_at":"2026-07-05T10:03:32.860545+00:00"}