{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RP7CZJHA5FLFZWWARGOPQJWX4Q","short_pith_number":"pith:RP7CZJHA","schema_version":"1.0","canonical_sha256":"8bfe2ca4e0e9565cdac0899cf826d7e4168fe25ab574fe42c9f0406929c5eb0c","source":{"kind":"arxiv","id":"2505.04950","version":3},"attestation_state":"computed","paper":{"title":"Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andrew Bradley, Fabio Cuzzolin, Julian F. P. Kooij, Keivan Shariatmadar, Neil Yorke-Smith, Shireen Kudukkil Manchingal","submitted_at":"2025-05-08T05:10:38Z","abstract_excerpt":"Despite AI's impressive achievements, including recent advances in generative and large language models, there remains a significant gap in the ability of AI systems to handle uncertainty and generalize beyond their training data. AI models consistently fail to make robust enough predictions when facing unfamiliar or adversarial data. Traditional machine learning approaches struggle to address this issue, due to an overemphasis on data fitting, while current uncertainty quantification approaches suffer from serious limitations. This position paper posits a paradigm shift towards epistemic arti"},"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":"2505.04950","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-08T05:10:38Z","cross_cats_sorted":[],"title_canon_sha256":"befccf658c3c9996eb471be4e16ac7a9636cb4bfdc014182319bba8cf4407b2e","abstract_canon_sha256":"bc67583182c1c11e2997ae966e8e8ace80efedaf35cc39ea753a9c93c8d51f53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:16.631836Z","signature_b64":"wWy9XXYS+gxYR9oXmQB6WnF+dajTTY5CUhQAdX+Z6QjaDB+3tknF7b2fjB/XnPjhMI1c4reywFPps7jd8xifCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bfe2ca4e0e9565cdac0899cf826d7e4168fe25ab574fe42c9f0406929c5eb0c","last_reissued_at":"2026-07-05T11:28:16.631355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:16.631355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andrew Bradley, Fabio Cuzzolin, Julian F. P. Kooij, Keivan Shariatmadar, Neil Yorke-Smith, Shireen Kudukkil Manchingal","submitted_at":"2025-05-08T05:10:38Z","abstract_excerpt":"Despite AI's impressive achievements, including recent advances in generative and large language models, there remains a significant gap in the ability of AI systems to handle uncertainty and generalize beyond their training data. AI models consistently fail to make robust enough predictions when facing unfamiliar or adversarial data. Traditional machine learning approaches struggle to address this issue, due to an overemphasis on data fitting, while current uncertainty quantification approaches suffer from serious limitations. This position paper posits a paradigm shift towards epistemic arti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04950","kind":"arxiv","version":3},"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/2505.04950/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":"2505.04950","created_at":"2026-07-05T11:28:16.631412+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04950v3","created_at":"2026-07-05T11:28:16.631412+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04950","created_at":"2026-07-05T11:28:16.631412+00:00"},{"alias_kind":"pith_short_12","alias_value":"RP7CZJHA5FLF","created_at":"2026-07-05T11:28:16.631412+00:00"},{"alias_kind":"pith_short_16","alias_value":"RP7CZJHA5FLFZWWA","created_at":"2026-07-05T11:28:16.631412+00:00"},{"alias_kind":"pith_short_8","alias_value":"RP7CZJHA","created_at":"2026-07-05T11:28:16.631412+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25182","citing_title":"What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26990","citing_title":"Decision-Aligned Evaluation of Uncertainty Quantification","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25882","citing_title":"Conformalised imprecise inference for robust extrapolation under limited data","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07908","citing_title":"Statistical inference with belief functions: A survey","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q","json":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q.json","graph_json":"https://pith.science/api/pith-number/RP7CZJHA5FLFZWWARGOPQJWX4Q/graph.json","events_json":"https://pith.science/api/pith-number/RP7CZJHA5FLFZWWARGOPQJWX4Q/events.json","paper":"https://pith.science/paper/RP7CZJHA"},"agent_actions":{"view_html":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q","download_json":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q.json","view_paper":"https://pith.science/paper/RP7CZJHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04950&json=true","fetch_graph":"https://pith.science/api/pith-number/RP7CZJHA5FLFZWWARGOPQJWX4Q/graph.json","fetch_events":"https://pith.science/api/pith-number/RP7CZJHA5FLFZWWARGOPQJWX4Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q/action/storage_attestation","attest_author":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q/action/author_attestation","sign_citation":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q/action/citation_signature","submit_replication":"https://pith.science/pith/RP7CZJHA5FLFZWWARGOPQJWX4Q/action/replication_record"}},"created_at":"2026-07-05T11:28:16.631412+00:00","updated_at":"2026-07-05T11:28:16.631412+00:00"}