{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MPYHTCHMSQ4AAXKRB5IAZBVKF3","short_pith_number":"pith:MPYHTCHM","schema_version":"1.0","canonical_sha256":"63f07988ec9438005d510f500c86aa2eda51545bf79318ba4344c67ec1febb37","source":{"kind":"arxiv","id":"2501.13075","version":1},"attestation_state":"computed","paper":{"title":"Evolution and The Knightian Blindspot of Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Elliot Meyerson, Joel Lehman, Kenneth O. Stanley, Tarek El-Gaaly, Tarin Ziyaee","submitted_at":"2025-01-22T18:38:41Z","abstract_excerpt":"This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world. Such robustness relates to Knightian uncertainty (KU) in economics, i.e. uncertainty that cannot be quantified, which is excluded from consideration in ML's key formalisms. This paper aims to identify this blind spot, argue its importance, and catalyze research into addressing it, which we believe is necessary to create truly robust open-world AI. To help illuminate the blind spot, we contrast one area of ML, reinforcement lea"},"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":"2501.13075","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-22T18:38:41Z","cross_cats_sorted":[],"title_canon_sha256":"2541d56c6f3e8d7b589e4ee5201b59934204a41998482ca22aae4a213b23c19f","abstract_canon_sha256":"f6f1788a507962639b16adce2973741d59fcc3074d0c653d105fbe1ed87b0299"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:02.545598Z","signature_b64":"uMIauaPw1Szy26m0BlVWtKLnaLPN+vU2yjNDL6bM15UHU9Rg1GAoS5y4koHbRLq5NDc2nw1JgVWcu1VuSjo+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63f07988ec9438005d510f500c86aa2eda51545bf79318ba4344c67ec1febb37","last_reissued_at":"2026-07-05T10:04:02.545190Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:02.545190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolution and The Knightian Blindspot of Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Elliot Meyerson, Joel Lehman, Kenneth O. Stanley, Tarek El-Gaaly, Tarin Ziyaee","submitted_at":"2025-01-22T18:38:41Z","abstract_excerpt":"This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world. Such robustness relates to Knightian uncertainty (KU) in economics, i.e. uncertainty that cannot be quantified, which is excluded from consideration in ML's key formalisms. This paper aims to identify this blind spot, argue its importance, and catalyze research into addressing it, which we believe is necessary to create truly robust open-world AI. To help illuminate the blind spot, we contrast one area of ML, reinforcement lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13075","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/2501.13075/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":"2501.13075","created_at":"2026-07-05T10:04:02.545245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.13075v1","created_at":"2026-07-05T10:04:02.545245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13075","created_at":"2026-07-05T10:04:02.545245+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPYHTCHMSQ4A","created_at":"2026-07-05T10:04:02.545245+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPYHTCHMSQ4AAXKR","created_at":"2026-07-05T10:04:02.545245+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPYHTCHM","created_at":"2026-07-05T10:04:02.545245+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.16814","citing_title":"Stable diffusion models reveal a persisting human and AI gap in visual creativity","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3","json":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3.json","graph_json":"https://pith.science/api/pith-number/MPYHTCHMSQ4AAXKRB5IAZBVKF3/graph.json","events_json":"https://pith.science/api/pith-number/MPYHTCHMSQ4AAXKRB5IAZBVKF3/events.json","paper":"https://pith.science/paper/MPYHTCHM"},"agent_actions":{"view_html":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3","download_json":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3.json","view_paper":"https://pith.science/paper/MPYHTCHM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.13075&json=true","fetch_graph":"https://pith.science/api/pith-number/MPYHTCHMSQ4AAXKRB5IAZBVKF3/graph.json","fetch_events":"https://pith.science/api/pith-number/MPYHTCHMSQ4AAXKRB5IAZBVKF3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3/action/storage_attestation","attest_author":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3/action/author_attestation","sign_citation":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3/action/citation_signature","submit_replication":"https://pith.science/pith/MPYHTCHMSQ4AAXKRB5IAZBVKF3/action/replication_record"}},"created_at":"2026-07-05T10:04:02.545245+00:00","updated_at":"2026-07-05T10:04:02.545245+00:00"}