{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RUA3FFJKXCNKPO6VSRO6ZCU7UX","short_pith_number":"pith:RUA3FFJK","schema_version":"1.0","canonical_sha256":"8d01b2952ab89aa7bbd5945dec8a9fa5e50df9c5ddbaf8e3318562bb1ff31be7","source":{"kind":"arxiv","id":"2602.10085","version":3},"attestation_state":"computed","paper":{"title":"CODE-SHARP: Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Antoine Cully, Pierluigi Vito Amadori, Richard Bornemann","submitted_at":"2026-02-10T18:51:39Z","abstract_excerpt":"A core quality of general intelligence is the ability to open-endedly expand and evolve its set of mastered skills autonomously. While recent Foundation Model (FM) driven approaches have shown promising results towards this goal, they typically rely on significant human-in-the-loop engineering, limiting their transferability to novel environments. To address this, we introduce Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs (CODE-SHARP), a framework that leverages FMs to open-endedly grow and evolve an archive of Python programs encoding skills to train "},"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":"2602.10085","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-10T18:51:39Z","cross_cats_sorted":[],"title_canon_sha256":"7cdb6d015161a2f56b95623749ee294b879511c5f9e3f209b8aeac9e593c0c1c","abstract_canon_sha256":"aa4593ed408e2781ffe8fa39c74357bd0a353ca59f84e731affcd7fb64df4fd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:57.149119Z","signature_b64":"/Cd1BdOiDZ9JV3tN1sUAEzpxCpyOv4ICwyicD7SUKMVCGFH8uKTY80sMnUYCgxNLABEYPr6zkM2N1h0nqixvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d01b2952ab89aa7bbd5945dec8a9fa5e50df9c5ddbaf8e3318562bb1ff31be7","last_reissued_at":"2026-05-22T01:03:57.148347Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:57.148347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CODE-SHARP: Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Antoine Cully, Pierluigi Vito Amadori, Richard Bornemann","submitted_at":"2026-02-10T18:51:39Z","abstract_excerpt":"A core quality of general intelligence is the ability to open-endedly expand and evolve its set of mastered skills autonomously. While recent Foundation Model (FM) driven approaches have shown promising results towards this goal, they typically rely on significant human-in-the-loop engineering, limiting their transferability to novel environments. To address this, we introduce Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs (CODE-SHARP), a framework that leverages FMs to open-endedly grow and evolve an archive of Python programs encoding skills to train "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.10085","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/2602.10085/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":"2602.10085","created_at":"2026-05-22T01:03:57.148446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.10085v3","created_at":"2026-05-22T01:03:57.148446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.10085","created_at":"2026-05-22T01:03:57.148446+00:00"},{"alias_kind":"pith_short_12","alias_value":"RUA3FFJKXCNK","created_at":"2026-05-22T01:03:57.148446+00:00"},{"alias_kind":"pith_short_16","alias_value":"RUA3FFJKXCNKPO6V","created_at":"2026-05-22T01:03:57.148446+00:00"},{"alias_kind":"pith_short_8","alias_value":"RUA3FFJK","created_at":"2026-05-22T01:03:57.148446+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/RUA3FFJKXCNKPO6VSRO6ZCU7UX","json":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX.json","graph_json":"https://pith.science/api/pith-number/RUA3FFJKXCNKPO6VSRO6ZCU7UX/graph.json","events_json":"https://pith.science/api/pith-number/RUA3FFJKXCNKPO6VSRO6ZCU7UX/events.json","paper":"https://pith.science/paper/RUA3FFJK"},"agent_actions":{"view_html":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX","download_json":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX.json","view_paper":"https://pith.science/paper/RUA3FFJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.10085&json=true","fetch_graph":"https://pith.science/api/pith-number/RUA3FFJKXCNKPO6VSRO6ZCU7UX/graph.json","fetch_events":"https://pith.science/api/pith-number/RUA3FFJKXCNKPO6VSRO6ZCU7UX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX/action/storage_attestation","attest_author":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX/action/author_attestation","sign_citation":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX/action/citation_signature","submit_replication":"https://pith.science/pith/RUA3FFJKXCNKPO6VSRO6ZCU7UX/action/replication_record"}},"created_at":"2026-05-22T01:03:57.148446+00:00","updated_at":"2026-05-22T01:03:57.148446+00:00"}