{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:F2OFCIGHCBXDO55TVQFQVEKJF5","short_pith_number":"pith:F2OFCIGH","schema_version":"1.0","canonical_sha256":"2e9c5120c7106e3777b3ac0b0a91492f43a45b71f9d6e283b3be8292f3f78c9e","source":{"kind":"arxiv","id":"2504.01915","version":2},"attestation_state":"computed","paper":{"title":"Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.NE","authors_text":"Antoine Cully, Lisa Coiffard, Paul Templier","submitted_at":"2025-04-02T17:18:21Z","abstract_excerpt":"Policy optimization seeks the best solution to a control problem according to an objective or fitness function, serving as a fundamental field of engineering and research with applications in robotics. Traditional optimization methods like reinforcement learning and evolutionary algorithms struggle with deceptive fitness landscapes, where following immediate improvements leads to suboptimal solutions. Quality-diversity (QD) algorithms offer a promising approach by maintaining diverse intermediate solutions as stepping stones for escaping local optima. However, QD algorithms require domain expe"},"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":"2504.01915","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2025-04-02T17:18:21Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"81d8e9ffde3a6286bce1a82c5402810883d9944996cae33771411889da655c02","abstract_canon_sha256":"98700471621454a9948d646a0b70c16c5dc9f4391c1af13b1fbbf02e91d41ead"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:23.706594Z","signature_b64":"+sHXRvnORsIkit7Cym0qKYpto4xoIlazSqgoMqSJ6zQIxq2gRxHQkkKupgcZQeAIq9BfmOu21nP6pF01CLL2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e9c5120c7106e3777b3ac0b0a91492f43a45b71f9d6e283b3be8292f3f78c9e","last_reissued_at":"2026-07-05T10:44:23.706131Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:23.706131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.NE","authors_text":"Antoine Cully, Lisa Coiffard, Paul Templier","submitted_at":"2025-04-02T17:18:21Z","abstract_excerpt":"Policy optimization seeks the best solution to a control problem according to an objective or fitness function, serving as a fundamental field of engineering and research with applications in robotics. Traditional optimization methods like reinforcement learning and evolutionary algorithms struggle with deceptive fitness landscapes, where following immediate improvements leads to suboptimal solutions. Quality-diversity (QD) algorithms offer a promising approach by maintaining diverse intermediate solutions as stepping stones for escaping local optima. However, QD algorithms require domain expe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01915","kind":"arxiv","version":2},"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/2504.01915/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":"2504.01915","created_at":"2026-07-05T10:44:23.706187+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.01915v2","created_at":"2026-07-05T10:44:23.706187+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01915","created_at":"2026-07-05T10:44:23.706187+00:00"},{"alias_kind":"pith_short_12","alias_value":"F2OFCIGHCBXD","created_at":"2026-07-05T10:44:23.706187+00:00"},{"alias_kind":"pith_short_16","alias_value":"F2OFCIGHCBXDO55T","created_at":"2026-07-05T10:44:23.706187+00:00"},{"alias_kind":"pith_short_8","alias_value":"F2OFCIGH","created_at":"2026-07-05T10:44:23.706187+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/F2OFCIGHCBXDO55TVQFQVEKJF5","json":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5.json","graph_json":"https://pith.science/api/pith-number/F2OFCIGHCBXDO55TVQFQVEKJF5/graph.json","events_json":"https://pith.science/api/pith-number/F2OFCIGHCBXDO55TVQFQVEKJF5/events.json","paper":"https://pith.science/paper/F2OFCIGH"},"agent_actions":{"view_html":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5","download_json":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5.json","view_paper":"https://pith.science/paper/F2OFCIGH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.01915&json=true","fetch_graph":"https://pith.science/api/pith-number/F2OFCIGHCBXDO55TVQFQVEKJF5/graph.json","fetch_events":"https://pith.science/api/pith-number/F2OFCIGHCBXDO55TVQFQVEKJF5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5/action/storage_attestation","attest_author":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5/action/author_attestation","sign_citation":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5/action/citation_signature","submit_replication":"https://pith.science/pith/F2OFCIGHCBXDO55TVQFQVEKJF5/action/replication_record"}},"created_at":"2026-07-05T10:44:23.706187+00:00","updated_at":"2026-07-05T10:44:23.706187+00:00"}