{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SQDMVHISITAQEE6QBSHB5NAT6C","short_pith_number":"pith:SQDMVHIS","schema_version":"1.0","canonical_sha256":"9406ca9d1244c10213d00c8e1eb413f0881ac585ccff0facf72bf6e385a53e36","source":{"kind":"arxiv","id":"2405.15143","version":4},"attestation_state":"computed","paper":{"title":"Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Cong Lu, Jeff Clune, Shengran Hu","submitted_at":"2024-05-24T01:45:27Z","abstract_excerpt":"Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to superhuman performance across a wide variety of challenging problems including Atari games and robotic control, but requires manually designing heuristics to guide exploration (i.e., determine which states to save and explore from, and what actions to consider next), which is time-consuming and infeasible in general. To resolve this, we propose Intellig"},"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":"2405.15143","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-24T01:45:27Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"d74ef4b36d5550eb1f3a523efa12dbde2380586f4dd2d2d81306808f3398a656","abstract_canon_sha256":"92e01adefe9681054121b42467c29e18e0a889a6223fa5df94e4ade0e7a8d415"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:40.728367Z","signature_b64":"+RcB+kTG1cdKMrq9MQ25mCYBK9PAgA9yoHsWPBfjPKMj4ce9+KpYPlgw3biu+ijqRaVJLe+Lrj3tOXYKYSd3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9406ca9d1244c10213d00c8e1eb413f0881ac585ccff0facf72bf6e385a53e36","last_reissued_at":"2026-07-05T10:10:40.727947Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:40.727947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Cong Lu, Jeff Clune, Shengran Hu","submitted_at":"2024-05-24T01:45:27Z","abstract_excerpt":"Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to superhuman performance across a wide variety of challenging problems including Atari games and robotic control, but requires manually designing heuristics to guide exploration (i.e., determine which states to save and explore from, and what actions to consider next), which is time-consuming and infeasible in general. To resolve this, we propose Intellig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15143","kind":"arxiv","version":4},"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/2405.15143/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":"2405.15143","created_at":"2026-07-05T10:10:40.728001+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.15143v4","created_at":"2026-07-05T10:10:40.728001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15143","created_at":"2026-07-05T10:10:40.728001+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQDMVHISITAQ","created_at":"2026-07-05T10:10:40.728001+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQDMVHISITAQEE6Q","created_at":"2026-07-05T10:10:40.728001+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQDMVHIS","created_at":"2026-07-05T10:10:40.728001+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21228","citing_title":"Sakana Fugu Technical Report","ref_index":124,"is_internal_anchor":false},{"citing_arxiv_id":"2509.19349","citing_title":"ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution","ref_index":87,"is_internal_anchor":false},{"citing_arxiv_id":"2408.08435","citing_title":"Automated Design of Agentic Systems","ref_index":179,"is_internal_anchor":false},{"citing_arxiv_id":"2408.06292","citing_title":"The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery","ref_index":70,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C","json":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C.json","graph_json":"https://pith.science/api/pith-number/SQDMVHISITAQEE6QBSHB5NAT6C/graph.json","events_json":"https://pith.science/api/pith-number/SQDMVHISITAQEE6QBSHB5NAT6C/events.json","paper":"https://pith.science/paper/SQDMVHIS"},"agent_actions":{"view_html":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C","download_json":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C.json","view_paper":"https://pith.science/paper/SQDMVHIS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.15143&json=true","fetch_graph":"https://pith.science/api/pith-number/SQDMVHISITAQEE6QBSHB5NAT6C/graph.json","fetch_events":"https://pith.science/api/pith-number/SQDMVHISITAQEE6QBSHB5NAT6C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C/action/storage_attestation","attest_author":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C/action/author_attestation","sign_citation":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C/action/citation_signature","submit_replication":"https://pith.science/pith/SQDMVHISITAQEE6QBSHB5NAT6C/action/replication_record"}},"created_at":"2026-07-05T10:10:40.728001+00:00","updated_at":"2026-07-05T10:10:40.728001+00:00"}