{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4IVDJDDIYWJFQA64N3VAJU7VRX","short_pith_number":"pith:4IVDJDDI","schema_version":"1.0","canonical_sha256":"e22a348c68c5925803dc6eea04d3f58df64dbce87b686bb799c84f69196532e6","source":{"kind":"arxiv","id":"2508.13174","version":2},"attestation_state":"computed","paper":{"title":"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","q-fin.CP","stat.ML"],"primary_cat":"cs.AI","authors_text":"Binqi Chen, Guoyi Shao, Hongjun Ding, Jinsheng Huang, Luchen Liu, Lutong Zou, Ming Zhang, Taian Guo, Zhengyang Mao","submitted_at":"2025-08-10T11:19:24Z","abstract_excerpt":"Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures. Backtesting is computationally intensive, inherently sequential, and sensitive to specific strategy parameters. Correlation-based metrics, though efficient, as"},"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":"2508.13174","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-10T11:19:24Z","cross_cats_sorted":["cs.LG","q-fin.CP","stat.ML"],"title_canon_sha256":"8b7924790342c3d8247ea993281fefd502f534d13089418d888740982127b834","abstract_canon_sha256":"fdbd71bfbbf4eb73a8c9f23c04ff02625bde944d068a9b604fb7b3075e092769"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:44.568586Z","signature_b64":"WW6M7fHtHpYolggfLdXxoU/ORP7OtkTckQhgP3E5FS0z2627C3cWuCTRXWymvMZQCt/eBvcpB5sX/bwZBM8dAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e22a348c68c5925803dc6eea04d3f58df64dbce87b686bb799c84f69196532e6","last_reissued_at":"2026-06-03T01:05:44.568060Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:44.568060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","q-fin.CP","stat.ML"],"primary_cat":"cs.AI","authors_text":"Binqi Chen, Guoyi Shao, Hongjun Ding, Jinsheng Huang, Luchen Liu, Lutong Zou, Ming Zhang, Taian Guo, Zhengyang Mao","submitted_at":"2025-08-10T11:19:24Z","abstract_excerpt":"Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures. Backtesting is computationally intensive, inherently sequential, and sensitive to specific strategy parameters. Correlation-based metrics, though efficient, as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13174","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/2508.13174/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":"2508.13174","created_at":"2026-06-03T01:05:44.568134+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.13174v2","created_at":"2026-06-03T01:05:44.568134+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13174","created_at":"2026-06-03T01:05:44.568134+00:00"},{"alias_kind":"pith_short_12","alias_value":"4IVDJDDIYWJF","created_at":"2026-06-03T01:05:44.568134+00:00"},{"alias_kind":"pith_short_16","alias_value":"4IVDJDDIYWJFQA64","created_at":"2026-06-03T01:05:44.568134+00:00"},{"alias_kind":"pith_short_8","alias_value":"4IVDJDDI","created_at":"2026-06-03T01:05:44.568134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.20625","citing_title":"AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX","json":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX.json","graph_json":"https://pith.science/api/pith-number/4IVDJDDIYWJFQA64N3VAJU7VRX/graph.json","events_json":"https://pith.science/api/pith-number/4IVDJDDIYWJFQA64N3VAJU7VRX/events.json","paper":"https://pith.science/paper/4IVDJDDI"},"agent_actions":{"view_html":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX","download_json":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX.json","view_paper":"https://pith.science/paper/4IVDJDDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.13174&json=true","fetch_graph":"https://pith.science/api/pith-number/4IVDJDDIYWJFQA64N3VAJU7VRX/graph.json","fetch_events":"https://pith.science/api/pith-number/4IVDJDDIYWJFQA64N3VAJU7VRX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX/action/storage_attestation","attest_author":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX/action/author_attestation","sign_citation":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX/action/citation_signature","submit_replication":"https://pith.science/pith/4IVDJDDIYWJFQA64N3VAJU7VRX/action/replication_record"}},"created_at":"2026-06-03T01:05:44.568134+00:00","updated_at":"2026-06-03T01:05:44.568134+00:00"}