{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:52L7CXAJC6F6X5OAIN3XPLWPEM","short_pith_number":"pith:52L7CXAJ","schema_version":"1.0","canonical_sha256":"ee97f15c09178bebf5c0437777aecf230b133da79c7435314b3c7033b3d13fdb","source":{"kind":"arxiv","id":"2502.16789","version":2},"attestation_state":"computed","paper":{"title":"AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Jiarui Yang, Jiayao Mai, Jinrui Chen, Keze Wang, Liang Lin, Yongsen Zheng, Zechuan Chen, Ziyi Tang","submitted_at":"2025-02-24T02:56:46Z","abstract_excerpt":"Alpha mining, a critical component in quantitative investment, focuses on discovering predictive signals for future asset returns in increasingly complex financial markets. However, the pervasive issue of alpha decay, where factors lose their predictive power over time, poses a significant challenge for alpha mining. Traditional methods like genetic programming face rapid alpha decay from overfitting and complexity, while approaches driven by Large Language Models (LLMs), despite their promise, often rely too heavily on existing knowledge, creating homogeneous factors that worsen crowding and "},"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":"2502.16789","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2025-02-24T02:56:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b990d32e235cb13bb48799315e84fe45e1d4e4cf993c86e48a99ce5677b03c02","abstract_canon_sha256":"1af13bd4f759148c27df9d81080f757a9c0f5a11414bcb516dcd34f27b54332a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:18.547877Z","signature_b64":"KrpIoiUZXpmw97qzeOs6lj0Lg3FAKyNwCBUOgGbjf7Rx15vlLC3c43YdvJLxTubLU9jhQdVNE8SzL6DFYVL7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee97f15c09178bebf5c0437777aecf230b133da79c7435314b3c7033b3d13fdb","last_reissued_at":"2026-07-05T11:18:18.547386Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:18.547386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Jiarui Yang, Jiayao Mai, Jinrui Chen, Keze Wang, Liang Lin, Yongsen Zheng, Zechuan Chen, Ziyi Tang","submitted_at":"2025-02-24T02:56:46Z","abstract_excerpt":"Alpha mining, a critical component in quantitative investment, focuses on discovering predictive signals for future asset returns in increasingly complex financial markets. However, the pervasive issue of alpha decay, where factors lose their predictive power over time, poses a significant challenge for alpha mining. Traditional methods like genetic programming face rapid alpha decay from overfitting and complexity, while approaches driven by Large Language Models (LLMs), despite their promise, often rely too heavily on existing knowledge, creating homogeneous factors that worsen crowding and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16789","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/2502.16789/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":"2502.16789","created_at":"2026-07-05T11:18:18.547446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.16789v2","created_at":"2026-07-05T11:18:18.547446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16789","created_at":"2026-07-05T11:18:18.547446+00:00"},{"alias_kind":"pith_short_12","alias_value":"52L7CXAJC6F6","created_at":"2026-07-05T11:18:18.547446+00:00"},{"alias_kind":"pith_short_16","alias_value":"52L7CXAJC6F6X5OA","created_at":"2026-07-05T11:18:18.547446+00:00"},{"alias_kind":"pith_short_8","alias_value":"52L7CXAJ","created_at":"2026-07-05T11:18:18.547446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08285","citing_title":"Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2511.18850","citing_title":"Cognitive Alpha Mining via LLM-Driven Code-Based Evolution","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09601","citing_title":"Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM","json":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM.json","graph_json":"https://pith.science/api/pith-number/52L7CXAJC6F6X5OAIN3XPLWPEM/graph.json","events_json":"https://pith.science/api/pith-number/52L7CXAJC6F6X5OAIN3XPLWPEM/events.json","paper":"https://pith.science/paper/52L7CXAJ"},"agent_actions":{"view_html":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM","download_json":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM.json","view_paper":"https://pith.science/paper/52L7CXAJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.16789&json=true","fetch_graph":"https://pith.science/api/pith-number/52L7CXAJC6F6X5OAIN3XPLWPEM/graph.json","fetch_events":"https://pith.science/api/pith-number/52L7CXAJC6F6X5OAIN3XPLWPEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM/action/storage_attestation","attest_author":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM/action/author_attestation","sign_citation":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM/action/citation_signature","submit_replication":"https://pith.science/pith/52L7CXAJC6F6X5OAIN3XPLWPEM/action/replication_record"}},"created_at":"2026-07-05T11:18:18.547446+00:00","updated_at":"2026-07-05T11:18:18.547446+00:00"}