{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PIHKAKCCVFPQ22TVFBRINFZDID","short_pith_number":"pith:PIHKAKCC","schema_version":"1.0","canonical_sha256":"7a0ea02842a95f0d6a75286286972340c62ee00196138017d0f06fe88eb4ee6a","source":{"kind":"arxiv","id":"2412.20138","version":7},"attestation_state":"computed","paper":{"title":"TradingAgents: Multi-Agents LLM Financial Trading Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG"],"primary_cat":"q-fin.TR","authors_text":"Di Luo, Edward Sun, Wei Wang, Yijia Xiao","submitted_at":"2024-12-28T12:54:06Z","abstract_excerpt":"Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, the multi-agent systems' potential to replicate real-world trading firms' collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical ana"},"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":"2412.20138","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.TR","submitted_at":"2024-12-28T12:54:06Z","cross_cats_sorted":["cs.AI","cs.CE","cs.LG"],"title_canon_sha256":"5c50e38e46846cd67a2ec4a65e1a381eeda92788930f883ef478e46c1fefc489","abstract_canon_sha256":"1e39fddcf074122323ab5709d6f04b669f5d35c86000144471fa705ffc8b7f3a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:51.927905Z","signature_b64":"m2DItITGH4U28H4pjbkvN2nRqjoOvtnklANVNwuTv+SbIAFhq6eoNniwZHTrNpkai9YDNNMAgfJ7ydEnAR7oAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a0ea02842a95f0d6a75286286972340c62ee00196138017d0f06fe88eb4ee6a","last_reissued_at":"2026-07-05T11:14:51.927407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:51.927407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TradingAgents: Multi-Agents LLM Financial Trading Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG"],"primary_cat":"q-fin.TR","authors_text":"Di Luo, Edward Sun, Wei Wang, Yijia Xiao","submitted_at":"2024-12-28T12:54:06Z","abstract_excerpt":"Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, the multi-agent systems' potential to replicate real-world trading firms' collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical ana"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20138","kind":"arxiv","version":7},"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/2412.20138/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":"2412.20138","created_at":"2026-07-05T11:14:51.927473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20138v7","created_at":"2026-07-05T11:14:51.927473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20138","created_at":"2026-07-05T11:14:51.927473+00:00"},{"alias_kind":"pith_short_12","alias_value":"PIHKAKCCVFPQ","created_at":"2026-07-05T11:14:51.927473+00:00"},{"alias_kind":"pith_short_16","alias_value":"PIHKAKCCVFPQ22TV","created_at":"2026-07-05T11:14:51.927473+00:00"},{"alias_kind":"pith_short_8","alias_value":"PIHKAKCC","created_at":"2026-07-05T11:14:51.927473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":38,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25447","citing_title":"The Interplay of Harness Design and Post-Training in LLM Agents","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22719","citing_title":"Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking","ref_index":97,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11537","citing_title":"MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08283","citing_title":"Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08285","citing_title":"Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05404","citing_title":"Harnessing Generalist Agents for Contextualized Time Series","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04505","citing_title":"Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04075","citing_title":"Large Language Models Hack Rewards, and Society","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02282","citing_title":"POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00939","citing_title":"FinCom: A Financial Multi-Agent Demo with Disagree-or-Commit Deliberation","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30877","citing_title":"A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31461","citing_title":"CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28850","citing_title":"Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24490","citing_title":"Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29194","citing_title":"AI Trading's Alpha Singularity: Emergent Market Reasoning through Agent-to-Agent Self-Evolution","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29771","citing_title":"CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27864","citing_title":"FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23007","citing_title":"MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2503.22693","citing_title":"Bridging Language Models and Financial Analysis","ref_index":107,"is_internal_anchor":false},{"citing_arxiv_id":"2504.02181","citing_title":"A Survey of Scaling in Large Language Model Reasoning","ref_index":229,"is_internal_anchor":false},{"citing_arxiv_id":"2601.11650","citing_title":"Large Language Model Agent for User-friendly Chemical Process Simulations","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21975","citing_title":"Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2510.18586","citing_title":"TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16358","citing_title":"LEAF: A Living Benchmark for Event-Augmented Forecasting","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16895","citing_title":"The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID","json":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID.json","graph_json":"https://pith.science/api/pith-number/PIHKAKCCVFPQ22TVFBRINFZDID/graph.json","events_json":"https://pith.science/api/pith-number/PIHKAKCCVFPQ22TVFBRINFZDID/events.json","paper":"https://pith.science/paper/PIHKAKCC"},"agent_actions":{"view_html":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID","download_json":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID.json","view_paper":"https://pith.science/paper/PIHKAKCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20138&json=true","fetch_graph":"https://pith.science/api/pith-number/PIHKAKCCVFPQ22TVFBRINFZDID/graph.json","fetch_events":"https://pith.science/api/pith-number/PIHKAKCCVFPQ22TVFBRINFZDID/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID/action/storage_attestation","attest_author":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID/action/author_attestation","sign_citation":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID/action/citation_signature","submit_replication":"https://pith.science/pith/PIHKAKCCVFPQ22TVFBRINFZDID/action/replication_record"}},"created_at":"2026-07-05T11:14:51.927473+00:00","updated_at":"2026-07-05T11:14:51.927473+00:00"}