{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LCN3AL6DS6VVF2UXHVAA3IZBRV","short_pith_number":"pith:LCN3AL6D","schema_version":"1.0","canonical_sha256":"589bb02fc397ab52ea973d400da3218d7651254ddef83ff50719c43a77f77824","source":{"kind":"arxiv","id":"2404.14777","version":2},"attestation_state":"computed","paper":{"title":"ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jintai Chen, Ling Yue, Sixue Xing, Tianfan Fu","submitted_at":"2024-04-23T06:30:53Z","abstract_excerpt":"Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge. Recognizing the potential of advanced clinical trial tools that aggregate and predict based on the latest medical data, we propose an integrated solution to enhance their accessibility and utility. We introduce Clinical Agent System (ClinicalAgent), a clinical multi-agent system designed for clinical trial tasks, leveraging GPT-4, multi-agent architectures, LEAST-TO-MOST, 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":"2404.14777","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-23T06:30:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9e338a7e9348df7b6707351f1ddce7cec231f37e79eb3523eccf67aed89c796e","abstract_canon_sha256":"f07dedb1109c79f27acb774896232221bb687707b71ec2d13f5ca0f79693e512"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:46:24.360828Z","signature_b64":"9YADHWwpIt6fL6rp+Dz7XXfg1zw00C0aBkPfCY6mT51p3ppPqVpbI4HkUnaLJqWXtEpDgguqMOCO0IlJhRZsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"589bb02fc397ab52ea973d400da3218d7651254ddef83ff50719c43a77f77824","last_reissued_at":"2026-07-05T08:46:24.360312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:46:24.360312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jintai Chen, Ling Yue, Sixue Xing, Tianfan Fu","submitted_at":"2024-04-23T06:30:53Z","abstract_excerpt":"Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge. Recognizing the potential of advanced clinical trial tools that aggregate and predict based on the latest medical data, we propose an integrated solution to enhance their accessibility and utility. We introduce Clinical Agent System (ClinicalAgent), a clinical multi-agent system designed for clinical trial tasks, leveraging GPT-4, multi-agent architectures, LEAST-TO-MOST, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14777","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/2404.14777/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":"2404.14777","created_at":"2026-07-05T08:46:24.360381+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14777v2","created_at":"2026-07-05T08:46:24.360381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14777","created_at":"2026-07-05T08:46:24.360381+00:00"},{"alias_kind":"pith_short_12","alias_value":"LCN3AL6DS6VV","created_at":"2026-07-05T08:46:24.360381+00:00"},{"alias_kind":"pith_short_16","alias_value":"LCN3AL6DS6VVF2UX","created_at":"2026-07-05T08:46:24.360381+00:00"},{"alias_kind":"pith_short_8","alias_value":"LCN3AL6D","created_at":"2026-07-05T08:46:24.360381+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/LCN3AL6DS6VVF2UXHVAA3IZBRV","json":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV.json","graph_json":"https://pith.science/api/pith-number/LCN3AL6DS6VVF2UXHVAA3IZBRV/graph.json","events_json":"https://pith.science/api/pith-number/LCN3AL6DS6VVF2UXHVAA3IZBRV/events.json","paper":"https://pith.science/paper/LCN3AL6D"},"agent_actions":{"view_html":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV","download_json":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV.json","view_paper":"https://pith.science/paper/LCN3AL6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14777&json=true","fetch_graph":"https://pith.science/api/pith-number/LCN3AL6DS6VVF2UXHVAA3IZBRV/graph.json","fetch_events":"https://pith.science/api/pith-number/LCN3AL6DS6VVF2UXHVAA3IZBRV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV/action/storage_attestation","attest_author":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV/action/author_attestation","sign_citation":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV/action/citation_signature","submit_replication":"https://pith.science/pith/LCN3AL6DS6VVF2UXHVAA3IZBRV/action/replication_record"}},"created_at":"2026-07-05T08:46:24.360381+00:00","updated_at":"2026-07-05T08:46:24.360381+00:00"}