{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PGWOZBQSMU7OO6MH5TRLKTINKP","short_pith_number":"pith:PGWOZBQS","schema_version":"1.0","canonical_sha256":"79acec8612653ee77987ece2b54d0d53e11b73b54103bbd64814a6995a6c0842","source":{"kind":"arxiv","id":"2410.17657","version":3},"attestation_state":"computed","paper":{"title":"ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shuyang Jiang, Yanfeng Wang, Yusheng Liao, Yu Wang","submitted_at":"2024-10-23T08:19:18Z","abstract_excerpt":"Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. However, current LLMs are limited to text-based communication, hindering their ability to interact with diverse forms of information in clinical environments. Despite clinical agents succeeding in diverse signal interaction, they are oriented to a single clinical scenario and hence fail for broader applications. To evaluate clinical agents holistically, we propose ClinicalAgent Bench~(CAB), a comprehensive medical agent benchmark consi"},"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":"2410.17657","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-23T08:19:18Z","cross_cats_sorted":[],"title_canon_sha256":"b22bc664a22c1001a9c0cda3dd91f315e87aece5a12772943faf53a96907096d","abstract_canon_sha256":"792fd280e083c4f7254d1efbed0a454b45e52216496bfbbeea26fd39fd766ad1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:37.457465Z","signature_b64":"yEb1EcmGoKDX4wmDOGPYpLqQ/klxuHtBZSCDnOKY0Jpwl3g7pkeT4V0vkIlhig10hyMGH2KE61GYbDqemiXCCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79acec8612653ee77987ece2b54d0d53e11b73b54103bbd64814a6995a6c0842","last_reissued_at":"2026-07-05T11:21:37.456921Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:37.456921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shuyang Jiang, Yanfeng Wang, Yusheng Liao, Yu Wang","submitted_at":"2024-10-23T08:19:18Z","abstract_excerpt":"Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. However, current LLMs are limited to text-based communication, hindering their ability to interact with diverse forms of information in clinical environments. Despite clinical agents succeeding in diverse signal interaction, they are oriented to a single clinical scenario and hence fail for broader applications. To evaluate clinical agents holistically, we propose ClinicalAgent Bench~(CAB), a comprehensive medical agent benchmark consi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17657","kind":"arxiv","version":3},"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/2410.17657/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":"2410.17657","created_at":"2026-07-05T11:21:37.456981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.17657v3","created_at":"2026-07-05T11:21:37.456981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17657","created_at":"2026-07-05T11:21:37.456981+00:00"},{"alias_kind":"pith_short_12","alias_value":"PGWOZBQSMU7O","created_at":"2026-07-05T11:21:37.456981+00:00"},{"alias_kind":"pith_short_16","alias_value":"PGWOZBQSMU7OO6MH","created_at":"2026-07-05T11:21:37.456981+00:00"},{"alias_kind":"pith_short_8","alias_value":"PGWOZBQS","created_at":"2026-07-05T11:21:37.456981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17679","citing_title":"PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2507.12261","citing_title":"Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP","json":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP.json","graph_json":"https://pith.science/api/pith-number/PGWOZBQSMU7OO6MH5TRLKTINKP/graph.json","events_json":"https://pith.science/api/pith-number/PGWOZBQSMU7OO6MH5TRLKTINKP/events.json","paper":"https://pith.science/paper/PGWOZBQS"},"agent_actions":{"view_html":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP","download_json":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP.json","view_paper":"https://pith.science/paper/PGWOZBQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.17657&json=true","fetch_graph":"https://pith.science/api/pith-number/PGWOZBQSMU7OO6MH5TRLKTINKP/graph.json","fetch_events":"https://pith.science/api/pith-number/PGWOZBQSMU7OO6MH5TRLKTINKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP/action/storage_attestation","attest_author":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP/action/author_attestation","sign_citation":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP/action/citation_signature","submit_replication":"https://pith.science/pith/PGWOZBQSMU7OO6MH5TRLKTINKP/action/replication_record"}},"created_at":"2026-07-05T11:21:37.456981+00:00","updated_at":"2026-07-05T11:21:37.456981+00:00"}