{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YDFMTRVWX4J2PLMK4YXNZTVDHF","short_pith_number":"pith:YDFMTRVW","schema_version":"1.0","canonical_sha256":"c0cac9c6b6bf13a7ad8ae62edccea3397c84eca4603c06ef8949ae05e271a068","source":{"kind":"arxiv","id":"2508.02808","version":1},"attestation_state":"computed","paper":{"title":"Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Daniel Alexander Alber, Daniel Freedman, Danielle Rigau, Diana Ruan, Eric Karl Oermann, Kang Zhang, Kyunghyun Cho, Motaz Nashawaty, Radhika Dua, Siddhant Dogra, Young Joon (Fred) Kwon","submitted_at":"2025-08-04T18:28:03Z","abstract_excerpt":"Radiological imaging is central to diagnosis, treatment planning, and clinical decision-making. Vision-language foundation models have spurred interest in automated radiology report generation (RRG), but safe deployment requires reliable clinical evaluation of generated reports. Existing metrics often rely on surface-level similarity or behave as black boxes, lacking interpretability. We introduce ICARE (Interpretable and Clinically-grounded Agent-based Report Evaluation), an interpretable evaluation framework leveraging large language model agents and dynamic multiple-choice question answerin"},"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":"2508.02808","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-04T18:28:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0f06b480559264abda2149188dbd6b39509111833f1efc0bd4a2cf4eaf420ebd","abstract_canon_sha256":"5fdc485bd2eeda1e12d9e3c6193db1f111f44f38a2d177c7848bf4d575821c94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:31.296567Z","signature_b64":"+QVsoLfwcP6b+rFxQFUiggv1JRu8BYoqn7UgrTmS12qjdndBpH+0Y5Yu2qsq5kjLwihevM4FT/VXDBhQmz4uBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0cac9c6b6bf13a7ad8ae62edccea3397c84eca4603c06ef8949ae05e271a068","last_reissued_at":"2026-07-05T11:48:31.295908Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:31.295908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Daniel Alexander Alber, Daniel Freedman, Danielle Rigau, Diana Ruan, Eric Karl Oermann, Kang Zhang, Kyunghyun Cho, Motaz Nashawaty, Radhika Dua, Siddhant Dogra, Young Joon (Fred) Kwon","submitted_at":"2025-08-04T18:28:03Z","abstract_excerpt":"Radiological imaging is central to diagnosis, treatment planning, and clinical decision-making. Vision-language foundation models have spurred interest in automated radiology report generation (RRG), but safe deployment requires reliable clinical evaluation of generated reports. Existing metrics often rely on surface-level similarity or behave as black boxes, lacking interpretability. We introduce ICARE (Interpretable and Clinically-grounded Agent-based Report Evaluation), an interpretable evaluation framework leveraging large language model agents and dynamic multiple-choice question answerin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02808","kind":"arxiv","version":1},"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/2508.02808/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":"2508.02808","created_at":"2026-07-05T11:48:31.296026+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02808v1","created_at":"2026-07-05T11:48:31.296026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02808","created_at":"2026-07-05T11:48:31.296026+00:00"},{"alias_kind":"pith_short_12","alias_value":"YDFMTRVWX4J2","created_at":"2026-07-05T11:48:31.296026+00:00"},{"alias_kind":"pith_short_16","alias_value":"YDFMTRVWX4J2PLMK","created_at":"2026-07-05T11:48:31.296026+00:00"},{"alias_kind":"pith_short_8","alias_value":"YDFMTRVW","created_at":"2026-07-05T11:48:31.296026+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09584","citing_title":"CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF","json":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF.json","graph_json":"https://pith.science/api/pith-number/YDFMTRVWX4J2PLMK4YXNZTVDHF/graph.json","events_json":"https://pith.science/api/pith-number/YDFMTRVWX4J2PLMK4YXNZTVDHF/events.json","paper":"https://pith.science/paper/YDFMTRVW"},"agent_actions":{"view_html":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF","download_json":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF.json","view_paper":"https://pith.science/paper/YDFMTRVW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02808&json=true","fetch_graph":"https://pith.science/api/pith-number/YDFMTRVWX4J2PLMK4YXNZTVDHF/graph.json","fetch_events":"https://pith.science/api/pith-number/YDFMTRVWX4J2PLMK4YXNZTVDHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF/action/storage_attestation","attest_author":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF/action/author_attestation","sign_citation":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF/action/citation_signature","submit_replication":"https://pith.science/pith/YDFMTRVWX4J2PLMK4YXNZTVDHF/action/replication_record"}},"created_at":"2026-07-05T11:48:31.296026+00:00","updated_at":"2026-07-05T11:48:31.296026+00:00"}