{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CILXW3GL3HEVWXL5DKA3A47SDY","short_pith_number":"pith:CILXW3GL","schema_version":"1.0","canonical_sha256":"12177b6ccbd9c95b5d7d1a81b073f21e21b706e9200e78080a2a7da91264674d","source":{"kind":"arxiv","id":"2606.28556","version":1},"attestation_state":"computed","paper":{"title":"IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anchal Nema, Ashutosh Joshi, Deepak Bansal, Dilek Hakkani-Tur, Korosh Vatanparvar, Maria Xenochristou, Mohammad Abuzar Hashemi, Nivedita Wadhwa, Prasad Kasu, Prashams S Jain, Rebecca Abraham, Wilko Schulz-Mahlendorf, Will Kimbrough","submitted_at":"2026-06-26T19:18:16Z","abstract_excerpt":"Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging. However, existing medical AI benchmarks are fragmented: some support multi-turn dialogues but lack images, while others provide multimodal inputs but focus on single-turn QA tasks. To address this gap, we introduce IMCBench, an image-grounded, multi-turn medical conversation benchmark that pairs real, publicly available clinical images with synthetic patient profiles to simulate realistic patient"},"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":"2606.28556","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-26T19:18:16Z","cross_cats_sorted":[],"title_canon_sha256":"737635f1dae534c99dddff609a006ec51a3e142923a3fcd86ba861b96bee3125","abstract_canon_sha256":"c88754c44743b245692d3ac750f8ad6d00e2243c50df9efdf629b35e31fbf552"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T00:15:18.436363Z","signature_b64":"T9BunG5CX+vXTtJm8S7epybxNV/NC0nOU0W9XKzOajSbMfEBxRHHGa2msM1RUxMLpdZGtWfNQp4Ln7Ox01MVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12177b6ccbd9c95b5d7d1a81b073f21e21b706e9200e78080a2a7da91264674d","last_reissued_at":"2026-06-30T00:15:18.435904Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T00:15:18.435904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anchal Nema, Ashutosh Joshi, Deepak Bansal, Dilek Hakkani-Tur, Korosh Vatanparvar, Maria Xenochristou, Mohammad Abuzar Hashemi, Nivedita Wadhwa, Prasad Kasu, Prashams S Jain, Rebecca Abraham, Wilko Schulz-Mahlendorf, Will Kimbrough","submitted_at":"2026-06-26T19:18:16Z","abstract_excerpt":"Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging. However, existing medical AI benchmarks are fragmented: some support multi-turn dialogues but lack images, while others provide multimodal inputs but focus on single-turn QA tasks. To address this gap, we introduce IMCBench, an image-grounded, multi-turn medical conversation benchmark that pairs real, publicly available clinical images with synthetic patient profiles to simulate realistic patient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28556","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/2606.28556/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":"2606.28556","created_at":"2026-06-30T00:15:18.435982+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.28556v1","created_at":"2026-06-30T00:15:18.435982+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28556","created_at":"2026-06-30T00:15:18.435982+00:00"},{"alias_kind":"pith_short_12","alias_value":"CILXW3GL3HEV","created_at":"2026-06-30T00:15:18.435982+00:00"},{"alias_kind":"pith_short_16","alias_value":"CILXW3GL3HEVWXL5","created_at":"2026-06-30T00:15:18.435982+00:00"},{"alias_kind":"pith_short_8","alias_value":"CILXW3GL","created_at":"2026-06-30T00:15:18.435982+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/CILXW3GL3HEVWXL5DKA3A47SDY","json":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY.json","graph_json":"https://pith.science/api/pith-number/CILXW3GL3HEVWXL5DKA3A47SDY/graph.json","events_json":"https://pith.science/api/pith-number/CILXW3GL3HEVWXL5DKA3A47SDY/events.json","paper":"https://pith.science/paper/CILXW3GL"},"agent_actions":{"view_html":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY","download_json":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY.json","view_paper":"https://pith.science/paper/CILXW3GL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.28556&json=true","fetch_graph":"https://pith.science/api/pith-number/CILXW3GL3HEVWXL5DKA3A47SDY/graph.json","fetch_events":"https://pith.science/api/pith-number/CILXW3GL3HEVWXL5DKA3A47SDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY/action/storage_attestation","attest_author":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY/action/author_attestation","sign_citation":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY/action/citation_signature","submit_replication":"https://pith.science/pith/CILXW3GL3HEVWXL5DKA3A47SDY/action/replication_record"}},"created_at":"2026-06-30T00:15:18.435982+00:00","updated_at":"2026-06-30T00:15:18.435982+00:00"}