{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3S4TSQ7RZDS7PE3M57BGFUS2KH","short_pith_number":"pith:3S4TSQ7R","schema_version":"1.0","canonical_sha256":"dcb93943f1c8e5f7936cefc262d25a51e528f3ca61dbbbf8066d2d3b4d24b1cb","source":{"kind":"arxiv","id":"2605.30716","version":1},"attestation_state":"computed","paper":{"title":"Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jiahao Cheng, Mahdi S. Hosseini, Vincent Quoc-Huy Trinh, Zhiyuan Yang","submitted_at":"2026-05-29T01:15:13Z","abstract_excerpt":"Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the complexity of case-level reasoning, where a single case may contain multiple WSIs with heterogeneous tissues and ambiguous findings. We present a simple token-efficient vision--language model for case-level synoptic report generation that remains practical under constrained GPU memory. Our architecture follows a minimal three-component design: a frozen pathology patch encoder, a lightweight two-layer MLP vision-langua"},"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":"2605.30716","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T01:15:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1553700689f2eef696accdb05e78102a51ea8526d36d3c51c06b3d970fc3f4e9","abstract_canon_sha256":"f27ee8c8f5ebe2d082688fd035e6d2a699222bdde40eaf10c11fd99a45558817"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:11.988430Z","signature_b64":"od40GS25OqHzBVAuMRcrkzpKHQp9D6LApdjkYXuOEDw3ZUdS+UBm4O+olBtMVB9cWIZpdMPpNUgsGerlzwx5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcb93943f1c8e5f7936cefc262d25a51e528f3ca61dbbbf8066d2d3b4d24b1cb","last_reissued_at":"2026-06-01T01:03:11.987476Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:11.987476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jiahao Cheng, Mahdi S. Hosseini, Vincent Quoc-Huy Trinh, Zhiyuan Yang","submitted_at":"2026-05-29T01:15:13Z","abstract_excerpt":"Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the complexity of case-level reasoning, where a single case may contain multiple WSIs with heterogeneous tissues and ambiguous findings. We present a simple token-efficient vision--language model for case-level synoptic report generation that remains practical under constrained GPU memory. Our architecture follows a minimal three-component design: a frozen pathology patch encoder, a lightweight two-layer MLP vision-langua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30716","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/2605.30716/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":"2605.30716","created_at":"2026-06-01T01:03:11.987619+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30716v1","created_at":"2026-06-01T01:03:11.987619+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30716","created_at":"2026-06-01T01:03:11.987619+00:00"},{"alias_kind":"pith_short_12","alias_value":"3S4TSQ7RZDS7","created_at":"2026-06-01T01:03:11.987619+00:00"},{"alias_kind":"pith_short_16","alias_value":"3S4TSQ7RZDS7PE3M","created_at":"2026-06-01T01:03:11.987619+00:00"},{"alias_kind":"pith_short_8","alias_value":"3S4TSQ7R","created_at":"2026-06-01T01:03:11.987619+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/3S4TSQ7RZDS7PE3M57BGFUS2KH","json":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH.json","graph_json":"https://pith.science/api/pith-number/3S4TSQ7RZDS7PE3M57BGFUS2KH/graph.json","events_json":"https://pith.science/api/pith-number/3S4TSQ7RZDS7PE3M57BGFUS2KH/events.json","paper":"https://pith.science/paper/3S4TSQ7R"},"agent_actions":{"view_html":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH","download_json":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH.json","view_paper":"https://pith.science/paper/3S4TSQ7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30716&json=true","fetch_graph":"https://pith.science/api/pith-number/3S4TSQ7RZDS7PE3M57BGFUS2KH/graph.json","fetch_events":"https://pith.science/api/pith-number/3S4TSQ7RZDS7PE3M57BGFUS2KH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH/action/storage_attestation","attest_author":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH/action/author_attestation","sign_citation":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH/action/citation_signature","submit_replication":"https://pith.science/pith/3S4TSQ7RZDS7PE3M57BGFUS2KH/action/replication_record"}},"created_at":"2026-06-01T01:03:11.987619+00:00","updated_at":"2026-06-01T01:03:11.987619+00:00"}