{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LTX6WV2PEWE4UZR3SDSIBQ5Y3B","short_pith_number":"pith:LTX6WV2P","schema_version":"1.0","canonical_sha256":"5cefeb574f2589ca663b90e480c3b8d84a05201e2b7f6095c4ba92b1d048c10d","source":{"kind":"arxiv","id":"2605.18111","version":1},"attestation_state":"computed","paper":{"title":"How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Intesar Tahmid, Md Fahim, Md Farhad Alam Bhuiyan, Mir Sazzat Hossain, Rafid Ahmed, Tasnimul Hossain Tomal","submitted_at":"2026-05-18T09:20:32Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benefited from these developments. However, despite Bangla being one of the most widely spoken languages globally, there exists no established MedVQA benchmark for it. To address this gap, we introduce BanglaMedVQA, a dataset comprising clinically validated image-question-answer pairs, along with a compr"},"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":"2605.18111","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-18T09:20:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0584199c7229885cbe9454cd530234b58bf0a2fbc967370e39035b30a9e32254","abstract_canon_sha256":"5dbed0ef96221aac44454784970853d0fc897aed53f0e861dfbed3b234a8e0aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:16.554429Z","signature_b64":"7S6pxd3MJ6Tf058XoO99r7aW8rk5qKaimo3WQVJlXR2X4hREqqaO+3HfT4+tNi7h/UppL7GPmt1x2SXYcY8iAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cefeb574f2589ca663b90e480c3b8d84a05201e2b7f6095c4ba92b1d048c10d","last_reissued_at":"2026-05-20T00:05:16.553452Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:16.553452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Intesar Tahmid, Md Fahim, Md Farhad Alam Bhuiyan, Mir Sazzat Hossain, Rafid Ahmed, Tasnimul Hossain Tomal","submitted_at":"2026-05-18T09:20:32Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benefited from these developments. However, despite Bangla being one of the most widely spoken languages globally, there exists no established MedVQA benchmark for it. To address this gap, we introduce BanglaMedVQA, a dataset comprising clinically validated image-question-answer pairs, along with a compr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18111","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/2605.18111/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T23:41:59.166219Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.411528Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"39b6dd06554d51d1695a026b31bc3c171964612781947132eda5d339dd33d398"},"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":"2605.18111","created_at":"2026-05-20T00:05:16.553557+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18111v1","created_at":"2026-05-20T00:05:16.553557+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18111","created_at":"2026-05-20T00:05:16.553557+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTX6WV2PEWE4","created_at":"2026-05-20T00:05:16.553557+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTX6WV2PEWE4UZR3","created_at":"2026-05-20T00:05:16.553557+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTX6WV2P","created_at":"2026-05-20T00:05:16.553557+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/LTX6WV2PEWE4UZR3SDSIBQ5Y3B","json":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B.json","graph_json":"https://pith.science/api/pith-number/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/graph.json","events_json":"https://pith.science/api/pith-number/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/events.json","paper":"https://pith.science/paper/LTX6WV2P"},"agent_actions":{"view_html":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B","download_json":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B.json","view_paper":"https://pith.science/paper/LTX6WV2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18111&json=true","fetch_graph":"https://pith.science/api/pith-number/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/graph.json","fetch_events":"https://pith.science/api/pith-number/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/action/storage_attestation","attest_author":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/action/author_attestation","sign_citation":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/action/citation_signature","submit_replication":"https://pith.science/pith/LTX6WV2PEWE4UZR3SDSIBQ5Y3B/action/replication_record"}},"created_at":"2026-05-20T00:05:16.553557+00:00","updated_at":"2026-05-20T00:05:16.553557+00:00"}