{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HA2IZ5V7LKG3RIAWJGCUXXIM7V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ed8489fb8a62708fa282d8f6a4891091221be3e3efd3b2972ac950b9fd1fc65e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-13T10:13:26Z","title_canon_sha256":"ffc01af23ca88b665c4c46e9b7276bddfd51b2858aa0d463d0769df96349e8df"},"schema_version":"1.0","source":{"id":"2505.08414","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08414","created_at":"2026-07-05T11:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08414v1","created_at":"2026-07-05T11:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08414","created_at":"2026-07-05T11:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"HA2IZ5V7LKG3","created_at":"2026-07-05T11:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"HA2IZ5V7LKG3RIAW","created_at":"2026-07-05T11:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"HA2IZ5V7","created_at":"2026-07-05T11:02:30Z"}],"graph_snapshots":[{"event_id":"sha256:7ea58915b657efaac73f33a179f1ce8b600fa475c79e70e007972f4474e51183","target":"graph","created_at":"2026-07-05T11:02:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.08414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current deep learning models are mostly task specific and lack a user-friendly interface to operate. We present Meta-EyeFM, a multi-function foundation model that integrates a large language model (LLM) with vision foundation models (VFMs) for ocular disease assessment. Meta-EyeFM leverages a routing mechanism to enable accurate task-specific analysis based on text queries. Using Low Rank Adaptation, we fine-tuned our VFMs to detect ocular and systemic diseases, differentiate ocular disease severity, and identify common ocular signs. The model achieved 100% accuracy in routing fundus images to","authors_text":"Can Can Xue, Charumathi Sabanayagam, Ching-Yu Cheng, Ecosse Lamoureux, Jiangnan He, Jie Xu, Jost B. Jonas, Kai Yu, Lavanya Raghavan, Lee Ching Linette Phang, Ming-Chih Ho, Preeti Gupta, Qingsheng Peng, Quan V. Hoang, Rachel Shujuan Chong, Rick Siow Mong Goh, Sahil Thakur, Seang Mei Saw, Songhomitra Panda-Jonas, Tien Yin Wong, Vinay Nangia, Wei-Chi Wu, Xiaofeng Lei, Xinxing Xu, Yang Bai, Yang Zhou, Ya Xing Wang, Yih Chung Tham, Yong Liu, Zann Lee, Zhi Da Soh","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-13T10:13:26Z","title":"An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08414","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:88f265a73f7220ebb6dd6a00bad8c52ba8f63fb0d30141aba13c739a3970c6ca","target":"record","created_at":"2026-07-05T11:02:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ed8489fb8a62708fa282d8f6a4891091221be3e3efd3b2972ac950b9fd1fc65e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-13T10:13:26Z","title_canon_sha256":"ffc01af23ca88b665c4c46e9b7276bddfd51b2858aa0d463d0769df96349e8df"},"schema_version":"1.0","source":{"id":"2505.08414","kind":"arxiv","version":1}},"canonical_sha256":"38348cf6bf5a8db8a01649854bdd0cfd4bbc238b3ecffc248ccec9392d1a9837","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38348cf6bf5a8db8a01649854bdd0cfd4bbc238b3ecffc248ccec9392d1a9837","first_computed_at":"2026-07-05T11:02:30.241894Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:30.241894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ROmX1W3KQAUyM2ygBKe6xM7jSmQLfTdlP+FbscWOQ8UcTIRRTxBVCREQDHRQ9cgTgQisbY+wjHhSy/Jb7gAWCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:30.242502Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.08414","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88f265a73f7220ebb6dd6a00bad8c52ba8f63fb0d30141aba13c739a3970c6ca","sha256:7ea58915b657efaac73f33a179f1ce8b600fa475c79e70e007972f4474e51183"],"state_sha256":"b0e3c8aba0ef75a8539fe031f0002c29268890fcf2949a9fd7add249161bd9cc"}