{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4SDA4YGDDNKTLKGDPBTEMGWGFE","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":"bf911459e683b42849fbdd62211bd51cba831d7d745a0fda194b856f4180c56c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T15:58:36Z","title_canon_sha256":"c072a55b2ac62dcab4764a3cb520847cbf9cd6c95054d278f955abd126307da3"},"schema_version":"1.0","source":{"id":"2406.12738","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12738","created_at":"2026-07-05T08:33:50Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12738v1","created_at":"2026-07-05T08:33:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12738","created_at":"2026-07-05T08:33:50Z"},{"alias_kind":"pith_short_12","alias_value":"4SDA4YGDDNKT","created_at":"2026-07-05T08:33:50Z"},{"alias_kind":"pith_short_16","alias_value":"4SDA4YGDDNKTLKGD","created_at":"2026-07-05T08:33:50Z"},{"alias_kind":"pith_short_8","alias_value":"4SDA4YGD","created_at":"2026-07-05T08:33:50Z"}],"graph_snapshots":[{"event_id":"sha256:2c9a68beaeebe7e023ddcf0bd19e17ea5642a6e1345d794703aea9cd5f3b0d69","target":"graph","created_at":"2026-07-05T08:33:50Z","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/2406.12738/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The development of effective machine learning methodologies for enhancing the efficiency and accuracy of clinical systems is crucial. Despite significant research efforts, managing a plethora of diversified clinical tasks and adapting to emerging new tasks remain significant challenges. This paper presents a novel paradigm that employs a pre-trained large language model as a universal clinical multi-task decoder. This approach leverages the flexibility and diversity of language expressions to handle task topic variations and associated arguments. The introduction of a new task simply requires ","authors_text":"Hongjian Song, Jiang Bian, Jiawen Zhang, Shun Zheng, Xumeng Wen, Yujiang Wu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T15:58:36Z","title":"Large Language Model as a Universal Clinical Multi-task Decoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12738","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":"sha256:8c43937b020fcf2fb4365fb904d67c8a0323d87797f42c7426e3e3431d009b72","target":"record","created_at":"2026-07-05T08:33:50Z","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":"bf911459e683b42849fbdd62211bd51cba831d7d745a0fda194b856f4180c56c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T15:58:36Z","title_canon_sha256":"c072a55b2ac62dcab4764a3cb520847cbf9cd6c95054d278f955abd126307da3"},"schema_version":"1.0","source":{"id":"2406.12738","kind":"arxiv","version":1}},"canonical_sha256":"e4860e60c31b5535a8c37866461ac6293eaba2ea6f0030f48fb0df042f781ebe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4860e60c31b5535a8c37866461ac6293eaba2ea6f0030f48fb0df042f781ebe","first_computed_at":"2026-07-05T08:33:50.736685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:33:50.736685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jRP3rZZpu5mSVAMzVKqHgjlCjsvd5oEZzKjKPZn+Ab3nuBYCybXruaf9IcohQEh+Z/2WMnrFmeizsdpZVVNNCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:33:50.737095Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.12738","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c43937b020fcf2fb4365fb904d67c8a0323d87797f42c7426e3e3431d009b72","sha256:2c9a68beaeebe7e023ddcf0bd19e17ea5642a6e1345d794703aea9cd5f3b0d69"],"state_sha256":"ac28515f552d970215209b9b552036e05942284b085a5dfb171d159cb50f7017"}