{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T2FHXSRATL7AB6AHJVFF226PP3","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":"920b551de7132107c6ad5f1162289c44d93f1bca357a68545f75bf692f5eb643","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-03-05T15:48:43Z","title_canon_sha256":"c4e8dd7c2fbeaf69f28f6d3545003171dbba3302c7e41c2f276e2a4764a7732a"},"schema_version":"1.0","source":{"id":"2603.05308","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.05308","created_at":"2026-05-20T00:04:27Z"},{"alias_kind":"arxiv_version","alias_value":"2603.05308v2","created_at":"2026-05-20T00:04:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.05308","created_at":"2026-05-20T00:04:27Z"},{"alias_kind":"pith_short_12","alias_value":"T2FHXSRATL7A","created_at":"2026-05-20T00:04:27Z"},{"alias_kind":"pith_short_16","alias_value":"T2FHXSRATL7AB6AH","created_at":"2026-05-20T00:04:27Z"},{"alias_kind":"pith_short_8","alias_value":"T2FHXSRA","created_at":"2026-05-20T00:04:27Z"}],"graph_snapshots":[{"event_id":"sha256:cdae43a0084da38f12c1fc98deefc2bb3acb77cb0f76fec1aa9c0467ffa69dd4","target":"graph","created_at":"2026-05-20T00:04:27Z","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/2603.05308/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters. Trained on high-quality synthetic data newly developed in this study, Med-V1 substantially outperforms (+27.0% to +71.3%) its base models on ","authors_text":"Aidong Zhang, Charalampos S. Floudas, Donald C. Comeau, Guangzhi Xiong, Joey Chan, Lauren He, Michael F. Chiang, Nicholas Wan, Qiao Jin, Robert Leaman, Yifan Peng, Yifan Yang, Yin Fang, Zhiyong Lu, Zhizheng Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-03-05T15:48:43Z","title":"Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.05308","kind":"arxiv","version":2},"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:d2749a3a8da379170a1e4b45879a890204bed351713b33932b4f58a32be4af82","target":"record","created_at":"2026-05-20T00:04:27Z","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":"920b551de7132107c6ad5f1162289c44d93f1bca357a68545f75bf692f5eb643","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-03-05T15:48:43Z","title_canon_sha256":"c4e8dd7c2fbeaf69f28f6d3545003171dbba3302c7e41c2f276e2a4764a7732a"},"schema_version":"1.0","source":{"id":"2603.05308","kind":"arxiv","version":2}},"canonical_sha256":"9e8a7bca209afe00f8074d4a5d6bcf7eecf424162637dadbe20954368a38eb9c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e8a7bca209afe00f8074d4a5d6bcf7eecf424162637dadbe20954368a38eb9c","first_computed_at":"2026-05-20T00:04:27.575930Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:27.575930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kVJB7BbT71Nvitctb3bsKpNypS3jCigAR4lAFsmlHiDLWDNIIvAAiIz/N/vYb4Lrt/SvKRyuU+hXMoyYHdNOBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:27.576816Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.05308","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2749a3a8da379170a1e4b45879a890204bed351713b33932b4f58a32be4af82","sha256:cdae43a0084da38f12c1fc98deefc2bb3acb77cb0f76fec1aa9c0467ffa69dd4"],"state_sha256":"a87240afa5619cb78a26834fb1aa8ad93da3ff0571a45f052d4ad92fa2e37867"}