{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LMUCEZGLFMWKXR7KEC65OURMPX","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":"c9e4463fd186b5d569b7299539c328c01b8c78a6839c1761f187688a61469615","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-23T15:33:38Z","title_canon_sha256":"27633d8327f36063b11622d2bc762c542ac99d83ee6b92c9957959b69e14015f"},"schema_version":"1.0","source":{"id":"2409.15127","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.15127","created_at":"2026-07-05T10:43:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.15127v3","created_at":"2026-07-05T10:43:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.15127","created_at":"2026-07-05T10:43:39Z"},{"alias_kind":"pith_short_12","alias_value":"LMUCEZGLFMWK","created_at":"2026-07-05T10:43:39Z"},{"alias_kind":"pith_short_16","alias_value":"LMUCEZGLFMWKXR7K","created_at":"2026-07-05T10:43:39Z"},{"alias_kind":"pith_short_8","alias_value":"LMUCEZGL","created_at":"2026-07-05T10:43:39Z"}],"graph_snapshots":[{"event_id":"sha256:74118bdc847f5f49947f71fb51b0dd2cd94605f1fb2b2f75dbff4e54b37dbbe7","target":"graph","created_at":"2026-07-05T10:43:39Z","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/2409.15127/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study leverages optimized context retrieval to enhance open-source Large Language Models (LLMs) for cost-effective, high performance healthcare AI. We demonstrate that this approach achieves state-of-the-art accuracy on medical question answering at a fraction of the cost of proprietary models, significantly improving the cost-accuracy Pareto frontier on the MedQA benchmark. Key contributions include: (1) OpenMedQA, a novel benchmark revealing a performance gap in open-ended medical QA compared to multiple-choice formats; (2) a practical, reproducible pipeline for context retrieval optimi","authors_text":"Ashwin Kumar Gururajan, Dario Garcia-Gasulla, Jordi Bayarri-Planas","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-23T15:33:38Z","title":"Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.15127","kind":"arxiv","version":3},"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:f127e62826a440205b02061dcc0466b9da047f9157dc6a5f1a96b5852f7381a4","target":"record","created_at":"2026-07-05T10:43:39Z","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":"c9e4463fd186b5d569b7299539c328c01b8c78a6839c1761f187688a61469615","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-23T15:33:38Z","title_canon_sha256":"27633d8327f36063b11622d2bc762c542ac99d83ee6b92c9957959b69e14015f"},"schema_version":"1.0","source":{"id":"2409.15127","kind":"arxiv","version":3}},"canonical_sha256":"5b282264cb2b2cabc7ea20bdd7522c7dfc2d4e5cada26d72498479b5c04269e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b282264cb2b2cabc7ea20bdd7522c7dfc2d4e5cada26d72498479b5c04269e4","first_computed_at":"2026-07-05T10:43:39.349912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:39.349912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bt9fGTY2yjgpc9V/bbfEwhXKZywSOJwkt3HlsKBDNN3aswescH+DILkEiPODShYzpuo9qABYAi8P3NGmWO3BCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:39.350522Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.15127","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f127e62826a440205b02061dcc0466b9da047f9157dc6a5f1a96b5852f7381a4","sha256:74118bdc847f5f49947f71fb51b0dd2cd94605f1fb2b2f75dbff4e54b37dbbe7"],"state_sha256":"ed002d4c66951cedd41f7b8a6d8040d25251be4daf7cf3aeb47b12169e5c56b8"}