{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LMUCEZGLFMWKXR7KEC65OURMPX","short_pith_number":"pith:LMUCEZGL","canonical_record":{"source":{"id":"2409.15127","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-23T15:33:38Z","cross_cats_sorted":[],"title_canon_sha256":"27633d8327f36063b11622d2bc762c542ac99d83ee6b92c9957959b69e14015f","abstract_canon_sha256":"c9e4463fd186b5d569b7299539c328c01b8c78a6839c1761f187688a61469615"},"schema_version":"1.0"},"canonical_sha256":"5b282264cb2b2cabc7ea20bdd7522c7dfc2d4e5cada26d72498479b5c04269e4","source":{"kind":"arxiv","id":"2409.15127","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"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LMUCEZGLFMWKXR7KEC65OURMPX","target":"record","payload":{"canonical_record":{"source":{"id":"2409.15127","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-23T15:33:38Z","cross_cats_sorted":[],"title_canon_sha256":"27633d8327f36063b11622d2bc762c542ac99d83ee6b92c9957959b69e14015f","abstract_canon_sha256":"c9e4463fd186b5d569b7299539c328c01b8c78a6839c1761f187688a61469615"},"schema_version":"1.0"},"canonical_sha256":"5b282264cb2b2cabc7ea20bdd7522c7dfc2d4e5cada26d72498479b5c04269e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:39.350522Z","signature_b64":"bt9fGTY2yjgpc9V/bbfEwhXKZywSOJwkt3HlsKBDNN3aswescH+DILkEiPODShYzpuo9qABYAi8P3NGmWO3BCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b282264cb2b2cabc7ea20bdd7522c7dfc2d4e5cada26d72498479b5c04269e4","last_reissued_at":"2026-07-05T10:43:39.349912Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:39.349912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.15127","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5snAUVoayaisAvU8XyBE8TpmesKjrglPv92wnzeNh9Nw/sLORIWiKpaG4xF3VSC1+sQkSyYrwV2fkTop5ZeRDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:36:48.256034Z"},"content_sha256":"f127e62826a440205b02061dcc0466b9da047f9157dc6a5f1a96b5852f7381a4","schema_version":"1.0","event_id":"sha256:f127e62826a440205b02061dcc0466b9da047f9157dc6a5f1a96b5852f7381a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LMUCEZGLFMWKXR7KEC65OURMPX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ashwin Kumar Gururajan, Dario Garcia-Gasulla, Jordi Bayarri-Planas","submitted_at":"2024-09-23T15:33:38Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.15127","kind":"arxiv","version":3},"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/2409.15127/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j/WgsiBVsTknZmU6iUFWEcjxw+4ETCsZvkOS5lLZoH8po0QChnL/35XgZqG9M5gF9ZwHk3z7DZCWllQ8kDmhAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:36:48.256537Z"},"content_sha256":"74118bdc847f5f49947f71fb51b0dd2cd94605f1fb2b2f75dbff4e54b37dbbe7","schema_version":"1.0","event_id":"sha256:74118bdc847f5f49947f71fb51b0dd2cd94605f1fb2b2f75dbff4e54b37dbbe7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LMUCEZGLFMWKXR7KEC65OURMPX/bundle.json","state_url":"https://pith.science/pith/LMUCEZGLFMWKXR7KEC65OURMPX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LMUCEZGLFMWKXR7KEC65OURMPX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T08:36:48Z","links":{"resolver":"https://pith.science/pith/LMUCEZGLFMWKXR7KEC65OURMPX","bundle":"https://pith.science/pith/LMUCEZGLFMWKXR7KEC65OURMPX/bundle.json","state":"https://pith.science/pith/LMUCEZGLFMWKXR7KEC65OURMPX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LMUCEZGLFMWKXR7KEC65OURMPX/bundle.json"},"state":{"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"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AYD+eR6Em7Sx+EO6pQR6XUII2Ts6uGLM4KU52sbC1LGUdMIOUbUEyRorf1hB3M1ySNshJLKY6Rs/X/ETffLRAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T08:36:48.260122Z","bundle_sha256":"f6ef8c1c8f9be05184c4bb00679bfbf75555702daf809bb29e7966f14fefed42"}}