{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YOUZSYZV57YLCXDRFL4JTUT2RB","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":"0f752ed53a8a96ac01096e25026c18eb7b05813d5c97e4d982a7eff421484376","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-18T11:20:52Z","title_canon_sha256":"cc7e544ea9ebca7cc5b5b18cf4abf195f39670feed8e57544457e8c7a509280d"},"schema_version":"1.0","source":{"id":"2507.13822","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13822","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13822v1","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13822","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_12","alias_value":"YOUZSYZV57YL","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_16","alias_value":"YOUZSYZV57YLCXDR","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_8","alias_value":"YOUZSYZV","created_at":"2026-07-05T11:39:23Z"}],"graph_snapshots":[{"event_id":"sha256:0650132cf35533477937682113b396eacc5df13dada6655de4b0f5cf0f2ab4c6","target":"graph","created_at":"2026-07-05T11:39:23Z","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/2507.13822/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Drug side effects are a major global health concern, necessitating advanced methods for their accurate detection and analysis. While Large Language Models (LLMs) offer promising conversational interfaces, their inherent limitations, including reliance on black-box training data, susceptibility to hallucinations, and lack of domain-specific knowledge, hinder their reliability in specialized fields like pharmacovigilance. To address this gap, we propose two architectures: Retrieval-Augmented Generation (RAG) and GraphRAG, which integrate comprehensive drug side effect knowledge into a Llama 3 8B","authors_text":"Afshin Beheshti, Andre Daniels, Diego Galeano, Pinar Avci, Reza Rassol, Shad Nygren","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-18T11:20:52Z","title":"RAG-based Architectures for Drug Side Effect Retrieval in LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13822","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":"sha256:784453847f0b30981cce82f233888f48cb65cf31ae5c3e018dbe7dc2bfd1c6bd","target":"record","created_at":"2026-07-05T11:39:23Z","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":"0f752ed53a8a96ac01096e25026c18eb7b05813d5c97e4d982a7eff421484376","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-18T11:20:52Z","title_canon_sha256":"cc7e544ea9ebca7cc5b5b18cf4abf195f39670feed8e57544457e8c7a509280d"},"schema_version":"1.0","source":{"id":"2507.13822","kind":"arxiv","version":1}},"canonical_sha256":"c3a9996335eff0b15c712af899d27a885a14eba14247e6cad42d4a1b23a6f1a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3a9996335eff0b15c712af899d27a885a14eba14247e6cad42d4a1b23a6f1a8","first_computed_at":"2026-07-05T11:39:23.834850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:39:23.834850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e+EhaOg5ayEp2tlxHfYlmGmF3klbkzEQjuP//9Ap9wigBGRcTx+nrbBFgVozMrv+jWA19uA0GGdMEy52V6wMAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:39:23.835408Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.13822","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:784453847f0b30981cce82f233888f48cb65cf31ae5c3e018dbe7dc2bfd1c6bd","sha256:0650132cf35533477937682113b396eacc5df13dada6655de4b0f5cf0f2ab4c6"],"state_sha256":"e0745a9b4a8ff69f56b24a25ba87a2f86beb97cfa5368957ff2ff897a9549e8e"}