{"version":1,"type":"story","url":"https://digestai.news/story/clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence","json":"https://digestai.news/story/clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence.json","markdown":"https://digestai.news/story/clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence.md","slug":"clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence","headline":"CLEAR framework improves medical LLM accuracy via cross-source evidence adjudication","summary":"Researchers have introduced CLEAR, an agentic framework designed to resolve conflicts in medical information processed by large language models. While retrieval-augmented generation (RAG) allows LLMs to access current data, retrieved sources often contain irrelevant or contradictory information, which can degrade the factual accuracy of model outputs. CLEAR addresses this by independently generating candidate answers from three distinct pathways: the model's internal parametric knowledge, locally curated medical corpora, and dynamically retrieved external evidence.\n\nThe framework employs an aggregation verifier to jointly evaluate these candidates, analyzing supporting evidence, provenance, and source quality to identify areas of agreement or conflict. An adjudication module then determines whether to preserve the initial conclusion or revise it using override-guard and challenge-audit mechanisms. If conflicts remain unresolved, the system triggers targeted follow-up searches and re-adjudication processes. This approach aims to ensure that medical LLMs provide reliable, evidence-grounded responses despite the inherent instability of external data sources.","keyPoints":["CLEAR is an agentic framework for adjudicating conflicting medical evidence in LLMs.","It generates answers from parametric, curated, and retrieved sources before verification.","Unresolved conflicts trigger targeted follow-up searches to ensure factual accuracy."],"whyItMatters":"Enhances the reliability of medical AI by systematically resolving conflicts between internal model knowledge and external retrieved data, reducing hallucinations in critical healthcare applications.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-09-16T04:00:00Z","updatedAt":"2026-09-16T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.AI","title":"CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine","url":"https://arxiv.org/abs/2609.16301","publishedAt":"2026-09-16T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"CLEAR framework improves medical LLM accuracy via cross-source evidence adjudication\", 16 September 2026, https://digestai.news/story/clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence","publisher":"Digest AI","title":"CLEAR framework improves medical LLM accuracy via cross-source evidence adjudication","datePublished":"2026-09-16T04:00:00Z","url":"https://digestai.news/story/clear-framework-improves-medical-llm-accuracy-via-cross-source-evidence"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}