NEWS · RESEARCH · #359
CLEAR: agentic cross-source evidence adjudication framework for LLMs in medicine (arXiv:2609.16301v1)
The authors propose CLEAR, an agentic framework that generates candidate answers from three complementary sources—parametric model knowledge, locally curated corpora, and dynamically retrieved evidence—and uses an aggregation verifier to evaluate candidates, provenance, and source quality. An adjudication module applies override-guard and challenge-audit mechanisms to preserve or revise conclusions and triggers targeted follow-up search and re-adjudication when conflicts remain.
KEY POINTS
- The authors propose CLEAR, an agentic framework that generates candidate answers from three complementary sources—parametric model knowledge, locally curated corpora, and dynamically retrieved evidence—and uses an aggregation verifier to evaluate candidates, provenance, and source quality.
- An adjudication module applies override-guard and challenge-audit mechanisms to preserve or revise conclusions and triggers targeted follow-up search and re-adjudication when conflicts remain.
- CLEAR targets the core problem of reconciling conflicting and evolving medical evidence with static LLM knowledge, proposing a structured adjudication pipeline that could improve grounding and safety of medical LLM outputs.
WHY IT MATTERS
CLEAR targets the core problem of reconciling conflicting and evolving medical evidence with static LLM knowledge, proposing a structured adjudication pipeline that could improve grounding and safety of medical LLM outputs.