An LLM agent using retrieval and summary uncertainty as training rewards and inference filters produces more factual, useful multi-omics summaries and better downstream survival predictions.
Mlomics: Can- cer multi-omics database for machine learning
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Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning
An LLM agent using retrieval and summary uncertainty as training rewards and inference filters produces more factual, useful multi-omics summaries and better downstream survival predictions.