Evolutionary Search for Automated Design of Uncertainty Quantification Methods

• URL: http://arxiv.org/abs/2604.03473v1
• Date: Fri, 03 Apr 2026 21:41:41 GMT
• Title: Evolutionary Search for Automated Design of Uncertainty Quantification Methods
• Abstract summary: Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and complexity.<n>We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs.<n>Our results indicate that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design.
• Score: 59.4676699732568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform strong manually-designed baselines, achieving up to 6.7% relative ROC-AUC improvement across 9 datasets while generalizing robustly out-of-distribution. Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies: Claude models consistently design high-feature-count linear estimators, while Gpt-oss-120B gravitates toward simpler and more interpretable positional weighting schemes. Surprisingly, only Sonnet 4.5 and Opus 4.5 reliably leverage increased method complexity to improve performance -- Opus 4.6 shows an unexpected regression relative to its predecessor. Overall, our results indicate that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design.

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