LLM-AR: LLM-powered Automated Reasoning Framework
Rick Chen Joseph Ternasky Aaron Ontoyin Yin Xianling Mu Fuat Alican Yigit Ihlamur
作者信息
Abstract
Large language models (LLMs) can already identify patterns and reason effectively, yet their variable accuracy hampers adoption in high-stakes decision-making applications. In this paper, we study this issue from a venture capital perspective by predicting idea-stage startup success based on founder traits. (i) To build a reliable prediction model, we introduce LLM-AR, a pipeline inspired by neural-symbolic systems that distils LLM-generated heuristics into probabilistic rules executed by the ProbLog automated-reasoning engine. (ii) An iterative policy-evolution loop incorporates association-rule mining to progressively refine the prediction rules.
On unseen folds, LLM-AR achieves 59.5% precision and 8.7% recall, 5.9x the random baseline precision, while exposing every decision path for human inspection. The framework is interpretable and tunable via hyperparameters, showing promise to extend into other domains.引用本文复制引用
Rick Chen,Joseph Ternasky,Aaron Ontoyin Yin,Xianling Mu,Fuat Alican,Yigit Ihlamur.LLM-AR: LLM-powered Automated Reasoning Framework[EB/OL].(2025-10-24)[2026-04-04].https://arxiv.org/abs/2510.22034.学科分类
计算技术、计算机技术
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