3 papers
cs.AI2025
LLM-AR: LLM-powered Automated Reasoning Framework
Rick Chen, Joseph Ternasky, Aaron Ontoyin Yin +3
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…
cs.AI2025
Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
Xianling Mu, Joseph Ternasky, Fuat Alican +1
Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled dat…
cs.AI2025
Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework
Jack Preuveneers, Joseph Ternasky, Fuat Alican +1
We present a novel framework that bridges the gap between the interpretability of decision trees and the advanced reasoning capabilities of large language models (LLMs) to predict…