3 citations · 4 across the 14 of their papers we have counts for
8 papers · 1 filter
Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm
Nathan Li, Aikins Laryea, Yigit Ihlamur
Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested in a systematic way. This p…
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6
Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a target outcome while avoiding l…
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…
VCBench: Benchmarking LLMs in Venture Capital
Rick Chen, Joseph Ternasky, Afriyie Samuel Kwesi +7
Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark…
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…
Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
Ben Griffin, Aaron Ontoyin Yin, Diego Vidaurre +4
Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable. We introduce Random Rule Forest (…
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…
SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance
Xisen Wang, Yigit Ihlamur, Fuat Alican
LLM based agents have recently demonstrated strong potential in automating complex tasks, yet accurately predicting startup success remains an open challenge with few benchmarks an…