1 citations · 1 across the 7 of their papers we have counts for
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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…
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 (…