2 citations · 2 across the 4 of their papers we have counts for
5 papers
Automating Forecasting Question Generation and Resolution for AI Evaluation
Nikos I. Bosse, Peter Mühlbacher, Jack Wildman +2
Forecasting future events is highly valuable in decision-making and is a robust measure of general intelligence. As forecasting is probabilistic, developing and evaluating AI forec…
Deep Research Bench: Evaluating AI Web Research Agents
FutureSearch, :, Nikos I. Bosse +7
Amongst the most common use cases of modern AI is LLM chat with web search enabled. However, no direct evaluations of the quality of web research agents exist that control for the…
Towards a Realistic Long-Term Benchmark for Open-Web Research Agents
Peter Mühlbacher, Nikos I. Bosse, Lawrence Phillips
We present initial results of a forthcoming benchmark for evaluating LLM agents on white-collar tasks of economic value. We evaluate agents on real-world "messy" open-web research…
Critical Parameters for Loop and Bernoulli Percolation
Peter Mühlbacher
We consider a class of random loop models (including the random interchange process) that are parametrised by a time parameter . Intuitively, larger means more randomn…
Bounds on the norm of Wigner-type random matrices
László Erdős, Peter Mühlbacher
We consider a Wigner-type ensemble, i.e. large hermitian random matrices with centered independent entries and with a general matrix of variances $S_{xy}=\mathb…