10 citations · 25 across the 9 of their papers we have counts for
10 papers
Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
Evelyn Duesterwald, Benjamin Elder, Lilian Ngweta +2
Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given…
VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
Ankita Rajaram Naik, Anupama Murthi, Benjamin Elder +6
Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation. We introduce V…
Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling
Benjamin Elder, Anupama Murthi, Jungkoo Kang +4
Large language models (LLMs) increasingly rely on external tools and APIs to execute complex tasks specified in natural language. Evaluating such tool calling capabilities in reali…
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves
Jiri Navratil, Benjamin Elder, Matthew Arnold +2
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Jiri Navratil, Benjamin Elder, Matthew Arnold +2
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…
Learning Prediction Intervals for Model Performance
Benjamin Elder, Matthew Arnold, Anupama Murthi +1
Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using te…