10 citations · 29 across the 12 of their papers we have counts for
12 papers
Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies
Chris Schneider, Kriti Faujdar, Philipp Schoenegger +1
Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as ind…
Separable Expert Architecture: Toward Privacy-Preserving LLM Personalization via Composable Adapters and Deletable User Proxies
Chris Schneider, Philipp Schoenegger, Ben Bariach
Current model training approaches incorporate user information directly into shared weights, making individual data removal computationally infeasible without retraining. This pape…
Verifiable Semantics for Agent-to-Agent Communication
Philipp Schoenegger, Matt Carlson, Chris Schneider +1
Multiagent AI systems require consistent communication, but we lack methods to verify that agents share the same understanding of the terms used. Natural language is interpretable…
How people use Copilot for Health
Beatriz Costa-Gomes, Pavel Tolmachev, Eloise Taysom +15
We analyze over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational AI about health. We devel…
Prompt Engineering Large Language Models' Forecasting Capabilities
Philipp Schoenegger, Cameron R. Jones, Philip E. Tetlock +1
Large language model performance can be improved in a large number of ways. Many such techniques, like fine-tuning or advanced tool usage, are time-intensive and expensive. Althoug…
Outcome-based Reinforcement Learning to Predict the Future
Benjamin Turtel, Danny Franklin, Kris Skotheim +2
Reinforcement Learning with Verifiable Rewards (RLVR) has been an effective approach for improving Large Language Models' reasoning in domains such as coding and mathematics. Here,…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
LLMs Can Teach Themselves to Better Predict the Future
Benjamin Turtel, Danny Franklin, Philipp Schoenegger
We present an outcome-driven fine-tuning framework that enhances the forecasting capabilities of large language models (LLMs) without relying on human-curated reasoning samples. Ou…
Can AI Understand Human Personality? -- Comparing Human Experts and AI Systems at Predicting Personality Correlations
Philipp Schoenegger, Spencer Greenberg, Alexander Grishin +2
We test the abilities of specialised deep neural networks like PersonalityMap as well as general LLMs like GPT-4o and Claude 3 Opus in understanding human personality. Specifically…
Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy
Philipp Schoenegger, Indre Tuminauskaite, Peter S. Park +1
Human forecasting accuracy in practice relies on the 'wisdom of the crowd' effect, in which predictions about future events are significantly improved by aggregating across a crowd…
Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament
Philipp Schoenegger, Peter S. Park
Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence. However, research on the ability of large language models to provid…
Diminished Diversity-of-Thought in a Standard Large Language Model
Peter S. Park, Philipp Schoenegger, Chongyang Zhu
We test whether Large Language Models (LLMs) can be used to simulate human participants in social-science studies. To do this, we run replications of 14 studies from the Many Labs…