23 citations · 27 across the 15 of their papers we have counts for
5 papers · 1 filter
Learning to Clarify Underspecified Intents Under Limited Interaction
Pranav M R, Manuel Cherep, Pattie Maes +1
AI assistants receive requests that leave out information needed for a good outcome, for example about users' preferences or goals. They must then either speculate or ask for more…
Probing Perceptual Priors of MLLMs via Gibbs Sampling with Interpretable Generative Controls
Manuel Cherep, Pattie Maes, Nikhil Singh
A model's behavior on a task is jointly determined by the input it receives and the prior it brings in, i.e. the distribution over stimuli it implicitly expects. Interpretability r…
Position: Behavioral Systems Require Behavioral Tests
Manuel Cherep, Nikhil Singh, Pattie Maes
Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation meth…
A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
Manuel Cherep, Chengtian Ma, Abigail Xu +3
Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from ou…
LLM Agents Are Hypersensitive to Nudges
Manuel Cherep, Pattie Maes, Nikhil Singh
LLMs are being set loose in complex, real-world environments involving sequential decision-making and tool use. Often, this involves making choices on behalf of human users. Howeve…