Ask WhAI:Probing Belief Formation in Role-Primed LLM Agents
Keith Moore, Jun W. Kim, David Lyu, Jeffrey Heo, Ehsan Adeli

TL;DR
Ask WhAI is a framework for analyzing and testing belief states in multi-agent language models, revealing how priors and reasoning influence diagnostic decisions in complex scenarios.
Contribution
It introduces a systems-level approach to inspect, perturb, and understand belief formation in role-primed LLM agents during multi-agent interactions.
Findings
Agents mirror real-world disciplinary biases.
Beliefs often rely on canonical studies and resist counterevidence.
The framework enables detailed interrogation of belief dynamics.
Abstract
We present Ask WhAI, a systems-level framework for inspecting and perturbing belief states in multi-agent interactions. The framework records and replays agent interactions, supports out-of-band queries into each agent's beliefs and rationale, and enables counterfactual evidence injection to test how belief structures respond to new information. We apply the framework to a medical case simulator notable for its multi-agent shared memory (a time-stamped electronic medical record, or EMR) and an oracle agent (the LabAgent) that holds ground truth lab results revealed only when explicitly queried. We stress-test the system on a multi-specialty diagnostic journey for a child with an abrupt-onset neuropsychiatric presentation. Large language model agents, each primed with strong role-specific priors ("act like a neurologist", "act like an infectious disease specialist"), write to a shared…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Multi-Agent Systems and Negotiation · Topic Modeling
