Charles Fleming

Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending restaurants or planning travel. In these scenarios, users rarely articulate every preference explicitly; instead, much of what they care about remains latent, waiting to be inferred. This raises a fundamental question: Can LLMs uncover and reason about such latent information through conversation? We address this problem by introducing a benchmark for evaluating latent information discovery---the ability of LLMs to reveal and utilize hidden user attributes through multi-turn interaction. The benchmark spans three progressively realistic settings: the 20 Questions game, Personalized Question Answering, and Personalized Text Summarization. All tasks share a tri-agent framework (User–Assistant–Judge) enabling turn-level evaluation of elicitation. Our results reveal that while LLMs can indeed surface latent information through dialogue, their success varies dramatically with context: from 10% to 100%, depending on task complexity, topic, model, and number of hidden attributes. This benchmark provides the first systematic framework for studying latent information discovery in personalized interaction, highlighting that effective preference inference remains an open frontier for building adaptive AI systems.

While Large Language Model (LLM) safety has focused on single-agent, white-box settings, the adoption of Multi-Agent Systems (MAS) creates a critical blind spot: \textit{supply chain vulnerabilities in MAS ecosystems} . These systems often rely on third-party agents accessed via black-box APIs, c...