Branching Minds

We study how AI systems form, maintain, and revise interpretations over time, especially in ambiguous or multi-agent settings. Our work focuses on misunderstanding, overconfidence, premature convergence, perspective shifts, and the mechanisms that help models remain open to corrective evidence.

More broadly, we are interested in the dynamics of meaning: how competing interpretations emerge, how prior reasoning trajectories shape future beliefs, and how AI systems can detect when they may be confidently wrong and recover more effectively. The goal is to develop AI systems that are more epistemically flexible, corrigible, and robust under uncertainty.