이해범 연구실

I am an Assistant Professor in the School of Electrical Engineering at Korea University. Previously, I was a postdoctoral researcher at KAIST, working with Prof. Juho Lee, and at Mila and Université de Montréal, working with Prof. Yoshua Bengio. I received my Ph.D. from KAIST, where I was advised by Prof. Sung Ju Hwang. Agents must know what to trust, what to question, and what to do next. I build explicit world models that serve as the working memory of language-model agents: small, testable, uncertainty-aware workspaces where knowledge is checked against evidence, repaired where it fails, and consolidated for future use. Knowledge stated as executable hypotheses (equations, programs, causal graphs, logical rules) that make testable predictions and expose the mechanisms and assumptions behind them. The small, active set of variables, hypotheses, and verified models that an agent needs for its current inference, test, or decision, held with uncertainty over alternatives. Language models know a great deal about the world, but their knowledge is stored in parameters. It is not attached to the evidence that supports it, it carries no calibrated estimate of its reliability, and when part of it is wrong for a particular system, there is no explicit place where the error can be found and repaired. My research asks how an agent that knows many things can also know what to trust, what to question, and what to do next. The question is becoming urgent. In domains where experiments are costly, generating candidate hypotheses is now far cheaper than testing them, so the bottleneck of intelligent behavior shifts from generation to verification and selection: deciding which explanations to believe, which experiment would best tell them apart, and which part of existing knowledge to r