이재윤 연구실

Logical Consistency & Calibration are methods for improving reasoning coherence, confidence estimation, and factual reliability in large language models. We study techniques that reduce hallucinations, maintain logical consistency across long reasoning chains, and align model confidence with prediction accuracy. Lightweight Structural Modifications for LLMs are efficient architectural adaptations that enhance reasoning, verification, and inference capabilities without requiring large-scale retraining. Our work explores scalable and modular approaches that improve model performance while preserving computational efficiency. Efficient Expert-Domain Adaptation is an approach for adapting foundation models to specialized scientific and expert domains using minimal supervision and efficient fine-tuning strategies. We focus on transferring domain-specific knowledge while maintaining strong general reasoning and language understanding abilities. Reinforcement Learning is a research area that studies adaptive decision-making through interaction and feedback, with a strong focus on applying RL to NLP and LLMs. Constraint-Guided Learning is a research area that incorporates logical, structural, and domain-specific constraints into learning and inference processes to improve consistency, robustness, and controllability. Our work focuses on integrating prior knowledge and formal rules into machine learning systems for more reliable and safer predictions. Brain Foundation Models is a research area that explores biologically inspired architectures and neural representations for building scalable, adaptive, and generalizable AI systems. Our research investigates computational principles motivated by brain mechanisms to improve learning efficiency and adaptability. Neuro-symbolic Appro