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Researcher profile

Jaewook Lee

Jaewook Lee contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and LanguageMachine Learningcond-mat.mes-hallCryptography and Securityphysics.comp-phquant-ph

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Published work

5 published item(s)

preprint2026arXiv

Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction

Large Language Models (LLMs) demonstrate strong reasoning and self-correction abilities in high-resource languages like English, but their performance remains limited in low-resource languages such as Korean. In this study, we investigate whether reinforcement learning (RL) can enhance Korean reasoning abilities to a degree comparable to English. Our findings reveal that RL alone yields limited improvements when applied to models lacking inherent Korean reasoning capabilities. To address this, we explore several fine-tuning strategies and show that aligning the model's internal reasoning processes with Korean inputs-particularly by tuning Korean-specific neurons in early layers-is key to unlocking RL's effectiveness. We introduce a self-correction code-switching dataset to facilitate this alignment and observe significant performance gains in both mathematical reasoning and self-correction tasks. Ultimately, we conclude that the crucial factor in multilingual reasoning enhancement is not injecting new linguistic knowledge, but effectively eliciting and aligning existing reasoning capabilities. Our study provides a new perspective on how internal translation and neuron-level tuning contribute to multilingual reasoning alignment in LLMs.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues

Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring systems to provide personalized support, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dialogue settings. However, existing dialogue-based KT approaches often ignore question difficulty modeling and rely on opaque latent representations from LLMs, hindering accurate and interpretable prediction. In this work, we propose an interpretable difficulty-aware conversational KT framework built upon LLMs, which explicitly models students' abilities and the difficulty of tutor-posed tasks at each turn. The framework incorporates the original textual question and the next tutor-posed task to estimate the student's knowledge state and the difficulty of the upcoming turn. Furthermore, it integrates Item Response Theory to map LLM's outputs into student ability and question difficulty parameters, enabling interpretable prediction of student performance grounded in cognitive theories of learning. We evaluate the framework on two tutor-student dialogue datasets. Both quantitative and qualitative results show that our framework outperforms existing KT baselines, meanwhile generating interpretable outputs consistent with cognitive theory.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Stability Analysis of Sharpness-Aware Minimization

Sharpness-aware minimization (SAM) is a recently proposed training method that seeks to find flat minima in deep learning, resulting in state-of-the-art performance across various domains. Instead of minimizing the loss of the current weights, SAM minimizes the worst-case loss in its neighborhood in the parameter space. In this paper, we demonstrate that SAM dynamics can have convergence instability that occurs near a saddle point. Utilizing the qualitative theory of dynamical systems, we explain how SAM becomes stuck in the saddle point and then theoretically prove that the saddle point can become an attractor under SAM dynamics. Additionally, we show that this convergence instability can also occur in stochastic dynamical systems by establishing the diffusion of SAM. We prove that SAM diffusion is worse than that of vanilla gradient descent in terms of saddle point escape. Further, we demonstrate that often overlooked training tricks, momentum and batch-size, are important to mitigate the convergence instability and achieve high generalization performance. Our theoretical and empirical results are thoroughly verified through experiments on several well-known optimization problems and benchmark tasks.

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preprint2022arXiv

Comment on Transferability and Input Transformation with Additive Noise

Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification of deep learning models. More importantly, an adversarial example generated from a specific model can also deceive other models without modification. We call this phenomenon ``transferability". Here, we analyze the relationship between transferability and input transformation with additive noise by mathematically proving that the modified optimization can produce more transferable adversarial examples.

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preprint2022arXiv

First-principles theory of extending the spin qubit coherence time in hexagonal boron nitride

Negatively charged boron vacancies (VB-) in hexagonal boron nitride (h-BN) are a rapidly developing qubit platform in two-dimensional materials for solid-state quantum applications. However, their spin coherence time (T2) is very short, limited to a few microseconds owing to the inherently dense nuclear spin bath of the h-BN host. As the coherence time is one of the most fundamental properties of spin qubits, the short T2 time of VB- could significantly limit its potential as a promising spin qubit candidate. In this study, we theoretically proposed two materials engineering methods, which can substantially extend the T2 time of the VB- spin by four times more than its intrinsic T2. We performed quantum many-body computations by combining density functional theory and cluster correlation expansion and showed that replacing all the boron atoms in h-BN with the 10B isotope leads to the coherence enhancement of the VB- spin by a factor of three. In addition, the T2 time of the VB- can be enhanced by a factor of 1.3 by inducing a curvature around VB-. Herein, we elucidate that the curvature-induced inhomogeneous strain creates spatially varying quadrupole nuclear interactions, which effectively suppress the nuclear spin flip-flop dynamics in the bath. Importantly, we find that the combination of isotopic enrichment and strain engineering can maximize the VB- T2, yielding 207.2 and 161.9 μs for single- and multi-layer h-10BN, respectively. Furthermore, our results can be applied to any spin qubit in h-BN, strengthening their potential as material platforms to realize high-precision quantum sensors, quantum spin registers, and atomically thin quantum magnets.

0 citations0 reviewsNo code linkedOpen access