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Researcher profile

Seungjun Lee

Seungjun Lee contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and LanguageComputer Visioncond-mat.mtrl-sciMachine LearningComputational Geometrycond-mat.mes-halleess.IVphysics.comp-phRobotics

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Published work

9 published item(s)

preprint2026arXiv

Emergence of unconventional magnetic order in strain-engineered RuO2/TiO2 superlattices

The spin ordering in RuO2 remains a highly debated topic, owing to its elusive nature, with reports ranging from a nonmagnetic ground state to signatures of unconventional magnetic order. Here we provide the first unambiguous, and direct evidence of unconventional magnetism in epitaxial, fully strained RuO2/TiO2 superlattices on TiO2 (110) substrate grown by hybrid molecular beam epitaxy. Polarized neutron reflectometry reveals a finite magnetic moment localized within the compressively strained RuO2 layers, consistent with predictions obtained from first-principles calculations. Complementary density functional theory and X-ray photoemission spectroscopy show that epitaxial strain drives the Ru 4d states toward the Fermi level, triggering a Stoner-type instability that stabilizes non-compensated magnetic order. These unique results reveal that RuO2 exhibits unconventional magnetic states under epitaxial strain, which are not accessible in bulk and establish strain engineering as a powerful route to uncover and control magnetic phases in RuO2 and related oxides.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

EXAONE 3.0 7.8B Instruction Tuned Language Model

We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RLDX-1 Technical Report

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, long-term memory, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including data synthesis for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. $π_{0.5}$ and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while $π_{0.5}$ and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Self-Supervised Automatic Post-Editing Data Generation Tool

Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, we develop a self-supervised data generation tool, deployable as a web application, that minimizes human supervision and constructs personalized APE data from a parallel corpus for several language pairs with English as the target language. Data-centric APE research can be conducted using this tool, involving many language pairs that have not been studied thus far owing to the lack of suitable data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection

Human-Object Interaction detection is a holistic visual recognition task that entails object detection as well as interaction classification. Previous works of HOI detection has been addressed by the various compositions of subset predictions, e.g., Image -> HO -> I, Image -> HI -> O. Recently, transformer based architecture for HOI has emerged, which directly predicts the HOI triplets in an end-to-end fashion (Image -> HOI). Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths. CPC learning enforces all the possible predictions from permuted inference sequences to be consistent. This simple scheme makes the model learn consistent representations, thereby improving generalization without increasing model capacity. Our experiments demonstrate the effectiveness of our method, and we achieved significant improvement on V-COCO and HICO-DET compared to the baseline models. Our code is available at https://github.com/mlvlab/CPChoi.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Identifying Physical Law of Hamiltonian Systems via Meta-Learning

Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying a functional or shared expression of the Hamiltonian is very difficult. It requires carefully designed experiments and the researcher's insight that comes from years of experience. We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law. We show that a well meta-trained learner can identify the shared representation of the Hamiltonian by evaluating our method on several types of physical systems with various experimental settings.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Learning on Radar Centric 3D Object Detection

Even though many existing 3D object detection algorithms rely mostly on camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather and lighting conditions. On the other hand, radar is resistant to such conditions. However, research has found only recently to apply deep neural networks on radar data. In this paper, we introduce a deep learning approach to 3D object detection with radar only. To the best of our knowledge, we are the first ones to demonstrate a deep learning-based 3D object detection model with radar only that was trained on the public radar dataset. To overcome the lack of radar labeled data, we propose a novel way of making use of abundant LiDAR data by transforming it into radar-like point cloud data and aggressive radar augmentation techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Largest triangles in a polygon

We study the problem of finding maximum-area triangles that can be inscribed in a polygon in the plane. We consider eight versions of the problem: we use either convex polygons or simple polygons as the container; we require the triangles to have either one corner with a fixed angle or all three corners with fixed angles; we either allow reorienting the triangle or require its orientation to be fixed. We present exact algorithms for all versions of the problem. In the case with reorientations for convex polygons with $n$ vertices, we also present $(1-\varepsilon)$-approximation algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Versatile Physical Properties of a Novel Two-Dimensional Materials Composed of Group IV-V Elements

Owing to the fascinating physical characteristics of two-dimensional (2D) materials and their heterostructure, much effort has been devoted to exploring their basic physical properties as well as discovering other novel 2D materials. Herein, based on first-principles calculations, we propose novel 2D material groups with the form A$_2$B$_2$, composed of group IV (A = C, Si, Ge, or Sn) and V (B = N, P, As, Sb, or Bi) elements; the group forms two stable phases with the $P\bar{6}m2$ ($\mathcal{M}$ phase) and $P\bar{3}m1$ ($\mathcal{I}$ phase) crystal symmetries. We found that a total of 40 different freestanding A$_2$B$_2$ compounds were dynamically stable and displayed versatile physical properties, such as insulating, semiconducting, or metallic properties, depending on their elemental compositions. Our calculation results further revealed that the newly proposed 2D materials expressed high electrical and thermal transport properties. Additionally, some of the compounds that contained heavy elements exhibited non-trivial topological properties due to strong spin-orbit interactions.

0 citations0 reviewsNo code linkedOpen access