arXiv subjects

Changhee Lee

Publications and source records attributed to Changhee Lee.

At least 19 recordsLinked to original sources

arXiv2026

$H-T$ Phase diagram of CeRh$_{2}$As$_{2}$: Refinement of the parity-switch scenario

The superconductivity of CeRh$_2$As$_2$ has drawn attention due to its first-order transition in magnetic fields. At first glance, the multiple superconducting (SC) phases as well as the first-order transition appear consistent with the parity-switch scenario, which emphasizes the role of strong Rashba spin-orbit coupling enabled by the locally non-centrosymmetric crystal structure. However, experimental phase diagrams exhibit notable deviations from this simple picture: thermodynamic measurements reveal a nearly vertical phase boundary of the high-field SC phase despite the orbital depairing effect, while transport measurements show that the initial slope of the high-field SC phase is steeper than that of the low-field SC phase. Here, we show that these discrepancies can be understood by considering the combined effects of nonsymmorphic band structure and coexisting antiferromagnetic order. We demonstrate that the symmetry-enforced electronic structure around the Dirac node and type-II Van Hove saddle points near the X point in the Brillouin zone boundary becomes more anisotropic with increased interlayer hopping amplitude, and this anisotropic band structure naturally accounts for the anomalous initial slope of the odd-parity SC phase. Meanwhile, a phenomenological theory incorporating coexisting antiferromagnetism explains the nearly vertical thermodynamic phase boundary as a consequence of field-enhanced odd-parity superconductivity enabled by the over-suppression of Pauli depairing.

cond-mat.supr-con↗

arXiv2026

Reentrant superconductivity enabled by spin-orbit coupling: Application to UTe$_2$

Reentrant superconductivity has been understood primarily in terms of the Jaccarino-Peter field-compensation effect or from a change of the strength in the pairing interaction. However, neither mechanism appears able to entirely explain the remarkable phase diagram of UTe$_2$. Here we propose a generic theory of the field-enhancement of opposite-spin Cooper pairings which does not necessitate the coexistence of magnetism or the vicinity of a magnetic quantum critical point. Our analytical treatment shows that the reentrance has its origin in the interplay of the sublattice degrees of freedom and spin-orbit coupling, which can can strikingly enhance opposite-spin Cooper pairings at strong Zeeman fields. Based on these results, we show that a pairing state with B$_{3u}$ symmetry can reproduce the highly anisotropic phase diagram of the reentrant superconducting state of UTe$_2$.

cond-mat.supr-con↗

arXiv2026

Representing Time Series as Structured Programs for LLM Reasoning

Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, time series lie outside their native textual modality, raising a fundamental question: how should time series be represented so that LLMs can reason about them effectively? Existing work typically serializes raw numerical sequences or fine-tunes pre-trained LLMs on time-series data. These approaches place the burden of extracting temporal structure directly on the LLM, creating a modality mismatch that often degrades performance on long sequences and introduces substantial computational overhead. In this work, we introduce Time-Series-to-Structured-Program representation (T2SP), a deterministic, training-free method that represents a time series as a structured symbolic program. T2SP decomposes time series into trends, periods, and salient events, expressing them in a program-friendly format aligned with the textual and code-like modalities on which LLMs are natively trained. By shifting temporal-structure extraction from the model to the representation itself, T2SP enables off-the-shelf LLMs to leverage their existing reasoning capabilities for time-series understanding. We evaluate T2SP on three reasoning tasks -- editing, captioning, and question answering -- where it consistently improves performance, reduces reasoning time, and lowers failure rates compared with raw-string representations. Our results demonstrate that T2SP provides an effective interface between time series and LLMs.

cs.LG↗

arXiv2026

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability. However, achieving this vital level of interpretability is particularly challenging when these DLMs operate as black-box systems (e.g., via APIs), where access to internal model states (e.g., parameters, gradients) is restricted. Despite numerous efforts, existing explanation methods often fail to concurrently satisfy three key desiderata: (i) inference-time efficiency, (ii) black-box compatibility without inducing out-of-distribution behavior, and (iii) comprehensible explanations grounded in the input's linguistic structure. To address these challenges, we propose a method that explains predictions of DLMs by selecting a small, informative subset of input words. We formulate this as an amortized optimization problem, enabling efficient one-shot inference without the need for input-specific search. Our selection policy is trained via REINFORCE-style policy gradients, allowing discrete word selection in a fully gradient-free setting. To enhance interpretability and align with human linguistic intuition, we integrate graph-structured knowledge into this selection process, fostering linguistically coherent subsets that result in explanations both highly informative and cognitively meaningful to end-users. We evaluated our method on diverse DLM architectures and multiple real-world datasets. It consistently identifies word subsets with enhanced discriminative power and stronger alignment with linguistically salient cues, outperforming both conventional black-box compatible methods and gradient-based approaches that are given oracle access to the black-box model's gradients for a more challenging benchmark. Our code is available at here.

cs.AI↗

arXiv2026

Agentic Molecular Recovery via Molecule-Aware Exploration

Text-guided molecular generation with LLMs often yields invalid SMILES. We argue that invalid drafts should be addressed through a shift from validity-oriented repair to identity-preserving molecular recovery: the objective is not only to restore chemical validity, but also to preserve target-relevant structural cues and recover the molecular identity implied by the description. This perspective reveals the limitations of existing correction strategies. Post-hoc repair can recover validity while distorting key structures, LLM-only correction can introduce unintended global drift, and generic agentic correction remains constrained by greedy single-candidate trajectories even when equipped with executable RDKit edit tools. To address these limitations, we propose AMREC, which couples molecule-aware mismatch tracking with expanded candidate exploration and trajectory-level selection. On invalid ChEBI-20 drafts from three backbone models, AMREC achieves the strongest overall recovery profile across structural, exact-match, and string-level metrics.

cs.AI↗

arXiv2026

Position: AI Lock-In Is in Progress, and We Must Be Prepared

AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of generative AI's societal impacts (including unemployment risk and labor market disruption). However, an equally important dimension remains underexplored: the risk inherent in dependence on AI systems themselves. In this position paper, we argue that AI safety research should address AI Lock-In, the phenomenon whereby excessive reliance on AI systems leads to human deskilling, diminishes human capacity for independent functioning, and creates systemic vulnerabilities when AI systems become unavailable or compromised. We highlight that AI Lock-In is a systemic threat that is already emerging at individual, societal, and national levels, one that could be dramatically amplified by AI service disruptions or geopolitical conflicts. Drawing on detailed scenarios, we investigate how AI Lock-In emerges and escalates across multiple levels, ranging from individual skill atrophy to national-scale infrastructure failures. To address this, we provide guidance on how such risks can be mitigated and prepared for at each level. We contend that proactively addressing AI Lock-In before such dependencies become entrenched, or even irreversible, is essential for preserving individual autonomy and national security.

cs.AI↗

arXiv2026

Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values

As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a critical requirement for safety and trust. However, current uncertainty quantification methods primarily operate at the output level, often failing to distinguish whether uncertainty arises from the model's lack of knowledge or from ambiguity in the user's input. While input-centric uncertainty quantification has recently emerged as a promising direction, it remains relatively underexplored and typically relies on coarse, input-level information. Consequently, users are provided with scalar uncertainty scores that offer little actionable guidance on which parts of the input should be clarified to improve reliability. To address this limitation, we propose Shapley-based input uncertainty Quantification (ShaQ), a framework for span-level attribution of input-induced uncertainty. Our approach models ambiguous spans in the input as players in a cooperative game and quantifies their contributions using Shapley values, defined via the weighted average of marginal reductions in conditional entropy obtained by clarifying each span coalition. Unlike existing input-level approaches, our formulation captures complex interactions among spans and provides a principled decomposition in which individual attributions sum exactly to the total input-induced uncertainty. We evaluate ShaQ on the AmbigQA and AmbiEnt benchmarks, where it achieves state-of-the-art performance in ambiguity detection. We further demonstrate its utility on MediTOD, showing that ShaQ can localize under-specified clinical utterances and facilitate human-AI collaboration in high-stakes settings. Overall, ShaQ improves uncertainty estimation and provides actionable insights for targeted input clarification.

cs.AI↗

arXiv2026

INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns. To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series. INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.

cs.LG↗

arXiv2026

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization

Time-series generative models often lack control over temporal granularity, forcing users to accept whatever granularity the model produces. To enable truly user-driven generation, we introduce TimeTok, a unified framework for Granularity-Controllable Time-Series Generation (GC-TSG), which generates time series at any target granularity from any coarser input (e.g., rough sketches) or from scratch. At the core of TimeTok is a hierarchical tokenization strategy that maps time series into an ordered sequence of tokens, from coarse to fine temporal granularity. Our autoregressive generation process operates across these granularity levels, producing token blocks that are decoded back into continuous time series. This design naturally enables GC-TSG - including standard generation - within a single framework, where controlling the number of token blocks provides explicit control over output detail. Experiments show that TimeTok excels at GC-TSG tasks while achieving state-of-the-art performance in standard generation. Furthermore, we showcase TimeTok's potential as a foundational tokenizer by training on multiple datasets with heterogeneous temporal granularities, verifying strong transferability that consistently outperforms models trained on individual datasets. To our knowledge, this is the first unified framework that covers the full generative spectrum for time series, offering a valuable foundation for models that benefit from diverse temporal granularities.

cs.AI↗

arXiv2026

Vivifying LIME: Visual Interactive Testbed for LIME Analysis

Explainable Artificial Intelligence (XAI) has gained importance in interpreting model predictions. Among leading techniques for XAI, Local Interpretable Model-agnostic Explanations (LIME) is most frequently utilized as it notably helps people's understanding of complex models. However, LIME's analysis is constrained to a single image at a time. Besides, it lacks interaction mechanisms for observing the LIME's results and direct manipulations of factors affecting the results. To address these issues, we introduce an interactive visualization tool, LIMEVis, which improves the analysis workflow of LIME by enabling users to explore multiple LIME results simultaneously and modify them directly. With LIMEVis, we could conveniently identify common features in images that a model seems to mainly consider for category classification. Additionally, by interactively modifying the LIME results, we could determine which segments in an image influence the model's classification.

cs.HC↗

arXiv2025

A Visual Analytics System to Understand Behaviors of Multi Agents in Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) is a branch of machine learning in which agents interact and learn optimal policies through trial and error, addressing complex scenarios where multiple agents interact and learn in the same environment at the same time. Analyzing and understanding these complex interactions is challenging, and existing analysis methods are limited in their ability to fully reflect and interpret this complexity. To address these challenges, we provide MARLViz, a visual analytics system for visualizing and analyzing the policies and interactions of agents in MARL environments. The system is designed to visually show the difference in behavior of agents under different environment settings and help users understand complex interaction patterns. In this study, we analyzed agents with similar behaviors and selected scenarios to understand the interactions of the agents, which made it easier to understand the strategies of agents in MARL.

cs.HC↗

arXiv2025

PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting

Generating automated reports for 3D positron emission tomography (PET) is an important and challenging task in medical imaging. PET plays a vital role in oncology, but automating report generation is difficult due to the complexity of whole-body 3D volumes, the wide range of potential clinical findings, and the limited availability of annotated datasets. To address these challenges, we introduce PETARSeg-11K, the first large-scale, publicly available dataset that provides lesion-level correspondence between 3D PET/CT volumes and free-text radiological findings. It comprises 11,356 lesion descriptions paired with 3D segmentations. Second, we propose PETAR-4B, a 3D vision-language model designed for mask-aware, spatially grounded PET/CT reporting. PETAR-4B jointly encodes PET, CT, and 3D lesion segmentation masks, using a 3D focal prompt to capture fine-grained details of lesions that normally comprise less than 0.1% of the volume. Evaluations using automated metrics show PETAR-4B substantially outperforming all 2D and 3D baselines. A human study involving five physicians -- the first of its kind for automated PET reporting -- confirms the model's clinical utility and establishes correlations between automated metrics and expert judgment. This work provides a foundational dataset and a novel architecture, advancing 3D medical vision-language understanding in PET.

cs.CV↗

arXiv2025

Incommensuration in odd-parity antiferromagnets

Inversion-asymmetric antiferromagnets (AFMs) with odd-parity spin-polarization pattern have been proposed as a new venue for spintronics. These AFMs require commensurate ordering to ensure an effective time-reversal symmetry, which guarantees a strictly antisymmetric spin polarization of the electronic states. Recently, nonsymmorphic centrosymmetric crystals have been identified as a broad class of materials which could exhibit unit-cell doubling magnetism with odd-parity spin-polarization. Here we investigate the stability of these states against incommensuration. We first demonstrate that the symmetry conditions which permit a p-wave spin polarization pattern also permit the existence of a non-relativistic Lifshitz invariant in the phenomenological Ginzburg-Landau free energy. This implies magnetism with an incommensurate ordering vector, independent of its microscopic origin. AFMs with f- or h-wave spin-polarization are also prone to incommensurability, especially when they have an itinerant origin. Here the symmetry which ensures the odd-parity spin-polarization also guarantees the existence of van Hove saddle points off the time-reversal-invariant momenta, which promote incommensurate spin fluctuations in quasi-two-dimensional electronic systems. Finally, we study the effect of weak spin-orbit coupling in locally noncentrosymmetric materials and find that it favors antiferromagnetic phases with in-plane magnetic moments. However, the inclusion of the spin-orbit coupling also introduces a new mechanism for driving incommensuration. Our results imply that odd-parity AFMs are likely to be preceded by an incommensurate phase, or emerge directly from the normal state via a first order transition. These conclusions are consistent with the phase diagram of several candidate materials.

cond-mat.mes-hall↗

arXiv2025

Stochastic Encodings for Active Feature Acquisition

Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.

cs.LG↗

arXiv2025

Inversion-asymmetric itinerant antiferromagnets by the space group symmetry

We investigate the appearance of an inversion-asymmetric antiferromagnetism due to an itinerant mechanism in nonsymmorphic systems with magnetic ions at Wyckoff position of multiplicity 2. The key symmetries which underpin the existence of such phases are established, and we derive a Landau free energy from a general microscopic electronic Hamiltonian. Our analysis reveals that the stable antiferromagnetic order is largely determined by the symmetries of Wyckoff position, the nature of the nesting between electronic bands, and the presence of anisotropy or nesting in high-symmetry planes of the Brillouin zone. We illustrate our conclusions with specific microscopic models.

cond-mat.str-el↗

arXiv2024

BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched EMR

Pediatric Emergency Department (PED) overcrowding presents a significant global challenge, prompting the need for efficient solutions. This paper introduces the BioBridge framework, a novel approach that applies Natural Language Processing (NLP) to Electronic Medical Records (EMRs) in written free-text form to enhance decision-making in PED. In non-English speaking countries, such as South Korea, EMR data is often written in a Code-Switching (CS) format that mixes the native language with English, with most code-switched English words having clinical significance. The BioBridge framework consists of two core modules: "bridging modality in context" and "unified bio-embedding." The "bridging modality in context" module improves the contextual understanding of bilingual and code-switched EMRs. In the "unified bio-embedding" module, the knowledge of the model trained in the medical domain is injected into the encoder-based model to bridge the gap between the medical and general domains. Experimental results demonstrate that the proposed BioBridge significantly performance traditional machine learning and pre-trained encoder-based models on several metrics, including F1 score, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier score. Specifically, BioBridge-XLM achieved enhancements of 0.85% in F1 score, 0.75% in AUROC, and 0.76% in AUPRC, along with a notable 3.04% decrease in the Brier score, demonstrating marked improvements in accuracy, reliability, and prediction calibration over the baseline XLM model. The source code will be made publicly available.

cs.CL↗

arXiv2024

Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination \textit{without} sacrificing calibration. Our method employs weighted sampling within a contrastive learning framework, assigning lower penalties to samples with similar survival outcomes. This aligns well with the assumption that patients with similar event times share similar clinical statuses. Consequently, when augmented with the commonly used negative log-likelihood loss, our approach significantly improves discrimination performance without directly manipulating the model outputs, thereby achieving better calibration. Experiments on multiple real-world clinical datasets demonstrate that our method outperforms state-of-the-art deep survival models in both discrimination and calibration. Through comprehensive ablation studies, we further validate the effectiveness of our approach through quantitative and qualitative analyses.

cs.LG↗

arXiv2024

DSG-KD: Knowledge Distillation from Domain-Specific to General Language Models

The use of pre-trained language models fine-tuned to address specific downstream tasks is a common approach in natural language processing (NLP). However, acquiring domain-specific knowledge via fine-tuning is challenging. Traditional methods involve pretraining language models using vast amounts of domain-specific data before fine-tuning for particular tasks. This study investigates emergency/non-emergency classification tasks based on electronic medical record (EMR) data obtained from pediatric emergency departments (PEDs) in Korea. Our findings reveal that existing domain-specific pre-trained language models underperform compared to general language models in handling N-lingual free-text data characteristics of non-English-speaking regions. To address these limitations, we propose a domain knowledge transfer methodology that leverages knowledge distillation to infuse general language models with domain-specific knowledge via fine-tuning. This study demonstrates the effective transfer of specialized knowledge between models by defining a general language model as the student model and a domain-specific pre-trained model as the teacher model. In particular, we address the complexities of EMR data obtained from PEDs in non-English-speaking regions, such as Korea, and demonstrate that the proposed method enhances classification performance in such contexts. The proposed methodology not only outperforms baseline models on Korean PED EMR data, but also promises broader applicability in various professional and technical domains. In future works, we intend to extend this methodology to include diverse non-English-speaking regions and address additional downstream tasks, with the aim of developing advanced model architectures using state-of-the-art KD techniques. The code is available in https://github.com/JoSangYeon/DSG-KD.

cs.CL↗