arXiv subjects

Hyunsoo Kim

Publications and source records attributed to Hyunsoo Kim.

At least 19 recordsLinked to original sources

arXiv2026

Biodegradable, Millimeter-Scale Light-Emitting Sensors for Distributed Environmental Monitoring-Functional Pixie Dust

Methods for large-area, precise monitoring across natural environments are of growing interest due to pressing needs for sustainable management of rapidly increasing anthropogenic activities. Established approaches involve sparse spatial sampling and/or sequential measurements, while emerging techniques exploit miniaturized electronics or passive optical methods. Various constraints in scalability, costs, robustness, operational range and other factors create a need for alternatives. Here, we introduce a concept that overcomes many of these limitations through the combined use of chemically induced light emission and chemically responsive optical filter elements in millimeter-scale systems that we refer to as functional pixie dust (fPD) sensors, designed specifically for monitoring natural water systems during nighttime to eliminate background optical interference and to enhance remote analysis. These floating devices act as Lagrangian tracers to follow surface flows and to simultaneously measure the concentrations of key chemical species along their trajectories. Optimized designs exploit environmentally compatible constituent materials that are also degradable through natural processes to benign end products, thereby eliminating the need for recovery. Spatially and spectrally resolved ratiometric measurement schemes ensure robust operation and ability to address practical requirements in range, operational lifetime, time response and sensitivity. Demonstrations include distributed measurements of pH, Hg2+, and NO2-, each of relevance to industrial discharge, toxic metal contamination, and nitrogen-rich runoff, adapted for static concentration gradients, flow-driven transport conditions, and outdoor aquatic settings. The results establish a framework for environmental sensing using degradable, self-powered microsystems capable of scalable deployment and remote readout.

physics.app-ph↗

arXiv2026

The self-organized vacancy order in Pr$_9$Ge$_{16}$

In this work, we report the discovery of a new crystal structure on the Ge-rich side of the Pr-Ge binary phase diagram. Using a high-temperature flux technique, we grew single crystals of $Pr_9Ge_{16}$, which adopt a previously unreported orthorhombic $Fdd$2 structure type featuring ordered Ge vacancies. We present the anisotropic magnetic properties and identify the crystallographic $b$ axis perpendicular to the crystal plane as the magnetic easy axis. Temperature-dependent resistivity measurements reveal metallic behavior with a distinct anomaly at $T_{\mathrm{C}}$ = 14.3 K. Hall resistivity data indicate that electron-like carriers dominate, with a carrier concentration on the order of $10^{27}~\mathrm{m}^{-3}$. The magnetic order is readily suppressed by a magnetic field of approximately 0.4 T applied along the easy $b$ axis.

cond-mat.mtrl-sci↗

arXiv2026

Self-Improving Diffusion Classifiers with Minority Preference Optimization

Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strongly tied to the pretraining data distribution: they perform well in majority, high-density regions of the data manifold, but are significantly less accurate in minority, low-density regions. Although prior works on minority sampling have focused on generating more minority-like images, what minority sampling fundamentally enables beyond generation remains underexplored. In this paper, we reveal a direct relationship between minority sampling in generation and the perception capability of diffusion classifiers. Specifically, we show that enhancing minority sampling broadens the coverage of underrepresented regions on the data manifold, thereby improving diffusion-based recognition. To exploit this connection, we propose \textit{Self-Improving Diffusion Classifiers with Minority Preference Optimization} (MiPO), which fine-tunes a pretrained diffusion model using minority preference rewards. Using only arbitrary caption data, MiPO generates candidate samples, rewards those that better cover minority regions, and optimizes the model with LoRA and Group Relative Policy Optimization, without additional image data, external foundation models, or external reward models. This enables stable, prompt-adaptive minority sampling and translates low-density generative coverage into improved zero-shot diffusion classification. To sum up, we show that diffusion classifier perception is biased toward majority regions, demonstrate that this bias can be alleviated through minority preference optimization, and evaluate MiPO on five standard datasets.

cs.CV↗

arXiv2026

Resilient $j$=3/2 superconductivity in topological semimetal YPtBi

Cooper pairing in most of the known fermionic superfluids occurs via spin-1/2 quasiparticle interactions that lead to spin-singlet or spin-triplet pairing. In the topological semimetal YPtBi, strong spin-orbit coupling results in a band inversion between highly symmetric $s$- and $p$-like electronic bands and a degeneracy at the $\Gamma$ point that ensures the manifold of $j$=3/2 quasiparticle states thrive near the Fermi level, where superconducting pairing occurs. Here we study the effects of magnetic and nonmagnetic disorder and carrier density on this exotic superconducting pairing state. By varying levels of disorder and carrier densities by nearly two and three orders of magnitude, respectively, we show that the superconducting critical temperature of YPtBi has a remarkable robustness, with little variation across this span. Our results suggest that superconductivity in YPtBi may reside in a regime where phase stiffness, rather than pair formation, governs the transition temperature. The insensitivity of Cooper pairing to dramatic changes in quasiparticle environment in a $j$=3/2 superconductor highlights a new form of protection realized in topological high-spin superconductors.

cond-mat.supr-con↗

arXiv2026

Campbell penetration depth in a single crystal of heavy fermion superconductor CeCoIn$_5$

The temperature and magnetic field dependent magnetic penetration depth, $\lambda_m(T,H)$, was measured in a single crystal of a heavy fermion superconductor CeCoIn$_5$ using a frequency-domain tunnel diode resonator. In addition to the London penetration depth, which yields the superfluid density, measurements in a finite DC magnetic field provide Campbell penetration depth, $\lambda_C(T,H)$, which is directly linked to the true (unrelaxed) critical current density, $J_c$. The measured $\lambda_C(H)$ in CeCoIn$_5$ deviates significantly from the conventional $\sim \sqrt{H}$ behavior, and its slope changes abruptly at the characteristic magnetic field values. Considering that our sample is in the clean limit, we interpret this deviation as a fingerprint of the vortex lattice symmetry change. The temperature dependence $J_c(T)$ of CeCoIn$_5$ calculated from $\lambda_C(T)$ is nearly $T$-linear over the entire temperature range, also in stark contrast to expectations in a conventional type-II superconductor. Our results provide new evidence for unconventional superconductivity in CeCoIn$_5$ from the never-before-measured Campbell penetration depth.

cond-mat.supr-con↗

arXiv2026

Characterization of the 20-inch Photomultiplier Tubes for RENE Detector

To address the Reactor Antineutrino Anomaly (RAA) observed in neutrino experiments, the Reactor Experiment for Neutrino and Exotics (RENE) has been initiated using a liquid scintillation detector. In this study, we investigate the characteristics of two 20-inch Hamamatsu R12860 photomultiplier tubes (PMTs) intended for installation in the RENE detector. The charge and timing responses of the PMTs were evaluated at both the nominal and target gains expected during actual operation. In particular, gain non-uniformity arising from the large-diameter photocathode with a box-and-line type dynode structure was examined, and the maximum gain variation was measured. The occurrence rate, timing, and charge distributions of late pulses and afterpulses were also investigated to characterize the specific response features of the R12860 PMT. The results reported in this study will aid in the interpretation of signals from the RENE detector and serve as a reference for estimating potential systematic uncertainties in RENE data. Furthermore, these findings are expected to provide valuable information for other experiments employing the same type of PMTs.

physics.ins-det↗

arXiv2026

Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey

Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety rescue. While individual robots show local autonomy, realistic tasks demand coordination among multiple agents sharing vast streams of sensor data. Communication is indispensable, yet transmitting comprehensive data can overwhelm networks, especially when a system-level orchestrator or cloud-based MLLM fuses multimodal inputs for route planning or anomaly detection. These tasks are often initiated by high-level natural language instructions. This intent serves as a filter for resource optimization: by understanding the goal via MLLMs, the system can selectively activate relevant sensing modalities, dynamically allocate bandwidth, and determine computation placement. Thus, R2X is fundamentally an intent-to-resource orchestration problem where sensing, communication, and computation are jointly optimized to maximize task-level success under resource constraints. This survey examines how integrated design paves the way for multi-robot coordination under MLLM guidance. We review state-of-the-art sensing modalities, communication strategies, and computing approaches, highlighting how reasoning is split between on-device models and powerful edge/cloud servers. We present four end-to-end demonstrations (sense -> communicate -> compute -> act): (i) digital-twin warehouse navigation with predictive link context, (ii) mobility-driven proactive MCS control, (iii) a FollowMe robot with a semantic-sensing switch, and (iv) real-hardware open-vocabulary trash sorting via edge-assisted MLLM grounding. We emphasize system-level metrics -- payload, latency, and success -- to show why R2X orchestration outperforms purely on-device baselines.

cs.RO↗

arXiv2026

Correlation-Weighted Multi-Reward Optimization for Compositional Generation

Text-to-image models produce images that align well with natural language prompts, but compositional generation has long been a central challenge. Models often struggle to satisfy multiple concepts within a single prompt, frequently omitting some concepts and resulting in partial success. Such failures highlight the difficulty of jointly optimizing multiple concepts during reward optimization, where competing concepts can interfere with one another. To address this limitation, we propose Correlation-Weighted Multi-Reward Optimization (CMO), a framework that leverages the correlation structure among concept rewards to adaptively weight each attribute concept in optimization. By accounting for interactions among concepts, CMO balances competing reward signals and emphasizes concepts that are partially satisfied yet inconsistently generated across samples, improving compositional generation. Specifically, we decompose multi-concept prompts into pre-defined concept groups (e.g., objects, attributes, and relations) and obtain reward signals from dedicated reward models for each concept. We then adaptively reweight these rewards, assigning higher weights to conflicting or hard-to-satisfy concepts using correlation-based difficulty estimation. By focusing optimization on the most challenging concepts within each group, CMO encourages the model to consistently satisfy all requested attributes simultaneously. We apply our approach to train state-of-the-art diffusion models, SD3.5 and FLUX.1-dev, and demonstrate consistent improvements on challenging multi-concept benchmarks, including ConceptMix, GenEval 2, and T2I-CompBench. The code is available at https://github.com/TheDarkKnight-21th/CMO.

cs.AI↗

arXiv2026

Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-weighted Adaptation

With the increasing versatility of text-to-image diffusion models, the ability to selectively erase undesirable concepts (e.g., harmful content) has become indispensable. However, existing concept erasure approaches primarily focus on removing unsafe concepts without providing guidance toward corresponding safe alternatives, which often leads to failure in preserving the structural and semantic consistency between the original and erased generations. In this paper, we propose a novel framework, PAIRed Erasing (PAIR), which reframes concept erasure from simple removal to consistency-preserving semantic realignment using unsafe-safe pairs. We first generate safe counterparts from unsafe inputs while preserving structural and semantic fidelity, forming paired unsafe-safe multimodal data. Leveraging these pairs, we introduce two key components: (1) Paired Semantic Realignment, a guided objective that uses unsafe-safe pairs to explicitly map target concepts to semantically aligned safe anchors; and (2) Fisher-weighted Initialization for DoRA, which initializes parameter-efficient low-rank adaptation matrices using unsafe-safe pairs, encouraging the generation of safe alternatives while selectively suppressing unsafe concepts. Together, these components enable fine-grained erasure that removes only the targeted concepts while maintaining overall semantic consistency. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving effective concept erasure while preserving structural integrity, semantic coherence, and generation quality.

cs.CV↗

arXiv2026

Neural Particle Automata: Learning Self-Organizing Particle Dynamics

We introduce Neural Particle Automata (NPA), a Lagrangian generalization of Neural Cellular Automata (NCA) from static lattices to dynamic particle systems. Unlike classical Eulerian NCA where cells are pinned to pixels or voxels, NPA model each cell as a particle with a continuous position and internal state, both updated by a shared, learnable neural rule. This particle-based formulation yields clear individuation of cells, allows heterogeneous dynamics, and concentrates computation only on regions where activity is present. At the same time, particle systems pose challenges: neighborhoods are dynamic, and a naive implementation of local interactions scale quadratically with the number of particles. We address these challenges by replacing grid-based neighborhood perception with differentiable Smoothed Particle Hydrodynamics (SPH) operators backed by memory-efficient, CUDA-accelerated kernels, enabling scalable end-to-end training. Across tasks including morphogenesis, point-cloud classification, and particle-based texture synthesis, we show that NPA retain key NCA behaviors such as robustness and self-regeneration, while enabling new behaviors specific to particle systems. Together, these results position NPA as a compact neural model for learning self-organizing particle dynamics.

cs.NE↗

arXiv2026

MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation

Modern recommender systems trained on domain-specific data often struggle to generalize across multiple domains. Cross-domain sequential recommendation has emerged as a promising research direction to address this challenge; however, existing approaches face fundamental limitations, such as reliance on overlapping users or items across domains, or unrealistic assumptions that ignore privacy constraints. In this work, we propose a new framework, MergeRec, based on model merging under a new and realistic problem setting termed data-isolated cross-domain sequential recommendation, where raw user interaction data cannot be shared across domains. MergeRec consists of three key components: (1) merging initialization, (2) pseudo-user data construction, and (3) collaborative merging optimization. First, we initialize a merged model using training-free merging techniques. Next, we construct pseudo-user data by treating each item as a virtual sequence in each domain, enabling the synthesis of meaningful training samples without relying on real user interactions. Finally, we optimize domain-specific merging weights through a joint objective that combines a recommendation loss, which encourages the merged model to identify relevant items, and a distillation loss, which transfers collaborative filtering signals from the fine-tuned source models. Extensive experiments demonstrate that MergeRec not only preserves the strengths of the original models but also significantly enhances generalizability to unseen domains. Compared to conventional model merging methods, MergeRec consistently achieves superior performance, with average improvements of up to 17.21% in Recall@10, highlighting the potential of model merging as a scalable and effective approach for building universal recommender systems. The source code is available at https://github.com/DIALLab-SKKU/MergeRec.

cs.IR↗

arXiv2025

Magnetic field-tuned magnetic order and metamagnetic criticality in non-stoichiometric CeAuBi$_2$

We present a detailed study of magnetization, resistivity, heat capacity, and X-ray and neutron powder diffraction measurements performed on single crystals of non-stoichiometric CeAuBi$_2$, Au deficiency 18$\%$, a strongly correlated antiferromagnet with N\'eel temperature T$_N$ = 13.2 K. Field-dependent magnetization measurements reveal a large magnetic anisotropy at low temperatures with an easy axis along the crystallographic c-axis, in which direction a spin-flop transition exhibits strong features in magnetization, specific heat, and resistivity at H$_c$ = 75 kOe. The constructed temperature-field phase diagram connects this transition to the suppression of magnetic order, which evolves from a second-order nature into a first-order transition that bifurcates at the spin-flop into three transitions below 1 K. The smoothed nature of the metamagnetic transitions in non-stoichiometric CeAuBi$_2$ is well described by an Ising model with weak quenched disorder, suggesting that the presence of Au vacancies is sufficient to smear the complex metamagnetic behavior and tune the critical behavior of magnetic order.

cond-mat.str-el↗

arXiv2025

Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades.

cs.LG↗

arXiv2025

A Theorem-Proving-Based Evaluation of Neural Semantic Parsing

Graph-matching metrics such as Smatch are the de facto standard for evaluating neural semantic parsers, yet they capture surface overlap rather than logical equivalence. We reassess evaluation by pairing graph-matching with automated theorem proving. We compare two approaches to building parsers: supervised fine-tuning (T5-Small/Base) and few-shot in-context learning (GPT-4o/4.1/5), under normalized and unnormalized targets. We evaluate outputs using graph-matching, bidirectional entailment between source and target formulas with a first-order logic theorem prover, and well-formedness. Across settings, we find that models performing well on graph-matching often fail to produce logically equivalent formulas. Normalization reduces incidental target variability, improves well-formedness, and strengthens logical adequacy. Error analysis shows performance degrades with increasing formula complexity and with coordination, prepositional phrases, and passive voice; the dominant failures involve variable binding and indexing, and predicate naming. These findings highlight limits of graph-based metrics for reasoning-oriented applications and motivate logic-sensitive evaluation and training objectives together with simplified, normalized target representations. All code and data for our experiments are publicly available.

cs.CL↗

arXiv2025

Human-AI Collaborative Bot Detection in MMORPGs

In Massively Multiplayer Online Role-Playing Games (MMORPGs), auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions require explainable justification to avoid legal and user experience issues. In this paper, we present a novel framework for detecting auto-leveling bots by leveraging contrastive representation learning and clustering techniques in a fully unsupervised manner to identify groups of characters with similar level-up patterns. To ensure reliable decisions, we incorporate a Large Language Model (LLM) as an auxiliary reviewer to validate the clustered groups, effectively mimicking a secondary human judgment. We also introduce a growth curve-based visualization to assist both the LLM and human moderators in assessing leveling behavior. This collaborative approach improves the efficiency of bot detection workflows while maintaining explainability, thereby supporting scalable and accountable bot regulation in MMORPGs.

cs.AI↗

arXiv2025

RENE experiment for the sterile neutrino search using reactor neutrinos

This paper summarizes the details of the Reactor Experiment for Neutrinos and Exotics (RENE) experiment. It covers the detector construction, Monte Carlo (MC) simulation study, and physics expectations. The primary goal of the RENE project is to investigate the sterile neutrino oscillation at $\Delta{m}^{2}_{41}\sim 2\,{\rm{eV}^{2}}$. which overlap with the allowed region predicted by the Reactor Antineutrino Anomaly (RAA). On the other hand, the STEREO and PROSPECT experiments have excluded certain regions of the parameter space with 95 \% confidence level (C.L.), while the joint study conducted by RENO and NEOS suggests possible indications of sterile neutrinos at $\Delta{m}^{2}_{41}\sim2.4\,{\rm{eV}^{2}}$ and $\sim{1.7}{\,\rm{eV}^{2}}$ with sin$^{2}\theta_{41} < 0.01$. Accordingly, a more meticulous investigation of these remaining regions continues to be a scientifically valuable endeavor. This paper reports the technical details of the detector and physics objectives.

hep-ex↗

arXiv2025

AC magnetic measurements with a self-oscillating LC circuit and its application to university education

Understanding the magnetic properties of matter plays a key role in materials physics. However, university education on fundamental magnetism is limited to a theoretical survey because of the lack of appropriate apparatus that can be applied for laboratory courses at the undergraduate level. In this work, we introduce an AC magnetometer based on the Colpitts self-oscillator with an inductor coil as a probe. We show that this type of self-oscillator can be adopted in a typical university laboratory course to learn the principles of magnetic measurement and to understand the fundamental magnetism of matter. We demonstrate the exceptional stability of the circuit with a working frequency range of ~10 kHz to 10 MHz and excellent performance to detect the diamagnetic signal from a superconductor at cryogenic temperature.

physics.ed-ph↗

arXiv2025

When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class

Open-source pre-trained models hold great potential for diverse applications, but their utility declines when their training data is unavailable. Data-Free Image Synthesis (DFIS) aims to generate images that approximate the learned data distribution of a pre-trained model without accessing the original data. However, existing DFIS meth ods produce samples that deviate from the training data distribution due to the lack of prior knowl edge about natural images. To overcome this limitation, we propose DDIS, the first Diffusion-assisted Data-free Image Synthesis method that leverages a text-to-image diffusion model as a powerful image prior, improving synthetic image quality. DDIS extracts knowledge about the learned distribution from the given model and uses it to guide the diffusion model, enabling the generation of images that accurately align with the training data distribution. To achieve this, we introduce Domain Alignment Guidance (DAG) that aligns the synthetic data domain with the training data domain during the diffusion sampling process. Furthermore, we optimize a single Class Alignment Token (CAT) embedding to effectively capture class-specific attributes in the training dataset. Experiments on PACS and Ima geNet demonstrate that DDIS outperforms prior DFIS methods by generating samples that better reflect the training data distribution, achieving SOTA performance in data-free applications.

cs.CV↗