arXiv subjects

Seungjun Lee

Publications and source records attributed to Seungjun Lee.

At least 19 recordsLinked to original sources

arXiv2026

Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data

Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient because evaluating dense coordinate grids is computationally expensive. Existing coordinate-pruning strategies rely on local error functions that can oversample correlated points and incur substantial overhead from full-grid error evaluation. We introduce Refined Grid Sampling (RGS), which combines stratified grid sampling, error-based refinement, and dynamic grid-resolution adjustment. This design reduces spatial redundancy without evaluating errors over the full grid. On high-resolution Navier--Stokes fields, RGS achieves higher reconstruction accuracy within a fixed wall-clock budget and reaches the accuracy of full-grid training up to $6\times$ faster than the evaluated baselines.

cs.LG↗

arXiv2026

Quasicrystalline oxide interfaces by twisting SrTiO3 membranes

Quasicrystals reveal that long-range order can exist without translational periodicity, but deterministic routes for designing strongly bonded quasicrystalline interfaces remain unexplored. Here, we show that such interfaces can be engineered by growing epitaxial SrTiO3 membranes on a sacrificial layer, releasing them, twisting them by prescribed angles, and bonding them onto a chosen substrate. SrTiO3 bilayers twisted by 45° form an octagonal quasicrystalline interface with eight-fold rotational symmetry, whereas trilayers assembled with successive 30° rotations form a dodecagonal interface with twelve-fold symmetry. Selected-area and nano-beam electron diffraction, multislice electron ptychography, atomic-resolution dark field imaging, Fourier analysis, and real-space tiling establish long-range quasicrystalline order with electronic reconstruction evident from electron energy-loss spectroscopy after annealing. Atomistic simulations based on machine-learned interatomic potentials show that octagonal order emerges as the bilayer twist angle approaches 45°. Polarization-dependent X-ray absorption spectroscopy in 1.5 nm twisted bilayers reveals a twist-dependent electronic response, and terahertz-field-induced second-harmonic generation circular dichroism identifies a symmetry-dependent nonlinear optical response at the 45° quasicrystalline condition. Twisted oxide membranes therefore provide an extendable route to engineer quasiperiodic order and couple it to interfacial bonding, orbital reconstruction, and nonlinear optical response in complex oxides.

cond-mat.mtrl-sci↗

arXiv2026

DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models

Hybrid reasoning models can answer directly or spend extra tokens on extended thinking. A practical router should choose between these modes for each query, so easy problems avoid unnecessary reasoning and hard problems receive enough budget to finish the answer. Existing routers move in this direction, but they typically require labeled training data or fix thinking budgets up front, ignoring answer-level evidence from the model itself. We introduce DART, a training-free routing framework that samples two cheap no-think drafts, accepts direct answering when the drafts agree, and predicts a thinking budget from draft entropy when they disagree. Across the main comparisons, DART preserves or improves always-thinking accuracy in most settings while reducing thinking-token use. Accuracy improves by up to +9.0 points on Olympiad-level math and by up to +22.5 points on code under execution-based equivalence, while thinking-token use drops by 32-73%. The Stage~1 signal extends across model scales (0.6B--32B), model families, and API-only hosted settings, with no labeled data and no gradient updates required. Our code is available at https://github.com/js-lee-AI/DART.

cs.AI↗

arXiv2026

Latent Information Sharing for Accelerating Federated Learning

Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, hindering the efficient training of a global model. In this study, we propose a novel latent information sharing scheme that directly mitigates data heterogeneity across clients. Our theoretical and empirical results show that sharing a small amount of hidden-layer activations significantly improves training efficiency while preserving convergence guarantees and data privacy. Furthermore, we compare our method with existing FL approaches designed to address client drift, including FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, and demonstrate superior model accuracy under a fixed round budget without incurring excessive communication overhead. Overall, this work presents a promising new knowledge aggregation scheme and provides a comprehensive analysis of the impact of activation sharing on federated optimization.

cs.LG↗

arXiv2026

Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling

Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampling that ignores spatial heterogeneity. Existing adaptive sampling methods partially address this issue by prioritizing high-error samples, but typically operate at the point level, often leading to redundant sampling in localized regions and insufficient coverage of the domain. We propose ACES (Adaptive Coverage-aware Efficient Sampling), a structured sampling framework that improves training efficiency by decoupling coverage and importance. ACES constructs adaptive spatial partitions to ensure domain coverage and reduce redundancy, and applies region-level importance weighting to prioritize informative regions during training. We provide a theoretical analysis showing that adaptive partitioning reduces gradient variance by increasing within-region homogeneity, and that controlled bias in region-level weighting may improve optimization efficiency relative to standard unbiased estimators. Experiments on scientific field learning tasks demonstrate that ACES achieves faster convergence and lower error than uniform and pointwise adaptive sampling baselines, with the largest gains in fields with highly localized complexity.

cs.LG↗

arXiv2026

Emergent superconductivity and non-reciprocal transport in a van der Waals Dirac semimetal/antiferromagnet heterostructure

We investigate emergent superconductivity and non-reciprocal transport (magnetochiral anisotropy, superconducting diode effect) at the heterointerface of two non-superconducting van der Waals (vdW) materials, the Dirac semimetal ZrTe$_2$ and the antiferromagnetic iron chalcogenide FeTe, grown using molecular beam epitaxy. We show from electrical transport measurements that two-dimensional (2D) superconductivity arises at the heterointerface below a critical temperature $T_c \sim 10$~K. In the superconducting transition region, non-reciprocal transport, characterized by the magneto-chiral anisotropy, exhibits a magnitude comparable to that observed in topological insulators, and is enhanced by a factor of three when the heterostructure is capped with a 2D vdW ferromagnet (CrTe$_2$). Below $T_c$, the superconducting diode effect exhibits an efficiency of 29\%. With strong spin-orbit coupling in ZrTe$_2$, these epitaxial heterostructures provide an attractive epitaxial vdW platform for exploring unconventional superconductivity in Dirac semimetals and for developing non-reciprocal devices for superconducting electronics.

cond-mat.supr-con↗

arXiv2026

Observation of mirror-odd and mirror-even spin texture in ultrathin epitaxially strained RuO2 films

Recently, rutile ruthenium dioxide (RuO$_2$) has attracted renewed interest due to expectations of prominent altermagnetic spin splitting. However, accumulating experimental evidence suggests that, in its bulk and thick-film forms, RuO$_2$ does not display any form of magnetic ordering. Despite this, the spin structure of RuO$_2$ remains largely unexplored in the ultrathin limit, where substrate-imposed epitaxial strain can be substantial. Here, we use spin-resolved angle-resolved photoemission spectroscopy, supported by ab initio calculations, to reveal the electronic structure of 2-nanometer-thick epitaxial RuO$_2$ heterostructures. We observe an unconventional spin texture characterized by the coexistence of mirror-even and mirror-odd momentum-dependent components. A comprehensive symmetry analysis rules out nonmagnetic origins of this spin texture. These findings suggest an emergent nonrelativistic spin structure enabled by epitaxial strain in the ultrathin limit, marking a distinct departure from the behavior of relaxed or bulk RuO$_2$. Our work opens previously unexplored perspectives for exploring symmetry-breaking mechanisms and spin textures in oxide heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry-isolated magnetoelectric electro-optic effects in noncentrosymmetric metals

We classify the symmetry-constrained forms of the Berry curvature dipole $\mathbf{D}$, gyrotropic magnetic tensor $\mathbf{K}$, and magnetoelectric electro-optic (EO) tensor $\mathbf{G}$, which describe metallic optical and EO effects in time-reversal symmetric, noncentrosymmetric metals. We identify 11 space groups (SGs) in which $\mathbf{D}$ and $\mathbf{K}$ vanish by symmetry while $\mathbf{G}$ remains allowed, thereby providing a more direct route to observing the recently predicted magnetoelectric EO effects associated with $\mathbf{G}$. First-principles based calculations confirm that $\mathbf{D}$ and $\mathbf{K}$ vanish for representative materials, while $\mathbf{G}$ remains allowed and tunable via Fermi level shifting. We further show that the choices of SG and experimental configuration provide complementary paths for isolating $\mathbf{G}$-driven EO effects, including cases where $\mathbf{D}$ and $\mathbf{K}$ are also symmetry-allowed. In an oblique-incidence geometry, the $\mathbf{D}$-driven response produces a helicity-even absorption or gain correction, whereas the $\mathbf{G}$-driven response couples the $s$ and $p$ optical sectors and produces a bias-induced circular dichroism with a characteristic $\sinθ\cosθ$ angular dependence. This provides a direct experimental route for separating the $\mathbf{D}$- and $\mathbf{G}$-driven EO signatures in noncentrosymmetric metals.

cond-mat.mes-hall↗

arXiv2026

Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator

Embodied navigation aims to build agents that interpret multimodal goals, reason in 3D space, and reach target destinations reliably in the real world. However, progress remains constrained by the lack of scalable, high-fidelity, and physically grounded interactive environments. Although real-world scanned datasets offer visual realism, they are limited by scale. In contrast, synthetic simulators scale more easily but often exhibit large sim-to-real gaps. We introduce Image2Sim, a real-time neural simulation framework that constructs high-quality interactive environments from posed RGB-D image sequences. The central idea is to decouple 3D spatial anchoring from photorealistic observation synthesis. For scene construction, Image2Sim uses a feed-forward feature Gaussian model that lifts posed RGB-D observations into a 3D feature-Gaussian representation in a single pass. For rendering, we propose a Geometry-Aware One-Step Pixel Flow model that transforms sparse and noisy Gaussian projections into high-quality panoramic RGB-D observations. Image2Sim also serves as a fully automated embodied data engine that generates high-fidelity observations, executable actions, and diverse navigation instructions at scale. It converts large collections of videos and images into nearly 20K interactive scenes and synthesizes more than 10 million navigation training samples. Navigation models trained entirely in these neural environments achieve strong improvements on major benchmarks and transfer effectively to real-world zero-shot settings. These results suggest that scalable neural simulation can serve as a practical training substrate for embodied navigation at scale.

cs.CV↗

arXiv2026

NoPA: Non-Parametric Online 3D Scene Graph Generation

Classic 3D scene graph generation approaches fail to work in real-time due to the heavy computational cost of environment mapping and the need to generate intermediate point-cloud representations. To alleviate this issue, a recent work eschews point clouds in favor of a lightweight Gaussian distribution for each object. This approximation drastically speeds up inference and enables real-time 3D scene graph generation. However, the representation has two key weaknesses. \textbf{1)} Each object is approximated by a single 3D Gaussian, which causes a severe loss of 3D geometric detail. \textbf{2)} The discrepancy between this approximation and the true object geometry exacerbates the inaccurate merging of object candidates during online inference. To address these issues, we propose \textbf{NoPA}, which represents each object as a separate non-parametric distribution. This formulation retains 3D geometric information while preserving real-time inference of the parametric Gaussian formulation. To build upon our novel object representation, we propose a tailored merging strategy to recover coherent object instances. Specifically, we leverage maximum mean discrepancy on kernel density estimates to enable robust merging of object candidates during online exploration while minimizing added computational complexity. The key is to maintain a fixed particle set per object. Furthermore, to rectify the relation loss caused by misclassified objects, NoPA propagates relationships between objects with high affinity. Experiments show that NoPA substantially outperforms current methods without sacrificing real-time inference speed.

cs.CV↗

arXiv2026

Epitaxial Strain Activates Altermagnetic Spin-Splitting Torques in RuO2(100)

The altermagnetic nature of rutile RuO2 remains under active debate: bulk measurements indicate a nearly nonmagnetic ground state, whereas thin-film studies have reported symmetry-dependent transport signatures consistent with altermagnetism. Here, we provide experimental evidence that altermagnetic spin splitting in RuO2 is a strain-stabilized emergent state rather than an intrinsic bulk property. Angular-resolved spin-torque measurements reveal a symmetry-selected spin Hall response characteristic of altermagnetic spin splitting, which is strongest in the strained regime but progressively suppressed as the lattice relaxes toward the bulk limit. Complementary magnetic measurements further reveal enhanced coercivity and exchange-bias behavior exclusively in strained films, indicating the emergence of a strain-stabilized magnetic state. First-principles calculations reproduce the strain-dependent evolution of the Neel order and spin-split electronic structure, supporting the experimental observations. Together, these results establish altermagnetic spin splitting in RuO2 as a strain-stabilized emergent state and provide a unified explanation for the long-standing discrepancy between bulk and thin-film observations.

cond-mat.mtrl-sci↗

arXiv2026

Interfacial transport driven by electrohydrodynamic instability at the plasma-liquid interface

Interfacial dynamics play a central role in transport processes across the plasma-liquid interface. While the strong electric field in the plasma sheath can destabilize the liquid surface and induce species transfer from the liquid into the plasma, the mechanistic relationship between surface instability and interfacial transport remains poorly understood. Here, we investigate the transport of sodium species in an atmospheric-pressure helium plasma in contact with a negatively DC-biased NaCl electrolyte using high-speed imaging, laser Mie scattering, and optical emission spectroscopy. The results show that Na transport is mediated by droplet emission from the liquid surface and proceeds through three sequential stages: surface deformation, Taylor cone formation, and electrospray. The threshold voltage required for Na I optical emission decreases with decreasing surface tension, in good agreement with the marginal condition for electrohydrodynamic (EHD) instability predicted by linear perturbation analysis. These findings demonstrate that EHD-driven droplet emission is the primary mechanism carrying sodium ions from the liquid into the plasma, where they are neutralized and excited. More broadly, this work establishes surface instability as the active transport channel governing the injection of dissolved species from the liquid into the plasma, creating a unique reaction network of plasma chemistry.

physics.plasm-ph↗

arXiv2026

Strain-Driven Altermagnetic Spin Splitting Effect in RuO$_2$

The non-relativistic spin-momentum locking in altermagnets gives rise to a time-reversal-odd spin Hall effect, known as the altermagnetic spin-splitting effect (ASSE). Although ASSE was first reported in RuO$_2$, subsequent experiments have yielded inconsistent results, leaving its spin-transport mechanism unclear. Here, we systematically investigate how strain, crystal orientation, and the Hubbard $U$ parameter influence the magnetic ground state and spin Hall response of RuO$_2$. Guided by recent experimental observations, we find that $U$ is likely smaller than the value required to induce intrinsic magnetism, suggesting that bulk RuO$_2$ and (001)/(101) RuO$_2$ thin films grown on TiO$_2$ are nonmagnetic in the absence of extrinsic effects. In contrast, (100) and (110) films exhibit strain-induced altermagnetic spin splitting, leading to a strong ASSE even without Hubbard $U$ corrections. These results reconcile previous experimental discrepancies and provide design guidelines for RuO$_2$-based spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

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.

cs.RO↗

arXiv2026

EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence

Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure monitoring. However, traditional pipelines rely on downlinking all captured images before analysis, introducing delays of hours to days due to restricted communication bandwidth. To address these bottlenecks, emerging systems perform onboard machine learning to prioritize which images to transmit. However, these solutions typically treat each satellite as an isolated compute node, limiting scalability and efficiency. Redundant inference across satellites and tasks further strains onboard power and compute costs, constraining mission scope and responsiveness. We present EarthSight, a distributed runtime framework that redefines satellite image intelligence as a distributed decision problem between orbit and ground. EarthSight introduces three core innovations: (1) multi-task inference on satellites using shared backbones to amortize computation across multiple vision tasks; (2) a ground-station query scheduler that aggregates user requests, predicts priorities, and assigns compute budgets to incoming imagery; and (3) dynamic filter ordering, which integrates model selectivity, accuracy, and execution cost to reject low-value images early and conserve resources. EarthSight leverages global context from ground stations and resource-aware adaptive decisions in orbit to enable constellations to perform scalable, low-latency image analysis within strict downlink bandwidth and onboard power budgets. Evaluations using a prior established satellite simulator show that EarthSight reduces average compute time per image by 1.9x and lowers 90th percentile end-to-end latency from first contact to delivery from 51 to 21 minutes compared to the state-of-the-art baseline.

cs.LG↗

arXiv2026

Strain Engineering of Altermagnetic Symmetry in Epitaxial RuO$_2$ Films

The magnetic ground state of RuO$_2$ has been under intense debate. Using first-principles calculations, we show that compressive strain along [001] direction stabilizes an altermagnetic phase in RuO$_2$ thin films grown on (100) and (110) TiO$_2$ substrates. We further identify that compressive strain enhances the density of states near the Fermi level, resulting in a Fermi surface instability and the emergence of altermagnetism. The magnitude of strain and the associated increase in the density of states can be tuned by varying the film thickness, as systematically confirmed by x-ray diffraction and photoemission spectroscopy measurements. Symmetry analysis further reveals that (100) RuO$_2$ hosts an ideal altermagnetic order, whereas broken symmetry in (110) films leads to an uncompensated ferrimagnetic state. Finally, we discuss the effects of Hubbard $U$ parameters and evaluate the realistic tunneling magnetoresistance of (100) RuO$_2$.

cond-mat.mtrl-sci↗

arXiv2026

Twist-Controlled Modulation of Quantum Emitters in a Van der Waals Bilayer

Stacking and twisting two dimensional materials has garnered enormous attention across the condensed matter and the nanophotonic communities. The surge of interest stems from the emergence of novel photophysical phenomena that arise due to the interlayer coupling of the individual layers. Here, we demonstrate that the twist degree of freedom can modulate a single quantum emitter at room temperature. We employ a van der Waals homobilayer of hexagonal boron nitride (hBN) and model the emission properties of quantum emitters as a function of the twist angle. Density functional theory results show that the embedded emitters are strongly influenced by the twist angle and the stacking of the top hBN layer. We consequently engineer these systems experimentally, and demonstrate in-situ tuning of embedded quantum emitters by mechanically twisting the top hBN layer, achieving tunability of over 30 nm (~ 100 meV). Our work demonstrates that mechanical twisting can be harnessed to modulate the embedded quantum emitters in a vdW material, marking a crucial step towards a programmable on-chip quantum circuitry.

cond-mat.mtrl-sci↗

arXiv2026

EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding

Understanding a 3D scene immediately with its exploration is essential for embodied tasks, where an agent must construct and comprehend the 3D scene in an online and nearly real-time manner. In this study, we propose EmbodiedSplat, an online feed-forward 3DGS for open-vocabulary scene understanding that enables simultaneous online 3D reconstruction and 3D semantic understanding from the streaming images. Unlike existing open-vocabulary 3DGS methods which are typically restricted to either offline or per-scene optimization setting, our objectives are two-fold: 1) Reconstructs the semantic-embedded 3DGS of the entire scene from over 300 streaming images in an online manner. 2) Highly generalizable to novel scenes with feed-forward design and supports nearly real-time 3D semantic reconstruction when combined with real-time 2D models. To achieve these objectives, we propose an Online Sparse Coefficients Field with a CLIP Global Codebook where it binds the 2D CLIP embeddings to each 3D Gaussian while minimizing memory consumption and preserving the full semantic generalizability of CLIP. Furthermore, we generate 3D geometric-aware CLIP features by aggregating the partial point cloud of 3DGS through 3D U-Net to compensate the 3D geometric prior to 2D-oriented language embeddings. Extensive experiments on diverse indoor datasets, including ScanNet, ScanNet++, and Replica, demonstrate both the effectiveness and efficiency of our method. Check out our project page in https://0nandon.github.io/EmbodiedSplat/.

cs.CV↗