Showing 1–50 of 227 results for author: Lim, D

Local Minkowski units in non-abelian extensions with cyclic Sylow $p$-subgroups

Authors: Wan Lee, Donghyeok Lim

Abstract: We establish a criterion for the existence of a local Minkowski unit at $p$ that applies to all Galois extensions with Galois group isomorphic to the direct product of a non-$p$-group and a cyclic $p$-group. As applications, we construct non-abelian extensions admitting a local Minkowski unit at $p$ under various ramification conditions and analyze the Iwasawa module structure of units in… ▽ More We establish a criterion for the existence of a local Minkowski unit at $p$ that applies to all Galois extensions with Galois group isomorphic to the direct product of a non-$p$-group and a cyclic $p$-group. As applications, we construct non-abelian extensions admitting a local Minkowski unit at $p$ under various ramification conditions and analyze the Iwasawa module structure of units in $\mathbb{Z}_p$-extensions of number fields. We also extend our study to the case where the group of $p$-power roots of unity is nontrivial. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 29 pages

MSC Class: 11R33; 11R23; 11R27

Extensions of several famous combinatorial identities via hypergeometric functions

Authors: Arjun Kumar Rathie, Feng Qi, Dongkyu Lim

Abstract: The objective of this paper is to develop an extension of Kummer's second theorem and to establish generalized forms of four classical combinatorial identities---Knuth's old sum (also known as Reed--Dawson's combinatorial identity), Riordan's combinatorial identity, Gould's combinatorial identity, and Touchard's combinatorial identity---using a hypergeometric-series approach. Several new identitie… ▽ More The objective of this paper is to develop an extension of Kummer's second theorem and to establish generalized forms of four classical combinatorial identities---Knuth's old sum (also known as Reed--Dawson's combinatorial identity), Riordan's combinatorial identity, Gould's combinatorial identity, and Touchard's combinatorial identity---using a hypergeometric-series approach. Several new identities also arise as special cases of our main results. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Comments: 16 pages

MSC Class: Primary 05A10; Secondary 05A15; 11B68; 30C05; 33C05; 33C15; 33C20; 40A25

Construction Of Non-Odd Galois Representations With Large Image

Authors: Donghyeok Lim, Christian Maire

Abstract: In this work, we construct Galois representations with large image that are not GL r -odd (or, equivalently, not regular at infinity). More precisely, for every prime p {\v e} 3, every integer r {\v e} 2, and every integer a P rp1 'p'1q r q{2, r '2s satisfying a '' r pmod 2q, we construct a continuous Galois representation $ρ$\,: G Q __ GL r pQ p q whose image is commensurable with GL r pZ p q and… ▽ More In this work, we construct Galois representations with large image that are not GL r -odd (or, equivalently, not regular at infinity). More precisely, for every prime p {\v e} 3, every integer r {\v e} 2, and every integer a P rp1 'p'1q r q{2, r '2s satisfying a '' r pmod 2q, we construct a continuous Galois representation $ρ$\,: G Q __ GL r pQ p q whose image is commensurable with GL r pZ p q and satisfies |trp$ρ$pcqq| '' a, where c denotes a complex conjugation. We also obtain analogous results for the orthogonal group O r pQ p q. △ Less

Submitted 5 August, 2026; originally announced August 2026.

On the upper bound for the vorticity growth of bi-rotational Euler flows without swirl

Authors: Khakim Egamberganov, Deokwoo Lim

Abstract: For $d\geq 4$, we consider incompressible Euler flows in $\mathbb{R}^{d}$ with bi-rotational symmetry and without swirl. Our first result gives the local wellposedness of the Yudovich-type solution. The second result provides global wellposedness up to $d\leq 6$. In particular, it shows that the rate of growth of the vorticity maximum coincides with the rate from axisymmetric flows without swirl,… ▽ More For $d\geq 4$, we consider incompressible Euler flows in $\mathbb{R}^{d}$ with bi-rotational symmetry and without swirl. Our first result gives the local wellposedness of the Yudovich-type solution. The second result provides global wellposedness up to $d\leq 6$. In particular, it shows that the rate of growth of the vorticity maximum coincides with the rate from axisymmetric flows without swirl, which was obtained in the paper by the second author and Jeong (Arch. Ration. Mech. Anal. 249(3):32, 2025) and Shao--Wei--Zhang (Acta Math. Sin. (Engl. Ser.), 42(3):663-679, 2026). △ Less

Submitted 29 July, 2026; originally announced July 2026.

Comments: 22 pages, 1 figure

MSC Class: 76B47; 35Q35

High-Frequency Magnetohydrodynamic Waves with Substantial Energy in the Solar Polar Corona

Authors: Yuhang Gao, Hui Tian, Richard Morton, Tom Van Doorsselaere, Daye Lim, Mingzhe Guo, Jiansen He, Zhenyong Hou

Abstract: The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between… ▽ More The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between theories and observations. Using high-cadence, high-resolution extreme-ultraviolet imaging from Solar Orbiter's Extreme Ultraviolet Imager, we identify a previously hidden population of high-frequency MHD waves in coronal plumes of the solar polar region. An analysis of the detected propagating kink waves shows that over one-third have periods shorter than 100 s, a population largely undetected by earlier instruments. Power spectral analysis demonstrates that these high-frequency waves carry substantial energy flux, which are significantly underestimated in lower-cadence data. These results suggest that high-frequency MHD waves may contribute importantly to the energy budget of the solar polar corona and could play a role in solar wind acceleration, highlighting the value of high-resolution observations for probing energy transport in magnetized space and astrophysical plasmas. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 14 pages, 4 figures, 1 table

Journal ref: National Science Review, Volume 13, Issue 14, July 2026, nwag370

Resonance-Induced Sign Reversal of Optical Gradient Forces and Three-Dimensional Singularity Trapping

Authors: Jinsheng Lu, Soon Wei Daniel Lim, Federico Capasso

Abstract: In atomic physics, tuning the light frequency across a resonance reverses the trapping force between bright and dark field regions, yet a unified analytical description of this principle applicable to photonic resonators in general has not been established. Here we show that sweeping the incident wavelength through a resonance in the optical response of the particle or device induces a $π$ phase s… ▽ More In atomic physics, tuning the light frequency across a resonance reverses the trapping force between bright and dark field regions, yet a unified analytical description of this principle applicable to photonic resonators in general has not been established. Here we show that sweeping the incident wavelength through a resonance in the optical response of the particle or device induces a $π$ phase shift, reversing the gradient force from attractive to repulsive. A generalized Fano line-shape model captures this quantitatively across physically distinct resonant systems, from plasmonic nanoparticles to high-Q metasurfaces, with full-wave simulations confirming the predictions in every case. Building on this framework, three-dimensional singularity trapping of silicon nanoparticles is demonstrated using counter-propagating vector beams and metasurfaces, with trapping potential depths competitive with conventional bright-field traps. These results establish a platform-independent design principle for controlling optical forces in resonant systems, with broad implications for optical manipulation, quantum optomechanics, and precision metrology. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 6 pages, 4 figures

A universal scaling between damping time and period of quasi-periodic pulsations from solar EUV brightenings to X-ray stellar flares

Authors: Daye Lim, Tom Van Doorsselaere, Valery M. Nakariakov, S. Krishna Prasad, David Berghmans, Laura A. Hayes, Kyung-Suk Cho, Sujin Kim

Abstract: Recent high spatial and temporal resolution extreme-ultraviolet (EUV) imaging observations have revealed that quasi-periodic pulsations (QPPs), a ubiquitous signature of impulsive energy release in solar and stellar flares, are also present in much smaller-scale coronal events known as EUV brightenings. Whether QPPs observed across such disparate spatial and energetic scales share a common physica… ▽ More Recent high spatial and temporal resolution extreme-ultraviolet (EUV) imaging observations have revealed that quasi-periodic pulsations (QPPs), a ubiquitous signature of impulsive energy release in solar and stellar flares, are also present in much smaller-scale coronal events known as EUV brightenings. Whether QPPs observed across such disparate spatial and energetic scales share a common physical origin remains an open question. Here we analyse 2,146 EUV brightenings observed with Solar Orbiter/EUI and 300 EUV solar flares observed with SDO/AIA, identifying 185 brightenings and 89 flares exhibiting statistically significant damped QPPs. We show that the relationship between damping time and oscillation period follows a common power-law scaling for EUV brightenings and EUV solar flares, consistent with previously reported X-ray QPPs spanning both solar and stellar flares. The persistence of this scaling over a wide range of energies and scales suggests that QPPs are governed by a common underlying physical mechanism. △ Less

Submitted 19 July, 2026; originally announced July 2026.

Comments: 9 pages, 4 figures, accepted for publication in ApJL

Machine Learning-Driven Design of Mixed-Pitch Grating Couplers for Co-Packaged Optics Applications

Authors: Yu Dian Lim, Yun Da Chua, Wai Cheung Ma, Yeow Kheng Lim, Chuan Seng Tan

Abstract: A mixed-pitch grating coupler which can couple a wide range of wavelengths is preferred in its application in co-packaged optics (CPO). However, the design and optimization of such grating coupler is complex. In this work, we developed software with integrated deep neural network (DNN) model to automatically design the mixed-pitch grating coupler from user-specified peak wavelengths and full-width… ▽ More A mixed-pitch grating coupler which can couple a wide range of wavelengths is preferred in its application in co-packaged optics (CPO). However, the design and optimization of such grating coupler is complex. In this work, we developed software with integrated deep neural network (DNN) model to automatically design the mixed-pitch grating coupler from user-specified peak wavelengths and full-width half-maximum (FWHM) values. We first trained the DNN model with 10,000 rows of grating parameters-power spectrum datasets, where the power spectrum was simulated using finite-difference time domain (FDTD) technique. Upon training, we tested the model using ~1,000 different combinations of peak wavelengths and FWHM values. Among the combinations, 822 attempts have <15% error, while 351 attempts have <5% error when comparing the user-specified and FDTD-verified spectrum. Meanwhile, comparing the user-specified and FDTD-verified peak wavelengths, 844 attempts have peak wavelengths with absolute error (AE) < 2 nm. For FWHMs, 738 attempts have FWHM values with AE < 10 nm. We have also developed a graphical-user interface (GUI) to ease the usage of this software. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Transcoders for Investigating Deception in Language Models

Authors: Darius Lim, Nathan Leow, Xin Wei Chia

Abstract: Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour. In this paper, we investigate the use of transcoders to analyse deceptive behaviour in language models, a behaviour that poses a safety and security risk. Using a Qwen3-4B model with pre-trained transcoders, specifically per-layer transcoders (PLTs),… ▽ More Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour. In this paper, we investigate the use of transcoders to analyse deceptive behaviour in language models, a behaviour that poses a safety and security risk. Using a Qwen3-4B model with pre-trained transcoders, specifically per-layer transcoders (PLTs), we construct attribution graphs that capture feature activations and inter-feature dependencies, allowing circuit-level analysis of deception. Through feature steering and circuit analysis, we identified a dictionary of deception-related features and show that these features exert a stronger influence on deceptive outputs, as they produce predictable shifts between deceptive and non-deceptive responses. These findings suggest that deception emerges from internal model mechanisms and highlight the potential of transcoders for behavioural monitoring and early detection of security vulnerabilities related to malicious behaviours in language models. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Domain-incremental audio classification using domain-specific experts and prototype classifier

Authors: Jongyeon Park, Do-Hyeon Lim, Sang-won Park, Hong Kook Kim, Kyungdeuk Ko, Hyeongcheol Geum, Jeong Eun Lim

Abstract: This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data at once. We approached domain-incremental learning (DIL) as a frozen-feature replay problem. At each incremental stage, one or two compact experts are trained and then kept fixed;… ▽ More This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data at once. We approached domain-incremental learning (DIL) as a frozen-feature replay problem. At each incremental stage, one or two compact experts are trained and then kept fixed; at the final stage, the penultimate features from all frozen experts are concatenated and used to train a lightweight per-class prototype classifier solely on cached features. This design prevents catastrophic forgetting by preserving each expert models at inference. To retain earlier-domain knowledge without storing raw audio, some experts were trained with DeepInversion-based generative replay. A cross-stage regression imputer was trained to fill the expert feature slots that did not yet exist at an ealier stage. We submit four fully DIL-compliant systems: three systems based on diverse frozen five-expert backbones and their cross-stack ensemble achieving 78.15% micro / 77.03% macro on the development set, outperforming every individual backbone on both evaluations. △ Less

Submitted 22 June, 2026; originally announced June 2026.

Comments: DCASE 2026 challenge Task7, 4 pages

The Roasting Marshmallows Program with IGRINS on Gemini South V: Atmosphere of MASCARA-1b is Enriched in Refractory Elements

Authors: Krishna Kanumalla, Michael R. Line, Martina Chiarella, Matteo Brogi, Peter C. B. Smith, Jorge A. Sanchez, Yayaati Chachan, Joshua Lothringer, Joost P. Wardenier, Hayley Beltz, Carlos Saffe, Emily K. Deibert, Megan Weiner Mansfield, Stefan Pelletier, Vivien Parmentier, Yeon-ho Choi, Swaetha Ramkumar, Arjun B. Savel, Luis Welbanks, Jacob L. Bean, Vatsal Panwar, Tomás Azevedo Silva, Lorenzo Pino, Yuya Hayashi, Dongwook Lim , et al. (53 additional authors not shown)

Abstract: Ultra-hot Jupiters (UHJs; $T_{\rm eq} \gtrsim 2000$ K) enable simultaneous detection of volatile (ice-forming) and refractory (rock-forming) species in planetary atmospheres, providing a powerful diagnostic of planet formation and atmospheric processing. We present a comprehensive high-resolution cross-correlation spectroscopy (HRCCS) analysis of the UHJ MASCARA-1b ($T_{\rm eq} \approx 2600$ K) us… ▽ More Ultra-hot Jupiters (UHJs; $T_{\rm eq} \gtrsim 2000$ K) enable simultaneous detection of volatile (ice-forming) and refractory (rock-forming) species in planetary atmospheres, providing a powerful diagnostic of planet formation and atmospheric processing. We present a comprehensive high-resolution cross-correlation spectroscopy (HRCCS) analysis of the UHJ MASCARA-1b ($T_{\rm eq} \approx 2600$ K) using the IGRINS and IGRINS-2 spectrographs. We detect robust (SNR$>$4) signals from H$_2$O, CO, OH, Fe I, Mg I, Ca I, and Ti I, marking the most complete atmospheric inventory of MASCARA-1b to date. Using a chemically consistent atmospheric inference framework, we constrain elemental abundances to a typical precision of $\approx$0.2 dex, retrieving a solar atmospheric metallicity ([M/H]$_\odot$ $= 0.07^{+0.17}_{-0.13}$ $\approx 1.2\times$ solar), a C/O ratio (C/O $= 0.65^{+0.08}_{-0.08}$) consistent with solar value (C/O $=$ 0.59), an enhanced refractory abundance ([R/H]$_\odot$ $= 0.40^{+0.23}_{-0.17} \approx 2.5\times$ solar; $\approx 3.8\times$ stellar), and a moderately super-solar refractory-to-volatile ratio ([R/V]$_\odot$ $= 0.36^{+0.11}_{-0.09}$ $\approx 2.3\times$ solar). Comparison with formation models suggests that MASCARA-1b most likely accreted material between the soot-H$_2$O or H$_2$O-CO snowlines (at 68$\%$ confidence). We additionally find stellar values for atmospheric Ti/Mg and Ca/Mg ratios (at 68$\%$ confidence). The Mg/Fe is also found to be consistent with stellar value at 95$\%$ confidence. Therefore, we do not find strong indication of nightside cold trapping in MASCARA-1b. As homogeneous refractory-to-volatile measurements expand across the UHJ population, particularly with upcoming Extremely Large Telescopes, these diagnostics will enable statistically robust tests of emerging trends in giant planet formation and atmospheric evolution. △ Less

Submitted 5 June, 2026; originally announced June 2026.

Comments: 20 pages, 12 figures, accepted for publication in AJ

Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows

Authors: M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen, Cedric Lim, Laura Wynter

Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orchestration paradigm. Concretely, we compare (i) a DeclarativeAgent that reads three domain-specific skill files at inferen… ▽ More We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orchestration paradigm. Concretely, we compare (i) a DeclarativeAgent that reads three domain-specific skill files at inference time and decides its own control flow, (ii) an ImperativeAgent based on a programmatic state machine with explicit phases, and (iii) an unscaffolded baseline agent modeled after the $τ$-Knowledge benchmark agent. Our ImperativeAgent is motivated by externalised-control inference as in Recursive Language Models and graph-based orchestration frameworks. We formalise the three agents as policy classes within a decentralised partially-observable Markov decision process and analyse their information-theoretic and structural properties; we then test the predicted differences empirically on five language models and two retrieval regimes. Our results show that retrieval quality is a dominant bottleneck for AI agents: when evidence is incomplete or skewed, all agents degrade substantially, and skill files cannot recover lost performance. Under high-quality retrieval, however, declarative skills consistently improve accuracy on procedural tasks and reduce orchestration errors, while the imperative state machine's brittleness does not reliably improve task success or compliance. △ Less

Submitted 5 June, 2026; originally announced June 2026.

Beyond Point Estimates: Reliable Evaluation of Prediction Performance Metrics under Clustered Data

Authors: Taekwon Hong, Daeyoung Lim, Woojung Bae

Abstract: Prediction performance metrics such as accuracy and the F1 score are typically reported as single numbers, with no measure of uncertainty. The omission has been tolerable in exploratory settings, where model evaluation is used for informal comparison rather than formal decision-making. But as machine learning is deployed in real-world applications, evaluation results are increasingly used to suppo… ▽ More Prediction performance metrics such as accuracy and the F1 score are typically reported as single numbers, with no measure of uncertainty. The omission has been tolerable in exploratory settings, where model evaluation is used for informal comparison rather than formal decision-making. But as machine learning is deployed in real-world applications, evaluation results are increasingly used to support binary decisions -- whether a model meets a required standard or not -- making uncertainty quantification essential. The problem is compounded when data are dependent, as in repeated measurements, clustered subjects, or time series, where variability is harder to assess and easy to underestimate. We develop a unified framework that links a broad class of performance metrics through their representation as smooth functionals of confusion-matrix probabilities. This representation allows the use of the cluster-robust sandwich variance estimator to obtain asymptotically valid confidence intervals, hypothesis tests, and paired model comparisons for both binary and multiclass problems under clustered data. We also provide power and sample size approximations based on pilot data, enabling principled study design for model evaluation. Simulations show that the proposed methods achieve near-nominal coverage across a range of dependence structures, while naive methods underestimate variability. A real-data application further illustrates how accounting for clustering can materially change conclusions. These results offer a practical foundation for uncertainty quantification and study design in prediction performance evaluation, in settings where decisions should be justified under dependent and clustered data. △ Less

Submitted 2 June, 2026; v1 submitted 2 June, 2026; originally announced June 2026.

MSC Class: 62D20

Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift

Authors: Doyeong Lim, Seungyoon Lee, In Cheol Bang

Abstract: Artificial-intelligence surrogates can support second-by-second thermal-hydraulic forecasting, but models selected and frozen offline may become condition-locked once deployed outside their pretraining envelope. This study develops a guarded continual-adaptation framework for experimental thermal-hydraulic loop data in which role-separated agents - Monitor, Diagnosis, Adaptation, Safety-Auditor, a… ▽ More Artificial-intelligence surrogates can support second-by-second thermal-hydraulic forecasting, but models selected and frozen offline may become condition-locked once deployed outside their pretraining envelope. This study develops a guarded continual-adaptation framework for experimental thermal-hydraulic loop data in which role-separated agents - Monitor, Diagnosis, Adaptation, Safety-Auditor, and Orchestrator - diagnose error signatures, prioritize candidate model families, and review promotions, while deterministic champion-challenger gates and background shadow learning retain final authority over model replacement. Seven surrogate families were screened by blocked three-fold cross-validation, and a temporal Fourier neural operator was selected as the initial champion for 60-s-history-to-10-s-trajectory forecasting on two held-out transients, with three seeds per adaptive mode. Static deployment gave a channel-averaged MAE of 7.06 and a 56.8% warning-exceedance ratio; rule-based adaptation reduced MAE to 6.54, whereas shadow refresh alone remained close to Static. The MA-Full mode, in which the role-separated multi-agent council reviews every evaluated stream step, achieved the lowest mean error, 5.72, and 35.8% exceedance, corresponding to a 19.0% improvement over Static. Paired bootstrap intervals against Static excluded zero, although intervals among adaptive modes overlapped and the six paired units limit broad statistical claims. Validated promotions from the neural operator to Transformer and graph neural network indicate that logged, gate-controlled adaptation can support auditable surrogate evolution while deterministic gates retain deployment authority. △ Less

Submitted 2 June, 2026; originally announced June 2026.

Tame Galois Groups, Linking Numbers and Mildness

Authors: Julian Feuerpfeil, Oussama Hamza, Donghyeok Lim

Abstract: Let $p$ be an odd prime and let $S$ be a set of tame primes. We denote by $G_S$ the Galois group of the maximal pro-$p$ extension of $\mathbb{Q}$ unramified outside $S$. We prove that for every finite set of tame primes $S_0$ with $|S_0|\geq 2$, there exists a set $S_1$ consisting of two tame primes such that $G_{S_0\cup S_1}$ has cohomological dimension $2$. This refines a result of Labute. Mor… ▽ More Let $p$ be an odd prime and let $S$ be a set of tame primes. We denote by $G_S$ the Galois group of the maximal pro-$p$ extension of $\mathbb{Q}$ unramified outside $S$. We prove that for every finite set of tame primes $S_0$ with $|S_0|\geq 2$, there exists a set $S_1$ consisting of two tame primes such that $G_{S_0\cup S_1}$ has cohomological dimension $2$. This refines a result of Labute. More generally, we establish an analogous result for number fields not containing a primitive $p$-th root of unity, under a suitable splitting condition. Our approach answers a question of Labute, from his seminal paper on mild groups, and combines weighted Zassenhaus filtrations, graph-theoretic methods, and Koch-type presentations. As an application, we solve several cohomological Galois inverse problems with prescribed ramification and splitting. We also provide numerical examples and statistics. △ Less

Submitted 31 May, 2026; originally announced June 2026.

Comments: 22 pages

MSC Class: 11R32; 20E18; 20F14; 20F05; 20F40; 05C25

How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks

Authors: Dong-Won Lim

Abstract: Inverse Kinematics (IK) plays a critical role in robotic motion planning and control. The IK solutions of a robot manipulator could be done by conventional ways such as geometric, algebraic, or Jacobian methods, which have drawbacks. The Artificial Neural Networks (ANNs) have become a promising alternative for approximating IK solutions due to their generalization ability and computational efficie… ▽ More Inverse Kinematics (IK) plays a critical role in robotic motion planning and control. The IK solutions of a robot manipulator could be done by conventional ways such as geometric, algebraic, or Jacobian methods, which have drawbacks. The Artificial Neural Networks (ANNs) have become a promising alternative for approximating IK solutions due to their generalization ability and computational efficiency. This approach basically trains only a few samples of the end effector that are recorded for the solution of the IK problem. However, a fundamental question remains: how many training samples are sufficient to achieve reliable and accurate IK predictions? This study investigates the mathematical framework of relating the size of training datasets and the accuracy of ANN-based IK solvers. Using an articulated robotic manipulator, we generate varying amounts of joint-position pairs to train feedforward neural networks and assess their accuracy, convergence, and generalization capability. The results reveal more training samples than 125 did not contribute to the improvement of the model efficiency that the comparable measure dealing with the approximation accuracy over the sampling size, offering valuable insight into data efficiency. This work provides practical guidance for optimizing the data sizing of ANN solutions, balancing computational cost and model accuracy for real-world robotic applications. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Comments: 14 pages, 5 figures

Still non-accelerating: age-bias correction in supernova cosmology is robust to host-progenitor age mapping

Authors: Chul Chung, Junhyuk Son, Seunghyun Park, Suk-Jin Yoon, Hyejeon Cho, Dongwook Lim, Young-Wook Lee

Abstract: We re-examine the claim by Wiseman et al. (2026) that progenitor-age bias has a negligible impact on cosmological inferences from Type Ia supernovae (SNe Ia). We show that their inferred host-age-Hubble residual (HR) slope is severely underestimated because their combined SN Ia sample spans an unusually wide redshift range ($0.04 < z < 0.42$), over which the mean host age evolves by $\sim$\,3 Gyr.… ▽ More We re-examine the claim by Wiseman et al. (2026) that progenitor-age bias has a negligible impact on cosmological inferences from Type Ia supernovae (SNe Ia). We show that their inferred host-age-Hubble residual (HR) slope is severely underestimated because their combined SN Ia sample spans an unusually wide redshift range ($0.04 < z < 0.42$), over which the mean host age evolves by $\sim$\,3 Gyr. As a result, SNe Ia spanning substantial host-age differences are effectively assigned similar HR values prior to regression, artificially flattening the inferred age-HR relation. In addition, their application of the Pantheon+ host-mass correction further suppresses the slope, but the underlying dust model is highly incompatible with the measured dust attenuation curves of galaxies. We also demonstrate that our age bias correction is robust to uncertainties in host-progenitor age mapping arising from different choices of the SN Ia delay-time distribution. The reduced progenitor-age evolution argued by Wiseman et al. (2026) must, by the same logic, be accompanied by a steeper inferred progenitor-age-HR slope. When these two effects are consistently combined in computing the redshift-dependent magnitude correction, the final correction, and hence the resulting cosmological impact, remain largely unchanged from Son et al. (2025). △ Less

Submitted 20 May, 2026; originally announced May 2026.

Comments: 5 pages, 2 figures, submitted to MNRAS

EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing

Authors: Inwoo Hwang, Donggeun Lim, Hojun Jang, Young Min Kim

Abstract: With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applications. However, egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an online framework for reconstructing long-term full-body motion from noisy egocentric input. While exist… ▽ More With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applications. However, egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an online framework for reconstructing long-term full-body motion from noisy egocentric input. While existing generative frameworks can robustly handle noisy and sparse measurements, they assume a fixed-length observation window is available and are thus not suitable for real-time applications. Faster inference often relies on autoregressive prediction, sacrificing robustness. In contrast, we adopt a diffusion-based method with a temporally asymmetric noise schedule inspired by Diffusion Forcing. Specifically, our approach models temporally evolving uncertainty and incrementally denoises states as new streaming observations arrive. Combined with a noise-robust imputation strategy, EgoForce progressively generates stable and coherent full-body motion under strict causal constraints. Experiments demonstrate that our online framework outperforms existing online and offline methods, enabling long-horizon, full-body motion reconstruction in challenging egocentric scenarios. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Comments: Project page: https://inwoohwang.me/EgoForce

Giant critical response in a driven-dissipative quantum gas

Authors: Ross C. Schofield, Daniel Lim, Himadri S. Dhar, Robert A. Nyman, Akshay K. Verma, Edmund Clarke, Jon Heffernan, Florian Mintert, Rupert F. Oulton

Abstract: Systems close to a phase transition turn weak perturbations into large responses. At equilibrium, this amplification is closely linked to criticality: fluctuations grow, dynamics slow, and a common soft mode controls the response. Whether this correspondence survives in driven-dissipative quantum systems, sustained by continuous pumping and loss away from thermal equilibrium, remains an open quest… ▽ More Systems close to a phase transition turn weak perturbations into large responses. At equilibrium, this amplification is closely linked to criticality: fluctuations grow, dynamics slow, and a common soft mode controls the response. Whether this correspondence survives in driven-dissipative quantum systems, sustained by continuous pumping and loss away from thermal equilibrium, remains an open question. Here we show experimentally that it does. In a room-temperature semiconductor photon Bose-Einstein condensate, the critical slowing of spontaneous intensity fluctuations and the amplification of weak pump perturbations are measured independently. Both peak at the same condensate population, $\bar{n}_c = 1250$, where the dimensionless slowing factor and susceptibility reach the same value, $\bar{n}_c/2 = 625$. A single weakly damped collective photon-reservoir mode governs both effects. This fluctuation-response correspondence in a finite open quantum gas establishes critical susceptibility as a measurable dynamical signature of condensation, with peak gain set by system size. △ Less

Submitted 11 May, 2026; originally announced May 2026.

Comments: 6 pages, 5 figures

Chemical signatures of planetary systems in their host stars. Near-infrared spectroscopy of four planet-hosting wide binaries

Authors: Dongwook Lim, Sol Yun, Andreas J. Koch-Hansen, Sang-Hyun Chun, Young Sun Lee, Young-Wook Lee

Abstract: An important open question in exoplanet studies is whether planets leave detectable chemical fingerprints on their host stars. While several studies have suggested possible planetary chemical signatures in planet-hosting stars, their origin remains debated because of stellar birth conditions and evolutionary effects. Wide binaries, whose components share a common formation environment, provide an… ▽ More An important open question in exoplanet studies is whether planets leave detectable chemical fingerprints on their host stars. While several studies have suggested possible planetary chemical signatures in planet-hosting stars, their origin remains debated because of stellar birth conditions and evolutionary effects. Wide binaries, whose components share a common formation environment, provide an ideal testbed for identifying planetary signatures. Such signatures are often characterized by differential abundance trends with condensation temperature (Tc), which traces the partitioning between gaseous and rocky planetary material. We investigate whether these trends are associated with planetary architectures in wide binaries. We obtained high-resolution NIR spectra of four planet-hosting wide binaries. We measured abundances for both components and analyzed differential abundances in each system. We also compiled literature measurements for planet-hosting and non-hosting wide binaries and compared their Tc trends. WASP-160 A/B and WASP-127/TYC 4916-897-1 exhibit significant abundance trends with Tc, while HD 20782/HD 20781 shows a weaker correlation and K2-54/K2-54 B is consistent with a flat relation. The trends are diverse, including both volatile- and refractory-enhanced patterns in planet-hosting stars. Literature comparisons indicate that extreme Tc slopes may occur more frequently among planet-hosting wide binaries, particularly at large separations, although the statistics remain limited by sample size and definition. Our results indicate that chemical signatures in planet-hosting wide binaries are not universal but vary across systems. While planetary architectures may be associated with some host-star abundance patterns, multiple processes are likely to contribute. Larger samples are essential for disentangling planetary signatures from stellar and binary effects. △ Less

Submitted 11 May, 2026; originally announced May 2026.

Comments: 19 pages, 9 figures, accepted for publication in A&A

TriBench-Ko: Evaluating LLM Risks in Judicial Workflows

Authors: Haesung Lee, Gyubin Choi, Eun-Ju Lee, So-Min Lee, Youkang Ko, Dogyoon Lim, Sung-Kyoung Jang, Yohan Jo

Abstract: Large language models (LLMs) are increasingly integrated into legal workflows. However, existing benchmarks primarily address proxy tasks, such as bar examination performance or classification, which fail to capture the performance and risks inherent in day-to-day judicial processes. To address this, we publicly release TriBench-Ko, a Korean benchmark designed to evaluate potential deployment risk… ▽ More Large language models (LLMs) are increasingly integrated into legal workflows. However, existing benchmarks primarily address proxy tasks, such as bar examination performance or classification, which fail to capture the performance and risks inherent in day-to-day judicial processes. To address this, we publicly release TriBench-Ko, a Korean benchmark designed to evaluate potential deployment risks of LLMs within the context of verified judicial task requirements. It covers four core tasks: jurisprudence summarization, precedent retrieval, legal issue extraction, and evidence analysis. It jointly assesses model behavior across multiple deployment risk categories, including inaccuracy (hallucination, omission, statutory misapplication), biases (demographic, overcompliance), inconsistencies (prompt sensitivity, non-determinism), and adjudicative overreach. Each item is structured to systematically assess both task performance and a specific risk type based on real judicial decisions. Our evaluation of a range of contemporary LLMs reveals that many models frequently manifest significant risks, most notably struggling with precedent retrieval and failing to capture critical legal information. We provide a comprehensive diagnosis of these LLMs and pinpoint critical areas where LLM-generated outputs in judicial contexts necessitate rigorous inspection and caution. Our dataset and code are available at https://github.com/holi-lab/TriBench-Ko △ Less

Submitted 5 May, 2026; originally announced May 2026.

Comments: 10 pages

HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats

Authors: Rebecca Soskin Hicks, Mikhail Trofimov, Dominick Lim, Rahul K. Arora, Foivos Tsimpourlas, Preston Bowman, Michael Sharman, Chi Tong, Kavin Karthik, Arnav Dugar, Akshay Jagadeesh, Khaled Saab, Johannes Heidecke, Ashley Alexander, Nate Gross, Karan Singhal

Abstract: Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Professional, an open benchmark for evaluating large language models on real tasks that clinicians bring to ChatGPT in the course of their work. The benchmark is organized around three common use cases central to clinical p… ▽ More Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Professional, an open benchmark for evaluating large language models on real tasks that clinicians bring to ChatGPT in the course of their work. The benchmark is organized around three common use cases central to clinical practice: care consult, writing and documentation, and medical research. Each example includes a physician-authored conversation with ChatGPT for Clinicians and is scored via rubrics written and iteratively adjudicated by three or more physicians across three phases. HealthBench Professional examples were carefully selected for quality, representativeness, and difficulty for OpenAI's current frontier models, to enable continued measurement of progress. Difficult examples for recent OpenAI models were enriched by roughly 3.5 times relative to the candidate pool of 15,079 examples. Additionally, about one-third of examples involve physicians conducting deliberate adversarial testing of models. As a strong baseline, we also collected human physician responses for all tasks (unbounded time, specialist-matched, web access). The best scoring system, GPT-5.4 in ChatGPT for Clinicians, outperforms base GPT-5.4, all other models, and human physicians. We hope HealthBench Professional provides the healthcare AI community a measure to track frontier model progress in real-world clinical tasks and build systems that clinicians can trust to improve care. △ Less

Submitted 30 April, 2026; originally announced April 2026.

Comments: Data link in paper; Blog: https://openai.com/index/making-chatgpt-better-for-clinicians/

Estimator-Aligned Prospective Sample Size Determination for Designs Using Inverse Probability of Treatment Weighting

Authors: Taekwon Hong, Daeyoung Lim, Woojung Bae, Yong Ma

Abstract: In observational studies, accurately characterizing variance is critical for sample size determination, yet unaccounted-for variability from propensity score estimation and the resulting weights limit the accuracy of standard variance approximations for design. Existing approaches often rely on heuristics or randomized controlled trial (RCT) formulas that treat weights as fixed, potentially misali… ▽ More In observational studies, accurately characterizing variance is critical for sample size determination, yet unaccounted-for variability from propensity score estimation and the resulting weights limit the accuracy of standard variance approximations for design. Existing approaches often rely on heuristics or randomized controlled trial (RCT) formulas that treat weights as fixed, potentially misaligning prospective design with the causal estimator used at analysis. We propose an estimator-aligned framework for prospective sample size determination based on generalized estimating equations (GEE) and stacked M-estimation. By merging the propensity score model and marginal structural model (MSM) into a single system of estimating equations, the method propagates nuisance-model uncertainty and directly targets the large-sample variance of the IPTW estimator. For study planning, we estimate a pilot-based large-sample variance factor and introduce a bootstrap stabilization procedure that accounts for both within- and between-pilot variability. The framework applies uniformly across binary, count, and continuous outcomes through link-specific GEE representations under a common design principle. Simulation studies motivated by post-marketing safety and healthcare cost applications demonstrate that anchoring design to this variance improves power calibration relative to conventional RCT-style formulas, particularly in settings with weight instability, outcome sparsity, or heavy-tailed variability. △ Less

Submitted 23 April, 2026; originally announced April 2026.

Comments: 22 pages for main manuscript, 4 pages for supplementary appendix

MSC Class: 62D20

Acts of Configuration: Rethinking Provenance, Temporality and Legitimacy in Post-Mortem Agents

Authors: Kellie Yu Hui Sim, Pin Sym Foong, Darryl Lim, John-Henry Lim, Kenny Tsu Wei Choo

Abstract: Work on persona-persistent post-mortem agents typically frames design around a life/death binary. This framing neglects a consequential yet under-theorised condition: when individuals remain alive but have impaired decisional capacity. Drawing on a multi-phase workshop in which participants trained and reflected on an AI agent for Advance Care Planning, we examined how people reason about agentic… ▽ More Work on persona-persistent post-mortem agents typically frames design around a life/death binary. This framing neglects a consequential yet under-theorised condition: when individuals remain alive but have impaired decisional capacity. Drawing on a multi-phase workshop in which participants trained and reflected on an AI agent for Advance Care Planning, we examined how people reason about agentic delegation post-capacity loss. Initially, participants favoured bounded agents grounded in first-party authorship and representational fidelity over autonomous or evolving stand-ins. However, temporality introduced novel ideas like adjacent use driven by persona persistence over functional expansion: agents should evolve while users retain capacity, remain static once capacity is lost, but somehow inform adjacent post-mortem uses. We discuss the implications of these findings and propose that the configuration of agents for post-capacity use reshapes our understanding of provenance, temporality, and legitimacy for post-mortem agents. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: Accepted at DIS 2026 (PWiP). 5 pages, 2 figures

ACM Class: H.5.0

A scalable platform for nanometer-scale quantum confinement

Authors: Christina M. Spaegele, Mehdi Rezaee, Thomas Werkmeister, Soon Wei Daniel Lim, Kailyn Vaillancourt, Joon-Suh Park, Paul Chevalier, Ido Kaminer, Philip Kim, Federico Capasso, Michele Tamagnone

Abstract: Overcoming the limitations of current nanofabrication techniques to achieve nanoscale feature sizes is essential for achieving new regimes of light-matter interactions at extreme frequencies and length scales. Here, we demonstrate a scalable nanofabrication platform capable of producing in-plane feature sizes down to 1.75 nm, pushing the boundaries of current top-down nanofabrication techniques. U… ▽ More Overcoming the limitations of current nanofabrication techniques to achieve nanoscale feature sizes is essential for achieving new regimes of light-matter interactions at extreme frequencies and length scales. Here, we demonstrate a scalable nanofabrication platform capable of producing in-plane feature sizes down to 1.75 nm, pushing the boundaries of current top-down nanofabrication techniques. Using precise thickness control of atomic layer deposition (ALD) and employing widely spaced oxide nanofins, we transform conventional ALD into a surface structuring method that produces nanolaminates with sub-10 nm periodicities over large areas. The resulting nanostructures can be used as a one-dimensional gate array to control charge carriers in two-dimensional materials. As an initial demonstration, we integrate the platform with graphene and perform electron transport measurements. In the presence of the gate array enabled by the nanolaminate, we observe satellite Dirac peaks consistent with band-structure modulation, suggestive of quantum-confinement effects. Our platform paves the way for exploring previously inaccessible regimes of nanoscale light-matter interactions, holding significant promise for applications in short wavelength optics, electronics, and polaritonics. △ Less

Submitted 10 April, 2026; originally announced April 2026.

How Robust is the Cosmic Distance with Tip of Red Giant Branch against Stellar Population Variations?

Authors: Chul Chung, Young-Wook Lee, Suk-Jin Yoon, Yong -Cheol Kim, Sang-Il Han, Hyejeon Cho, Dongwook Lim, Young-Lo Kim, Sohee Jang, Seungsoo Hong, Seunghyun Park, Junhyuk Son, Myung Gyoon Lee

Abstract: The tip of the red giant branch (TRGB) provides a key standard candle for extragalactic distance measurements and for refining the Hubble constant. We test its robustness by quantifying how metallicity, $α$-element enhancement, age, and initial helium abundance modulate the TRGB luminosity, using synthetic composite color--magnitude diagrams in the $I$ and $F814W$ bands. We find that metallicity a… ▽ More The tip of the red giant branch (TRGB) provides a key standard candle for extragalactic distance measurements and for refining the Hubble constant. We test its robustness by quantifying how metallicity, $α$-element enhancement, age, and initial helium abundance modulate the TRGB luminosity, using synthetic composite color--magnitude diagrams in the $I$ and $F814W$ bands. We find that metallicity and $α$-element enhancement are the primary drivers of TRGB variation, while age introduces only a modest effect and helium abundance is negligible. At fixed age and helium content, increasing the mean metallicity by 0.5 dex or the $α$-element enhancement by 0.3 dex produces the well-known systematic dimming of 0.046 and 0.050 mag, respectively, in $M_I^{\rm TRGB}$, and of 0.093 and 0.044 mag, respectively, in $M_{F814W}^{\rm TRGB}$. By comparison, changes in age of 3~Gyr and in initial helium abundance of 0.10 yield minor luminosity shifts, with average changes of 0.031 and 0.009~mag, respectively, in $M_I^{\rm TRGB}$, and of 0.035 and 0.027 mag, respectively, in $M_{F814W}^{\rm TRGB}$, substantially smaller than those caused by variations in metallicity or $α$-element enhancement. For mixed stellar populations under typical stellar-halo metallicity conditions, the net variation in $M_I^{\rm TRGB}$ arising from each combination of the $α$-element enhancement, age, and initial helium abundance remains below 0.028~mag, well within reported systematic uncertainties. Together, these results reaffirm the TRGB as a highly robust distance indicator and support its continued use as an independent anchor for precision cosmology in the era of the Hubble-tension debate. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Comments: Accepted for publication in ApJ, 15 pages, 11 figures, 4 tables

Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability

Authors: Akzhol Almukhametov, Doyeong Lim, Rui Hu, Yang Liu

Abstract: Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable. Meeting this need calls for surrogate models that combine predictive fidelity, millisecond-scale inference, and robustness to partial observability. In this work, we present a physics-informed message-passing Graph Neur… ▽ More Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable. Meeting this need calls for surrogate models that combine predictive fidelity, millisecond-scale inference, and robustness to partial observability. In this work, we present a physics-informed message-passing Graph Neural Network coupled with a Neural Ordinary Differential Equation (GNN-ODE) to addresses all three requirements simultaneously. We represent the whole system as a directed sensor graph whose edges encode hydraulic connectivity through flow/heat transfer-aware message passing, and we advance the latent dynamics in continuous time via a controlled Neural ODE. A topology-guided missing-node initializer reconstructs uninstrumented states at rollout start; prediction then proceeds fully autoregressively. The GNN-ODE surrogate achieves satisfactory results for the system dynamics prediction. On held-out simulation transients, the surrogate achieves an average MAE of 0.91 K at 60 s and 2.18 K at 300 s for uninstrumented nodes, with $R^2$ up to 0.995 for missing-node state reconstruction. Inference runs at approximately 105 times faster than simulated time on a single GPU, enabling 64-member ensemble rollouts for uncertainty quantification. To assess sim-to-real transfer, we adapt the pretrained surrogate to experimental facility data using layerwise discriminative fine-tuning with only 30 training sequences. The learned flow-dependent heat-transfer scaling recovers a Reynolds-number exponent consistent with established correlations, indicating constitutive learning beyond trajectory fitting. The model tracks a steep power change transient and produces accurate trajectories at uninstrumented locations. △ Less

Submitted 18 May, 2026; v1 submitted 8 April, 2026; originally announced April 2026.

Defect-free arrays at the thousand-atom scale in a 4-K cryogenic environment

Authors: Desiree Lim, Hadriel Mamann, Grégoire Pichard, Lilian Bourachot, Arvid Lindberg, Clotilde Hamot, Hugo Le Bars, Florian Fasola, Siddhy Tan, Gwennolé Cournez, Sylvain Dutartre, Thierry Cartry, Sylvain Lemettre, Richard Hostein, Julien Paris, Franck Ferreyrol, Andréa Collardey, Adrien Signoles, Thierry Lahaye, Corentin Monmeyran, Bruno Ximenez

Abstract: We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping lasers at different wavelengths and by min… ▽ More We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping lasers at different wavelengths and by minimizing other atom losses during the rearrangement and imaging processes, we demonstrate the preparation of defect-free arrays with up to 1024 atoms. Our cryogenic design opens exciting prospects for analog and digital quantum computing. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Comments: 8 pages, 5 figures

Any-Subgroup Equivariant Networks via Symmetry Breaking

Authors: Abhinav Goel, Derek Lim, Hannah Lawrence, Stefanie Jegelka, Ningyuan Huang

Abstract: The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant architectures are usually highly constrained, designed for symmetries chosen a priori, and not applicable to datasets with other symmetries. This precludes the development of flexible, multi-modal foundation models capable of… ▽ More The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant architectures are usually highly constrained, designed for symmetries chosen a priori, and not applicable to datasets with other symmetries. This precludes the development of flexible, multi-modal foundation models capable of processing diverse data equivariantly. In this work, we build a single model -- the Any-Subgroup Equivariant Network (ASEN) -- that can be simultaneously equivariant to several groups, simply by modulating a certain auxiliary input feature. In particular, we start with a fully permutation-equivariant base model, and then obtain subgroup equivariance by using a symmetry-breaking input whose automorphism group is that subgroup. However, finding an input with the desired automorphism group is computationally hard. We overcome this by relaxing from exact to approximate symmetry breaking, leveraging the notion of 2-closure to derive fast algorithms. Theoretically, we show that our subgroup-equivariant networks can simulate equivariant MLPs, and their universality can be guaranteed if the base model is universal. Empirically, we validate our method on symmetry selection for graph and image tasks, as well as multitask and transfer learning for sequence tasks, showing that a single network equivariant to multiple permutation subgroups outperforms both separate equivariant models and a single non-equivariant model. △ Less

Submitted 19 March, 2026; originally announced March 2026.

Comments: Accepted at ICLR 2026

VCA: Vision-Click-Action Framework for Precise Manipulation of Segmented Objects in Target Ambiguous Environments

Authors: Donggeon Kim, Seungwon Jan, Hyeonjun Park, Daegyu Lim

Abstract: The reliance on language in Vision-Language-Action (VLA) models introduces ambiguity, cognitive overhead, and difficulties in precise object identification and sequential task execution, particularly in environments with multiple visually similar objects. To address these limitations, we propose Vision-Click-Action (VCA), a framework that replaces verbose textual commands with direct, click-based… ▽ More The reliance on language in Vision-Language-Action (VLA) models introduces ambiguity, cognitive overhead, and difficulties in precise object identification and sequential task execution, particularly in environments with multiple visually similar objects. To address these limitations, we propose Vision-Click-Action (VCA), a framework that replaces verbose textual commands with direct, click-based visual interaction using pretrained segmentation models. By allowing operators to specify target objects clearly through visual selection in the robot's 2D camera view, VCA reduces interpretation errors, lowers cognitive load, and provides a practical and scalable alternative to language-driven interfaces for real-world robotic manipulation. Experimental results validate that the proposed VCA framework achieves effective instance-level manipulation of specified target objects. Experiment videos are available at https://robrosinc.github.io/vca/. △ Less

Submitted 26 February, 2026; originally announced February 2026.

Comments: Submitted to UR 2026

An attention economy model of co-evolution between content quality and audience selectivity

Authors: Masaki Chujyo, Isamu Okada, Hitoshi Yamamoto, Dongwoo Lim, Fujio Toriumi

Abstract: Human attention has become a scarce and strategically contested resource in digital environments. Content providers increasingly engage in excessive competition for visibility, often prioritizing attention-grabbing tactics over substantive quality. Despite extensive empirical evidence, however, there is a lack of theoretical models that explain the fundamental dynamics of the attention economy. He… ▽ More Human attention has become a scarce and strategically contested resource in digital environments. Content providers increasingly engage in excessive competition for visibility, often prioritizing attention-grabbing tactics over substantive quality. Despite extensive empirical evidence, however, there is a lack of theoretical models that explain the fundamental dynamics of the attention economy. Here, we develop a minimal mathematical framework to explain how content quality and audience attention coevolve under limited attention capacity. Using an evolutionary game approach, we model strategic feedback between providers, who decide how much effort to invest in production, and consumers, who choose whether to search selectively for high-quality content or to engage passively. Analytical and numerical results reveal three characteristic regimes of content dynamics: collapse, boundary, and coexistence. The transitions between these regimes depend on how effectively audiences can distinguish content quality. When audience discriminability is weak, both selective attention and high-quality production vanish, leading to informational collapse. When discriminability is sufficient and incentives are well aligned, high- and low-quality content dynamically coexist through feedback between audience selectivity and providers' effort. These findings identify two key conditions for sustaining a healthy information ecosystem: adequate discriminability among audiences and sufficient incentives for high-effort creation. The model provides a theoretical foundation for understanding how institutional and platform designs can prevent the degradation of content quality in the attention economy. △ Less

Submitted 6 February, 2026; originally announced February 2026.

Detection of Gravitational Anomaly at Low Acceleration from a Highest-quality Sample of 36 Wide Binaries with Accurate 3D Velocities

Authors: K. -H. Chae, B. -C. Lee, X. Hernandez, V. G. Orlov, D. Lim, D. A. Turnshek, Y. -W. Lee

Abstract: We set out to accurately measure gravity in the low-acceleration range $(10^{-11},10^{-9})$ m s$^{-2}$ from 3D motions of isolated wide binary stars. Gaia DR3 provides precise measurements of the four sky-plane components of the 3D relative displacement and velocity ($\mathbf{r}, \mathbf{v}$) for a wide binary, but not comparably precise line-of-sight (radial) separation and relative velocity… ▽ More We set out to accurately measure gravity in the low-acceleration range $(10^{-11},10^{-9})$ m s$^{-2}$ from 3D motions of isolated wide binary stars. Gaia DR3 provides precise measurements of the four sky-plane components of the 3D relative displacement and velocity ($\mathbf{r}, \mathbf{v}$) for a wide binary, but not comparably precise line-of-sight (radial) separation and relative velocity $v_{r}$. Based on our new observations and the public databases/publications, we assemble a sample of 36 nearby (distance $<150$pc) wide binaries in the low-acceleration regime with accurate values of $v_{r}$ (uncertainty $< 100$ m s$^{-1}$). Kinematic contaminants such as undetected stellar companions are well under control using various observational diagnostics such as Gaia's ruwe parameter, the color-magnitude diagram, multi-epoch observations of radial velocities, Speckle interferometric follow-up observations, and requiring Hipparcos-Gaia proper motion consistency. For the parameter $Γ\equiv \log_{10}\sqrtγ$ with $γ\equiv G/G_{\rm N}$ (where $G$ is a parameter generalizing Newton's constant $G_{\rm N}$ in elliptical orbits), we find $Γ=0.102_{-0.021}^{+0.023}$, inconsistent with standard gravity at $4.9σ$, giving a gravity boost factor of $γ=1.600_{-0.141}^{+0.171}$. Four wide binaries have 3D relative velocities exceeding their estimated Newtonian escape velocities with $1<v_{\rm obs}/v_{\rm escN}\le1.2$. These systems are unlikely to be chance associations and are expected in a nonstandard paradigm such as Milgromian dynamics (MOND). The hypothesis that Newtonian gravity can be extrapolated to the low-acceleration limit is falsified by this independent study with accurate 3D velocities. Future radial velocity monitoring and Speckle interferometric imaging for larger samples will be useful to refine the present result. △ Less

Submitted 9 February, 2026; v1 submitted 29 January, 2026; originally announced January 2026.

Comments: 28 pages (main part) + 39 pages (appendix), submitted to the AAS journals with a minor correction of Appendix E

Strain-Dependent Wetting of Graphene

Authors: Darren Wayne Lim, Xavier R. Advincula, William C. Witt, Angelos Michaelides, Fabian L. Thiemann, Christoph Schran

Abstract: Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the contact angle made by a liquid droplet against the surface, yet experimental measurements for graphene span a wide range, with no consensus for free-standing graphene. Here, we use a machine learning potential with ab initi… ▽ More Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the contact angle made by a liquid droplet against the surface, yet experimental measurements for graphene span a wide range, with no consensus for free-standing graphene. Here, we use a machine learning potential with ab initio accuracy to provide an atomistic first-principles prediction for this unsolved problem, finding a weakly hydrophilic contact angle of $72.1 \pm 1.5 °$. More importantly, we unveil that graphene's wetting properties are highly sensitive to mechanical strain: tensile strain makes graphene significantly less hydrophilic, while compressive strain induces coherent ripples around the droplet, resulting in pronounced anisotropic wetting and contact angle hysteresis. We show that there is a strong coupling between the three-phase contact line and the intrinsic thermal ripples of free-standing graphene, which contributes to this strain sensitivity. Our results demonstrate that the wettability of 2D membranes are governed not only by their chemistry but also by their dynamic morphology, introducing a new class of wetting behaviour unique to atomically thin materials that offers an additional explanation for variability in experimental measurements. These findings reveal that mechanical strain may be a practical route to controlling wetting in 2D nanomaterials-based technologies, with promising consequences for nanofluidic and nano-filtration applications. △ Less

Submitted 1 May, 2026; v1 submitted 27 January, 2026; originally announced January 2026.

Comments: [v1] Main: 19 pages, 3 figures, LaTeX. SI: 36 pages, 10 figures, LaTeX. [v2] Corrected "acknowledgements", removed unnecessary code from main.tex file. [v3] Major edits, additional data, change of format, change of title. Main: 20 pages, 3 figures, LaTeX. SI: 40 pages, 12 figures, LaTeX

OpenAI GPT-5 System Card

Authors: Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, Akshay Nathan, Alan Luo, Alec Helyar, Aleksander Madry, Aleksandr Efremov, Aleksandra Spyra, Alex Baker-Whitcomb, Alex Beutel, Alex Karpenko, Alex Makelov, Alex Neitz, Alex Wei, Alexandra Barr, Alexandre Kirchmeyer, Alexey Ivanov , et al. (461 additional authors not shown)

Abstract: This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in… ▽ More This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in the prompt). The router is continuously trained on real signals, including when users switch models, preference rates for responses, and measured correctness, improving over time. Once usage limits are reached, a mini version of each model handles remaining queries. This system card focuses primarily on gpt-5-thinking and gpt-5-main, while evaluations for other models are available in the appendix. The GPT-5 system not only outperforms previous models on benchmarks and answers questions more quickly, but -- more importantly -- is more useful for real-world queries. We've made significant advances in reducing hallucinations, improving instruction following, and minimizing sycophancy, and have leveled up GPT-5's performance in three of ChatGPT's most common uses: writing, coding, and health. All of the GPT-5 models additionally feature safe-completions, our latest approach to safety training to prevent disallowed content. Similarly to ChatGPT agent, we have decided to treat gpt-5-thinking as High capability in the Biological and Chemical domain under our Preparedness Framework, activating the associated safeguards. While we do not have definitive evidence that this model could meaningfully help a novice to create severe biological harm -- our defined threshold for High capability -- we have chosen to take a precautionary approach. △ Less

Submitted 1 May, 2026; v1 submitted 19 December, 2025; originally announced January 2026.

Comments: May 2026: Added monitorability evals and authors

Scale-robust Low Resistance Transport in Atomic Layer Deposited Topological Semimetal Wafers on Amorphous Substrate

Authors: Dong-Hyun Lim, Young-Min Song, Yeji Kim, Ae Rim Choi, Hyun-Mi Kim, Hyeongkeun Kim, Sujin Kwon, Bonggeun Shong, Justin Shih, Asir Intisar Khan, Il-Kwon Oh

Abstract: As data-centric computing advances, energy-efficient interconnects are increasingly critical for AI-driven systems. Traditional metal conductors face severe limitations at nanoscale due to increased resistivity from surface scattering. In response, this study demonstrates the first wafer-scale realization of an amorphous topological semimetal, tantalum phosphide (TaP), grown directly on amorphous… ▽ More As data-centric computing advances, energy-efficient interconnects are increasingly critical for AI-driven systems. Traditional metal conductors face severe limitations at nanoscale due to increased resistivity from surface scattering. In response, this study demonstrates the first wafer-scale realization of an amorphous topological semimetal, tantalum phosphide (TaP), grown directly on amorphous SiO2 substrates (without any seed layers) using low-temperature atomic layer deposition (ALD). The resulting TaP films exhibit unconventional resistivity scaling: decreasing resistivity with decreasing thickness, reaching 227 micro-ohm cm at ~2.3 nm film thickness. This behavior, observed without crystalline order or seed layers, indicates dominant surface conduction and establishes ALD-TaP as a promising candidate for back-end-of-line integration. The films also show excellent conformality, stoichiometry control, and thermal stability up to 600 degree C. A two-channel conduction model confirms surface-dominated transport in ultrathin regimes, further supported by enhanced conductivity in multi-stacked configurations. These findings highlight the potential of amorphous topological semimetals for future high-density, low-power electronic interconnects and expand the applicability of ALD for integrating novel quantum materials at scale. △ Less

Submitted 6 December, 2025; originally announced December 2025.

Comments: 9 pages, 3 figures

BRIC: Bridging Kinematic Plans and Physical Control at Test Time

Authors: Dohun Lim, Minji Kim, Jaewoon Lim, Sungchan Kim

Abstract: We propose BRIC, a novel test-time adaptation (TTA) framework that enables long-term human motion generation by resolving execution discrepancies between diffusion-based kinematic motion planners and reinforcement learning-based physics controllers. While diffusion models can generate diverse and expressive motions conditioned on text and scene context, they often produce physically implausible ou… ▽ More We propose BRIC, a novel test-time adaptation (TTA) framework that enables long-term human motion generation by resolving execution discrepancies between diffusion-based kinematic motion planners and reinforcement learning-based physics controllers. While diffusion models can generate diverse and expressive motions conditioned on text and scene context, they often produce physically implausible outputs, leading to execution drift during simulation. To address this, BRIC dynamically adapts the physics controller to noisy motion plans at test time, while preserving pre-trained skills via a loss function that mitigates catastrophic forgetting. In addition, BRIC introduces a lightweight test-time guidance mechanism that steers the diffusion model in the signal space without updating its parameters. By combining both adaptation strategies, BRIC ensures consistent and physically plausible long-term executions across diverse environments in an effective and efficient manner. We validate the effectiveness of BRIC on a variety of long-term tasks, including motion composition, obstacle avoidance, and human-scene interaction, achieving state-of-the-art performance across all tasks. △ Less

Submitted 22 December, 2025; v1 submitted 25 November, 2025; originally announced November 2025.

Comments: Accepted to AAAI'26

AI-Designed Photonics Gratings with Experimental Verification

Authors: Yu Dian Lim, Chuan Seng Tan

Abstract: Artificial Intelligence (AI) software based on transformer model is developed to automatically design gratings for possible integrations in ion traps to perform optical addressing on ions. From the user-defined (x,z) coordinates and full-width half-maximum (FWHM) values, the AI software can automatically generate the Graphic Design System (GDS) layout of the grating that shoots light towards the p… ▽ More Artificial Intelligence (AI) software based on transformer model is developed to automatically design gratings for possible integrations in ion traps to perform optical addressing on ions. From the user-defined (x,z) coordinates and full-width half-maximum (FWHM) values, the AI software can automatically generate the Graphic Design System (GDS) layout of the grating that shoots light towards the pre-defined (x,z) coordinates with built-in finite-difference time-domain (FDTD) simulation for performance verification. Based on the FDTD verification, AI-design gratings produced grating-to-free-space light that shoots towards the provided (x,z) target with < 2 micron deviations. For most attempts, the FWHM of FDTD simulation has < 2 micron deviations from the user-defined FWHM. The AI-designed gratings were successfully taped out and capable of producing output light for possible optical addressing of trapped ions. △ Less

Submitted 26 November, 2025; v1 submitted 25 November, 2025; originally announced November 2025.

Optimization of experimental parameters for laser-slowing and magneto-optical trapping of MgF molecules

Authors: Dongkyu Lim, Eunmi Chae

Abstract: Diatomic molecules are promising systems for quantum science applications due to their complex energy structures and strong dipole-dipole interactions. Achieving ultracold temperatures is essential for these applications, but the complexity of molecular energy levels requires precise optimization of experimental parameters for laser slowing and magneto-optical trapping (MOT). Here, we simulate and… ▽ More Diatomic molecules are promising systems for quantum science applications due to their complex energy structures and strong dipole-dipole interactions. Achieving ultracold temperatures is essential for these applications, but the complexity of molecular energy levels requires precise optimization of experimental parameters for laser slowing and magneto-optical trapping (MOT). Here, we simulate and optimize the complete process of slowing and trapping MgF molecules, from a buffer-gas beam source to MOT capture, using Bayesian optimization. By combining laser slowing and MOT simulations, we identify parameters that maximize the capture velocity and the ratio of trapped molecules. Our results demonstrate a maximum MOT capture velocity of 82.5 m/s, and 28.6% of the molecules that reach the MOT region are trapped under optimal conditions. These findings provide insights into experimental setups for MgF and similar molecules, offering a framework for advancing molecular laser cooling and quantum experiments. △ Less

Submitted 19 November, 2025; originally announced November 2025.

Comments: 12 pages, 10 figures, 4 tables

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

Authors: YongKyung Oh, Dong-Young Lim, Sungil Kim

Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled framework for such tasks, yet their performance is highly sensitive to the choice of control path constructed from discrete observations. Existing methods commonly employ fixed interpolation schemes, which impose simplistic… ▽ More Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled framework for such tasks, yet their performance is highly sensitive to the choice of control path constructed from discrete observations. Existing methods commonly employ fixed interpolation schemes, which impose simplistic geometric assumptions that often misrepresent the underlying data manifold, particularly under high missingness. We propose FlowPath, a novel approach that learns the geometry of the control path via an invertible neural flow. Rather than merely connecting observations, FlowPath constructs a continuous and data-adaptive manifold, guided by invertibility constraints that enforce information-preserving and well-behaved transformations. This inductive bias distinguishes FlowPath from prior unconstrained learnable path models. Empirical evaluations on 18 benchmark datasets and a real-world case study demonstrate that FlowPath consistently achieves statistically significant improvements in classification accuracy over baselines using fixed interpolants or non-invertible architectures. These results highlight the importance of modeling not only the dynamics along the path but also the geometry of the path itself, offering a robust and generalizable solution for learning from irregular time series. △ Less

Submitted 30 June, 2026; v1 submitted 13 November, 2025; originally announced November 2025.

Comments: Published at the 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026). https://ojs.aaai.org/index.php/AAAI/article/view/39643

Continuum Dropout for Neural Differential Equations

Authors: Jonghun Lee, YongKyung Oh, Sungil Kim, Dong-Young Lim

Abstract: Neural Differential Equations (NDEs) excel at modeling continuous-time dynamics, effectively handling challenges such as irregular observations, missing values, and noise. Despite their advantages, NDEs face a fundamental challenge in adopting dropout, a cornerstone of deep learning regularization, making them susceptible to overfitting. To address this research gap, we introduce Continuum Dropout… ▽ More Neural Differential Equations (NDEs) excel at modeling continuous-time dynamics, effectively handling challenges such as irregular observations, missing values, and noise. Despite their advantages, NDEs face a fundamental challenge in adopting dropout, a cornerstone of deep learning regularization, making them susceptible to overfitting. To address this research gap, we introduce Continuum Dropout, a universally applicable regularization technique for NDEs built upon the theory of alternating renewal processes. Continuum Dropout formulates the on-off mechanism of dropout as a stochastic process that alternates between active (evolution) and inactive (paused) states in continuous time. This provides a principled approach to prevent overfitting and enhance the generalization capabilities of NDEs. Moreover, Continuum Dropout offers a structured framework to quantify predictive uncertainty via Monte Carlo sampling at test time. Through extensive experiments, we demonstrate that Continuum Dropout outperforms existing regularization methods for NDEs, achieving superior performance on various time series and image classification tasks. It also yields better-calibrated and more trustworthy probability estimates, highlighting its effectiveness for uncertainty-aware modeling. △ Less

Submitted 18 November, 2025; v1 submitted 13 November, 2025; originally announced November 2025.

Journal ref: The Association for the Advancement of Artificial Intelligence 2026

Uniturbulence and Alfvén Wave Solar Model in MPI-AMRVAC

Authors: M. McMurdo, T. Van Doorsselaere, N. Magyar, L. Banovic, D. Lim

Abstract: The coronal heating problem remains a fundamental challenge in solar physics. While AWSoM-type models (Alfvén Wave Solar Model) have proven highly successful in reproducing the large-scale structure of the solar corona, they inherently neglect contributions from additional wave modes that arise when the effects of transverse structuring is fully incorporated into the magnetohydrodynamic (MHD) equa… ▽ More The coronal heating problem remains a fundamental challenge in solar physics. While AWSoM-type models (Alfvén Wave Solar Model) have proven highly successful in reproducing the large-scale structure of the solar corona, they inherently neglect contributions from additional wave modes that arise when the effects of transverse structuring is fully incorporated into the magnetohydrodynamic (MHD) equations. In this paper, we compare the roles of kink wave- and Alfvén wave-driven heating in sustaining a region of the solar atmosphere, using newly developed physics and radiative cooling modules within MPI-AMRVAC. We extend the existing MHD physics module in MPI-AMRVAC by incorporating additional Alfvén and kink wave energy contributions to the MHD equations. We examine their roles in heating the solar atmosphere and driving the solar wind. To validate our approach, we compare numerical results from Python-based simulations with those obtained using the UAWSoM module in MPI-AMRVAC. Furthermore, we assess the heating efficiency of kink waves relative to that of pure Alfvén waves through two parameter studies: (1) exploring how different Alfvén wave reflection rates impact the simulated atmosphere, and (2) varying the relative magnitudes of Alfvén and kink wave energy injections. Finally, we present results from a larger-scale domain, sustained entirely by kink wave-driven heating. Our results show that kink wave-driven (UAWSoM) models are able to sustain a stable atmosphere without requiring any artificial background heating terms, unlike traditional Alfvén-only models. We attribute this to the increased heating rate associated with kink waves compared with Alfvén waves, given the same energy injection. Kink waves can sustain a model plasma with temperature and density values representative of coronal conditions without resorting to ad hoc heating terms. △ Less

Submitted 31 October, 2025; originally announced October 2025.

Comments: 13 pages, 10 figures

Controllable Machine Unlearning via Gradient Pivoting

Authors: Youngsik Hwang, Dong-Young Lim

Abstract: Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimization (SOO) problem, face a critical trade-off between unlearning efficacy and model fidelity. This leads to three primary challenges: the risk of over-forgetting, a lack of fine-grained control over the unlearning proces… ▽ More Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimization (SOO) problem, face a critical trade-off between unlearning efficacy and model fidelity. This leads to three primary challenges: the risk of over-forgetting, a lack of fine-grained control over the unlearning process, and the absence of metrics to holistically evaluate the trade-off. To address these issues, we reframe MU as a multi-objective optimization (MOO) problem. We then introduce a novel algorithm, Controllable Unlearning by Pivoting Gradient (CUP), which features a unique pivoting mechanism. Unlike traditional MOO methods that converge to a single solution, CUP's mechanism is designed to controllably navigate the entire Pareto frontier. This navigation is governed by a single intuitive hyperparameter, the `unlearning intensity', which allows for precise selection of a desired trade-off. To evaluate this capability, we adopt the hypervolume indicator, a metric that captures both the quality and diversity of the entire set of solutions an algorithm can generate. Our experimental results demonstrate that CUP produces a superior set of Pareto-optimal solutions, consistently outperforming existing methods across various vision tasks. △ Less

Submitted 22 October, 2025; originally announced October 2025.

Support growth of vorticity for bi-rotational Euler flows in high dimensions

Authors: In-Jee Jeong, Deokwoo Lim

Abstract: We study incompressible Euler equations in $\mathbb{R}^d$ with $d \ge 4$ under bi-rotational symmetry without swirl, which reduces the Euler equations to a scalar vorticity advection in the first quadrant. We show that patch type initial vorticities exhibit infinite growth of the support diameter. We study incompressible Euler equations in $\mathbb{R}^d$ with $d \ge 4$ under bi-rotational symmetry without swirl, which reduces the Euler equations to a scalar vorticity advection in the first quadrant. We show that patch type initial vorticities exhibit infinite growth of the support diameter. △ Less

Submitted 25 January, 2026; v1 submitted 21 October, 2025; originally announced October 2025.

Comments: 13 pages

MSC Class: 76B47; 35Q35

A Semiconductor Photon Bose-Einstein Condensate as a Practical Light Source for Ranging Finding

Authors: Ross C. Schofield, Daniel Lim, Nathan R. Gemmell, Edmund Clarke, Ian Farrer, Aristotelis Trapalis, Jon Heffernan, Rupert F. Oulton

Abstract: Here we report the measurement of thermal photon statistics from a semiconductor photon Bose-Einstein condensate operating just above the condensation threshold. We identify a regime where coherent, single mode emission occurs while still demonstrating significant photon bunching. Taking advantage of the photon bunching, along with the continuous-wave operation and high photon flux, we demonstrate… ▽ More Here we report the measurement of thermal photon statistics from a semiconductor photon Bose-Einstein condensate operating just above the condensation threshold. We identify a regime where coherent, single mode emission occurs while still demonstrating significant photon bunching. Taking advantage of the photon bunching, along with the continuous-wave operation and high photon flux, we demonstrate optical range sensing using a photon Bose-Einstein condensate. We characterise the precision of the range measurement and analyse the dependence on the condensate's pump power and resulting coherence properties. △ Less

Submitted 15 October, 2025; originally announced October 2025.

Comments: 7 pages, 4 figures

Accessing the fine temporal scale of EUV brightenings and their quasi-periodic pulsations: 1 second cadence observations by Solar Orbiter/EUI

Authors: Daye Lim, Tom Van Doorsselaere, Nancy Narang, Laura A. Hayes, Emil Kraaikamp, Aadish Joshi, Konstantina Loumou, Cis Verbeeck, David Berghmans, Krzysztof Barczynski

Abstract: Small scale extreme ultraviolet (EUV) transient brightenings are observationally abundant and critically important to investigate. Determining whether they share the same physical mechanisms as larger scale flares would have significant implications for the coronal heating problem. A recent study has revealed that quasi periodic pulsations (QPPs), a common feature in both solar and stellar flares,… ▽ More Small scale extreme ultraviolet (EUV) transient brightenings are observationally abundant and critically important to investigate. Determining whether they share the same physical mechanisms as larger scale flares would have significant implications for the coronal heating problem. A recent study has revealed that quasi periodic pulsations (QPPs), a common feature in both solar and stellar flares, may also be present in EUV brightenings in the quiet Sun (QS). We aim to characterise the properties of EUV brightenings and their associated QPPs in both QS and active regions (ARs) using unprecedented 1 s cadence observations from Solar Orbiter/Extreme Ultraviolet Imager (EUI). We applied an automated detection algorithm to analyse statistical properties of EUV brightenings. QPPs were identified using complementary techniques optimised for both stationary and non stationary signals, including a Fourier based method, ensemble empirical mode decomposition, and wavelet analysis. Over 500000 and 300000 brightenings were detected in ARs and QS regions, respectively. Brightenings with lifetimes shorter than 3 s were detected, demonstrating the importance of high temporal resolution. QPP periods span from 5 to over 500 s and show similar distributions between AR and QS. We found a consistent power law scaling, with a weak correlation and a large spread, between QPP period and lifetime in EUV brightenings, solar, and stellar flares. The results support the interpretation that EUV brightenings may represent a small scale manifestation of the same physical mechanisms driving larger solar and stellar flares. Furthermore, the similarity in the statistical properties of EUV brightenings and their associated QPPs between AR and QS regions suggests that the underlying generation mechanisms may not strongly depend on the large scale magnetic environment. △ Less

Submitted 7 October, 2025; originally announced October 2025.

Comments: 17 pages, 16 figures, accepted for publication in A&A

Journal ref: A&A 704, A58 (2025)

Flatness-Aware Stochastic Gradient Langevin Dynamics

Authors: Stefano Bruno, Youngsik Hwang, Jaehyeon An, Sotirios Sabanis, Dong-Young Lim

Abstract: Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learning algorithms. Motivated by this view, we propose Flatness-Aware Stochastic Gradient Langevin Dynamics (fSGLD), a first-order optimization method that biases learning its dynamics toward flat basins while retaining the computational and memory efficienc… ▽ More Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learning algorithms. Motivated by this view, we propose Flatness-Aware Stochastic Gradient Langevin Dynamics (fSGLD), a first-order optimization method that biases learning its dynamics toward flat basins while retaining the computational and memory efficiency of SGD and SGLD. We provide a non-asymptotic theoretical analysis showing that fSGLD targets a flatness-biased Gibbs distribution under a theoretically prescribed coupling between the noise scale $σ$ and the inverse temperature $β$, together with explicit excess risk guarantees. We empirically evaluate fSGLD across standard optimizer benchmarks, Bayesian image classification, uncertainty quantification, and out-of-distribution detection, demonstrating consistently strong performance and reliable uncertainty estimates. Additional experiments confirm the effectiveness of the theoretically prescribed $β$-$σ$ coupling compared to decoupled choices. △ Less

Submitted 27 May, 2026; v1 submitted 2 October, 2025; originally announced October 2025.

Comments: Accepted by ICML 2026

Journal ref: ICML 2026

Automating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents

Authors: Yang Liu, Zaid Abulawi, Abhiram Garimidi, Doyeong Lim

Abstract: Modern engineering increasingly relies on vast datasets generated by experiments and simulations, driving a growing demand for efficient, reliable, and broadly applicable modeling strategies. There is also heightened interest in developing data-driven approaches, particularly neural network models, for effective prediction and analysis of scientific datasets. Traditional data-driven methods freque… ▽ More Modern engineering increasingly relies on vast datasets generated by experiments and simulations, driving a growing demand for efficient, reliable, and broadly applicable modeling strategies. There is also heightened interest in developing data-driven approaches, particularly neural network models, for effective prediction and analysis of scientific datasets. Traditional data-driven methods frequently involve extensive manual intervention, limiting their ability to scale effectively and generalize to diverse applications. In this study, we propose an innovative pipeline utilizing Large Language Model (LLM) agents to automate data-driven modeling and analysis, with a particular emphasis on regression tasks. We evaluate two LLM-agent frameworks: a multi-agent system featuring specialized collaborative agents, and a single-agent system based on the Reasoning and Acting (ReAct) paradigm. Both frameworks autonomously handle data preprocessing, neural network development, training, hyperparameter optimization, and uncertainty quantification (UQ). We validate our approach using a critical heat flux (CHF) prediction benchmark, involving approximately 25,000 experimental data points from the OECD/NEA benchmark dataset. Results indicate that our LLM-agent-developed model surpasses traditional CHF lookup tables and delivers predictive accuracy and UQ on par with state-of-the-art Bayesian optimized deep neural network models developed by human experts. These outcomes underscore the significant potential of LLM-based agents to automate complex engineering modeling tasks, greatly reducing human workload while meeting or exceeding existing standards of predictive performance. △ Less

Submitted 1 October, 2025; originally announced October 2025.

Scalable and Robust LLM Unlearning by Correcting Responses with Retrieved Exclusions

Authors: Junbeom Kim, Kyuyoung Kim, Jihoon Tack, Dongha Lim, Jinwoo Shin

Abstract: Language models trained on web-scale corpora risk memorizing and exposing sensitive information, prompting the need for effective machine unlearning. Prior methods mainly focus on input queries to suppress sensitive outputs, yet this often fails to eliminate the underlying knowledge and limits scalability. To address this, we propose Corrective Unlearning with Retrieved Exclusions (CURE), a novel… ▽ More Language models trained on web-scale corpora risk memorizing and exposing sensitive information, prompting the need for effective machine unlearning. Prior methods mainly focus on input queries to suppress sensitive outputs, yet this often fails to eliminate the underlying knowledge and limits scalability. To address this, we propose Corrective Unlearning with Retrieved Exclusions (CURE), a novel unlearning framework that verifies model outputs for leakage and revises them into safe responses. Specifically, CURE employs a lightweight corrector that is applied to the original model to verify whether outputs contain target knowledge and to rewrite them if any leakage is detected. To efficiently handle large-scale unlearning requests, CURE retrieves unlearning targets that are relevant to the initial response and provides them as in-context references to the corrector for detection and conditional revision. By leveraging this retrieval augmentation, the corrector can adapt to new unlearning requests without additional training. Extensive evaluations demonstrate that CURE substantially reduces information leakage, even from indirect queries where prior works fall short, while maintaining response quality and general utility. Moreover, it demonstrates robustness under continual unlearning scenarios, making it practical for real-world applications. △ Less

Submitted 30 September, 2025; originally announced September 2025.

ACM Class: I.2.6

A Hierarchy for Constant Communication Complexity

Authors: Andris Ambainis, Hartmut Klauck, Debbie Lim

Abstract: Similarly to the Chomsky hierarchy, we offer a classification of communication complexity measures such that these measures are organized into equivalence classes. Different from previous attempts of this endeavor, we consider two communication complexity measures as equivalent, if, when one is constant, then the other is constant as well, and vice versa. Most previous considerations of similar to… ▽ More Similarly to the Chomsky hierarchy, we offer a classification of communication complexity measures such that these measures are organized into equivalence classes. Different from previous attempts of this endeavor, we consider two communication complexity measures as equivalent, if, when one is constant, then the other is constant as well, and vice versa. Most previous considerations of similar topics have been using polylogarithmic input length as a defining characteristic of equivalence. In this paper, two measures ${\cal C}_1, {\cal C}_2$ are constant-equivalent, if and only if for all total Boolean (families of) functions $f:\{0, 1\}^n\times\{0, 1\}^n\rightarrow \{0, 1\}$ we have ${\cal C}_1(f)=O(1)$ if and only if ${\cal C}_2(f)=O(1)$. We identify five equivalence classes according to the above equivalence relation. Interestingly, the classification is counter-intuitive in that powerful models of communication are grouped with weak ones, and seemingly weaker models end up on the top of the hierarchy. △ Less

Submitted 12 March, 2026; v1 submitted 26 September, 2025; originally announced September 2025.

Comments: 30 pages, 7 figures

ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models

Authors: Jewon Lee, Wooksu Shin, Seungmin Yang, Ki-Ung Song, DongUk Lim, Jaeyeon Kim, Tae-Ho Kim, Bo-Kyeong Kim

Abstract: Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial computational overhead due to the large number of vision tokens. With the advent of "thinking with images" models, reasoning now extends beyond text to the visual domain. This capability motivates our two-stage "coarse-to-fi… ▽ More Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial computational overhead due to the large number of vision tokens. With the advent of "thinking with images" models, reasoning now extends beyond text to the visual domain. This capability motivates our two-stage "coarse-to-fine" reasoning pipeline: first, a downsampled image is analyzed to identify task-relevant regions; then, only these regions are cropped at full resolution and processed in a subsequent reasoning stage. This approach reduces computational cost while preserving fine-grained visual details where necessary. A major challenge lies in inferring which regions are truly relevant to a given query. Recent related methods often fail in the first stage after input-image downsampling, due to perception-driven reasoning, where clear visual information is required for effective reasoning. To address this issue, we propose ERGO (Efficient Reasoning & Guided Observation) that performs reasoning-driven perception-leveraging multimodal context to determine where to focus. Our model can account for perceptual uncertainty, expanding the cropped region to cover visually ambiguous areas for answering questions. To this end, we develop simple yet effective reward components in a reinforcement learning framework for coarse-to-fine perception. Across multiple datasets, our approach delivers higher accuracy than the original model and competitive methods, with greater efficiency. For instance, ERGO surpasses Qwen2.5-VL-7B on the V* benchmark by 4.7 points while using only 23% of the vision tokens, achieving a 3x inference speedup. The code and models can be found at: https://github.com/nota-github/ERGO. △ Less

Submitted 17 March, 2026; v1 submitted 26 September, 2025; originally announced September 2025.