April 2026 arXiv papers — page 104

Qian Wang, Calvin Ku, Jyh-Pin Chou, Peng-Jen Chen

Density functional theory with plane-wave basis sets is widely employed in computational materials science, including applications to isolated molecular systems. However, the inadequate description of electron correlation remains a fundamental limitation. Accurate correlation treatments based on many-body Hamiltonians require reliable representations of both

M. A. Lozande, E. A. Fajardo

We explore the realization of distinct analog black hole horizons within tilted Weyl semimetals by comparing two models with contrasting spectral properties. We demonstrate that a spatially varying tilt in the Weyl cone structure creates an effect analogous to the tilting of light cones near a gravitational black hole horizon. By analyzing wave packet dynami

Ziyan Wang, Teng Liu, Shang Wang, Sheng Fang

The Qinghai-Tibetan Plateau (QTP), Earth's "Third Pole", profoundly shapes the Asian monsoon and regional climate and exerts far-reaching influence on the global climate system. Yet its role in organizing planetary-scale climate interactions remains poorly quantified. Here we develop a climate network framework to explicitly resolve the planetary teleconnect

Junxi Wu, Kailin Huang, Dongjian Hu, Bin Chen

Detecting AI-generated text is an important but challenging problem. Existing likelihood-based detection methods are often sensitive to content complexity and may exhibit unstable performance. In this paper, our key insight is that modern Large Language Models (LLMs) undergo alignment (including fine-tuning and preference tuning), leaving a measurable distri

Seongbin Park, Eunjin Oh

In this paper, we study the many-to-many matching problem on planar point sets with integer coordinates: Given two disjoint sets $R,B \subset [\Delta]^2$ with $|R|+|B|=n$, the goal is to select a set of edges between $R$ and $B$ so that every point is incident to at least one edge and the total Euclidean length is minimized. In the general case that $R$ and

Tian-Cong Wang, Aleksandra Piorkowska - Kurpas, Marek Biesiada, He Gao

Einstein's Weak Equivalence Principle (WEP), the universality of free fall, is a fundamental component of general relativity and other metric theories of gravity. Its validity can be tested through the post-Newtonian parameter gamma, which quantifies the amount of spacetime curvature due to the presence of unit rest mass. In this paper, we use high-energy ne

Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen, Zaiwang Gu

Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but they are susceptible to local minima and noise overfitting. Although DMs can provide strong priors for Bayesian approaches, enforcing measurement consistency during the denoising pr

Weiyu Ma, Yongcheng Zeng, Yan Song, Xinyu Cui

Reinforcement Learning (RL) has achieved impressive success in post-training Large Language Models (LLMs) and Vision-Language Models (VLMs), with on-policy algorithms such as PPO, GRPO, and REINFORCE++ serving as the dominant paradigm. However, these methods discard all collected trajectories after a single gradient update, resulting in poor sample efficienc

Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang

Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single dominant language. In this work, we introduce x1, a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. To isolate the effect of reasoning-language choice, x

Yuheng Chen, Zhiyu Wu, Bowen Cheng, Tetsuro Takahashi

Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respond. In contrast, many real-world applications place LLMs in structured decision-making tasks, such as multiple-choice questions (MCQs), where abstention is discouraged or unavailable. We identify a systematic

Parthaw Goswami, Jaynto Goswami Deep

Retrieval augmented generation (RAG) has transformed text based question answering, yet its extension to visual domains remains hindered by fundamental challenges: bridging the modality gap between image queries and text heavy knowledge bases, constructing semantically meaningful visual knowledge bases, performing multihop reasoning over retrieved images, an

Chen Ma, Yunshu Li, Junhu Fu, Shuyu Liang

Clinical ultrasound analysis demands models that generalize across heterogeneous organs, views, and devices, while supporting interpretable workflow-level analysis. Existing methods often rely on task-wise adaptation, and joint learning may be unstable due to cross-task interference, making it hard to deliver workflow-level outputs in practice. To address th

Syed Muhammad Aqdas Rizvi

Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplor

Ellenor Geraffy, Kusha Sharma, Shahar Zuri, Faris Horani

Antiferromagnetic FePS3 has recently gained significant interest in its potential applications in spin-related devices. Here, we show that in-plane structural anisotropy has a major impact in shaping the optical responses of FePS3 single-crystals from the bulk form down to the monolayer limit. X-ray diffraction on a bulk FePS3 crystal confirms a distorted Fe

Sampriti Saha, Pranav Hemanth

Large Language Model (LLM) agents are increasingly extended at runtime via skill packages, structured natural-language instruction bundles loaded from a well-known directory. Community install tooling and registries exist, but two gaps persist: no public tool scores skill packages against Anthropic's published format specification, and no mechanism bundles r

Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang

As large language models (LLMs) evolve from conversational assistants into agents capable of handling complex tasks, they are increasingly deployed in high-risk domains. However, existing benchmarks largely rely on mixed queries and posterior evaluation, output-level scoring, which quantifies hallucination severity but offers limited insight into where and w

Zhihe Zhuang, Rodrigo A. González, Hongfeng Tao, Wojciech Paszke

An inherent assumption of perfect tracking in iterative learning control (ILC) is that there exists an ILC input such that the generated output can track the desired trajectory reference. This assumption may fail in practice, which gives rise to desired but untrackable tasks. This paper gives an end-to-end ILC design for repetitive untrackable tasks in close

Ruochen Wu, Xu Du, Karl H. Johansson, Apostolos I. Rikos

In modern large-scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. In this paper, we consider an unconstrained distributed optimization problem in which information exchange among nodes is governed by a directed communication graph. In our setup we focus

Isabella Novik, Hailun Zheng

We establish several new lower bounds on the $g$-numbers of simplicial spheres without large missing faces. For this class of spheres, we derive bounds on the $g$-numbers in terms of the independence numbers of their graphs, extending a result of Chudnovsky and Nevo. As a consequence, we show that flag $(d-1)$-spheres -- and more generally, flag normal $(d-1

Samuel Tiersma

We give an order-theoretic characterization of the essential image of the forgetful functor from the category of real/complex unital C*-algebras to the category of real/complex unital operator systems. It is based on the characterization of JB-algebras among the order unit spaces in terms of the existence of gauge-reversing bijections obtained by M. Roelands

Yihan Zhang, Ziyun Huang, Linqi Ye

Collecting embodied interaction data at scale remains costly and difficult due to the limited accessibility of conventional interfaces. We present a gamified data collection framework based on Unity that combines procedural scene generation, VR-based humanoid robot control, automatic task evaluation, and trajectory logging. A trash pick-and-place task protot

Xinru Yan, Boxi Cao, Yaojie Lu, Hongyu Lin

Native Omni-modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native integration gives rise to a critical yet underexplored phenomenon: modality preference. To bridge this gap, we first systematically quantify modality preference of OLLMs using a newly-curated conflict-based benchmark

Ana Isabel Silva Martins, Cosmoglobe Collaboration

The Cosmoglobe project is a global effort to jointly analyze complementary cosmological and astrophysical datasets, in order to better understand our Universe and its evolution. This paper describes the goals and motivations of the project, some of the main results and future prospects.

Greta Grassmann, Giancarlo Ruocco, Mattia Miotto

Biomolecular phase separation is typically attributed to the polymer physics of long, disordered chains. However, the underlying chemical grammar, i.e. the specific interactions between protein and RNA building blocks, remains poorly understood. We decouple those effects by screening the phase behavior of the complete dipeptide library in presence and absenc

Björn Assmann, Ulan Degenbaev

Many automated market makers can be understood through the geometry of their trading orbits, the sets of states reachable from one another through swaps. In prominent designs, this geometry is captured by a simple closed-form invariant such as the constant product $xy$ in Uniswap or a weighted geometric mean $x^w y^{1-w}$ in Balancer. This paper explains why

Yutang Ge, Guojiang Zhao, Sihang Li, Zheng Cheng

Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tuned LLMs as direct text-to-sequence generators, but this is data- and compute-hungry. With limited supervision, LLMs can produce coherent plans in text yet fail to reliably realize

Emil Hovad, Allan Peter Engsig-Karup

We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder localizes a particle as a heatmap peak (landmark) and a differentiable physics module embedded in the autoencoder constrains several landmarks over time (a trajectory) to satisfy known dynamics. The novel Physics-I

Hiroki Hasegawa, Aoba Tamura, Yukihiko Okada

Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings with $p>n$. To address this, we propose a novel estimation principle that reformulates the covariance structure into self-covariance and cross-covariance components. The resulting fr

Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu

Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types

Antonios Kritikos, Nikolaos Spanos, Athanasios Voulodimos

Domain generalization (DG) aims to maintain performance under domain shift, which in computer vision appears primarily as stylistic variations that cause models to overfit to domain-specific appearance cues rather than class semantics. To overcome this, recent methods use textual representations as stable, domain-invariant anchors. However, multimodal approa

Byeong Rok Ko

Axion dark matter searches use a microwave cavity for the resonant conversion of axions into photons to enhance experimental sensitivity, with the cavity generally configured as a two-port system for both signal pickup and cavity characterization measurements. In this study, we investigated cavity-mode couplings in such a two-port system and examined their i

Benteng Chen, Weida Wang, Shufei Zhang, Mingbao Lin

Large reasoning models that use long chain-of-thought excel at problem-solving yet waste compute on redundant checks. Curbing this overthinking is hard: training-time length penalties can cripple ability, while inference-time early-exit adds system overhead. To bridge this gap, we propose Step-GRPO, a novel post-training framework that internalizes dynamic e

Qinhao Chen, Linyang He, Nima Mesgarani

Existing feature-interpretation pipelines typically operate on uniformly sampled units or exhaustive feature sets, incurring massive costs on units irrelevant to target behaviors. To address this, we introduce the first CLT-native end-to-end pruning framework, PIE, which pioneers the paradigm of pruning first and interpreting later. PIE connects Pruning, aut

Yuheng Zhao, Yu-Hu Yan, Amit Attia, Tomer Koren

Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergence rates competitive with optimally tuned methods. While some parameter-free methods do not require the specific values of the problem parameters, they still rely on prior knowledge, such as the lower or upper

Shoujie Li, Changqing Guo, Jianle Xu, Hong Luo

Robotic manipulators for aerospace applications require a delicate balance between lightweight construction and fault-tolerant operation to satisfy strict weight limitations and ensure reliability in remote, hazardous environments. This paper presents Time-Division Multiplexing Actuation (TDMA), a practical approach for tendon-driven robots that significantl

Xianhao Wang, Xiaojian Ma, Haozhe Hu, Rongpeng Su

Generalist embodied agents must perform interactive, causally-dependent reasoning, continually interacting with the environment, acquiring information, and updating plans to solve long-horizon tasks before they could be adopted in real-life scenarios. For instance, retrieving an apple from a cabinet may require opening multiple doors and drawers before the a

Yang Cao, Wenchi Cheng, Jingqing Wang, Lifeng Wang

Wireless communication systems are increasingly vulnerable to sophisticated jamming attacks with the rapid evolution of jamming technologies and advanced signal processing techniques. While traditional anti-jamming techniques offer limited performance gains, active reconfigurable intelligent surfaces (RISs) have emerged as a promising channel-domain solution

Cheng Li, Weijian Huang, Jiarun Liu, Hao Yang

The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. However, pervasive biases, such as imbalanced disease prevalence, skewed anatomical region distributions, heterogeneous imaging protocols, and demographic disparities, pose significant challenges to the fairness an

Junnan Liu, Xinyan Liu, Peifeng Gao, Zhaobo Qi

In long-context decoding for LLMs and LMMs, attention becomes increasingly memory-bound because each decoding step must load a large amount of KV-cache data from GPU memory. Existing acceleration strategies often trade efficiency for accuracy by relying on heuristic pruning that may discard useful information. At a deeper level, they also tend to indiscrimin

Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong

Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of culturally appropriate translations in context. However, relevant knowledge may already be encoded in model parameters during large-scale pre-training. To incentivize the effective use of parametric knowledge

Yuze Jin, Xin Zhe Khooi, Ruyi Yao, Mun Choon Chan

Ring-based collective operations are widely used in distributed AI training due to their efficient bandwidth utilization. While ring communication excels at pipelining, its performance is heavily dependent on having synchronized step-wise progression. This presents a mismatch to the underlying network conditions in practice: collective operations are vulnera

Jiazhen Yang, Junjun Zheng, Kejia Chen, Xiangheng Kong

With the rapid development of generative models and multimodal content editing technologies, the key challenge faced by synthetic image detection (SID) lies in cross-distribution generalization to unknown generation sources. In recent years, visual foundation models (VFM), which acquire rich visual priors through large scale image-text alignment pretraining,

Yuhang Tu, Jinfan Wang, Hao Huang, Le Wang

Quantum classification faces two key challenges. First, the difficulty of distinguishing between different classes varies: some class pairs are easy to separate, while others are more challenging. Second, practical execution is affected by noise, finite sampling, and measurement overhead. To address these issues, we propose the Pauli-Space Discriminative-Rep

Kui-Yo Chen, Yat-Hin Suen

The conjugacy problem in braid groups has been extensively studied, particularly from an algorithmic perspective. Established methods based on Garside structures, such as initial summit sets and super summit sets, provide effective procedures for determining whether two braids are conjugate. In contrast, explicit structural descriptions of conjugacy classes

JiYang Wang, Jiawei Chen, Mengqi Xiao, Yu Cheng

Object level hallucination remains a central reliability challenge for vision language models (VLMs), particularly in binary object existence verification. Existing benchmarks emphasize aggregate accuracy but rarely disentangle whether errors stem from perceptual limitations or from the influence of contextual textual priors, leaving underlying failure mecha

Nils Leutenegger

CORRECTION (August 2026): an evaluation-mode defect affected the predictive-coding and STDP conditions of this study; those results should not be used pending re-computation. At V1 and 224px, predictive coding falls from rho = 0.056 to 0.016 and STDP from 0.064 to 0.037, so the claims that STDP leads among trained rules and that PC and STDP lead at V1/V2 are

Shihan Huang, Yue Chen, Richard Chen, Adam Wierman

Electric vehicles (EVs) play a vital role in achieving carbon neutrality. Various approaches have been developed for online optimal EV charging scheduling to maximize their environmental and economic benefits. Among them, Lyapunov optimization has gained wide adoption due to its ease of implementation, no need for predictions, and rigorous performance guaran

Mike Thelwall

Large Language Models (LLMs) can be helpful for literature search and summarisation, but retracted articles can confuse them. This article asks three open weights (offline) LLMs whether 161 high profile retracted articles had been retracted, performing a similar check for a benchmark multidisciplinary set of 34,070 non-retracted articles. Based on titles and

Hikaru Shindo, Henri Rößler, Quentin Delfosse, Kristian Kersting

Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizable policies. Relational concepts, such as "left of" or "close by", serve as foundational building blocks that structure how agents perceive and act. However, conventional approaches require human experts to manu

Phunyapa Suksomboon, Paulo Garcia

State estimation is a fundamental requirement in robotics, where the accurate determination of a robot's state is essential for stable operation despite inherent process disturbances and sensor noise. Traditionally, this is achieved through Kalman filtering, providing a statistically optimal estimate by balancing predictive models with noisy measurements. In

Anand Chitrao, Eknath Ghate

We determine the mod $p$ reductions of the semi-stable representations $V_{k, \mathcal{L}}$ of weight $k \in [p + 5, 2p]\cup[2p + 6, 3p + 1]$ and $v_p(\mathcal{L}) < 1-k/2$ for primes $p \geq 5$. In particular, this shows that the techniques introduced in [CG24] involving the $p$-adic and mod $p$ local Langlands correspondences can be used to compute the red

Victor Korolev, Mikhail Ivanov, Tatiana Kukanova, Artyom Rukavitsa

In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic processes satisfying the It{\^{o}} stochastic differential equations with unknown random drift and diffusion coefficients. We do not attract any additional information and use only the information contained in

Haoxuan Wang, Chen Wang

The deployment of long-context Large Language Models (LLMs) poses significant challenges due to the intense computational cost of self-attention and the substantial memory overhead of the Key-Value Cache (KV Cache). In this paper, we introduce \textit{HieraSparse}, a hierarchical KV Cache compression framework with acceleration kernels that leverage GPU spar

Naman Khandelwal, Bikash K. Behera, Ashok Kumar, Prasanta K. Panigrahi

Variational quantum algorithms offer a promising framework for solving eigenvalue problems on near-term quantum hardware, yet their applicability beyond electronic structure calculations remains relatively unexplored. In this work, we investigate the quantum computing of lattice vibrational and thermodynamical properties by applying the variational quantum e

Yuchen Pan, Soung Chang Liew

Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence generalizes beyond specific market patterns and how it should be trained and evaluated in noisy markets lacking ground truth. We propose a structured framework for training and evaluating such models. Central to o

Akash Samanta, Manish Pratap Singh, Debasis Chaudhuri

Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding representations that are entangled and brittle. We propose Class-Conditional Activation Regularization (CCAR) to explicitly engineer the feature space, imposing a block-diagonal structure via a soft inductive bias.

Changcang Qiao, Chen-Chia Hsu, Tao Zhang, Zhiming Sun

Topological materials ranging from topological insulators to semimetals host many novel quantum phenomena including quantum spin Hall effect and topological Fermi arcs. Transitions between these topological phases have attracted much research interest. We performed angle-resolved photoemission spectroscopy (ARPES) on WTe2 ranging from a monolayer to the bulk

Dongyi He, Yuanquan Gao, Bin Jiang, He Yan

Accurate traffic forecasting is crucial for intelligent transportation systems, supporting effective traffic management, congestion reduction, and informed urban planning. However, traditional models often fail to adequately capture the intricate spatio-temporal dependencies present in traffic data. To overcome these limitations, we introduce GAMMA-Net, a no

Xudong Li, Jiaxi Tan, Ziyin Zhou, Yan Zhong

Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demands not only accurate scores but also actionable, localized feedback. However, current MLLM-based methods adopt a single-look, language-only paradigm, which departs from human evidence-seeking judgment and yiel

Masakazu Teragaito

There are only three known strongly invertible hyperbolic L-space knots with braid index four and tunnel number two. They are t09284, t10496 and o9_34409 in the SnapPy census. In this paper, we give the first infinite family of strongly invertible hyperbolic L-space knots with braid index four and tunnel number two that includes t10496 and o9_34409.

Bo Yang, Lei Zhang, Bin Liu

We study the defocusing energy-critical inhomogeneous nonlinear Schr\"odinger equation \[ i\partial_tu+\Delta u=|x|^{-b}|u|^{\frac{4-2b}{d-2}}u, \qquad (t,x)\in\R\times\R^d, \] with initial data $u_0\in\dot H_x^1(\R^d)$, where $d\ge 3$ and $0<b<\min\{2,\frac d2\}$. We prove global well-posedness and scattering for arbitrary non-radial data. The main difficul

Yuhan Gao, Shuhao Kang, Xin He, Bing Li

Camouflaged object detection (COD) aims to segment objects that closely resemble their surrounding environments. Vision foundation models (VFMs) provide strong transferable representations for COD, but their large-scale architectures and full-token processing incur substantial computational overhead. To address this issue, we propose Certainty-Aware Token Sp

Hogyun Yu, Michaël Michaux, Donghyun Kim, Changhee Song

We present the second paper in our program characterizing the impact of massive neutrinos on the multiscale cosmic web using global topology and persistent homology. Building on the methodology established in Paper I, based on discrete Morse theory, we analyze a subset of the Quijote simulations to compute persistent diagrams, Betti curves, and additional to

Rudra Ranajee Saha, Laks V. S. Lakshmanan, Raymond T. Ng

The proliferation of online debate platforms and social media has led to an unprecedented volume of argumentative content on controversial topics from multiple perspectives. While this wealth of perspectives offers opportunities for developing critical thinking and breaking filter bubbles (Pariser 2011), the sheer volume and complexity of arguments make it c

Chenwei Zhang

This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with deep learning. It focuses on two areas: DNA reaction kinetics and cryogenic electron microscopy (cryo-EM). In the first part, we present ViDa, a biophysics-informed framework leveraging variational autoencoder

Yunpeng Zhou

Sparse Autoencoders (SAEs) have emerged as a powerful paradigm for disentangling feature superposition in transformer-based architectures, enabling precise control via activation steering. However, the theoretical foundations of compositional steering -- the simultaneous activation of distinct semantic latents -- remain under-explored. The prevailing Linear

Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh, Masashi Hamaya

Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-acceler

Tingting Liu, Jumin Qiu, Xintong Shi, Qiegen Liu

As the explosive growth of visual data increasingly strains the latency and energy limits of conventional electronic computing, optical analog computing has re-emerged as a disruptive paradigm for zero-power, speed-of-light information processing. Propelled by the unprecedented wave-manipulation capabilities of optical metasurfaces, this field is undergoing

Xu Cui, Xinyan Liu, Chen Yang, Zhaobo Qi

Fine-grained semantic segmentation of transmission-corridor point clouds is fundamental for intelligent power-line inspection. However, current progress is limited by realistic data scarcity and the difficulty of modeling global corridor structure and local geometric details in long, heterogeneous scenes. Existing public datasets usually provide only a few c

Nicolás Morales-Durán, Jingtian Shi, Cristian Voinea, Paweł Potasz

We investigate the effect of remote band mixing on the stability of fractional Chern insulators in a family of models that approximate continuum descriptions of moir\'e materials. Our results suggest that the experimentally observed asymmetry between filling fractions $\nu=1/3$ and $\nu=2/3$ in twisted MoTe$_2$ originates from a competition between a fractio

Haskelle T. White-Gianella, Joshua Krissansen-Totton

Arid terrestrial exoplanets are potentially abundant and are thus interesting targets in the search for life. In particular, M-dwarf planets such as those in the TRAPPIST-1 system may possess limited surface water, whereas early solar system terrestrials may have had small surface water inventories postmagma ocean solidification. On modern Earth, there is en

Ziwen Pan, Zihan Liang, Jad Kabbara, Ali Emami

Large language models (LLMs) tuned for safety often avoid acknowledging demographic differences, even when such acknowledgment is factually correct (e.g., ancestry-based disease incidence) or contextually justified (e.g., religious hiring preferences). This identity-blindness yields incorrect responses, unnecessary refusals, or generic "equal-treatment" defa

Martin Sundermann, Naoki Ito, Daisuke Takegami, Chun-Fu Chang

UCd$_{11}$ is an antiferromagnetic uranium intermetallic compound ($T_{\rm N}$ = 5.3K) with enhanced electron mass and uranium-uranium spacings nearly twice the Hill limit, suggesting a weakly hybridized 5$f$ electronic character. Various x-ray spectroscopy techniques indicate that uranium in UCd$_{11}$ adopts the formal U$^{3+}$ 5$f^3$ configuration, while

Hagen Holthusen, Kevin Linka, Ellen Kuhl

Can we learn the physics of matter in motion directly from images and video--and trust it? Answering this question requires integrating experiments, physics-based simulation, and data across traditionally separate disciplines. Much of this knowledge is visual and temporal rather than textual: images and videos encode structure, dynamics, and causality that e

Yixuan Wang

This thesis develops numerical and theoretical approaches for understanding and analyzing singularity formation in Partial Differential Equations (PDEs). The singularity formation in the Navier-Stokes Equation (NSE) is famously challenging as one of the seven Clay Prize problems. Unlike simpler equations such as the Nonlinear Heat (NLH) or Keller-Segel (KS)

Yiheng Chen, Zihui Ma, Peishi Jiang, Yilong Dai

Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In this paper, we propose Earth Foundation Model-guided Diffusion (EFDiff), a novel framework for super-resolution under extreme spatial degradation. EFDiff use

Shuyang He, Qingyang Liu, Jing Ma

Furstenberg's flow on the infinite-dimensional torus $\mathbb{T}^\omega$ is defined by \[ T (x_1, x_2, \ldots, x_\nu, \ldots) = (x_1 + \alpha, x_2 + h(x_1), \ldots, x_\nu + h(x_1 + (\nu-2)\beta), \ldots) \] with $\alpha\in \mathbb{R}$ satisfying certain diophantine conditions, $\beta\in \mathbb{R}\backslash\mathbb{Q},$ and $h: \mathbb{R}\to \mathbb{R}$ being

Jinchang Zhu, Jindong Li, Cheng Zhang, Jiahong Liu

Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interactions. Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. This paradigm fails to capture the associative structure of hum

Alfredo Metere

We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attestable peer trust, deny-by-default external connectivity, signed-module loading, and a tamper-evident audit trail -- typically regulated industries (financial services, healthcare, defense, government). The framework shi

Kai Yi

Defant and Zheng introduced a consecutive-pattern-avoiding stack sort map $SC_{\sigma}$, where the stack must avoid a consecutive pattern $\sigma$. Seidel and Sun disproved a conjecture in Defant and Zheng's paper about the maximum sort-number of a length $n$ permutation under $SC_{231}$. In this paper, we compute sort-numbers for each permutation of length

S. Tanda, S. Kashimoto, H. Yamamoto, K. Inagaki

The physics of a genuine one-dimensional system in which electrons are confined in one direction remains unclear. The actual electronic state of such a genuinely one-dimensional system has not been investigated in previous experiments, for they have all been conducted on quasi-one-dimensional specimens, namely in strongly anisotropic bulk crystals. Conventio

Zahid Hasan, Masud Ahmed, Nirmalya Roy

Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior approaches predominantly rely on the Poincar\'e ball model, which suffers from numerical instability, optimization, and computational challenges. We propose a novel, tractable, architecture-agnostic semantic

Haibin Jiao

Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks (RNN), convolutional neural networks (CNN), and transformers. Specifically, the Transformer encoder, with its unique attention mechanism an

Nges Brian Njungle, Eric Jahns, Michel A. Kinsy

Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphic Encryption (HE) enables computation directly on encrypted data, allowing neural network inference without revealing raw inputs. While prior works have largely focused on inference over a single encrypted imag

Vasudevarao Allu, Shobhit Kumar

Let $\mathcal{A}$ denote the class of analytic functions $f$ such that $f(0)=0$ and $f'(0)=1$ in the unit disk $\mathbb{D}:=\{z \in \mathbb{C}: |z|<1\}.$ We examine the properties of the class $\mathcal{C}(\varphi)$ defined as $\mathcal{C}(\varphi) := \left\{ f \in \mathcal{A} : 1+zf''(z)/f'(z) \prec \varphi(z):=1+z+ m/n\, \, z^2, \text{ with } 2m \le n,\tex

Nges Brian Njungle, Edwin P. Kayang, Mishel J. Paul, Michel A. Kinsy

Timing side-channel attacks exploit variations in program execution time to recover sensitive information. Cryptographic implementations are especially vulnerable to these attacks, since even small timing differences in operations such as modular exponentiation or key comparisons can be exploited to extract highly sensitive information, such as secret keys.

Shuang-Shuo Chu, Han-Qiu Zhang, Jian-Song Zhang, Wen-Xue Zhong

We propose a scheme to simultaneously cool multiple degenerate mechanical modes in optomechanical systems beyond the resolved sideband regime. In general, one of the main obstacles for cooling degenerate mechanical modes is the so-called dark-mode effect. The Duffing nonlinearities (mechanical nonlinearities) can be used to overcome the dark-mode effect of d

Jiaxin Zhang, Xiangyu Peng, Qinglin Chen, Qinyuan Ye

On-policy distillation (OPD) is an increasingly important paradigm for post-training language models. However, we identify a pervasive Scaling Law of Miscalibration: while OPD effectively improves task accuracy, it systematically traps models in severe overconfidence. We trace this failure to an information mismatch: teacher supervision is formed under privi

Yue Gruszecki, Elliot Anshelevich

We study Nash equilibria in strategic facility location games where clients are located in an arbitrary metric space. Specifically, there are $n$ clients, and the goal is to choose a facility from a set of given locations, so that the total distance from the clients to the facility is as small as possible. While some of the clients are always truthful, $k$ o

Guk Heon Kim, Minseo Kim, Kwangsan Chun, Jaemin Kim

Phase-field models for anisotropic fracture employ two complementary mechanisms: (i) the anisotropic crack density function, controlling direction-dependent fracture resistance, and (ii) the anisotropic strain energy, governing the fracture driving force. Although the unified framework was presented in Pranavi et al.[Comput. Mech., 73 (2024)], the distinct r

Sanju Mandal, Molla Basir Ahamed

In this paper, we study extremal problems for coefficient functionals associated with a distinguished subclass of holomorphic semigroup generators, denoted by $\mathcal{A}_{\beta}$ ($0 \le \beta \le 1$), defined on the unit disk $\mathbb{D}$. This class forms a natural filtration of the class $\mathcal{G}_0$ of infinitesimal generators, with the class $\math

Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz

Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination p

Yixuan Tang, Yi Yang

Merging separately trained LoRA adapters is a practical alternative to joint multi-task training, but it often hurts performance. Existing methods usually treat the LoRA update $\Delta W = BA$ as a single object and do not distinguish the two LoRA matrices. We show that the main source of LoRA merge interference comes from the output-side matrix $B$. Across

Chuyi Liu, Yifeng Guan, Jingyuan Li, Mao Su

We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conventional CG approaches that rely on a mean-field potential, SD-GLE utilizes a variational Bayesian framework with a random field prior to explicitly disentangle static spatial disord

Bo Yan, Weikai Lin, Yada Zhu, Song Wang

Multi-turn jailbreak attacks progressively erode LLM safety alignment across seemingly innocuous conversation turns, achieving success rates exceeding 90% against state-of-the-art models. Existing alignment-based and guardrail methods suffer from three key limitations: they require costly weight modification, evaluate each turn independently without modeling

Haibin Jiao

Vision Transformer (ViT) has brought new breakthroughs to the field of image classification by introducing the self-attention mechanism and Graph Convolutional Networks(GCN) have been proposed and successfully applied in data representation and analysis. However, there are key challenges which limit their further development: (1) The patch size selected by V

Yunshan Peng, Ji Wu, Wentao Bai, Yunke Bai

Reach and Frequency (R&F) contract advertising is an important form of widely used brand advertising. Unlike performance advertising, R&F contracts emphasize controllable delivery of UV and PV under given targeting, scheduling, and frequency control constraints. In practical systems, advertisers typically need to view the UV, PV change curves at different bu

Muhammad Junayed Hasan Zahed, Chieh Tsai, Salim Hariri, Hossein Rastgoftar

This paper presents an online reinforcement-learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands directly, the proposed method selects gain vectors online from a finite library of pre-certified stabilizing controllers, thereby preserving the structure of the underlying snap-based co

Sosui Moribe, Taketoshi Ushiama

Serendipity-oriented recommender systems expose users to unfamiliar items to counter filter bubbles, yet mere exposure does not ensure that users will understand or appreciate the content they encounter. We propose Peer Recommendation, a framework in which a user and an AI agent (Peer) with distinct preferences collaboratively explore unfamiliar content. Unl

Chongsheng Zhang, Hao Wang, Zelong Yu, Esteban Garces Arias

Imbalanced data are commonly present in real-world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text generation, the application of LLMs to the synthesis of relational/structured tabular data remains underexplored. Moreover, existing approaches lack an effective feedback mechanism to