April 2026 arXiv papers — page 150

M. Kriginsky, J. Leenaarts, J. de la Cruz Rodriguez, S. Danilovic

The solar chromosphere is a transition layer between the cool, dense photosphere and the hot, rarefied corona. This boundary region plays a key role in regulating energy transport and structuring the magnetic field throughout the solar atmosphere. Understanding its thermodynamic and magnetic properties is essential to model and interpret solar phenomena. Thi

Yahia Idriss Benalioua, Nathan Lhote, Pierre-Alain Reynier

In this work, we study minimization of rational functions given as appending streaming string transducers (aSST for short). We rely on an algebraic presentation of these functions, known as bimachines, to address the minimization of both states and registers of aSST. First, we show a bijection between a subclass of aSST and bimachines, which maps the numbers

Isabel Agadagba, Triphonia Kilasara, Takudzwa Tarutira, Noah Shumba

Digital payment systems have become a cornerstone of consumer finance in Africa. Prominent payment categories include money transfer applications, mobile money, cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs). While there are studies exploring how and why people use individual digital payment systems (both in Africa and beyond), we

Marius Mavridis, Juan De Gregorio, Raul Toral, David Sanchez

We show that short-range phoneme dependencies encode large-scale patterns of linguistic relatedness, with direct implications for quantitative typology and evolutionary linguistics. Specifically, using an information-theoretic framework, we argue that phoneme sequences modeled as second-order Markov chains essentially capture the statistical correlations of

Gengjia Chang, Xining Ge, Weijun Yuan, Zhan Li

Single-image super-resolution has progressed from deep convolutional baselines to stronger Transformer and state-space architectures, yet the corresponding performance gains typically come with higher training cost, longer engineering iteration, and heavier deployment burden. In many practical settings, multiple pretrained models with partially complementary

Artem Gadzhiev, Andrew Kislov

Providing AI agents with reliable long-term memory that does not hallucinate remains an open problem. Current approaches to memory for LLM agents -- sliding windows, summarization, embedding-based RAG, and flat fact extraction -- each reduce token cost but introduce catastrophic information loss, semantic drift, or uncontrolled hallucination about the user.

Anand Umashankar, Karam Tomotaki-Dawoud, Nicolai Schneider

Remote sensing archives are inherently distributed: Earth observation missions such as Sentinel-1, Sentinel-2, and Sentinel-3 have collectively accumulated more than 5 petabytes of imagery, stored and processed across many geographically dispersed platforms. Training machine learning models on such data in a centralized fashion is impractical due to data vol

Yiqing Wang

The Kolmogorov-Smirnov (KS) statistic is widely used in credit risk model monitoring and validation to assess discriminatory power. In practice, a material decline in KS often triggers governance review and requires validation teams to identify the breach source and the potential business risk. However, such diagnosis is frequently conducted on an ad hoc bas

Vincent S. Kather, Sylvain Haupert, Burooj Ghani, Dan Stowell

1. Natural sounds have been recorded for millions of hours over the previous decades using passive acoustic monitoring. Improvements in deep learning models have vastly accelerated the analysis of large portions of this data. While new models advance the state-of-the-art, accessing them using tools to harness their full potential is not always straightforwar

Tianqi Wang, Wenchao Du, Hongyu Yang

Diffusion-based sparse-view CT (SVCT) imaging has achieved remarkable advancements in recent years, thanks to its more stable generative capability. However, recovering reliable image content and visually consistent textures is still a crucial challenge. In this paper, we present a Progressively Texture-aware Diffusion (PTD) model, a coarse-to-fine learning

Michael Thorne

Over fifty years ago, Robert May applied random matrix theory to show that as ecological systems grow in size, stability decreases. What emerged from this and the critique that followed was decades of what has been called the complexity-stability debate. However, decades of critique over the assumptions that Robert May applied in carrying out his analysis ha

Liujie Zhang, Benzhe Ning, Rui Yang, Xiaoyan Yu

Reinforcement learning (RL) post-training has proven effective at unlocking reasoning, self-reflection, and tool-use capabilities in large language models. As models extend to omni-modal inputs and agentic multi-turn workflows, RL training systems face three interdependent challenges: heterogeneous data flows, operational robustness at scale, and the stalene

Tobias Binder, Edward Wang

Sommerfeld-enhanced annihilation cross sections in the presence of nearly zero-energy bound states can become so large that perturbative partial-wave unitarity appears to be violated. Previous literature incorporated the short-distance annihilation potential self-consistently into the computation of the Schr\"odinger wave function at the origin, leading to t

Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang

Voice imitation aims to transform source speech to match a reference speaker's timbre and speaking style while preserving linguistic content. A straightforward approach is to train on triplets of (source, reference, target), where source and target share the same content but target matches the reference's voice characteristics, yet such data is extremely sca

Ozioma C. Oguine, Elmira Rashidi, Pamela J. Wisniewski, Karla Badillo-Urquiola

Ecological Momentary Assessment (EMA) is widely used to study adolescents' experiences; yet, how the design of EMA platforms shapes engagement, research practices, and power dynamics in youth studies remains under-examined. We developed a youth-centered EMA platform prioritizing youth engagement and researcher support, and evaluated it through a case study o

Wenjun Xiong, Yan Chen, Mingya Long, Qizhai Li

High-dimensional inference methods often rely on coefficient sparsity, an assumption that can be restrictive when signals are dense but individually weak. In such settings, valid inference may still be possible if the covariates exhibit sparse conditional dependence. Motivated by this observation, we propose Neighborhood-Localized Nested Regression (NLNR), a

David Puertas-Ramirez, Raul Fernandez-Matellan, David Martin Gomez, Jesus G. Boticario

In vehicles with partial or conditional driving automation (SAE Levels 2-3), the driver remains responsible for supervising the system and responding to take-over requests. Therefore, reliable driver monitoring is essential for safe human-automation collaboration. However, most existing Driver Monitoring Systems rely on generalized models that ignore individ

Ningyan Zhu, Huacan Wang, Jie Zhou, Feiyu Chen

The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step research. This scale of adoption signals that two parallel arcs of development have reached an inflection point. First is a paradigm shift in AI engineering, evolving

Haolin Li, Shuyang Jiang, Ruipeng Zhang, Jiangchao Yao

While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning data. To address this issue, existing approaches typically distill chain-of-thought reasoning traces from large proprietary models via supervised fine-tuning, then conduct reinforcement learning (RL). These method

Hanbo Huang, Xuan Gong, Yiran Zhang, Hao Zheng

Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to black-box spoofing remains insufficiently evaluated. Existing evaluation methods often demand extensive datasets and white-box access to algorithmic internals, limiting their practical applicability. In this paper

Enrico Caprioglio, Luc Berthouze

Understanding what and how causal dynamical mechanisms generate collective phenomena is a central challenge in complexity science. Recent studies have focused on identifying the mechanisms underlying the synergistic interdependencies that characterise these phenomena in systems with fixed interaction structures. Yet, real-world systems displaying collective

M. W. Przewozniczek, B. Frej, M. M. Komarnicki, M. Prusik

In optimization problems, some variable subsets may have a joint non-linear or non-monotonical influence on the function value. Therefore, knowledge of variable dependencies may be crucial for effective optimization, and many state-of-the-art optimizers leverage it to improve performance. However, some real-world problem instances may be the subject of noise

Weixian Waylon Li, Jiaxin Zhang, Xianan Jim Yang, Tiejun Ma

Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (burying old-yet-permanent knowledge), simply overwriting outdated facts, or requiring an expensive LLM call at every ingestion step, leaving the

Wenqing Wu, Yi Zhao, Yuzhuo Wang, Siyou Li

Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing pressure on human reviewers. While large language models (LLMs), including those fine-tuned on peer review data, have shown promise in generating review comments, the absence of a dedicated benchmark has limited

Saswata Dasgupta, Boyuan Liu, Anastasia Fialkov, Furen Deng

High-mass X-ray binaries are one of the primary drivers of the 21-cm signal from Cosmic Dawn and Reionization, playing a leading role in the thermal history of the intergalactic medium. In traditional semi-numerical simulations, a deterministic scaling relation between the total X-ray luminosity of high-mass X-ray binaries, $L_{\rm X}$, and star formation ra

Bhavna Bose, Muhammad Faryad

Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of noisy intermediate-scale quantum (NISQ) hardware. Although most existing studies have focused primarily on quantum circuit noise in isolation, the combined influence of corrupted c

Tongyu Shi, Yutang Li, Zhanyuan Li, Qian Liu

Current large language models require hundreds of billions of parameters yet struggle with domain-specific reasoning and tool coordination in materials science. Here, we present MatBrain, a lightweight collaborative agent system with two synergistic models specialization for crystal materials research. MatBrain employs a dual-model architecture: Mat-R1 (30B

Zijian Ding, Fenghai Li, Ziyi Wang, Joel Chan

Research ideation requires navigating trade-offs across multiple evaluative dimensions, yet most AI-assisted ideation tools leave this multi-dimensional reasoning unsupported, or reducing evaluation to unipolar scales where "more is better". We present ResearchCube, a system that reframes evaluation dimensions as bipolar trade-off spectra (e.g., theory-drive

Zhiqiang Hou, Jian-Feng Zhu

In this paper, we obtain an improved H\"older regularity for quasiregular gradient mappings which was studied by Baernstein and Kovalev.

Xi-Wei Pan, Shi-Wen An, Jin-Guo Liu

Solving an NP-hard optimization problem often requires reformulating it for a specific solver -- quantum hardware, a commercial optimizer, or a domain heuristic. A tool for polynomial-time reductions between hard problems would let practitioners route any supported problem to any supported solver through a single interface. Building such a library at scale,

Gui-Long Jiang, Hai-Rui Wei

Implementation of high-dimensional (HD) quantum gates shows very promising perspectives for HD quantum computation. A bipartite quantum system with arbitrary dimensions $n$ and $m$ is termed a quNit-quMit. Here we propose a synthesis scheme to construct the quantum circuit for general quNit-quMit gates with controlled increment (CINC) gates and local gates.

Maria Gabriela Jordão Oliveira, Karl Michael Ziems, Nina Glaser

Krylov subspace methods are among the most extensively studied early fault-tolerant quantum algorithms for estimating ground-state energies of quantum systems. However, the rapid onset of ill-conditioning might make accurate energies difficult or even impossible to retrieve. In this communication, we analyse the numerical stability and statistical problems o

Preston T. Abadie, Donald J. Docimo

This work explores controllability and the control effort required for lithium-ion batteries. Battery packs have become a critical technology in both personal and professional applications as a means to store large amounts of energy. Management of cells in a pack becomes increasingly difficult though, with charging and discharging operations requiring more c

Denizalp Goktas, Gerardo Riaño-Briceño, Alif Abdullah, Aryan Nair

Foundation models have transformed natural language processing and computer vision, and a rapidly growing literature on time-series foundation models (TSFMs) seeks to replicate this success in forecasting. While recent open-source models demonstrate the promise of TSFMs, the field lacks a comprehensive and community-accepted model evaluation framework. We se

Elena Bannikova, Volodymyr Akhmetov, Peter Berczik, Serhii Skolota

Global m=1 asymmetries are observed in many self-gravitating astrophysical systems and are often interpreted as large-scale slow modes in near-Keplerian potentials. Prominent examples include eccentric nuclear disks in galactic centres, such as the double nucleus of M31. However, the origin and long-term stability of such modes remain unclear. We investigate

Azaz Ahmad, Pankaj Bhalla, Snehasish Nandy, Tanay Nag

We develop a semiclassical Boltzmann theory for frequency-dependent magneto-optical transport in Weyl semimetals (WSMs), incorporating momentum-dependent relaxation via a scattering matrix approach. The interplay of orbital magnetic moment, Weyl cone tilt, intervalley scattering, and electromagnetic driving is analyzed to obtain the full conductivity tensor

Ivano Colombaro, Marc Tudela-Pi

Modeling relaxation phenomena in complex media is central to understanding multiscale dynamics in materials science, bioengineering and condensed matter physics. Existing fractional-order models, while flexible, sometimes lack physical interpretability, closed-form time-domain expressions, and compatibility with physically realizable architectures. In this w

Denis S. Grebenkov, Michael Levitin, Iosif Polterovich

The study of the Dirichlet-to-Neumann map and the associated Steklov problem for the Laplace equation has been a central topic in spectral geometry over the past decade. In this survey, we consider a more general framework in which the Laplace equation is replaced by the Helmholtz equation. We examine how the properties of the Dirichlet-to-Neumann eigenvalue

Yichen Wang, Ervin Győri, Casey Tompkins, Xiamiao Zhao

Let $P_k$ denote the path with $k$ vertices, and $\mathrm{ex}_{\mathcal{OP}}(n,H^{\mathrm{ind}},\emptyset)$ be the maximum number of induced copies of $H$ in an $n$-vertex outerplanar graph. In this paper, we determine the exact value of $\mathrm{ex}_{\mathcal{OP}}(n,P_3^{\mathrm{ind}},\emptyset)$ for all $n$, and give an asymptotic value of $\mathrm{ex}_{\m

M. W. Przewozniczek, F. Chicano, R. Tinós, M. M. Komarnicki

Surrogates provide a cheap solution evaluation and offer significant leverage for optimizing computationally expensive problems. Usually, surrogates only approximate the original function. Recently, the perfect linear surrogates were proposed that ideally represent the original function. These surrogates do not mimic the original function. In fact, they are

Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim

We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents. However, the dynamics of multi-agent collaboration under privacy constraints remain poorly understood. In this work, we present $PAC\text{-}Bench$, a benchmark for systematic evaluation of multi-agent collabor

Xinda Wang, Zhengxu Hou, Yangshijie Zhang, Bingren Yan

Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that require either annotated data or powerful closed-source models. Inspired by recent work on unsupervised reinforcement learning for mathematical reasoning using confidence-based endogenous rewards, we investigate

Shanchuan Lin, Ceyuan Yang, Zhijie Lin, Hao Chen

We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces s

Claudia Bucur, Luca Lombardini

In this paper, we introduce a functional and a geometric setting for an obstacle problem for nonlocal minimal graphs. In particular we study existence of solutions, a priori estimates, and we prove the equivalence of the two settings. We then observe a striking stickiness phenomena when the fractional parameter is small and the data at infinity is not too la

Chengyu Liu, Xiang Zhou

Wasserstein gradient flows (WGFs) describe the evolution of probability distributions in Wasserstein space as steepest descent dynamics for a free energy functional. Computing the full path from an arbitrary initial distribution to equilibrium is challenging, especially in high dimensions. Eulerian methods suffer from the curse of dimensionality, while exist

Jinhua Wang, Biswa Sengupta

Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We present a methodology for LLM-assisted continuous code translation in which a large language model translates a production Rust codebase (648K LOC, 65 crates) into Python (41K LOC, 28 modules), with public agent

Do Won Kim, Cody Buntain, Giovanni Luca Ciampaglia

Social media feed algorithms infer user preferences from their past behaviors. Yet what drives engagement often diverges from what users value. We examine this gap between stated preferences (what users say they prefer) and revealed preferences (what their behavior suggests they prefer) among young adults, a group deeply embedded in algorithmically mediated

N. Nari, A. Suárez Mascareño, J. I. González Hernández, A. K. Stefanov

Context. The development of refined instruments and techniques for a detailed analysis of the radial velocities (RVs) of stars other than the Sun allows the presence of planetary signals of amplitude below 1 ms$^{-1}$ to be investigated. Long-term RV surveys allow the detection of Earth-like and super-Earth-like planets in the habitable zones of Sun-like sta

Luke Soneji, Simon Crampin, Marcin Mucha-Kruczynski

Low-energy electronic behavior in graphite crystals is highly dependent on the relative stacking arrangement of the constituent layers. Topologically non-trivial electronic states can arise due to interrupted rhombohedral (ABC) stacking, localized at the edges of the stacking region, but not in the case of Bernal (AB) stacking. Here, we study the electronic

Hojae Han, Jaejin Kim, Seung-won Hwang, Yu Jin Kim

This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first to ground predictions. One grounding strategy is direct execution of generated code, but even minor errors can cause failures. To address this, we introduce LLM-based pseudocode exe

Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang

We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin systems. We generalize the Behler-Parrinello (BP) ML architecture -- originally developed for quantum molecular dynamics -- to construct scalable and transferable ML models capable of capturing the intricate depend

Jinane Bazzi, Mariam Rakka, Fadi Kurdahi, Mohammed E. Fouda

The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fundamental inefficiencies in existing accelerators. While GPUs handle prefill workloads efficiently, the autoregressive decoding phase is dominated by GEMV operations that are inherently memory-bound, resulting i

Bin Han, Di Feng, Zexin Fang, Jie Wang

When users exercise data deletion rights under the General Data Protection Regulation (GDPR) and similar regulations, mobile network operators face a tradeoff: excessive machine unlearning degrades model accuracy and incurs retraining costs, yet existing pricing mechanisms for data retention require the server to know every user's private privacy and accurac

Stefan Lenz, Sotiris Michaelides, Moritz Rickert, Jonas Holtwick

Traditionally, industrial control systems (ICS) were designed without security in mind, prioritizing availability and real-time communication. As these systems increasingly become targets of powerful adversaries, security can no longer be neglected. Driven by flexibility and automation needs, ICS are transitioning from wired to 5G communication, introducing

Miit Daga, Swarna Priya Ramu

Fine-tuning pretrained image classifiers is standard practice, yet which individual samples are forgotten during this process, and whether forgetting patterns are stable or architecture dependent, remains unclear. Understanding these dynamics has direct implications for curriculum design, data pruning, and ensemble construction. We track per-sample correctne

I. Esra Buyuktahtakin

Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management sciences (OR/MS), which have long offered conceptual and methodological foundations for sequential decision-making under uncertainty. At t

Ricardo Bessa, Rui Claro, João Trindade, João Lourenço

The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI leverage the natural language processing capabilities of Large Language Models to generate machine code from natural language descriptions. In the cybersecurity field, red teams can also take advantage of generativ

Xuechun Zhang, Hongliang Lu

Let $n, r, s$ be three positive integers such that $n\geq 2s+5$. Let $K_r$ denote the complete graph of order $r$. Given a graph $F$, the anti-Ramsey number $ar(n,F)$ is defined as the minimum number $C$ such that any edge-coloring of $K_n$ with exactly $C$ colors contains a rainbow copy of $F$. Let $H$ be an edge-colored graph on $K_n$ with at least $g(n,s)

Eric Ngondiep

This paper proposes a strong second-order two-step explicit/implicit technique with spectral orthogonal basis Galerkin finite element method for solving a two-dimensional Gray-Scott model subject to appropriate initial and boundary conditions. The constructed approach discretizes at the first stage utilizing a second-order explicit method while a second-orde

Xiaoyang Chen, Xiaoyang Chen

This book provides a comprehensive and accessible introduction to the emerging field of AI for mathematics. It covers the core principles and diverse applications of using artificial intelligence to advance mathematical research. Through clear explanations, the text explores how AI can discover hidden mathematical patterns, assist in proving complicated theo

D. Ramsey, J. McKeown, J. P. Palastro

The evolution of a charged lepton in the field of an electromagnetic plane wave can be described as a superposition of Volkov states. Here we demonstrate that imposing specific momentum correlations among Volkov states produces a spatiotemporally structured wavepacket whose probability-density peak travels at an arbitrary, tailored velocity. This velocity ca

Pengfeng Li, Chen Huang, Chaoqun Hao, Hongyao Chen

Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. To address this, we pioneer METER to systematically benchmark LLMs across all three levels of the causa

Samuel Salfati

We compare two strategies for compressing the KV cache in transformer inference: rank reduction (discard dimensions) and quantization (keep all dimensions, reduce precision). At matched storage budgets across five models (124M-14B, MHA and GQA), we find that quantization consistently outperforms rank reduction by 4-364 PPL depending on model and compression

Alberto Boscaggin, Walter Dambrosio

We establish a link between different relativistic variants of the Kepler problem. In particular, we show that solutions of the special relativistic model with fixed energy can be reparameterized as solutions of a generalized Kepler equation with an additional $1/r^2$ term in the gravitational potential. This yields a dynamics of the same type as the Levi-Ci

Joshua Hatherley, Sandrine R. Schiller, Iwan Williams, Filippos Stamatiou

Postmortem avatars (PMAs) -- AI systems that simulate a deceased person by being fine-tuned on data they generated or that was generated about them -- have attracted growing scholarly attention, yet their potential role in clinical settings remains largely unexplored. This paper examines the ethics of deploying PMAs as therapeutic tools in grief therapy. Dra

Imtiaz Ul Hassan, Nik Bessis, Ardhendu Behera

Fine-grained human action recognition (FHAR) is challenging because visually similar actions differ by subtle spatio-temporal cues. Many recent systems enhance discriminability with extra modalities (e.g., pose, text, optical flow), but this increases annotation burden and computational cost. We introduce TAG-Head, a lightweight spatio-temporal graph head th

Imanol Miranda, Ander Salaberria, Eneko Agirre, Gorka Azkune

Dual-encoder Vision-Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional benchmarks. We argue that this limitation may stem less from deficient representations than from the standard inference protocol based on global cosine similarity. First, through controlled diagnostic experime

Yichen Wang, Xin Cheng, Ervin Győri, Xiamiao Zhao

We say a hypergraph $\mathcal{H}$ contains a hypergraph $\mathcal{G}$ as trace if there exists a vertex subset $S \subseteq V(\mathcal{H})$ such that $|S| = |V(\mathcal{G})|$ and $\{e \cap S: e \in E(\mathcal{H})\}$ contains $\mathcal{G}$ as a sub-hypergraph. We use $\mathrm{ex}_r(n, \mathrm{Tr}_r(\mathcal{G}))$ to denote the maximum number of hyperedges in

Zhangke Yang, Zhaoxu Meng

Tendon-bone enthesis connects tendon and bone, two mechanically dissimilar materials, while effectively minimizing stress concentrations, a capability rarely achieved in engineering materials. Its hierarchical organization and graded variations in composition or mineralization are widely recognized as key contributors to its exceptional performance. Here, we

Zuyu Ma, Yilin Song, Kai Yang, Xiaoyi Zhang

We study the threshold scattering problem for the energy-critical nonlinear Schr\"odinger equation with a repulsive inverse-square potential $\frac{a}{|x|^2} > 0$ in dimensions $d= 4, 5, 6$. On the energy level surface determined by the ground state of the energy-critical NLS without potential, we show that, despite the absence of a ground state in this sett

Vjekoslav Vulić, Neven Šantić, Hrvoje Buljan, Damir Aumiler

The manipulation of light in periodic structures is fundamental to the development of discrete photonics and provides a versatile platform for controlling light propagation in integrated and quantum photonic systems. This work reports the experimental observation of discrete one-dimensional (1D) solitons in a photonic lattice, optically induced in warm rubid

Qinyi Lu, Nan Liu, Wei Kang

We consider a coded caching problem with multiple demands under a privacy constraint. In this problem, a server with access to \(N\) files serves \(K\) users over a shared link, and each user requests \(L\) distinct files. The privacy constraint requires that each user obtain no information about the demands of the other users. We propose a new achievable sc

An Luo, Jie Ding

As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promising remedy is multi-bit image watermarking, which embeds a multi-bit message into an image so that a verifier can later detect whether the image is generated by someone and further identify the source by decodi

Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong, Hitesh Laxmichand Patel

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedicated framework for assessing human-centric alignment in vision-language systems. We offer two contributions to address this gap. First, we introduce Anthropogenic Regional Adaptati

Zhen Hong Yu, Yu Miao

In the present paper, we derive Berry-Esseen bounds for the estimation of diversity indices on countable alphabets. A general non-asymptotic convergence rate is established for the plug-in estimator of a wide class of indices, including Simpson's index and Re\'{n}yi's entropy. For the practically crucial case of Shannon entropy, we provide explicit Berry-Ess

Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya, Artem Filippov

This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal of this challenge was to develop detection models capable of distinguishing real images from generated ones in realistic scenarios: the images are often transformed (cropped, resize

Hartmut Löwen

When a cup of hot coffee is suddenly put into a cold environment, it cools down as a function of time $t$ until the internal temperature $T_\text{int}$ of the coffee equals the external ambient temperature $T_\text{ext}$. This instantaneous shock-freezing corresponds to an imposed cooling protocol of the external temperature $T_\text{ext}(t)$, ideally descri

Rajesh Kumar, Waqar Ali, Junaid Ahmed, Najma Imtiaz Ali

Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification optional. We introduce execution-grounded verification as a first-class principle: every code change must survive sandboxed execution before propagation. We instantiate this principle in AGENTFORGE, a multi-agent

Weidong Tang, Bohan Zhang, Zhixiang Chi, ZiZhang Wu

On-the-Fly Category Discovery (OCD) requires a model, trained on an offline support set, to recognize known classes while discovering new ones from an online streaming sequence. Existing methods focus heavily on offline training. They aim to learn discriminative representations on the support set so that novel classes can be separated at test time. However,

Yanting Li, Zhuoyang Jiang, Enyan Dai, Lei Wang

Goal-directed molecular generation requires satisfying heterogeneous constraints such as protein--ligand compatibility and multi-objective drug-like properties, yet existing methods often optimize these constraints in isolation, failing to reconcile conflicting objectives (e.g., affinity vs. safety), and struggle to navigate the non-differentiable chemical s

Adam B. Barrett, Borjan Milinkovic, Pedro A. M. Mediano, Fernando E. Rosas

The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious experience, to describe the quantity and quality of consciousness for any physical system that possesses it. IIT has generated considerable debate, which has engendered some misunderst

Lydia Blümel, Kai Sauerwald, Kenneth Skiba, Matthias Thimm

We show that deciding whether an argument a is stronger than an argument b with respect to the discussion-based semantics of Amgoud and Ben-Naim is decidable in polynomial time. At its core, this problem is about deciding whether, for two vertices in a graph, the number of walks of each length ending in those vertices is the same. We employ results from auto

Lea Karbevska, Liming Xu, Zehui Dai, Sara AlMahri

As global political tensions rise and the anticipation of additional tariffs from the United States on international trade increases, the issues of economic independence and supply chain resilience become more prominent. The importance of supply chain resilience has been further underscored by disruptions caused by the COVID-19 pandemic and the ongoing war i

Julen Larrucea, Marita Oliv, Jeanette Lorenz

We present a hardware-validated reference dataset for variational ground-state energy calculations of the hydrogen molecule H\(_2\) on several IBM Quantum processors available in 2026. Using a standardized workflow, we benchmark the impact of shot count, backend choice, optimization strategy, and runtime variability on the achievable energy accuracy relative

Kun Liu, Liqun Chen

The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty. Current paradigms, such as Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), frequently induce model sycophancy, while execution-based environments suffer from adversarial "Test Evasion" by unconstrained a

Julien Perradin, Simona Ispas, Anwar Hasmy, Bernard Hehlen

Nexus-CAT (Cluster Analysis Toolkit) is an open-source Python package for cluster detection and percolation analysis of atomistic simulation trajectories. Standard structural tools, such as the pair distribution function or structure factor, fail to capture the long-range connectivity changes underlying amorphous-amorphous transitions in glassy materials. Ne

Jiajun Zhou, Yadong Li, Xuanze Chen, Chen Ma

Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static and rigid routing strategies that enforce a uniform expert budget or coarse-grained expert toggles on all nodes. This limitation overlooks the varying discriminative difficulty of nodes and leads to under-fit

Özlem Tuğfe Demir, Emil Björnson

Many wireless systems divide the baseband processing between two locations, interconnected by a fronthaul. This paper examines the impact of fronthaul quantization on multiple-input multiple-output (MIMO) systems. Starting from a Bussgang-based analysis of quantized single-input single-output (SISO) channels, we extend the framework to MIMO and derive a capa

Yang Ji, Zonghao Chen, Zhihao Xue, Junqin Hu

Real-world image super-resolution is particularly challenging for diffusion models because real degradations are complex, heterogeneous, and rarely modeled explicitly. We propose a degradation-aware and structure-preserving diffusion framework for real-world SR. Specifically, we introduce Degradation-aware Token Injection, which encodes lightweight degradati

Yu Li, Zihao Qi, Yongjun Xu, James J. Zhang

We prove that there is no finitely generated symmetric operad of Gelfand-Kirillov dimension strictly between 1 and 2 that answers an open question posted in 2020. We also classify finitely generated prime symmetric operads of Gelfand-Kirillov dimension 1.

Gengjia Chang, Xining Ge, Weijun Yuan, Zhan Li

This paper presents our solution to the NTIRE 2026 Image Denoising Challenge (Gaussian color image denoising at fixed noise level $\sigma = 50$). Rather than proposing a new restoration backbone, we revisit the performance boundary of the mature Restormer architecture from two complementary directions: stronger data-centric training and more complete Test-Ti

Juhoon Lee, Joseph Seering

Large Language Model (LLM) agents offer a potentially-transformative path forward for generative social science but face a critical crisis of validity. Current simulation evaluation methodologies suffer from the "stopped clock" problem: they confirm that a simulation reached the correct final outcome while ignoring whether the trajectory leading to it was so

S. Aaron McClendon, Jorge Gallego-Feliciano, Stavros Zervoudakis, Antonios Saravanos

Large language model (LLM) agents show promise on realistic tool-use tasks, but deploying capable agents on modest hardware remains challenging. We study whether inference-time scaffolding alone, without any additional training compute, can improve the performance of a small model in complex multi-step environments. Operating on a single 24GB GPU, we evaluat

Victor Schulte, Michael Eichelbeck, Matthias Althoff

Reinforcement learning (RL) can be a powerful alternative to classical control methods when standard model-based control is insufficient, e.g., when deriving a suitable model is intractable or impossible. In many cases, however, the choice between model-based and RL-based control is not obvious. Due to the high computational costs of training RL agents, RL-b

Xiaozhe Li, Tianyi Lyu, Yizhao Yang, Liang Shan

Large Language Models (LLMs) struggle with long-horizon tasks due to the "context bottleneck" and the "lost-in-the-middle" phenomenon, where accumulated noise from verbose environments degrades reasoning over multi-turn interactions. To address this issue, we introduce a symbiotic framework that decouples context management from task execution. Our architect

Marieke Stolte, Jörg Rahnenführer, Andrea Bommert

Quantifying the similarity of two or more datasets has widespread applications in statistics and machine learning. The method choice is, however, difficult due to the abundance of proposed methods and the lack of neutral comparison studies, especially for categorical data. Here, the most promising methods are compared concerning their ability to detect certa

Francisco S. N. Lobo, Tiberiu Harko

We construct a class of stationary, axisymmetric, horizonless spacetimes whose curvature is generated entirely by smooth, localised differential rotation $\Omega(r)$, while the spatial geometry remains exactly flat. Despite vanishing ADM mass, these helicity-supported configurations exhibit non-trivial curvature, finite tidal forces, and a gravitomagnetic fi

Wuping Yang, H. Huang

The non-Hermitian skin effect (NHSE) refers to the accumulation of an extensive number of eigenstates at system boundaries under open boundary conditions (OBCs). As a dynamical consequence, wave packets in such systems drift and ultimately accumulate at a boundary, giving rise to the dynamical skin effect (DSE). While strong quasiperiodic potentials are know

Zekun Wang, Binghao Yue, Yichen Deng, Weitao Pan

High-speed switch packet scheduling demands both line-rate performance and programmability. Existing programmable hardware scheduling models, such as PIFO and PIEO, can express a broad range of scheduling algorithms; however, their semantics are restricted to packet-level ordering and cannot dynamically reorder buffered packets, which limits the support for

Jinying Yang, Qingqi Zeng, Yibo Wang, Meng Lyu

Magnetic topological semimetals exhibit emerging magneto-transport behaviors, such as the giant anomalous Hall effect (AHE), chiral Hall effect, and antisymmetric magnetoresistance. In this work, based on the magnetic Weyl semimetal Co3Sn2S2, we report an intriguing longitudinal domain-wall Hall magnetoresistance in multi-domain states. According to a multi-