April 2026 arXiv papers — page 99

Zan Kai Chong, Hiroyuki Ohsaki, Bryan Ng

Enterprise SLM deployment faces epistemic asymmetry: small models cannot self-correct reasoning errors, while frontier LLMs incur prohibitive costs and data sovereignty risks at scale. We propose Semantic Gradient Descent (SGDe), a teacher-student framework that compiles agentic workflows into discrete execution plans--DAG topologies, system prompts, and det

Dongmin Gang, Kibok Jeong, Taeyoon Kim, Soochang Lee

We introduce a refined version of the 3D index for 3-manifolds, building on the construction of the 3D $\mathcal{N}=2$ gauge theory $T[M]$ by Dimofte-Gaiotto-Gukov and Gang-Yonekura. The refined index is a superconformal index of $T[M]$ equipped with additional gradings that capture enhanced flavor symmetries of the effective theory. Our construction is base

Ryo Hayami

In this paper, we investigate a quandle structure induced by an augmented rack arising from a gauge transformation group. We construct a quandle from a principal bundle and its discrete generalization. When we see a group as a (discrete) principal bundle over a point, this quandle becomes equivalent to the generalized Alexander quandle for its inner automorp

Alexander Saikia, Chiara Di Vece, Zhehua Mao, Sierra Bonilla

Purpose: 3D reconstruction in minimally invasive surgery (MIS) enables enhanced surgical guidance through improved visualisation, tool tracking, and augmented reality. However, traditional RGB-based keypoint detection and matching pipelines struggle with surgical challenges, such as poor texture and complex illumination. We investigate whether using snapshot

Esther Banaian, Min Huang

A $k$-Markov number is a positive integer that appears in a positive integral solution to the Diophantine equation $x^2 + y^2 + z^2 + k(xy + xz + yz) = (3+3k)xyz$. This equation was introduced by Gyoda and Matsushita. When $k =0$, this definition recovers that of ordinary Markov numbers. The set of $k$-Markov numbers can be indexed by pairs of coprime positi

Linlin Li, Steven X. Ding, Jiahao Wang, Maiying Zhong

This paper deals with system representations in finite-sample signal subspaces and their application to data-driven fault detection. The first part addresses concepts of finite-sample image and kernel system representations and, associated with them, image and residual subspaces of finite-sample signals. On this basis, the equivalence between the fundamental

Hongyang Liu, Wei Yan

For the discrete memoryless sources with a countably infinite alphabet, we prove that for any positive integer $k$, there exists a corresponding probability interval such that if the largest symbol probability $p_{1}$ falls in this interval, the optimal code length for the symbol equals $k$. Furthermore, for infinite sources, we provide a criterion to determ

Hamza Kheddar, Yassine Himeur, Abbes Amira

This study presents an Adaptive Transfer Learning and Thresholding-based Deep Learning Model (ATL-TDLM) for automated breathing pattern recognition using thermal imaging. Unlike conventional methods that rely on sound-based respiratory data, our approach leverages hierarchical deep feature extraction and adaptive multi-thresholding (AMT) to enhance feature s

Ran Yi, Ruopeng Xu, Dongshu Zhao, Zhaoyang Zhang

This paper investigates the agent design for solving the wireless resource allocation problem without sufficient channel state information (CSI), which cannot be effectively solved via conventional method. In the considered wireless agent design, we provide the general sense-repair-decide-act workflow, which can be used to intelligently solve general wireles

Nur Mohammad Kazi, Ibteshum Khaled, Md. Luthful Hasan Galib, Ali Faruk Shihab

Recently computer vision has seen advancements mainly thanks to Transformer-based models. However many non-Transformer methods are still doing well being a direct competition of Transformer-based models. This review tries to present a comprehensive taxonomy of such methods and organize these methods into categories like convolution-based models, MLP-based mo

Bo Yin, Zhijun Wang, Quansheng Wu

The quantum metric is a fundamental ingredient of band quantum geometry and has recently at tracted intense interest, with most of its transport signatures appearing in the intrinsic second order nonlinear conductivity. In the clean limit, previous works argued that linear response conductivity is insensitive to the quantum metric, while the Berry curvature

Rekha Biswal

In this paper, we study numerical multiplicities of Demazure modules in the excellent filtration of $\mathfrak{sl}_2[t]$-modules $V(\xi)$, where $V(\xi)$ denotes the fusion product associated to a partition $\xi$. We express generating functions for the numerical multiplicities of level $m$ Demazure modules in excellent filtrations of $V(\xi)$ in terms of qu

Aaranay Aadi, Jai Gopal Singla, Nitant Dube

This study presents a Shape from Shading (SfS) framework to enhance sub-metre resolution lunar digital elevation models (DEMs) using imagery from the Orbiter High Resolution Camera (OHRC) aboard Chandrayaan-2. The framework applies SfS to an independent OHRC image of the same region, enabling SfS not just as a refinement tool, but as a source of new topograp

Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin, Sung-Feng Huang

Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive dat

H. Trinh, P. T. Nam, T. N. Nguyen

This paper provides a comprehensive framework for designing functional observers for linear systems subject to delayed output measurements. Moving beyond traditional methodologies, the proposed observer generates an estimate $\hat{z}(t)$ that predicts the current state functional $z(t)=Fx(t)$ using delayed data. By neutralizing sensing latency, the observer

Naoya Hatano, Ryota Kawasumi, Hiroki Saito, Hitoshi Tanaka

By using a Hedberg-type inequality, the Adams trace inequality is extended from Lebesgue spaces to product Morrey spaces.

Gaston Besanson

As generative AI commercializes, competitive advantage is shifting from model training toward inference, distribution, and routing. This paper develops a formal game-theoretic model of vertical foreclosure in inference markets, as the formal-model companion to Besanson and Celani (2026). The model isolates two foreclosure mechanisms operating without predato

Michał Marciniak, Marek Olechowski, Stefan Pokorski

In a recently proposed approach to testing models of inflation by Cosmic Microwave Background (CMB) radiation the reheating temperature is directly expressed in terms of the CMB observables. Its model independent bounds translate in a given model into narrow ranges of those observables. In that approach we analyse the polynomial class of the $\alpha$-attract

George Drayson

We present Jupiter-N, a hybrid reasoning model post-trained from Nemotron 3 Super, a fully open-source 120 billion parameter LLM. We target three objectives: (1) agentic capability via uncertainty-curated trajectories; (2) UK cultural alignment via synthetic data grounded in cultural norms; and (3) Welsh language support via parallel corpora and LLM-translat

Zhijiang Tang, Jiaxin Qi, Bing Zhao, Jianqiang Huang

As video generation models achieve unprecedented capabilities, the demand for robust video evaluation metrics becomes increasingly critical. Traditional metrics are intrinsically tailored for short-video evaluation, predominantly assessing frame-level visual quality and localized temporal smoothness. However, as state-of-the-art video generation models scale

Di Zhang, Zixun Yu, Fangzhou Liu, Yumeng Li

Metal-site density strongly influences oxygen reduction activity and selectivity in M-N-C electrocatalysts, but the descriptors that predict these trends remain under debate. Here, we compare spin state and the potential of zero charge as predictors of density-dependent oxygen reduction behavior in Fe-N-C and Co-N-C catalysts. Using constrained-magnetization

Bumsu Park, Youngmok Park, Chanho Park, Namyoon Lee

We study channel state information (CSI) compression for wideband frequency division duplex massive multiple-input multiple-output (MIMO) when the base station (BS) reconstructs CSI using an imperfect covariance model. Under matched second-order statistics, remote rate--distortion theory yields transform coding with reverse water-filling (RWF) over covarianc

Rongxiao Guo, Qingchao Chen

Millimeter-wave (mmWave) radar has shown great potential for contactless, privacy-preserving, and robust human sensing, yet existing mmWave-based human mesh reconstruction (HMR) studies are still limited by the lack of benchmarks for generalization analysis under configuration shifts and fair comparison of different algorithms. To address the limitation, we

Chanik Kang, Hyewon Suk, Haejun Chung

Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimization problem: mapping a desired optical output back to a physical 3D structure requires solving computationally expensive Maxwell's equations iteratively. Even with adjoint optimization, broadband design can r

William Cheah, David Treeby

A Galileo sequence \((a_n)\) is a sequence of positive integers whose partial sums $S_n$ satisfy $S_{2n}=kS_n$ for some $k>1$. In this paper we prove that every polynomial Galileo sequence is given by first differences of the form \(a_n= C\left(n^d-(n-1)^d\right)\). We then show that every positive Galileo sequence has a binary-tree representation. Finally,

Aman Devnani, Pramod Eyyunni

In this article, we provide an infinite family of examples to disprove a recent conjecture due to Ballantine and her collaborators on the injectivity of a class of maps, namely pre_k, defined on integer partitions. These maps arise from applying the sequence of elementary symmetric polynomials to integer partitions, where pre_k is associated with the kth pol

S. Gratton, Ph. L. Toint

A unified framework for first-order optimization algorithms fornonconvex unconstrained optimization is proposed that uses adaptivelypreconditioned gradients and includes popular methods such as full anddiagonal AdaGrad, AdaNorm, as well as an adpative variant of Muon. This framework also allows combining heterogeneous geometries across different groups of va

Shaoguang Wang, Weiyu Guo, Ziyang Chen, Xuming Hu

Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive computational cost of processing dense frame sequences. Prevailing solutions, which select a keyframe subset, typically rely on either a single visual-centric metric (e.g., CLIP similarity) or a static fusion of heuristic scores. This `

Mohamed Atouife, Jesse Jenkins

Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to

Chuyue Wang, Jie Feng, Yuxi Wu, Shenglin Yi

Human mobility prediction is a critical task but remains challenging due to its complexity and variability across populations and regions. Recently, large language models (LLMs) have made progress in zero-shot prediction, but existing methods suffer from limited interpretability (due to black-box reasoning), lack of iterative learning from new data, and poor

Krzysztof Kutak, Sándor Lökös

The 1D Mueller dipole model, its high energy limit, and its generalization were investigated. To address the ambiguity stemming from different definitions of the pseudorapidity ranges in experimental measurements, we propose the entropy as the function of the logarithm of the average multiplicity, $S(\ln\langle n\rangle$, as a universal observable. From the

Sebastiano A. Piccolo, Giorgio Terracina

Engineering projects are the result of the combined effort of their members. Yet, it has been documented that labor division withing projects is unevenly distributed: some project members are specialists undertaking only few tasks, whereas other are generalists and are responsible for the success of many tasks. Moreover, the latter are often facilitators of

Tetsuro Kubo, Atsushi Sasaki, Keita Murooka, Hisashi Kotegawa

We report $^{59}$Co nuclear quadrupole resonance (NQR) measurements on the Nd-based compound NdCo$_2$Zn$_{18}$Ga$_2$, which undergoes an antiferromagnetic transition at $T_{\rm N} = 1.5$ K. Although the NQR spectra show no detectable change across $T_{\rm N}$, the nuclear spin-lattice relaxation rate, $1/T_1$, exhibits a clear anomaly at $T_{\rm N}$. An anal

Ang Li, Chengyu Liu, Yue Wang

Transmitter-resolved radio map estimation (RME) from sparse measurements is essential for obtaining source-specific received-power information in wireless networks. This paper proposes SeaGAT, a Structured Edge-Aware Graph Attention Network for transmitter-resolved pointwise RME. For each target--transmitter query, SeaGAT constructs a target-centered graph f

Vinicius Santana Gomes

The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety. This paper challenges that framing, arguing that access restrictions, without governed alternatives, may displace risks rather than reduce them. The global concentration of compute infrastructure makes open-weight mod

Yumeng Zeng, Jeongrak Son, Mile Gu, Xiao Yuan

Quantum imaginary-time evolution (QITE) is a fundamental framework for preparing ground and thermal states, yet its computational cost scales significantly with the evolution duration $\tau$. Reducing this duration is critical for practical quantum advantage. Here, we establish a unified theoretical framework for the Mpemba effect in QITE -- a counterintuiti

Lexuan Liang, Tao Zou, Xuxiang Ta, Zekun Qiu

Text-attributed graphs integrate semantic information of node texts with topological structure, offering significant value in various applications such as document classification and information extraction. Existing approaches typically encode textual content using language models (LMs), followed by graph neural networks (GNNs) to process structural informat

Zhangsong Li

The low-degree polynomial framework has emerged as a powerful tool for providing evidence of statistical-computational gaps in high-dimensional inference. For detection problems, the standard approach bounds the low-degree advantage through an explicit orthonormal basis. However, this method does not extend naturally to estimation tasks, and thus fails to ca

Adi Harchol, Shahar Zuri, Rajesh Kumar Yadav, Nirman Chakraborty

Two-dimensional (2D) van der Waals (vdW) heterostructures (HSs) provide a versatile platform for tailoring electronic, optical, and magnetic properties via proximity effects at their interfaces. In this work, we explore the optical response of few-layer NiPS3/WSe2 HSs using low-temperature micro-photoluminescence ({\mu}-PL) and magneto-PL spectroscopy. The H

Bolun Huang, Zebin Zhu, Genrong Yu, Zhen Gao

The hallmark feature of photonic time crystals (PTCs) is the momentum bandgap, yet opening such a gap is extremely challenging, as it demands strong and rapid temporal modulation of the material properties. Recent theoretical advances have shown that resonance effects can substantially expand the momentum bandgap, and even give rise to a full (infinite) mome

Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li

Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such

Wei Chen, Lili Zhao, Zhi Zheng, HuiJun Hou

Multi-hop question answering (MHQA) enables accurate answers to complex queries by retrieving and reasoning over evidence dispersed across multiple documents. Existing MHQA approaches mainly rely on iterative retrieval-augmented generation, which suffer from the following two major issues. 1) Existing methods prematurely commit to surface-level entities rath

Bleuenn Bégoc, Sukhjit P. Singh, Giovanni Cichelli, Roberto Franco

We report experimental results on Rydberg facilitation, whereby Rydberg levels can be excited off-resonantly in the presence of a nearby Rydberg atom because of Rydberg-Rydberg interactions, for high-lying $S$, $P$ and $D$ levels in rubidium. Facilitation is detected both through an enhancement of the number of excited atoms for off-resonant excitation (eith

Hansa Kannan, Ram Prakash Babu, Trisha Ghosh, Arpita Mohapatra

Quartz Crystal Microbalance (QCM) sensors are widely used to study biological and soft-matter interfaces due to their exceptional sensitivity to mass loading and interfacial mechanical properties. While classical QCM theory assumes predominantly shear-wave coupling into a semi-infinite Newtonian liquid, finite liquid thickness and acoustic reflections give r

A. P. Veselov

We show that the Markov fractions introduced recently by Springborn coincide with the index of the Cohn matrices defined by Aigner. This provides a simple concatenation rule for the corresponding continued fractions on the Conway topograph. We also discuss the $q$-deformation of Markov fractions in the sense of Morier-Genoud and Ovsienko, as well as their ``

Mohit Dubey

Multi-agent systems (MAS) powered by large language models suffer from severe token inefficiency arising from two compounding sources: (i) unstructured parallel execution, where all agents activate simultaneously irrespective of input readiness; and (ii) unrestricted context sharing, where every agent receives the full accumulated context regardless of relev

Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou

Large language models (LLMs) have demonstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has shifted toward meta-reasoning as a promising direction for further improvement. However, most existing meta-reasoning methods remain episodic: they focus on executing complex meta-re

S. A. Desimone, L. Alonso Alemany

We present a methodological framework to discover linguistic and discursive patterns associated to different social groups through contrastive synthetic text generation and statistical analysis. In contrast with previous approaches, we aim to characterize subtle expressions of bias, instead of diagnosing bias through a pre-determined list of words or express

Yuezhou Hu, Jintao Zhang

Autoregressive video diffusion is emerging as a promising paradigm for streaming video synthesis, with step distillation serving as the primary means of accelerating inference. Whether speculative decoding, the dominant acceleration strategy for large language models, can be effectively adapted to autoregressive video generation remains an open question, bec

Zeguan Xiao, Lang Mo, Yun Chen, Lei Yang

Large Language Models (LLMs) often memorize sensitive or harmful information, necessitating effective machine unlearning techniques. While existing parameter-efficient unlearning methods have shown promise, they still struggle with the forget-retain trade-off. This can be attributed to their reliance on parameter importance metrics to identify parameters tha

Bayangmbe Mounmo, Sam Chien, Mile Mitrovic

Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self-supervised foundation model converting surface meshes into dense per-token embeddings. Shape combines a structured 3D latent grid, a multi-scale geometry-aware tokenizer (MAGNO) with cross-attention, and a tra

Chad M. Topaz

The Mapper algorithm from topological data analysis constructs a graph summarizing the shape of a high-dimensional dataset, and groups of data points identified within this graph are widely interpreted as evidence of distinct subtypes. However, the covariance structure of the data alone can make such groups appear differentiated, even when no subtypes are pr

Ryoma Takeuchi

T. Saito introduced FW-derivations and the modules of FW-differentials. He gave a regularity criterion in terms of the modules of FW-differentials. In this paper, we introduce logarithmic analogues of FW-derivations and the modules of FW-differentials. We study basic properties of them and give a logarithmic regularity criterion in terms of the modules of lo

Finn Schmidt, Jan Philip Wahle, Terry Ruas, Bela Gipp

Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Most metrics are developed on the Workshop on Machine Translation (WMT) benchmarks, raising concerns about their robustness to unseen domains. Prior studies that analyze unseen domains vary translation systems,

Zbigniew Drogosz, Natalia Łygan

We analyze the effect of using the Fermi-Dirac statistics, rather than its Boltzmann approximation, in numerical simulations of perfect spin hydrodynamics of particles with spin 1/2. The system considered is boost invariant, transversely homogeneous, with corrections to the baryon current and the energy-momentum tensor that are second order in the spin polar

Nick Andreasyan, Mikhail Struve, Alexey Popov, Maksim Nikolaev

RISC-V is emerging as a viable platform for automotive-grade embedded computing, with recent ISO 26262 ASIL-D certifications demonstrating readiness for safety-critical deployment in autonomous driving systems. However, functional safety in automotive systems is fundamentally a certification problem rather than a processor problem. The dominant costs arise f

Vasileios Toulatzis, Sofia Theodoridou, Ioannis Fudos

Ancient inscriptions frequently suffer missing or corrupted regions from fragmentation, erosion, or other damage, hindering reading, and analysis. We review prior image restoration methods and their applicability to inscription image recovery, then introduce MESA (Multi-Exemplar, Style-Aware) -an image-level restoration method that uses well-preserved exempl

Eashrat Jahan Muniya, Gernot Kronreif, Ander Biguri, Wolfgang Birkfellner

Soft-tissue deformation remains a major limitation in image-guided neurosurgery, where intra-operative anatomy can deviate substantially from pre-operative imaging due to brain shift, compromising navigation accuracy and surgical safety. Existing compensation methods often rely on intra-operative MRI, CT, or ultrasound, which are disruptive and difficult to

Lora R. Du, Guo-Niu Han

Inversion sequences, also known as subexcedant sequences, form a fundamental class of objects in enumerative combinatorics. In this paper, we study the joint distribution of five statistics on inversion sequences. While several statistics on inversion sequences have been extensively investigated, our contribution is to introduce the inversion number statisti

Hongde Yu, Thomas Brumme, Thomas Heine

Altermagnetism combines momentum-dependent spin splitting of opposite-spin channels with zero net magnetization, enabling electric-field control of spin transport that is robust against external magnetic fields. Although widely explored in inorganic systems, metal-free altermagnets with pi-spin splitting, particularly in two-dimensional organic frameworks, h

Yian Li, Yang Jiao, Bin Zhu, Tianwen Qian

Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge for multimodal large language models. Despite promising performance, recent multimodal large language models (MLLMs) often exhibit fragile reasoning traces in spatial intelligence tasks that involve consistent

Ou Wu

Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved samples through selection, augmentation, or poisoning, while the latter tunes model weights via masking, quantization, or low-rank adaptation. This paper establishes a unified \emph{data-parameter correspondenc

Juntaro Wada

We study the hydrodynamics of the Filtered Dark Matter (Filtered DM) scenario during a first-order phase transition (FOPT). In this scenario, the bubble wall is highly reflective of the dark matter (DM) fluid but transparent to radiation, making the hydrodynamic problem fundamentally different from that of the electroweak FOPT. Motivated by this property, we

Xinyi Zhang, Tanda Li, Jianxing Chen, Xinghao Chen

We perform a detailed asteroseismic analysis of four red giants observed by Kepler: KIC 9145955, KIC 9970396, KIC 9882316, and KIC 11968334. Our study is based on individual oscillation frequencies, with particular emphasis on gravity-dominated (g-dominated) mixed modes. These modes are highly sensitive to the deep stellar interior and serve as powerful diag

Yuan-Hao Wei

This paper proposes StrEBM, a structured latent energy-based model for source-wise structured representation learning. The framework is motivated by a broader goal of promoting identifiable and decoupled latent organization by assigning different latent dimensions their own learnable structural biases, rather than constraining the entire latent representatio

Denys Butenko, Mustafa Khan, Liusuo Wu, Jinlong Zhu

In this review, from crystallographic symmetry to amorphous local polyhedra arrangement and combinations, we examine inorganic solid state electrolytes through the lens of structure property relationships, with oxides, sulfides, and halides representing three major framework chemistries. Halide solid electrolytes and their derivatives, including mixed anion

Wanzhe Wang, Tong Zhang, Hao Xu, Shuai Wang

Fluid antenna system (FAS) becomes a promising paradigm for next-generation wireless networks, which enables position-flexible antenna elements that can dynamically adjust to more favorable channel conditions. However, the optimization of fluid antenna (FA) positions, beamforming, and power allocation in FA-assisted wireless networks is challenging, due to t

Quentin Cohen-Solal

In this article, we generalize Unbounded Minimax, the state-of-the-art search algorithm for zero sums two-player games with perfect information to the framework of multiplayer games with perfect information. We experimentally show that this generalized algorithm also achieves better performance than the main multiplayer search algorithms.

Zhanyu Shen, Sijie Cheng, Zhicheng Guo, Weiqin Wang

While large language models have achieved remarkable performance in complex tasks, they still need a memory system to utilize historical experience in long-term interactions. Existing memory methods (e.g., A-Mem, Mem0) place excessive emphasis on organizing interactions by frequently rewriting them, however, this heavy reliance on summarization risks dilutin

Cui Yakun, Xingqun Qi, TianTian Geng, Yuyao Zhang

Recent advances in Vision-Language Models (VLMs) have substantially enhanced their ability across multimodal video understanding benchmarks spanning temporal, action, object, and spatial understanding. However, we identify a critical yet overlooked issue: when embedded on-screen text contradicts the visual scene, existing VLMs systematically hallucinate, pri

Aleksander Ivanov

Given Polish space $\mathcal{Y}$ and a continuous language $L$ we study the corresponding logic $\mathsf{Iso}(\mathcal{Y})$-space $\mathcal{Y}_L$. We build a framework of generalized model theory towards analysis of Borel complexity of families of subsets of Effros spaces $\mathcal{F}(\mathcal{Y})^k_L\times \mathcal{F}(\mathsf{Iso} (\mathcal{Y}))^l$ correspo

Zihang Wang, Boris Sedlak, Schahram Dustdar

Edge computing enables AI inference closer to data sources, reducing latency and bandwidth costs. However, orchestrating AI services across the cloud-edge continuum remains challenging due to dynamic workloads and infrastructure variability. We present AIF-Router, an Active Inference--based routing framework that autonomously learns to balance latency, throu

Robert Fleischer

CP violation offers powerful probes to explore the quark-flavour sector, where decays of B mesons have been key players since decades. I discuss a variety of probes ranging from non-leptonic to rare B decays, offering exciting opportunities at the FCC in the era after the HL-LHC and Belle II.

Anis Hamadouche, Mathini Sellathurai

This paper investigates communication-efficient neural network transmission by exploiting structured symmetry constraints in convolutional kernels. Instead of transmitting all model parameters, we propose a degrees-of-freedom (DoF) based codec that sends only the unique coefficients implied by a chosen symmetry group, enabling deterministic reconstruction of

Manfred Droste, Vitaly Nürnberg

We introduce weighted finite finance automata (WFFA), a formal framework for modeling and analyzing quantitative properties of financial systems driven by uncertain economic variables such as stock prices, interest rates, and exchange rates. The model provides a compositional and language-theoretic approach to scenario-based financial analysis, enabling syst

Lamia Alyami, Anis Hamadouche, Amir Hussain

This paper presents a stochastic delayed differential model for rumor propagation during infodemic that incorporates human behavioral response, public skepticism and fact-checking mechanisms. A discrete time delay is introduced to model natural lags in information processing and institutional response. Additionally, we adopt additive stochastic perturbations

Jintao Ye, Xiaohua Zhu

We establish a Petersen-Wei type relative volume comparison theorem for weighted Riemannian manifolds under both $L^p$-bounds of the Bakry-Émery Ricci curvature and the gradient of potential function. As an application, we give a modified proof for a volume comparison and monotonicity of Kähler-Ricci flow established in a recent work of Tian-Zhang-Zhang-Zhu-

Yamen Ajjour, Carlotta Quensel, Nedim Lipka, Henning Wachsmuth

Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection, debating collaboratively for diverse answers, and countering hate speech. In this paper, we create the first benchmark for a standardized evaluation of LLM-based approaches to computational argumentation, e

Wu Xie, Changchao Liu, Fayuan Zhang, Zhenhong Tan

Altermagnetism, known for its non-relativistic spin-split band structures with yet compensated moments, is being intensively investigated. Discovering new altermagnetic materials with characteristics suitable for practical use remains an important ongoing task. Recently a metallic room-temperature altermagnet candidate Rb1-xV2Te2O with a layered structure an

Zixuan Tang, Shen Zhao

Large segmentation foundation models such as the Segment Anything Model (SAM) have reshaped promptable segmentation in natural images, and recent efforts have extended these models to medical images and volumetric settings. However, directly transferring a 3D SAM-style model to lesion segmentation remains challenging due to (i) weak spatial representational

Chenyun Yin, Youwei Xiao, Yuze Luo, Yuyang Zou

Equality saturation (EqSat) is a powerful optimization paradigm that compactly represents many equivalent programs in an e-graph and delays commitment until extraction selects a lowest-cost program. Making EqSat effective, therefore, requires not only domain-specific rewrite rules but also domain-specific strategies. Today, much of this strategy design is st

Bin Chen, Yujin Guo, Yong Luo, Juncheng Wei

We study ground states of the fermionic nonlinear Schr\"{o}dinger system $J_2(p)$ in $\R$, where $p>1$ denotes a polynomial exponent of the nonlinear term. It is known that the system $J_2(p)$ admits ground states for any $1<p<2$, while there is no ground state for $J_2(2)$. We prove that there is no ground state of $J_2(p)$ as $p\searrow 2$, which addresses

Marta I. Bracco, Jorge Malouf, Laurent Maimoun, Xavier Nogues

Background: Three-dimensional dual-energy X-ray absorptiometry reconstructs three-dimensional maps of the proximal femur's density distribution from standard hip scans, enabling the estimation of trabecular and cortical bone parameters. The aim of this study was to assess the agreement of these three-dimensional cortical and trabecular femur parameters acros

Zixuan Tang, Shen Zhao

Fine-grained medical image classification is challenged by subtle inter-class variations and visually ambiguous cases, where confidence estimates often exhibit uncertainty rather than being overconfident. In such scenarios, purely discriminative classifiers may achieve high overall accuracy yet still fail to distinguish between highly similar categories, lea

Dongwook Lee, Eunwoo Song, Che Hyun Lee, Heeseung Kim

While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interruptions (TPI) from the primary user's ongoing flow, leaving them vulnerable to contextual failures. To bridge this gap, we introduce TPI-Train, a dataset of 88K instances designed with speaker-aware hard negati

Raghunathan Ramakrishnan

Low-temperature hydrocarbon autooxidation involves radical intermediates whose reactivity depends not only on the stereochemistry of the intermediates themselves, but also on that of the transient species encountered along the reaction path. This study offers large-scale evidence for the importance of stereochemistry in low-temperature autooxidation by propa

Yaping Mao

For finite graphs $G$ and $H$, let $\RR(G,H)$ denote the isomorphism classes of Ramsey-minimal graphs for $(G,H)$. We prove two 1981 conjectures of Burr, Erd\H{o}s, Faudree, Rousseau, and Schelp: Ramsey-finiteness is preserved by adjoining disjoint matchings, and $(G,H)$ is Ramsey-infinite unless both graphs are odd stars or one graph has a $K_2$ component.

Partha Pratim Basumallick, Saheb Das, Bhaswati Mandal, Subhadip Sau

Gravitational lensing constitutes one of the most direct observational manifestations of spacetime curvature and provides a powerful probe of compact astrophysical objects. In this work, we present a comprehensive analysis of the bending of light in curved spacetime, beginning with the fundamental aspects of gravitational lensing and the Newtonian approximat

Wei He

Vision-Language Models (VLMs) excel at photorealistic generation, yet often struggle to represent abstract meaning such as idiomatic interpretations of noun compounds. To study whether high visual fidelity interferes with idiomatic compositionality under visual abstraction, we introduce DIVA, a controlled benchmark that replaces high-fidelity visual detail w

Zizhang Luo, Yuhao Luo, Youwei Xiao, Yansong Xu

Large language models are increasingly deployed as complex agentic systems that scale with task complexity. While prior work has extensively explored model- and system-level scaling, algorithm- and task-level scaling remain largely unaddressed, constraining the full potential of agentic systems. At the algorithm level, allocating additional inference-time co

M. Hosein Niroomand, Utku Şentürk

Fan-array wind generators (FAWGs) provide controlled turbulent inflow conditions that cannot be reproduced in conventional wind tunnels. Despite their increasing use in experimental studies, numerical modeling of FAWG-generated flows remains largely unexplored. The present study assesses the capability of Reynolds-Averaged Navier-Stokes (RANS) modeling to pr

Yuncheng Hua, Sion Weatherhead, Mehdi Jafari, Hao Xue

Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long-horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. We propose SOCIA-EVO, a dual-anchored evolutionary framework. SOCIA-EVO introduces:

K. A. Shahriar

Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which process all time steps uniformly and often incur substantial computational cost. However, real-world temporal signals typically exhibit heterogeneous structure, where informative patterns are sparsely distribute

Giuseppe De Giacomo, Timotheus Kampik, Lukas Kirchdorfer, Marco Montali

Just like traditional BPM systems, agentic BPM systems are built around a specification of the process under consideration. Their distinguishing feature, however, is that the execution of the process is driven by multiple autonomous decision-makers, referred to as agents. Since such agents cannot be fully controlled, the process specification is augmented wi

Glyn V. Morrill, Oriol Valentín

In this book we promote logical computational linguistics as opposed to statistical computational linguistics. In particular, we provide a logical semantic interface. This book assembles more than twenty years of research work on type logical grammar, and adds new ideas and material. Chains of statistical dependencies of less than one hundred per cent confid

Xi-Yao Guo, Hua Zheng, Wenchao Zhang, Li-Lin Zhu

We systematically investigate the scaling properties of the transverse momentum spectra for pions, kaons, and protons in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 62.4, and 200 GeV, as well as in U+U collisions at $\sqrt{s_{NN}}$ = 193 GeV, across different centrality classes, using experimental data from the collaborations at the

Jingzhou Jiang, Yixuan Tang, Yi Yang, Kar Yan Tam

When task-specific labels are not available, it becomes difficult to select an embedding model for a specific target corpus. Existing labelless measures based on kernel estimators or Gaussian mixes fail in high-dimensional space, resulting in unstable rankings. We propose a flow-based labelless representation embedding evaluation (FLARE), which utilizes norm

Shida Jiang, Shengyu Tao, Zihe Liu, Scott Moura

The ensemble Kalman filter (EnKF) is widely used for nonlinear and high-dimensional state estimation because it replaces complex covariance propagation with simple ensemble statistics. However, conventional EnKF implementations can become overconfident in the presence of measurement nonlinearity. The commonly used covariance inflation technique only partiall

Claude Carlet, Marko Čupić, Marko Ðurasevic, Domagoj Jakobovic

Monotone Boolean functions are a structurally important class of Boolean functions, but their restricted form imposes strong limitations on achievable nonlinearity. In this paper, we investigate whether evolutionary computation can evolve monotone Boolean functions with high nonlinearity, both in the balanced and imbalanced settings. We consider three soluti

Shiyao Xie, Jian Du

Clinical guidelines, typically developed by independent specialty societies, inherently exhibit substantial fragmentation, redundancy, and logical contradiction. These inconsistencies, particularly when applied to patients with multimorbidity, not only cause cognitive dissonance for clinicians but also introduce catastrophic noise into AI systems, rendering