October 2025 arXiv papers — page 125

Sung Gi Park

Let $X\subset \mathbb P^n$ be a degree $d$ hypersurface. We prove that $X$ is GIT stable if the minimal exponent $\widetilde \alpha(X)>\frac{n+1}{d}$ and GIT semistable if $\widetilde \alpha(X)=\frac{n+1}{d}$, resolving a question of Laza. Conversely, for GIT semistable cubic hypersurfaces, we prove a uniform lower bound for the minimal exponent, which impli

Xiangyuan Peng, Xingsi Dong, Si Wu

We propose Grid-like Code Quantization (GCQ), a brain-inspired method for compressing observation-action sequences into discrete representations using grid-like patterns in attractor dynamics. Unlike conventional vector quantization approaches that operate on static inputs, GCQ performs spatiotemporal compression through an action-conditioned codebook, where

Perapard Ngokpol, Kun Kerdthaisong, Pasin Buakhaw, Pitikorn Khlaisamniang

Large language models (LLMs) are increasingly used as role-playing agents, yet their capacity to faithfully and consistently portray version-specific characters -- for example, superheroes across comic and cinematic universes -- remains underexplored. Superhero canons such as Marvel and DC provide a rich testbed: decades of storytelling yield multiple incarn

Xiaocun Zong, Fan Yang, Shenheng Xu, Maokun Li

To address the issue of beam collapse resulting from instantaneous instability during switch transitions in beam tracking, this paper proposes a novel beam switching method based on a row-by-row switching code table. The paper first establishes an abstract model of the beam tracking application scenario and introduces the reconfigurable intelligent surface (

Yunnan Wang, Fan Lu, Kecheng Zheng, Ziyuan Huang

Multimodal large language models (MLLMs) integrate image features from visual encoders with LLMs, demonstrating advanced comprehension capabilities. However, mainstream MLLMs are solely supervised by the next-token prediction of textual tokens, neglecting critical vision-centric information essential for analytical abilities. To track this dilemma, we introd

Miao Zhang, Runhan Feng, Hongbo Tang, Yu Zhao

Mobile telecommunication networks are foundational to global infrastructure and increasingly support critical sectors such as manufacturing, transportation, and healthcare. The security and reliability of these networks are essential, yet depend heavily on accurate modeling of underlying protocols through state machines. While most prior work constructs such

Andrej Bogdanov, Rohit Chatterjee, Yunqi Li, Prashant Nalini Vasudevan

Prange's information set algorithm is a decoding algorithm for arbitrary linear codes. It decodes corrupted codewords of any $\mathbb{F}_2$-linear code $C$ of message length $n$ up to relative error rate $O(\log n / n)$ in $\mathsf{poly}(n)$ time. We show that the error rate can be improved to $O((\log n)^2 / n)$, provided: (1) the decoder has access to a po

Dingding Li, Chao Zhang

This work investigates the Sobolev regularity of solutions to perturbed fractional 1-Laplace equations. Under the assumption that weak solutions are locally bounded, we establish that the regularity properties are analogous to those observed in the superquadratic case. By introducing the threshold $\frac{p-1}{p}$, we divide the range of the parameter $s_p$ i

Ahamd Sheykhi, Leila Liravi

Starting from the Modified Newtonian Dynamics (MOND) theory and using an inverse approach, we construct a general form of the entropy expression associated with the horizon based on the entropic nature of gravity. Using the thermodynamics-gravity correspondence in the cosmological setup, we apply the corrected entropy expression and find the modified Friedma

Zichen Liu, Shao Yang, Xusheng Xiao

Mobile app markets host millions of apps, yet undesired behaviors (e.g., disruptive ads, illegal redirection, payment deception) remain hard to catch because they often do not rely on permission-protected APIs and can be easily camouflaged via UI or metadata edits. We present BINCTX, a learning approach that builds multi-modal representations of an app from

Ke Wang, Li-Na Sheng, Xue-Heng Zhang, Bei-Min Wu

The construction of the first phase of the High energy FRagment Separator (HFRS Phase-I) has already been completed and it is anticipated to start beam commissioning in autumn 2025. This paper presents the first order and higher order beam optics calculations for the HFRS Phase-I, using measured magnet data, and evaluates its experimental performance in prep

Amandip Sangha

We present a differential-geometric formulation of automatic differentiation (AD) based on jet functors and Weil algebras. In this framework, forward- and reverse-mode differentiation arise naturally as pushforward and cotangent pullback, while higher-order differentiation corresponds to evaluation in a Weil algebra. This construction provides a unified, coo

Xu He, Shu Wang, Kun Sun

Automated program repair (APR) techniques are effective in fixing inevitable defects in software, enhancing development efficiency and software robustness. However, due to the difficulty of generating precise specifications, existing APR methods face two main challenges: generating too many plausible patch candidates and overfitting them to partial test case

Siva Teja Kakileti, Bharath Govindaraju, Sudhakar Sampangi, Geetha Manjunath

Mammography, the current standard for breast cancer screening, has reduced sensitivity in women with dense breast tissue, contributing to missed or delayed diagnoses. Thermalytix, an AI-based thermal imaging modality, captures functional vascular and metabolic cues that may complement mammographic structural data. This study investigates whether a breast den

Jialu Zhang, Jialiang Gu, Wangmeiyu Zhang, José Pablo Cambronero

Online programming platforms such as Codeforces and LeetCode attract millions of users seeking to learn to program or refine their skills for industry interviews. A major challenge for these users is the Time Limit Exceeded (TLE) error, triggered when a program exceeds the execution time bound. Although designed as a performance safeguard, TLE errors are dif

Yuanhong Zeng, Anushri Dixit

In this work, we study risk-aware reinforcement learning for quadrupedal locomotion. Our approach trains a family of risk-conditioned policies using a Conditional Value-at-Risk (CVaR) constrained policy optimization technique that provides improved stability and sample efficiency. At deployment, we adaptively select the best performing policy from the family

Jaewan Park, Solbee Cho, Jay-Yoon Lee

Iterative retrieval-augmented generation (RAG) enables large language models to answer complex multi-hop questions, but each additional loop increases latency, costs, and the risk of introducing distracting evidence, motivating the need for an efficient stopping strategy. Existing methods either use a predetermined number of iterations or rely on confidence

Shruti Sarika Chakraborty, Peter Minary

Graph Transformers (GTs) have emerged as powerful architectures for graph-structured data, yet remain constrained by rigid designs and lack quantifiable interpretability. Current state-of-the-art GTs commit to fixed GNN types across all layers, missing potential benefits of depth-specific component selection, while their complex architectures become opaque w

Hendrik Ranocha, David I. Ketcheson

We propose a class of numerical methods for the nonlinear Schr\"odinger (NLS) equation that conserves mass and energy, is of arbitrarily high-order accuracy in space and time, and requires only the solution of a scalar algebraic equation per time step. We show that some existing spatial discretizations, including the popular Fourier spectral method, are in f

Sung-Soo Byun, Peter J. Forrester

Although for the most part classical, the topic of electrostatics finds to this day new applications. In this review we highlight several theoretical results on electrostatics, chosen to both illustrate general principles, and for their application in statistical mechanics and random matrix settings. The theoretical results include electrostatic potentials a

Ole Fredrik Brevig, Aleksei Kulikov, Kristian Seip, Ilya Zlotnikov

The class $A_\alpha^p$ consists of those analytic functions $f$ in the unit disc such that \[\|f\|_{\alpha,p}^p := |f(0)|^p+\int_0^1 \left(\frac{d}{dr} M_p^p(r,f)\right) (1-r^2)^{\alpha-1} \,dr < \infty,\] where $M_p^p(r,f)$ is the radial integral mean of $|f|^p$ and $0<\alpha, p <\infty$. For $\alpha>1$, $A_\alpha^p$ is the standard weighted Bergman space,

Yangyang Li

Early detection of Alzheimer's Disease (AD) is greatly beneficial to AD patients, leading to early treatments that lessen symptoms and alleviating financial burden of health care. As one of the leading signs of AD, language capability changes can be used for early diagnosis of AD. In this paper, I develop a robust classification method using hybrid word embe

Yuto Nakamizo, Ryuhei Miyazato, Hikaru Tanabe, Ryuta Yamakura

This paper presents the 5th place solution by our team, y3h2, for the Meta CRAG-MM Challenge at KDD Cup 2025. The CRAG-MM benchmark is a visual question answering (VQA) dataset focused on factual questions about images, including egocentric images. The competition was contested based on VQA accuracy, as judged by an LLM-based automatic evaluator. Since incor

Shihong Ding, Yihong Gu, Yuanshi Liu, Cong Fang

We study the Order-$k$ ($k \geq 4$) spiked tensor model for the tensor principal component analysis (PCA) problem: given $N$ i.i.d. observations of a $k$-th order tensor generated from the model $\mathbf{T} = \lambda \cdot v_*^{\otimes k} + \mathbf{E}$, where $\lambda > 0$ is the signal-to-noise ratio (SNR), $v_*$ is a unit vector, and $\mathbf{E}$ is a rand

Liviu Aolaritei, Boubacar Bangoura, Saverio Bolognani, Nicolas Lanzetti

Renewable generators must commit to day-ahead market bids despite uncertainty in both production and real-time prices. While forecasts provide valuable guidance, rare and unpredictable extreme events (so-called black swans) can cause substantial financial losses. This paper models the nomination problem as an instance of optimal transport-based distributiona

Himanshu Yadav, Thomas Bryan Smith, Peter Bubenik, Christopher McCarty

Recent work in the information sciences, especially informetrics and scientometrics, has made substantial contributions to the development of new metrics that eschew the intrinsic biases of citation metrics. This work has tended to employ either network scientific (topological) approaches to quantifying the disruptiveness of peer-reviewed research, or topic

Allen Daniel Sunny

Feature selection is a fundamental step in model development, shaping both predictive performance and interpretability. Yet, most widely used methods focus on predictive accuracy, and their performance degrades in the presence of correlated predictors. To address this gap, we introduce TangledFeatures, a framework for feature selection in correlated feature

Jia-lu Zhang, Mu-Hua Zhang

We establish a factorization relation between baryon quasi-distribution amplitudes (quasi-DAs) defined with gradient flow and their counterparts renormalized in the $\overline{MS}\,$ scheme. Working beyond the small flow-time limit, we perform a complete one-loop calculation that yields the full matching kernel and the associated Wilson-line linear divergenc

N. P. Acharya, S. Basnet, A. P. Misra, R. Khanal

We study the characteristics of small-amplitude nonlinear dust-ion-acoustic (DIA) solitary waves in active magnetized positive-ion-beam-driven dusty plasmas with the effects of nonadiabatic and adiabatic dust charge variations. In the model, we consider the ion-neutral collision and thereby consider the collision enhanced ion current to the dust-charging pro

Árpád Baricz, Pranav Kumar, Sanjeev Singh

This paper explores the asymptotic behaviour of the radii of convexity and uniform convexity for normalized Bessel functions with respect to large order. We provide detailed asymptotic expansions for these radii and establish recurrence relations for the associated coefficients. Additionally, we derive generalized bounds for the radii of convexity and unifor

D. M. Gokhfeld, S. V. Semenov, M. I. Petrov, I. V. Nemtsev

The influence of adding 23 nm NiO nanoparticles on the magnetic hysteresis loops and critical current density of the high-temperature superconductor YBa2Cu3O7-d has been investigated. The samples were prepared using a fast annealing method that prevents chemical interaction between the components and does not reduce the critical temperature of the supercondu

Jianting Tang, Dongshuai Li, Tao Wen, Fuyu Lv

In modern e-commerce search systems, dense retrieval has become an indispensable component. By computing similarities between query and item (product) embeddings, it efficiently selects candidate products from large-scale repositories. With the breakthroughs in large language models (LLMs), mainstream embedding models have gradually shifted from BERT to LLMs

Mario Rüttgers, Julian Vorspohl, Luca Mayolle, Benedikt Johanning-Meiners

Accurate simulations of the flow in the human airway are essential for advancing diagnostic methods. Many existing computational studies rely on simplified geometries or turbulence models, limiting their simulation's ability to resolve flow features such shear-layer instabilities or secondary vortices. In this study, direct numerical simulations were perform

Xu Shen, Qi Zhang, Song Wang, Zhen Tan

Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents and disrupt the trajectory. In this paper, we present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.

Marwa Abdulhai, Ryan Cheng, Aryansh Shrivastava, Natasha Jaques

Large Language Models (LLMs) interact with millions of people worldwide in applications such as customer support, education and healthcare. However, their ability to produce deceptive outputs, whether intentionally or inadvertently, poses significant safety concerns. The unpredictable nature of LLM behavior, combined with insufficient safeguards against hall

Graeme D. Berk, Kavan Modi, Simon Milz

The correlation structure of multitime quantum processes - succinctly described by quantum combs - is an important resource for many quantum information protocols and control tasks. Inspired by approaches for quantum states, we introduce quantifiers of the practical utility of quantum processes that satisfy monotonicity properties, thus overcoming shortcomin

Daiqi Gao, Ziping Xu, Aseel Rawashdeh, Predrag Klasnja

Measuring states in reinforcement learning (RL) can be costly in real-world settings and may negatively influence future outcomes. We introduce the Actively Observable Markov Decision Process (AOMDP), where an agent not only selects control actions but also decides whether to measure the latent state. The measurement action reveals the true latent state but

Shivangi Yadav, Arun Ross

An iris biometric system can be compromised by presentation attacks (PAs) where artifacts such as artificial eyes, printed eye images, or cosmetic contact lenses are presented to the system. To counteract this, several presentation attack detection (PAD) methods have been developed. However, there is a scarcity of datasets for training and evaluating iris PA

Changguang Dong, Qiujie Qiao

In [52], Parmenter and Pollicott establish an abstract criterion that gives a geometric construction of equilibrium states for a class of partially hyperbolic systems. We refine their criterion to cover a much broader class of diffeomorphisms, which include certain diffeomorphisms with exponential mixing property (with respect to volume), Katok maps and ``al

Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud, Sahar Abdelnabi

A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires inter-agent collaboration. LLMs enable nuanced protocols that account for unstructured private data, user constraints, and preferences. However, this design introduces new risks, including misalignment and attacks by mal

Ken-Ichi Nakamura, Toshiko Ogiwara

We study the propagation speed of bistable traveling waves in the classical two-component diffusive Lotka-Volterra system under strong competition. From an ecological perspective, the sign of the propagation speed determines the long-term outcome of competition between two species and thus plays a central role in predicting the success or failure of invasion

Mara Martinez, B. Veena S. N. Rao, S. M. Mallikarjunaiah

The accurate representation of numerous physical, chemical, and biological processes relies heavily on differential equations (DEs), particularly nonlinear differential equations (NDEs). While understanding these complex systems necessitates obtaining solutions to their governing equations, the derivation of precise approximations for NDEs remains a formidab

Shōta Inoue, Hirotaka Kobayashi, Yuichiro Toma

We investigate explicit extreme values of the argument of the Riemann zeta-function in short intervals. As an application, we improve the result of Conrey and Turnage-Butterbaugh concerning $r$-gaps between zeros of the Riemann zeta-function.

Yimeng Liu, Misha Sra, Jeevana Priya Inala, Chenglong Wang

AI-powered web agents have the potential to automate repetitive tasks, such as form filling, information retrieval, and scheduling, but they struggle to reliably execute these tasks without human intervention, requiring users to provide detailed guidance during every run. We address this limitation by automatically synthesizing reusable workflows from an age

Sathyanarayanan Ramamoorthy, Vishwa Shah, Simran Khanuja, Zaid Sheikh

This paper introduces MERLIN, a novel testbed system for the task of Multilingual Multimodal Entity Linking. The created dataset includes BBC news article titles, paired with corresponding images, in five languages: Hindi, Japanese, Indonesian, Vietnamese, and Tamil, featuring over 7,000 named entity mentions linked to 2,500 unique Wikidata entities. We also

Shun Ishii

In this paper, we study the Rasmussen-Tamagawa conjecture for abelian varieties with constrained prime power torsion. Previously, Rasmussen and Tamagawa have established the conjecture under the Generalized Riemann Hypothesis for abelian varieties of any dimension over any number field, and unconditionally for those over $\mathbb{Q}$ of dimension at most thr

Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin, Md Mofijul Islam

Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored.

Kyungryul Back, Seongbeom Park, Milim Kim, Mincheol Kwon

Large Vision-Language Models (LVLMs) have recently shown promising results on various multimodal tasks, even achieving human-comparable performance in certain cases. Nevertheless, LVLMs remain prone to hallucinations -- they often rely heavily on a single modality or memorize training data without properly grounding their outputs. To address this, we propose

Ziye Xia, Sergei S. Ospichev

In recent years, the rapid increase in academic publications across various fields has posed severe challenges for academic paper analysis: scientists struggle to timely and comprehensively track the latest research findings and methodologies. Key concept extraction has proven to be an effective analytical paradigm, and its automation has been achieved with

Haozhao Li, Linwei Zhang

In this paper, we show that on a compact K\"ahler manifold the Calabi flow can be extended as long as some space-time $L^p$ integrals of the scalar curvature are bounded.

Bingjie Zhang, Yibo Yang, Zhe Ren, Dandan Guo

Large language models (LLMs) have achieved remarkable success in diverse tasks, yet their safety alignment remains fragile during adaptation. Even when fine-tuning on benign data or with low-rank adaptation, pre-trained safety behaviors are easily degraded, leading to harmful responses in the fine-tuned models. To address this challenge, we propose GuardSpac

Weijie Shen, Yitian Liu, Yuhao Wu, Zhixuan Liang

Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presents several critical challenges: (1) Training new VLA models from scratch demands substantial computational resources and extensive datasets. Given the current scarcity of robot dat

Nam Le, Leo Yu Zhang, Kewen Liao, Shirui Pan

As deep neural networks power increasingly critical applications, stealthy backdoor attacks, where poisoned training inputs trigger malicious model behaviour while appearing benign, pose a severe security risk. Many existing defences are vulnerable when attackers exploit subtle distance-based anomalies or when clean examples are scarce. To meet this challeng

Nicolai T A Haydn

We consider expanding systems with invariant measures that are uniformly expanding everywhere except on a small measure set and show that the limiting statistics of hitting times for zero measure sets are compound Poisson provided the limits for the cluster size distributions exist. This extends previous results from neighbourhoods around single points to ne

Yani Feng, Michael K. Ng, Zhiwen Zhang

The Keller-Segel (KS) chemotaxis system is used to describe the overall behavior of a collection of cells under the influence of chemotaxis. However, solving the KS chemotaxis system and generating its aggregation patterns remain challenging due to the emergence of solutions exhibiting near-singular behavior, such as finite-time blow-up or concentration phen

Md Mahadi Hasan Nahid, Davood Rafiei, Weiwei Zhang, Yong Zhang

Schema linking -- the process of aligning natural language questions with database schema elements -- is a critical yet underexplored component of Text-to-SQL systems. While recent methods have focused primarily on improving SQL generation, they often neglect the retrieval of relevant schema elements, which can lead to hallucinations and execution failures.

T. M. Dunster, Amparo Gil, Diego Ruiz-Antolin, Javier Segura

Uniform asymptotic expansions are derived for the zeros of the reverse generalized Bessel polynomials of large degree $n$ and real parameter $a$. It is assumed that $-\Delta_{1} n+\frac{3}{2} \leq a \leq \Delta_{2} n$ for fixed arbitrary $\Delta_{1} \in (0,1)$ and bounded positive $\Delta_{2}$. For this parameter range at most one of the zeros is real, with

Young-Jun Kim, Jeong-Eun Lee, Giseon Baek, Seokho Lee

Tracing the water snowline in low-mass young stellar objects (YSOs) is important because dust grain growth is promoted and the chemical composition varies at the water snowline, which influences planet formation and its properties. In protostellar envelopes, the water snowline can be estimated as a function of luminosity using a relation derived from radiati

Yushi Du, Yixuan Li, Baoxiong Jia, Yutang Lin

Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been extensively developed for robotic arms, enabling compliant human-humanoid collaboration remains largely unexplored due to humanoids' complex whole-body dynamics. In this paper, we pr

Haolin Pan, Hongbin Zhang, Mingjie Xing, Yanjun Wu

Compiler pass auto-tuning is critical for enhancing software performance, yet finding the optimal pass sequence for a specific program is an NP-hard problem. Traditional, general-purpose optimization flags like -O3 and -Oz adopt a one-size-fits-all approach, often failing to unlock a program's full performance potential. To address this challenge, we propose

Shoichi Oshino, Yusuke Sakai, Marco Meyer-Conde, Takashi Uchiyama

Gravitational wave interferometers are disrupted by various types of nonstationary noise, referred to as glitch noise, that affect data analysis and interferometer sensitivity. The accurate identification and classification of glitch noise are essential for improving the reliability of gravitational wave observations. In this study, we demonstrated the effec

M. A. Teeti

This work proposes RIS-enabled channel signature modulation (RIS-CSM), a lightweight index modulation scheme for reconfigurable intelligent surfaces (RIS). An N-element RIS is partitioned into disjoint groups, each employing predetermined binary reflection patterns to generate distinct channel signatures at an $n_R$-antenna receiver, without RIS-side beamfor

Madabattula Rajesh Kumar, Srinivasa Rao Aravilli, Mustafa Saify, Shashank Srivastava

The evolution of Large Language Models from the Transformer architecture to models with trillions of parameters has shifted the primary bottleneck from model training to real time inference. Deploying these massive models is a complex distributed systems challenge constrained by memory bandwidth, computational throughput, and latency requirements. LLM infere

Dao-He Ma, Jin An

Topological nodal superconductors (SCs) have attracted considerable interest due to their gapless bulk excitations and exotic surface states. In this paper, by establishing a general framework of the effective theory for multi-orbital SCs, we realize a class of three-dimensional (3D) time-reversal (T )-invariant Dirac SCs, with their topologically protected

Yi Su, Xuchen Hua, Bingyan Xue, Yucheng Yao

We propose a lensed-fiber based coupling strategy for low-loss interconnection between single-mode fibers and anti-resonant fibers. By optimizing structural and geometric parameters, the design simultaneously achieves high coupling efficiency and suppressed back-reflection. Experimental results demonstrate an insertion loss of 1.2 dB and back-reflection of -

Hayato Nihei, Sou Nobukawa, Yusuke Sakemi, Kazuyuki Aihara

Reservoir computing (RC) establishes the basis for the processing of time-series data by exploiting the high-dimensional spatiotemporal response of a recurrent neural network to an input signal. In particular, RC trains only the output layer weights. This simplicity has drawn attention especially in Edge Artificial Intelligence (AI) applications. Edge AI ena

Mayank Keoliya, Seewon Choi, Rajeev Alur, Mayur Naik

Early event prediction (EEP) systems continuously estimate a patient's imminent risk to support clinical decision-making. For bedside trust, risk trajectories must be accurate and temporally stable, shifting only with new, relevant evidence. However, current benchmarks (a) ignore stability of risk scores and (b) evaluate mainly on tabular inputs, leaving tra

B. Cooper Boniece, José E. Figueroa-López, Tianwei Zhou

Volatility estimation is a central problem in financial econometrics, but becomes particularly challenging when jump activity is high, a phenomenon observed empirically in highly traded financial securities. In this paper, we revisit the problem of spot volatility estimation for an It\^o semimartingale with jumps of unbounded variation. We construct truncate

Xinhao Deng, Jingyou Chen, Linxiao Yu, Yixiang Zhang

Website Fingerprinting (WF) attacks exploit patterns in encrypted traffic to infer the websites visited by users, posing a serious threat to anonymous communication systems. Although recent WF techniques achieve over 90% accuracy in controlled experimental settings, most studies remain confined to single scenarios, overlooking the complexity of real-world en

Kazuya Horibe, Daichi G. Suzuki

In this study, we provide a relatively simple simulation framework for constructing artificial life (ALife) with both autonomous and evolutionary aspects by extending chemoton model. While the original chemoton incorporates metabolism, membrane, and genetic templates, it lacks a mechanism for phenotypic variation, preventing true evolutionary dynamics. To ad

Junyuan Gao, Weifeng Zhu, Shuowen Zhang, Yongpeng Wu

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base sta

P. H. Diamond, Y. Kosuga, P. L. Guillon, Ö. D. Gürcan

This paper discusses transport barrier formation and layering as consequences of jam formation. Extensive use is made of analogies with the theory of traffic flow in one dimension. The relation of flux jamming to motility induced phase separation (MIPS) is explained. Two routes to heat flux jamming are identified. The first is due to a rollover in the heat f

Evangelos Lamprou, Seong-Heon Jung, Mayank Keoliya, Lukas Lazarek

A wealth of state-of-the-art systems demonstrate impressive improvements in performance, security, and reliability on programs composed of opaque components, such as Unix shell commands. To reason about commands, these systems require partial specifications. However, creating such specifications is a manual, laborious, and error-prone process, limiting the p

Md Mahadi Hasan Nahid, Davood Rafiei

Retrieval plays a central role in multi-hop question answering (QA), where answering complex questions requires gathering multiple pieces of evidence. We propose PRISM, an agentic retrieval framework that leverages large language models (LLMs) in a structured loop to retrieve relevant evidence with high precision and recall. PRISM decomposes retrieval into t

Yichen Yu, Yifan Jiang, Mandy Lui, Qiao Jin

We introduce GenLARP, a virtual reality (VR) system that transforms personalized stories into immersive live action role-playing (LARP) experiences. GenLARP enables users to act as both creators and players, allowing them to design characters based on their descriptions and live in the story world. Generative AI and agents powered by Large Language Models (L

Haiquan Zhao, Chenhan Yuan, Fei Huang, Xiaomeng Hu

As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in static evaluation settings, face two major limitations in real-world applications: (1) they typically output only binary "safe/unsafe" labels, which can be interpreted inconsistently a

Anthony W. Thomas

The use of effective field theory offers a systematic way to improve calculations of nuclear reactions and the properties of atomic nuclei. Its successes have led to the widespread belief that the predictions of this approach are model independent. We explain why this is definitely not the case.

Lifu Tu, Yingbo Zhou, Semih Yavuz

Training effective multilingual embedding models presents unique challenges due to the diversity of languages and task objectives. Although small multilingual models (<1 B parameters) perform well on multilingual tasks generally, they consistently lag behind larger models (>1 B) in the most prevalent use case: retrieval. This raises a critical question: Can

Jin Qin, Zhikun Liu

We provide direct experimental evidence for a state-selection principle in a far-from-equilibrium system. Using the laser-driven growth of silicon nanowires as a uniquely clean and quantifiable platform, we show that a long-range ordered array emerges as the system spontaneously selects the periodicity that maximizes its collective light absorption. This est

Kieu-Anh Truong Thi, Huy-Hieu Pham, Duc-Trong Le

Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models. Existing methods primarily rely on modeling statistical correlations by aligning feature distributions or introducing statistical variation, yet they often overlook causal relatio

Dennis Belotserkovskiy, Mariana Landín, Charlie Ruppe, Lizzy Teryoshin

To a graph $G$ one associates the binomial edge ideal $J_G$ generated by a collection of binomials corresponding to the edges of $G$. In this paper, we study the asymptotic behavior of symbolic powers of $J_G$, its lexicographic initial ideal $\mathrm{in}_<(J_G)$, and its multigraded generic initial ideal $\mathrm{gin}(J_G)$. We focus on the Waldschmidt cons

Peini Cheng, Amir Bahmani

This study investigates the privacy risks associated with diffusion-based synthetic tabular data generation methods, focusing on their susceptibility to Membership Inference Attacks (MIAs). We examine two recent models, TabDDPM and TabSyn, by developing query-based MIAs based on the step-wise error comparison method. Our findings reveal that TabDDPM is more

Yilun Zheng, Dan Yang, Jie Li, Lin Shang

Retrieval-Augmented Generation (RAG) systems enable large language models (LLMs) instant access to relevant information for the generative process, demonstrating their superior performance in addressing common LLM challenges such as hallucination, factual inaccuracy, and the knowledge cutoff. Graph-based RAG further extends this paradigm by incorporating kno

Alexander Valverde, Brian Xu, Yuyin Zhou, Meng Xu

Scene reconstruction has emerged as a central challenge in computer vision, with approaches such as Neural Radiance Fields (NeRF) and Gaussian Splatting achieving remarkable progress. While Gaussian Splatting demonstrates strong performance on large-scale datasets, it often struggles to capture fine details or maintain realism in regions with sparse coverage

Yutian Zhao, Chao Du, Xiaosen Zheng, Tianyu Pang

Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs. Existing methods for diffusion models typically require access to model gradients or retraining, limiting their applicability in proprietary or large-scale settings. We propose a nonparametric attribution method that operates entirely on d

Mohammad Afzal Shadab, Howard A. Stone, Reed M. Maxwell

Surface meltwater from glaciers and ice sheets contributes significantly to sea-level rise, yet the processes governing its transport and retention within cold firn remain poorly constrained, particularly in multiple dimensions. Here we present a multidimensional, vertically integrated modeling framework for aquifers in cold firn that incorporates phase chan

Ricardo Gonzalez, Fannie Liu, Blair MacIntyre, David Saffo

Screen readers are audio-based software that Blind and Low Vision (BLV) people use to interact with computing devices, such as tablets and smartphones. Although this technology has significantly improved the accessibility of touchscreen devices, the sequential nature of audio limits the bandwidth of information users can receive and process. We introduce Tap

Miu Sumino, Mayu Ishii, Shun Kaizu, Daisuke Hisano

We propose a robust demodulation scheme for optical camera communication systems using an event-based vision sensor, combining OOK with toggle demodulation and a digital phase-locked loop. This is the first report to achieve a $\mathrm{BER} < 10^{-3}$ at 200m-60kbps and 400m-30kbps in outdoor experiments.

Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong

With the advancement of powerful large-scale reasoning models, effectively evaluating the reasoning capabilities of these models has become increasingly important. However, existing benchmarks designed to assess the reasoning abilities of large models tend to be limited in scope and lack the flexibility to adapt their difficulty according to the evolving rea

Zheye Deng, Weixiang Yan, Changlong Yu, Jiashu Wang

While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, produce inconsistent signals, and lack the end-to-end optimization required to learn a coherent strategy from market feedback. To address this, we introduce AlphaQuanter, a single-ag

Mary Graveman, Landen La Rue, Lillian MacArthur, Hunter Pesin

In this paper we study non-standard holomorphic structures on line bundles over the quantum projective line $\mathbb{C} P^1_q$. We show that there exist infinitely many non-gauge equivalent holomorphic structures on those line bundles. This gives a negative answer to a question raised by Khalkhali, Landi, and Van Suijlekom in 2011.

Zihao Fu, Ming Liao, Chris Russell, Zhenguang G. Cai

Large language models have achieved remarkable success but remain largely black boxes with poorly understood internal mechanisms. To address this limitation, many researchers have proposed various interpretability methods including mechanistic analysis, probing classifiers, and activation visualization, each providing valuable insights from different perspec

Ryuji Hakuno, Youichi Yanase

Superconductivity in UTe$_{2}$ has garnered significant attention, as it is widely recognized as a promising candidate for a spin-triplet superconductor. However, the symmetry of superconductivity and the microscopic origin of spin-triplet pairing remain subjects of debate. Nevertheless, various experiments imply an intimate coupling between magnetism and su

Subash Adhikari, M. Hasan Barbhuiya

The dissipation mechanisms in weakly collisional plasmas have been a longstanding topic of investigation, where significant progress has been made in recent years. A recent promising development is the use of the "scale-filtered" Vlasov-Maxwell equations to fully quantify the scale-by-scale energy balance, a feature that was absent when using fluid models in

Lingyu Mu, Hao Deng, Haibo Xing, Kaican Lin

The integration of large language models (LLMs) into recommendation systems has revealed promising potential through their capacity to extract world knowledge for enhanced reasoning capabilities. However, current methodologies that adopt static schema-based prompting mechanisms encounter significant limitations: (1) they employ universal template structures

Xiangyu Meng, Zixian Zhang, Zhenghao Zhang, Junchao Liao

While advanced methods like VACE and Phantom have advanced video generation for specific subjects in diverse scenarios, they struggle with multi-human identity preservation in dynamic interactions, where consistent identities across multiple characters are critical. To address this, we propose Identity-GRPO, a human feedback-driven optimization pipeline for

Liao Shen, Wentao Jiang, Yiran Zhu, Jiahe Li

Recent advances in image-to-video (I2V) generation have achieved remarkable progress in synthesizing high-quality, temporally coherent videos from static images. Among all the applications of I2V, human-centric video generation includes a large portion. However, existing I2V models encounter difficulties in maintaining identity consistency between the input

Saurabh Kataria, Yi Wu, Zhaoliang Chen, Hyunjung Gloria Kwak

Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data and can be adapted for various downstream tasks. They have been extensively applied to tasks in Natural Language Processing and Computer Vision with models such as GPT, BERT, and CLIP. They are now also increasingly gaining attention in time-series analy

Jian-wei Liu, Bing-rong Xu, Zhi-yan Song

In this paper, the branches of recursive and recurrent neural networks are classified in detail according to the network structure, training objective function and learning algorithm implementation. They are roughly divided into three categories: The first category is General Recursive and Recurrent Neural Networks, including Basic Recursive and Recurrent Ne

Wangtao Sun, Xiang Cheng, Jialin Fan, Yao Xu

We study whether self-learning can scale LLM-based agents without relying on human-curated datasets or predefined rule-based rewards. Through controlled experiments in a search-agent setting, we identify two key determinants of scalable agent training: the source of reward signals and the scale of agent task data. We find that rewards from a Generative Rewar