April 2026 arXiv papers — page 113

Geunyoung Jung, Soohong Kim, Kyungwoo Song, Jiyoung Jung

With the rise of pre-trained models in the 3D point cloud domain for a wide range of real-world applications, adapting them to downstream tasks has become increasingly important. However, conventional full fine-tuning methods are computationally expensive and storage-intensive. Although prompt tuning has emerged as an efficient alternative, it often suffers

Jon-Paul Cacioli

We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework and applying human psychometric methodology to LLM evaluation. The battery comprises 524 items across six cognitive domains (learning, metacognitive calibration, social cognition, attention, executive function

Yao Chen, Jiawei Sheng, Wenyuan Zhang, Tingwen Liu

The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation. Current CoT distillation methods mainly focus on transferring teacher-generated rationales for complex reasoning to student models. However, they do not adequately explore teache

Miguel Tierz

We derive explicit strong-field asymptotics for the normalized differential resistance in Hall field-induced resistance oscillations (HIRO) within the Vavilov-Aleiner-Glazman kinetic framework. For a single-harmonic density of states, the leading oscillation amplitude is set by the full backscattering rate $1/\tau(\pi)$. Extending the theory to a two-harmoni

Osasumwen Cedric Ogiesoba-Eguakun, Phani Kumar Inkollu, Rupesh Sah, Zia Rashid

Artificial Intelligence (AI), especially cloud platforms and large language models (LLMs), is changing how engineering is taught by making learning more interactive and flexible. However, in electrical engineering and energy systems, students often find power system dynamics difficult to understand because the concepts are abstract, math-heavy, and there are

Katarzyna Skowronek, Marek Arendarczyk, Anna K. Panorska, Tomasz J. Kozubowski

We propose a testing and estimation methodology for univariate and bivariate symmatric $\alpha$-stable distributions using a modified version of the Greenwood statistic. Originally designed for positive-valued random variables, the Greenwood statistic, and its modified version tailored for symmetric distributions, have been predominantly applied to univariat

S. S. Baturin

We comment on Ref.[D. Karlovets, D. Grosman, and I. Pavlov, Phys. Rev. Lett. 136, 085002 (2026)], which proposes a BMT-like equation for the mean kinetic orbital angular momentum (OAM) of vortex particles in accelerator fields and draws spin-like conclusions about depolarization, resonances, and control. We show that the proposed closure is not generally val

Deep Kumar Ganguly, Chandradithya S Jonnalagadda, Pratham Chintamani, Adithya Ananth

Cooperative equilibria are fragile. When agents learn alongside each other rather than in a fixed environment, the process of learning destabilizes the cooperation they are trying to sustain: every gradient step an agent takes shifts the distribution of actions its partner will play, turning a cooperative partner into a source of stochastic noise precisely w

Jingyuan Li, Xiaoyi Jiang, Fukang Wen, Wei Liu

Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet existing approaches typically parameterize the reverse rate matrix monolithically -- through proxies such as concrete scores (SEDD) or clean-data predictions (MDLM, GIDD) -- rather than aligning the parameteriza

Gauri Kale, Rahul Vishwakarma, Holly Diamond, Ava Hedayatipour

Predictive multiplicity and chaotic dynamics represent two fundamental challenges in machine learning that have evolved independently despite their conceptual connections. We bridge this gap by introducing horizon-constrained Rashomon sets, a theoretical framework that characterizes how model multiplicity evolves with prediction horizon in chaotic systems. U

Kaito Nakamura, Yuka Kotorii, Andrey O. Leonov

We present a comprehensive study of current-driven dynamics, transformations, and instabilities of skyrmioniums in chiral magnetic films, considering both isolated objects and collective states forming skyrmionium-based meta-matter. Using micromagnetic simulations combined with an analytical description based on the generalized Thiele equation, we clarify ho

Suman Raj, Siva Sai, Yogesh Simmhan, Kyle Chard

High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new oppor

Marco Aldi, Sergio Da Silva, Daniele Grandini

In this paper we consider families of mutually commuting endomorphisms of the generalized tangent bundle. We identify natural tensorial constraints extending the notion of a generalized K\"ahler structure to endomorphisms that are not necessarily generalized almost complex structures. These tensors form ideals whose generators we explicitly construct and stu

Jiguang Yu

In this workshop, we discuss several algorithms for mathematical programs with equilibrium constraints (MPECs). The unifying theme is that MPECs are optimization problems whose feasible set contains a lower-level equilibrium system, often written through complementarity or variational-inequality conditions. This destroys the smooth manifold or convex structu

M. C. Fernandez, R. O. J. Venero, L. S. Cidale, I. Araya

Radiation-driven winds of massive stars can be described within the modified CAK theory, which parametrises the radiation force through three key quantities: $\alpha$, $\delta$, and $k$. Different combinations of these parameters, together with rotation, result in three types of stationary solutions, namely fast (or classical), $\delta$-slow, and $\Omega$-sl

Lang Qin, Mandong Zhang, Wenting Song, Xiaohu Wu

This paper introduces a high-precision indoor positioning and tracking method that utilizes multi-site single-input single-output (SISO) radar systems. We propose a novel velocity synthesis-assisted (VSA) localization algorithm that iteratively refines target position estimates within range bins by fusing radial velocity measurements from multiple radars. Th

Yuta Konishi, Kento Yamamoto, Eisuke Sonomoto, Rikuho Takeda

Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable

Abdallah Alalem Albustami, Ahmad F. Taha, Sankaran Mahadevan

This paper addresses the classic problem of parameter estimation (PE) in multimachine power system models. Such models are typically described by a set of nonlinear differential-algebraic equations (DAE), where generator physics and network power flow equations are coupled. DAE models are well established in classic power system textbooks, but parameter iden

F. Cosco, R. Tuovinen, F. Plastina, N. Lo Gullo

Motivated by recent theoretical and experimental studies on the role of flatbands in the thermoelectric properties of Ni$_3$In$_{1-x}$Sn$_x$ compounds, we investigate electron transport in two minimal one-dimensional flatband models, the sawtooth and diamond chains, which differ in a crucial aspect: the flatband is separated from the dispersive band by a fin

Jing Fu, Bill Moran, Jose Nino-Mora

We study a finite time horizon Markov decision process (MDP) consisting of several groups of multi-action finite-state restless bandit processes, which are identical within each group. The bandit processes into different groups can be rather different. The bandit processes are subject to multiple weakly coupled constraints on their state and action variables

Markus Haltmeier, Nadja Gruber, Gyeongha Hwang

Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution. A central task is image reconstruction, where measured acoustic signals are used to recover the initial pressure distribution. For ideal point-like or line-like detectors, several efficient and fast reconstru

Enrui Liu, Pan Tang, Haiyang Miao, Qi Zhen

With the push toward 6G commercialization, Frequency Range 3 (FR3) bands, specifically 7.125-8.4 GHz and 14.8-15.3 GHz, have become focal points for achieving wide-area, high-capacity coverage. However, practical deployment is often limited by the physical aperture constraints of base station antennas. This study conducts comprehensive measurements in Urban

Prashant Rangarajan, Rajesh P. N. Rao

Active inference, a neurally-inspired model for inferring actions based on the free energy principle (FEP), has been proposed as a unifying framework for understanding perception, action, and learning in the brain. Active inference has previously been used to model ecologically important tasks such as navigation and planning, but scaling it to solve complex

Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee

Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting real-world applicability where data arises from heterogeneous disciplines with imbalanced sample availability and varying visual complexity

Yali Yuan, Yaosheng Liu, Qianqi Niu, Guang Cheng

Website fingerprinting (WF) attacks infer the websites visited by users from encrypted traffic in anonymous networks such as Tor. Existing deep learning methods achieve high accuracy under the single-tab assumption but degrade substantially when users open multiple tabs concurrently, producing interleaved traffic that transforms WF into an implicit demixing

Zhenbo Fu, Yuanzhe Zhang, Qiange Wang, Hao Yuan

Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) has emerged as a promising paradigm for enhancing LLM reasoning by retrieving multi-hop paths from KGs. However, existing KG-RAG frameworks often underperform in real-world scenarios because the pre-captured knowledge dependencies are not tailored to the downstream task or its evolving requirement

Pufan Zeng, Yilun Liu, Mingchen Dai, Mengyao Piao

Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initial step is seed curation; however, current methods lack quantifiable standards for selecting these seeds. Existing approaches rely on unscalable manual curation or bias-prone LLM extraction, treating cultural

Junhao Wen, Juncen Zhou, Junjie Huang

Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involvin

Yahya Emara, Mauricio Barba da Costa, Chi-Chih Chang, Cameron Freer

Speculative decoding (SD) accelerates language model inference by drafting tokens from a cheap proposal model and verifying them against an expensive target model via rejection sampling. Because rejection truncates the draft block at the first error, throughput degrades when draft and target diverge. Rather than rejecting draft tokens outright, we propose to

Nixie Sapphira Lesmana, Ling Feng, Kan Chen, Choy Heng Lai

Self-organized criticality (SOC) is widely proposed as a fundamental mechanism for collective behavior, yet its role in objective-driven, heterogeneous adaptive systems underpinning real complex systems remains less understood. We introduce EvoSK, a minimal evolutionary model in which agents perform memory dependent reinforcement learning on a rugged Sherrin

Zehao Wang, Lanjun Wang

Graph anomaly detection aims to identify irregular patterns in graph-structured data. Most unsupervised GNN-based methods rely on the homophily assumption that connected nodes share similar attributes. However, real-world graphs often exhibit attribute-level heterophily, where connected nodes have dissimilar attributes. Our analysis of attribute-level hetero

Xudong Yu

sPHENIX is a state-of-the-art experiment at the Relativistic Heavy Ion Collider (RHIC), dedicated to the study of heavy-flavor and jet physics. Its precision tracking system, combined with streaming readout, enables heavy-flavor measurements with high-statistics and essentially unbiased data samples. During the 2024 run, sPHENIX was fully commissioned and re

Yan Zhang, Xiong Zhao

Markerless 3D movement analysis from monocular video enables accessible biomechanical assessment in clinical and sports settings. However, most research-grade pipelines rely on GPU acceleration, limiting deployment on consumer-grade hardware and in low-resource environments. In this work, we optimize a monocular 3D biomechanics pipeline derived from the Mono

Xinge Liu, Terry Jingchen Zhang, Bernhard Schölkopf, Zhijing Jin

The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to evaluate the capabilities of these agents in research work. We introduce Stargazer, a scalable environment for evaluating AI agents on dynamic, iterative physics-grounded model-fitting tasks using inference on ra

Jiahui Geng, Qing Li, Fengyu Cai, Fakhri Karray

Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG), improving code discovery, reuse, and the reliability of LLM-based coding. Yet existing code IR models remain largely text-centric and often overlook the visual and structural aspects inherent in programming ar

Pingyang Gao, Xu Jiang, Jinzhi Lu

We develop an incomplete-contracting model with accounting-based covenants to study how covenant accounting adjustments are made and what properties they exhibit. Standard accounting rules (e.g., GAAP) can generate false-alarm errors or undue-optimism errors. The manager can exert costly effort to privately identify these errors and propose adjustments. If e

Mingxuan Jia, Wen Huang, Weixin Zhao, Xingyi Wang

How to synthesize a dataset while achieving differential privacy for AI model training is a meaningful but challenging problem. To address this problem, state-of-the-art methods first select original private dataset's multiple low-dimensional distributions that have the potential to approximate the distribution of original private dataset with high precision

Dylan Le, Joel McCandless, Carlos Varela, Radoslav Ivanov

This paper considers the problem of reachability analysis of control systems with optimal controllers, as a first step towards verifying the safety and correctness of such systems. Despite their appeal in guaranteeing task satisfaction through cost minimization, optimal controllers are often challenging to assure. In particular, as system dynamics grow in co

Juan Mei, Zhiwei Chen, Min Fang, Miaomiao Zhang

We present distances to 56 molecular clouds within $10\degr \leq l \leq 20\degr$ and $|b| \leq 5.25\degr$ from the Milky Way Imaging Scroll Painting (MWISP) $^{12}$CO survey, 47 of which are first-time determinations. The molecular clouds were identified using the DBSCAN algorithm, and their distances were measured with the model-calibrated color-distance me

Zhengbo Chen, Zhouningxin Wang, Xiao-Dong Zhang

For a simple graph $G$ with $n$ vertices, let $A_G$ denote the adjacency matrix of $G$, and let $\lambda_1(G) \geq \lambda_2(G) \geq \dots \geq \lambda_n(G)$ be its eigenvalues. For an integer $p \geq 2$, the positive $p$-energy and negative $p$-energy of $G$, denoted $\mathcal{E}^+_p(G)$ and $\mathcal{E}^-_p(G)$, are defined as follows: $\mathcal{E}^+_p(G)

Masashi Aiki, Mitsuo Higaki

This paper investigates the dynamics of closed vortex filaments in $\R^3$ governed by the Localized Induction Equation. Recently, Aiki and Higaki (2026) established the nonlinear orbital stability of circular vortex filaments under asymmetric perturbations, while identifying Lyapunov instability due to the linear growth of translation modes. Motivated by thi

Chen Zhao, Yunzhe Xu, Zhizhou Chen, Enxuan Gu

Ultra-high-definition (UHD) image restoration poses unique challenges due to the high spatial resolution, diverse content, and fine-grained structures present in UHD images. To address these issues, we introduce a progressive spectral decomposition for the restoration process, decomposing it into three stages: zero-frequency \textbf{enhancement}, low-frequen

Declan Gossink, Undurti S. Sainadh, Glenn S. Solomon

GaAs quantum dots grown by droplet etching epitaxy are high-quality solid-state sources of quantum light. Despite implementation in devices that exploit quantum phenomenon, a comprehensive review on the crystal growth of quantum dots grown by droplet etching epitaxy is absent, unlike for other quantum dot growth techniques such as the related droplet epitaxy

Markus Haltmeier, Lukas Neumann, Nadja Gruber, Gyeongha Hwang

Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-truth images that are often unavailable. This has motivated self-supervised approaches, which have primarily addressed denoising and, more recently, linear inverse problems. We address nonlinear inverse problems

Jin Cai, Bo Zhou

It is proved that for a graph of order $n$, where $n\ge 6$, if the signless Laplacian index is larger than or equal to certain value depending on $n$, then the graph contains a trebly chorded cycle, where the chords incident to a common vertex, unless it is one of two specified graphs.

Yanbin Wei, Chun Kang, Siwei Li, Haoxuan Che

Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real world, hypergraphs have significant practical applications in areas such as life sciences and social communities. Recent advancements in LVLMs have shown promise in understanding comp

Ming-Bin Chen, Jey Han Lau, Lea Frermann

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective understanding of the target topic. To operationalize CIG, we mo

Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury, Muhammad Ali Khan

Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema expertise. We demo FD-NL2SQL, a feedback-driven clinical NL2SQL assistant for SQLite-based oncology databases. Given a natural-language question, a schema-aware LLM decomposes it int

Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy, Aaron Goldgewert

We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow. The framework enables systematic evaluation of PINN performance across benchmark problems including 1D hyper

Tianyi GU, Yoshimasa Watanabe

Although the relationship between molecular gas content and the star formation rate (SFR) has been extensively studied in nearby galaxies, it remains controversial whether the star formation efficiency (SFE) depends on galactic structure. In particular, whether the SFE is suppressed in the bar region compared with other structures, and the physical origin of

Sam Beilis, Israel R. Curbelo

The online Ramsey game for graphs $G$ and $H$ is played on the infinite complete graph $K_\mathbb{N}$. Each round, Builder chooses an edge, and Painter colors it red or blue. The online Ramsey number $\tilde{r}(G,H)$ is the smallest integer $t$ for which Builder has a strategy that guarantees a red copy of $G$ or a blue copy of $H$ in at most $t$ rounds. We

Shiva Ahir, Prajna Bhat, Alex Doboli

Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rapidly propose alternative architectural realizations. However, single-shot LLM generation struggles to consistently produce designs that are both functionally correct and power-efficient. This paper proposes HYP

Weifeng Xie, Libo Wang, Yunliang Yue, Xiong Xu

Altermagnets (AMs) and fully compensated ferrimagnets (FC-FIMs) are emerging classes of magnetic materials that combine the advantages of antiferromagnets and ferromagnets. Here, we elucidate the mechanism behind the uniaxial strain-driven transformation from AM to FC-FIM and find that the accompanying non-relativistic valley polarization is positively corre

Chaowu Xue, Mengzhao Sun, Zixuan Zhou, Zhuoran Yao

Twisted graphene layers (TGLs) provide a powerful platform for investigating multiple quantum phenomena, yet their scalable deployment is hindered by the lack of reliable synthesis with precise angle. Here, benefited from a deeper understanding of the interplay between grain index and graphene growth kinetics, we report a scalable strategy to grow TGLs with

Aedan Mangan, Kehan Long, Ki Myung Brian Lee, Miheer Potdar

Continuum robots are well suited for navigating confined and fragile environments, such as vascular or endoluminal anatomy, where contact with surrounding structures is often unavoidable. While controlled contact can assist motion, unfavorable contact can degrade controllability, induce kinematic singularities, or introduce safety risks. We present a contact

Haoling Zhou, Shixuan Zhao, Chao Wang, Zhiqiang Lin

Apple Intelligence is a generative AI (GenAI) service provided by Apple on its devices. While offering a similar set of features as other similar GenAI services, Apple Intelligence is claimed to be designed with an extra focus on user security and privacy through a two-stage authentication and authorization design using anonymous access tokens. In this paper

Yirui Zhang, Zhixuan Fang

In the conventional principal-agent problem, a principal delegates a task to an agent and formulates a contract to incentivize the agent's actions on behalf of the principal. However, this framework overlooks the information that is possibly available during the delegation process in some scenarios. To address this limitation, we propose a novel model that i

Nokimul Hasan Arif, Qian Lou, Mengxin Zheng

Most LLM safety work studies single-agent models, but many real applications rely on multiple interacting agents. In these systems, prompt segmentation and inter-agent routing create attack surfaces that single-agent evaluations miss. We study \emph{conjunctive prompt attacks}, where a trigger key in the user query and a hidden adversarial template in one co

Tristan R. Cao, Gabriel Schebel, Arabella Quane, Hengdi Zhao

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn3Si2Te6, nonequilibrium reconfiguration of chiral

Rujiang Li, Muhammad Imran, Wencai Wang, Yongtao Jia

The introduction of nonlinearities into lattices with topological band structures has led to the discovery of various types of solitons. The Su-Schrieffer-Heeger (SSH) lattice, as the most fundamental topological model, has been extended into the nonlinear regime. In particular, nonlinear edge states and bulk solitons exhibiting intensity humps against a zer

Laura Zielinski, Justin Hsu

Backward stability is a desirable property for a well-designed numerical algorithm: given an input, a backward stable floating-point program produces the exact output for a nearby input. While automated tools for bounding the forward error of a numerical program are well-established, few existing tools target backward error analysis. We present a formal fram

Yulia Alexandr, Hao Duan, Guido Montúfar

We study the polynomial coefficients of lightning self-attention as coordinates of an algebraic variety. In the single-token case, we characterize the coefficient variety as a rank-constrained Chow-type variety and derive algebraic equations from this description. For multiple tokens, linear relations reduce the nonlinear geometry to coefficients involving i

Shuang Li, Jiaxu Leng, Changjiang Kuang, Mingpi Tan

VVI-ReID is a critical technique for all-day surveillance, where temporal information provides additional cues beyond static images. However, existing approaches rely heavily on fully supervised learning with expensive cross-modality annotations, limiting scalability. To address this issue, we investigate Unsupervised Learning for VVI-ReID (USL-VVI-ReID), wh

Sungyong Chung, Tina Radvand, Alireza Talebpour

As automated vehicles (AVs) enter mixed traffic, proactively anticipating the evolution of human driving behavior during critical interactions, such as lane changes, is essential. However, classical Evolutionary Game Theory (EGT) fails to capture the complexity of human decision-making during lane changes. Specifically, by strictly assuming independence betw

Yafet E. Sanchez Sanchez

We introduce an ansatz for force-free electrodynamics in Minkowski spacetime under which the nonlinear system reduces to a semilinear scalar wave equation depending on two spacetime variables. This reduction yields explicit time-dependent solutions, including type-changing configurations with finite energy per unit transverse area and a null kink-type exampl

Youxiu Yu, Hao Hu, Qianru Yang, Linyang Zou

Time-varying media, characterized by dynamic or spacetime-modulated constitutive parameters such as permittivity and permeability, have recently emerged as a transformative paradigm for advanced wave control, transcending the constraints imposed by temporal translation symmetry and energy conservation in static systems. By incorporating time as an active deg

Keisuke Gomi, Keiji Yanai

Cross-modal retrieval between food images and recipe texts is an important task with applications in nutritional management, dietary logging, and cooking assistance. Existing methods predominantly rely on dual-encoder architectures with separate image and text encoders, requiring complex alignment strategies and task-specific network designs to bridge the se

Donggyun Seo

This note studies the Burnside problem for homeomorphism groups of compact connected manifolds. For surfaces, we prove that the identity component of the homeomorphism group is torsion-free precisely when the surface is not the sphere, torus, projective plane, or Klein bottle. An extension argument based on the Tits alternative for mapping class groups then

Junxu Li

We introduce a Hybrid Quantum Residual Network (HQRN) and establish an exact functional correspondence between its state evolution and the dynamics of classical networks with residual connections. When inputs are restricted to the computational basis, the HQRN reduces to its classical analog, enabling the direct translation of optimized classical weights int

Jenny Ma, Sitong Wang, Joshua H. Kung, Lydia B. Chilton

Rules files (e.g., AGENTS\.md, CLAUDE\.md) are the primary mechanism for human-agent alignment when developers vibe code. However, they remain passive: it is not immediately apparent when rules are being used or followed, or how to improve them. To transform rules from passive text into active controls, we introduce ZORO, an interactive interface that integr

Mengmou Li, Lijun Zhu, Masaaki Nagahara

We show that a common Lyapunov matrix exists for the convex combination of two Hurwitz matrices if and only if the intersection of the set of strict Lyapunov matrices for one matrix and the set of non-strict Lyapunov matrices for the other is nonempty. This simple relaxation is useful for the convergence analysis of the augmented primal-dual gradient flow fo

Rethna Pulikkoonattu

We investigate a combinatorial puzzle in which $N$ apples and $N$ pears are distributed among baskets subject to two constraints: every basket must contain the same number of apples, and every basket must contain a distinct number of pears. We prove that the maximum number of baskets is the largest divisor of $N$ not exceeding $(1 + \sqrt{1+8N})/2$. For the

Weilun Wang, Zirui Wang, Wantong Li

Neuro-symbolic AI is gaining traction in domains such as large language models, scientific discovery, and autonomous systems due to its ability to combine perception with structured reasoning. However, its deployment is often constrained by high memory demands, diverse computation patterns, and complex hardware requirements. Existing hardware platforms strug

Jun Feng, Jiahui Tang, Zhicheng He, Hang Lv

Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving supplementary passages. However, as Large Language Models evolve with increasing robustness to noise, the necessity of adaptive retrieval warrants re-evaluation. In this paper, we rethink this necessity and prop

Selain K. Kasereka, Eric M. Mafuta, Fadi Al Machot, Emmanuel M. Kabengele

Tuberculosis (TB) remains a leading global infectious disease, causing approximately 1.3 million deaths and 10.6 million new infections annually. Classical compartmental ODE models are the standard epidemiological tool for TB, yet their fixed-parameter structure cannot adapt to time-varying dynamics, unmodeled effects, or heterogeneous real-world data. This

Hua-Rong Chu, Kuan-Chun Wang, Yao-Te Huang

Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). However, existing research lacks consideration of nuances across linguistic and cultural contexts, resulting in a gap between reported performance and in-the-wild effectiveness. To address this issue, this paper p

Lorenzo Ticozzi, Patricio A. Vela, Panagiotis Tsiotras

Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of such systems. Existing approaches broadly trade off between parametric methods that yield compact state representations but lack probabilistic structure, and Cosserat rod inference on factor graphs, which provid

Tim Launer, Jonas Hübotter, Marco Bagatella, Ido Hakimi

We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies a representative solution from multiple generations using their runtime execution signatures on test inputs. We find that FMV is an effective test-time inference strategy, substantially boosting performance

Reza Pirayeshshirazinezhad

We propose a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision for scientific machine learning under data scarcity. Unlike prior PINNs that rely on fixed or heuristic weighting of physics residuals and data loss, our approach introduces a learnable blending neuron that dynamica

Jiamin Jiang, Shanglin Lv, Jingrun Chen

Hyperbolic conservation laws govern a wide range of transport-driven dynamics featuring shocks, contact discontinuities, and complex wave interactions, posing distinct challenges for deep-learning-based surrogate modeling. While classical numerical methods provide robust and physically admissible solutions, their computational cost restricts applicability in

Yi Xie, Yangyang Xu, Yi Fan, Bo Liu

Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of smaller, more efficient LLMs that collectively match or even outperform a single large model. However, jointly updating multiple agents introduces compounding distribution shifts, makin

Le Cheng, Xiaoran Liu, Lingjin Kong, Haitao Zhao

Recovering a source signal from indirect measurements often requires estimating latent parameters, such as wireless channel states or MRI coil sensitivities, that cannot be directly observed. Here, we introduce Physics-Embedded Inverse Learning (PEIL), in which a learned estimator predicts these parameters and a fixed, physics-based inverse operator uses the

Taisuke Kobayashi

This paper proposes a novel method that incorporates empowerment when reasoning actions in reinforcement learning (RL), thereby achieving the flexibility of exploration-exploitation dilemma (EED). In previous methods, empowerment for promoting exploration has been provided as a bonus term to the task-specific reward function as an intrinsically-motivated RL.

Wladimir Silva

We present VoodooNet, a non-iterative neural architecture that replaces the stochastic gradient descent (SGD) paradigm with a closed-form analytic solution via Galactic Expansion. By projecting input manifolds into a high-dimensional, high-entropy "Galactic" space ($d \gg 784$), we demonstrate that complex features can be untangled without the thermodynamic

Dong-Uk Seo, Jinwoo Jeon, Eungchang Mason Lee, Hyun Myung

Gaussian splatting has recently gained traction as a compelling map representation for SLAM systems, enabling dense and photo-realistic scene modeling. However, its application to monocular SLAM remains challenging due to the lack of reliable geometric cues from monocular input. Without geometric supervision, mapping or tracking could fall in local-minima, r

Duy-Phuong Dao, Muhammad Taqiyuddin, Jahae Kim, Sang-Heon Lee

Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance imaging scans. In particular, predicting the evolution of a patients brain can aid in early intervention, prognosis, and treatment planning. In this study, we introduce CLIMB, Controllable Longitudinal brain I

Jinxing Li, Nicholas Ren, Cathy Chang, Hongkai Pan

Identity-document fraud detection is not a stationary binary classification problem. Adaptive attackers modify templates and fabrication pipelines, making historical fraud labels stale, and successful forgeries recur at scale as coherent campaigns. We therefore study layout-aware representation learning for open-set fraud discovery rather than only closed-se

ZhengXiao He, Huayu Li, Xiwen Chen, Janet M Roveda

Medical time series, such as electrocardiograms (ECG) and electroencephalograms (EEG), exhibit complex temporal dynamics and structured cross-channel dependencies, posing fundamental challenges for automated analysis. Conventional convolutional and recurrent models struggle to capture long-range dependencies, while Transformer-based approaches incur quadrati

Srikar Gouru, Ariel Felner, Jiaoyang Li

In this paper, we tackle the Multiple Watchman Route Problem (MWRP), which aims to find a set of paths that M watchmen can follow such that every location on the map can be seen by at least one watchman. First, we propose multiple methods to reduce the state space over which a search needs to be conducted by pruning map areas that are guaranteed to be seen e

Yunbei Zhang, Shuaicheng Niu, Chengyi Cai, Feng Liu

Test-Time Adaptation (TTA) for black-box models accessible only via APIs remains a largely unexplored challenge. Existing approaches such as post-hoc output refinement offer limited adaptive capacity, while Zeroth-Order Optimization (ZOO) enables input-space adaptation but faces high query costs and optimization challenges in the unsupervised TTA setting. We

Masashi Hasea, Minoru Soda, Takatsugu Masuda, Shinichi Itoh

We carried out inelastic neutron scattering experiments on a powdered sample of the antiferromagnetic (AFM) uniform spin-1/2 chain compound CuSb2O6.The magnetic excitations appear in the energy range of 1.8 to 13 meV at 2.5 K below the AFM transition temperature (TN = 8.7 K).The gap value (1.8 meV) is close to that evaluated from the specific heat (1.51 meV)

Bo Gao, Chang Liu, Yuyang Miao, Siyuan Ma

Recent advancements in Large Generative Models (LGMs) have revolutionized multi-modal generation. However, generating illustrated storybooks remains an open challenge, where prior works mainly decompose this task into separate stages, and thus, holistic multi-modal grounding remains limited. Besides, while safety alignment is studied for text- or image-only

Myke C. Cohen, Mingqian Zheng, Neel Bhandari, Hsien-Te Kao

AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in imperfectly cooperative scenarios, where people and AI only have partially aligned goals and objectives. This study compares a purely simulated dataset comprising 2,000 simulation

Jing Tang, Yuangang Deng

The controlled generation of nonclassical light beyond single photons remains a central challenge in quantum optics, due to the difficulty of enhancing multiphoton processes while suppressing lower-order excitations. Here we propose an interference-interaction-engineered scheme for programmable few-photon emission in a cavity-QED system of three atoms couple

Tang Jing, Yuangang Deng

The generation of high-performance nonclassical light remains a cornerstone of quantum technologies, yet faces a fundamental trade-off between emission purity and brightness. Here, we demonstrate that cavity-mediated many-body spin-exchange interactions provide a route to overcome this constraint by collectively amplifying spectral anharmonicity. In a cavity

Asif Akhtab Ronggon, Tasnuva Farheen

Current fault-tolerant quantum compilers allocate error budgets uniformly during resource estimation, causing suboptimal physical resource overhead. We optimize this allocation using a potential game formulation, where Nash Equilibrium yields a Pareto-optimal distribution across logical operations, T-state distillation, and rotation synthesis. An iterated be

Jixuan Leng, Si Si, Hsiang-Fu Yu, Vinod Raman

Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per prompt, discarding additional supervision available in preference datasets that typically contain multiple candidate responses. Motivated by this limitation, recent work explores gr

Weijie Wang, Songlong Xing, Zhengyu Zhao, Nicu Sebe

Poisoning input views of 3D reconstruction systems has been recently studied. However, we identify that existing studies simply backpropagate adversarial gradients through the 3D reconstruction pipeline as a whole, without uncovering the new vulnerability rooted in specific modules of the 3D reconstruction pipeline. In this paper, we argue that the structure

Tomasz Kociumaka, Jakob Nogler, Philip Wellnitz

In the decades-old Pattern Matching with Edits problem, given a length-$n$ string $T$ (the text), a length-$m$ string $P$ (the pattern), and a positive integer $k$ (the threshold), the task is to list the $k$-error occurrences of $P$ in $T$, that is, all fragments of $T$ whose edit distance to $P$ is at most $k$. The one-way communication complexity of Patte

Robert de Mello Koch, João P. Rodrigues

At finite $N$ the ring of gauge invariant operators is not freely generated. For problems of interest in physics, these rings are Cohen--Macaulay and admit a Hironaka decomposition, in which the full invariant ring is a free module over a polynomial ring generated by the primary invariants. The module basis is given by finitely many secondary invariants. Thi