March 2026 arXiv papers — page 78

Yuxuan Yan, Sitian Qian, Qi Zhao, Xingjian Zhang

Quantum Neural Networks (QNNs) are a promising class of quantum machine learning models with potential quantum advantages when implemented on scalable, error-corrected quantum computers. However, as system sizes increase, deploying QNNs becomes challenging. Similar to their classical counterparts, a key obstacle to their practical applications is that large-

Timothy M. Chan, Yuancheng Yu

We present an algorithm that computes the girth of the intersection graph of $n$ given line segments in the plane in $O(n^{1.483})$ expected time. This is the first such algorithm with $O(n^{3/2-\varepsilon})$ running time for a positive constant $\varepsilon$, and makes progress towards an open question posed by Chan (SODA 2023). The main techniques include

Md Kaykobad Reza, Ameya Patil, Edward Ayrapetian, M. Salman Asif

Multimodal large language models (MLLMs) achieve strong performance by jointly processing inputs from multiple modalities, such as vision, audio, and language. However, building such models or extending them to new modalities often requires large paired datasets and substantial computational resources. Since many pretrained MLLMs (e.g., vision-language or au

Ivan Dobrovolskyi

Large Language Models (LLMs) deployed as autonomous agents commonly use Retrieval-Augmented Generation (RAG), feeding retrieved documents into the context window, which creates two problems: the risk of hallucination grows with context length, and token cost scales linearly with dataset size. We propose the Reasoner-Executor-Synthesizer (RES) architecture, a

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized lea

Deepak Gupta, Dina Demner-Fushman, William Hersh, Steven Bedrick

Recent advances in large language models (LLMs) have made significant progress across multiple biomedical tasks, including biomedical question answering, lay-language summarization of the biomedical literature, and clinical note summarization. These models have demonstrated strong capabilities in processing and synthesizing complex biomedical information and

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based intrusion detection systems treat network flows as independent instances, thereby failing to exploit the relational dependencies inherent in network communications. To address thi

Xunzhou Chen, Tiancheng Sun, Yuxi, Lu

The radius valley, a bimodal feature in the size distribution of close-in small exoplanets, is widely interpreted as a signature of atmospheric loss and therefore provides a key constraint on the formation and atmospheric evolution of these planets. We investigate its dependence on host-star properties using 769 planets orbiting 558 stars, for which we deriv

Koki Hirano, Hiroyasu Tsukamoto

We propose a fully data-driven, Koopman-based framework for statistically robust control of discrete-time nonlinear systems with linear embeddings. Establishing a connection between the Koopman operator and contraction theory, it offers distribution-free probabilistic bounds on the state tracking error under Koopman modeling uncertainty. Conformal prediction

Tomohiro Nabika, Yui Hayashi, Masato Okada

Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes computationally expensive as the number of model parameters, data points, and candidate models increases, often rendering practical applications infeasible. We propose a GPU-accel

Jie Cui, Yao Zhang, Yang Zhang, Yi Luo

Determining the chemical structure for a single molecule on surface from spectroscopic data represents a challenging high-dimensional inverse problem. Tip-enhanced Raman spectroscopy (TERS) enables chemically specific imaging of single molecules with sub-nanometer spatial resolution, yet reconstructing complete molecular structures from TERS maps remains dif

Naisargi Kanabar, Seiichiro Higashiya, Haralabos Efstathiadis

Garnet-type Li$_{6.25}$Al$_{0.25}$La$_3$Zr$_2$O$_{12}$ (Al-LLZO) solid electrolytes are promising for all-solid-state batteries but are limited by interfacial resistance. In this work, dense and graded tri-layer Al-LLZO electrolytes were fabricated and tested in Li/Al-LLZO/NMC(111) full cells. After 25 cycles, the tri-layer cell delivered discharge capacity

Baocai Shan, Yuzhuang Xu, Wanxiang Che

Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce personalized text. While lightweight large language models (LLMs) make on-device auxiliary generation feasible, enabling deeply personalized, privacy-preserving, and real-time generative IMEs poses fundamental chal

Qihui Zhu, Shouwei Ruan, Xiao Yang, Hao Jiang

Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely confined to reactive planning from immediate observations, consistently failing in spatial reasoning across extensive spatiotemporal scales. Cognitive science reveals that Biological Intelligence (BI) thrives on "mental navigation": the strategic construction of sp

Hyoseok Park, Yeonsang Park

Long-context LLM inference is bottlenecked not by compute but by the O(n) memory bandwidth cost of scanning the KV cache at every decode step -- a wall that no amount of arithmetic scaling can break. Recent photonic accelerators have demonstrated impressive throughput for dense attention computation; however, these approaches inherit the same O(n) memory sca

Xuan Wei, J. Tang, Yu Tao, XiaoHan Zhang

Quasars,asextremelyluminousanddistantspecialcelestialbodiesintheuniverse,aredrivenbyacomplexsystemcomposedof supermassiveblackholesandsurroundingaccretiondisks.Thispaperadoptsatime-domainobservationstrategyandcombines the analysis of light curves with the construction of structure functions to indirectly reveal the physical essence of the central regionofqua

Zhongyi Li, Wan Tian, Jingyu Chen, Kangyao Huang

Multi-agent collaboration has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models, yet it suffers from interaction-level ambiguity that blurs generation, critique, and revision, making credit assignment across agents difficult. Moreover, policy optimization in this setting is vulnerable to heavy-tailed and noisy r

Xiao Chen, Li Ma

Recoil corrections, which appear at order $\mathcal{O}(\frac{1}{M})$, turn out to be crucial for the pentaquark molecules with heavy flavor. In the past, such corrections were typically regarded as negligible for the formation of heavy molecules. However, our research manifests the important impacts on the spectra of possible baryon-meson bound states. In ce

Nasser-Eddine Monir, Zakaria Baou

DATASHI is a new parallel English-Tashlhiyt corpus that fills a critical gap in computational resources for Amazigh languages. It contains 5,000 sentence pairs, including a 1,500-sentence subset with expert-standardized and non-standard user-generated versions, enabling systematic study of orthographic diversity and normalization. This dual design supports t

Ryo Ohashi, Momonari Kudo

While the numbers of superspecial curves of genus at most 3 are well understood, and several computational approaches have been developed to count superspecial curves of genus 4 with large automorphism groups, much less is known in higher genera. In this paper, we construct a feasible algorithm to enumerate superspecial hyperelliptic curves of genus 5 whose

Gianluca Fabiani, Michail E. Kavousanakis, Constantinos Siettos, Ioannis G. Kevrekidis

We address a numerical framework for the stability and bifurcation analysis of nonlinear partial differential equations (PDEs) in which the solution is sought in the function space spanned by physics-informed random projection neural networks (PI-RPNNs), and discretized via a collocation approach. These are single-hidden-layer networks with randomly sampled

Andrii Shportko

Large language models can rewrite text to embed hidden payloads while preserving surface-level meaning, a capability that opens covert channels between cooperating AI systems and poses challenges for alignment monitoring. We study the information-theoretic cost of such embedding. Our main result is that any steganographic scheme that preserves the semantic l

Mohammad Eslami, Dhanvinkumar Ganeshkumar, Saber Kazeminasab, Michael G. Morley

We present CataractSAM-2, a domain-adapted extension of Meta's Segment Anything Model 2, designed for real-time semantic segmentation of cataract ophthalmic surgery videos with high accuracy. Positioned at the intersection of computer vision and medical robotics, CataractSAM-2 enables precise intraoperative perception crucial for robotic-assisted and compute

Jialei Li, Xiaodong Liu

This paper addresses the inverse obstacle scattering problem of simultaneously reconstructing the obstacle geometry and boundary conditions from multi-frequency near-field backscattering data. We first establish rigorous high-frequency asymptotic expansions for the scattered near-field, leveraging pseudo-differential operators (PDOs) to characterize the inte

Yansong Lin, Zihan Cheng, Jielei Wang, Guoming Lua

Synthetic aperture radar automatic target recognition (SAR ATR) is of considerable importance in marine navigation and disaster monitoring. However, the coherent speckle noise inherent in SAR imagery often obscures salient target features, leading to degraded recognition accuracy and limited model generalization. To address this issue, this paper proposes a

Yashar Talebirad, Ali Parsaee, Csongor Y. Szepesvari, Amirhossein Nadiri

Many recent long-context and agentic systems address context-length limitations by adding hierarchical memory: they extract atomic units from raw data, build multi-level representatives by grouping and compression, and traverse this structure to retrieve content under a token budget. Despite recurring implementations, there is no shared formalism for compari

Mingle Zhou, Jiahui Liu, Jin Wan, Gang Li

Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Unsupervised Anomaly Detection (UAD). However, existing UCAD approaches that rely solely on visual information are insufficient to capture the manifold of normality in complex scenes

Limin Liao, Jun Sun, Junzhi Wang, Chao Deng

D-SIC is of crucial importance for the implementation of IBFD radios. Unfortunately, the achievable performance limit remains underexplored. To fill this gap, in this paper we aim to explore the performance limit, i.e., the minimum residual self-interference (RSI) of the most commonly used PH canceller, and provide the achievable pilot design accordingly. To

Assaf Bar-Natan, Yulan Qing, Kasra Rafi

We introduce a numerical invariant $\zeta(\Sigma)$ measuring the end-complexity of $\Sigma$ and use it to organize coarse-geometric features of Map($\Sigma$). Our main tool is the \emph{non-peripheral curve graph} $C_{\rm np}(\Sigma)$, whose vertices are those essential simple closed curves that cannot be pushed out of every compact subsurface, with edges gi

Minseok Kang, Minhyeok Lee, Minjung Kim, Jungho Lee

Weakly-supervised video scene graph generation (WS-VSGG) aims to parse video content into structured relational triplets without bounding box annotations and with only sparse temporal labeling, significantly reducing annotation costs. Without ground-truth bounding boxes, these methods rely on off-the-shelf detectors to generate object proposals, yet largely

Bi'an Du, Daizong Liu, Pufan Li, Wei Hu

Single-image 3D generation lies at the core of vision-to-graphics models in the real world. However, it remains a fundamental challenge to achieve reliable generalization across diverse semantic categories and highly variable structural complexity under sparse supervision. Existing approaches typically model objects in a monolithic manner or rely on a fixed

Bhagya Chembakottu, Martin P. Robillard

Developers rely on online tutorials to learn web application security, but tutorial quality varies. We reviewed 132 free security tutorials to examine topic coverage, authorship, and technical depth. Our analysis shows that most tutorials come from vendors and emphasize high-level explanations over concrete implementation guidance. Few tutorials provide comp

Kengo Kato, Boyu Wang

Vector quantile regression (VQR) is an optimal transport (OT)-based framework that extends linear quantile regression to vector-valued response variables and can be formulated as an OT problem with a mean-independence constraint. In this paper, we study Sinkhorn-type algorithms for VQR with entropic regularization, building on the authors' previous work

Yifei Dong, Fengyi Wu, Yilong Dai, Lingdong Kong

We study language-conditioned visual navigation (LCVN), in which an embodied agent is asked to follow a natural language instruction based only on an initial egocentric observation. Without access to goal images, the agent must rely on language to shape its perception and continuous control, making the grounding problem particularly challenging. We formulate

Koichiro Akiyama, Sota Hamaguchi, Hiromasa Shiraiwa, Shunsuke Shiomi

Inspired by biology's use of vesicles for targeted transport, many studies have propelled liposomes with active matter, creating synthetic systems that can be viewed as microscale biohybrid robots. Nevertheless, the underlying motility mechanisms from a hydrodynamic perspective are often unresolved, and reliable velocity control remains challenging. Here we

Lei Dong

A fundamental question in nonequilibrium statistical physics is whether effective equilibrium behavior can emerge at coarse-grained scales in strongly driven systems. Here, we investigate this question in the context of human mobility by analyzing five years of intercity flow data covering millions of travelers. While short-term flows are highly asymmetric,

Janardhan Kulkarni

Designing algorithms with provable guarantees that also work well in practice remains difficult, requiring both mathematical reasoning and careful implementation. Existing approaches that bridge worst-case theory and empirical performance, such as beyond-worst-case analysis and data-driven algorithm selection, typically assume prior distributional knowledge

Haoran Xi, Minghao Shao, Kimberly Milner, Venkata Sai Charan Putrevu

Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validated competition designs and evaluation practices that remain fair and evidence-based. This paper presents a cross-regional study of LLM-centered Capture-the-Flag competitions built

Tomoharu Oka, Ryo Ariyama, Tatsuya Kotani

We report the detection of a molecular cloud, CO+40.05-2.40, positionally coincident with the "heartbeating" GeV source Fermi J1913+0515 at the northern boundary of the SS 433/W50 system. Millimeter and submillimeter spectroscopy with the Nobeyama 45 m telescope and the James Clerk Maxwell Telescope shows that the cloud has physical properties typical of qui

Selva Salimi, David J. Warne, Christopher Drovandi

Ordinary differential equation (ODE) models are widely used to describe systems in many areas of science. To ensure these models provide accurate and interpretable representations of real-world dynamics, it is often necessary to infer parameters from data, which involves specifying the form of the ODE system as well as a statistical model describing the obse

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 yea

Yiwei Xie, Zheng Zhang, Ping Liu

Concept erasure techniques for text-to-video (T2V) diffusion models report substantial suppression of sensitive content, yet current evaluation is limited to checking whether the target concept is absent from generated frames, treating output-level suppression as evidence of representational removal. We introduce PROBE, a diagnostic protocol that quantifies

Xinyu Zhang

World models learn to simulate environment dynamics from experience, enabling sample-efficient reinforcement learning. But what do these models actually represent internally? We apply interpretability techniques--including linear and nonlinear probing, causal interventions, and attention analysis--to two architecturally distinct world models: IRIS (discrete

Jiachen Li, Soovadeep Bakshi, Jian Chu, Shihao Li

This paper presents a hierarchical two-stage framework for multi-robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed-form bid functions, and (2) each robot independently solves an optimal control problem for energy-minimal trajectories with a physics-based battery model, followed

Kesheng Chen, Wenjian Luo, Xin Lin, Zhen Song

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). H

Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu

Neural network controllers trained via behavior cloning are increasingly deployed in cyber-physical systems (CPS), yet practitioners lack tools to trace controller failures back to training data. Existing data attribution methods assume i.i.d.\ data and standard loss targets, ignoring CPS-specific properties: closed-loop dynamics, safety constraints, and tem

Da-Zheng Feng, Hao-Xuan Du

The human brain achieves its remarkable computational prowess not despite its inherent non-ideal factors noise, heterogeneity, structural irregularities, decentralized plasticity, systematic errors, and chaotic dynamics but precisely because of them. This paper systematically demonstrates that these traits, long dismissed as imperfections in classical neuros

Yawen Li, Tao Hu, Zhouhui Lian, Wan Tian

This paper studies generalization error bounds for Transformer models. Based on the offset Rademacher complexity, we derive sharper generalization bounds for different Transformer architectures, including single-layer single-head, single-layer multi-head, and multi-layer Transformers. We first express the excess risk of Transformers in terms of the offset Ra

Jian Xian Sim

Non-equilibrium dynamics of strongly and rapidly driven quantum many-body systems is poorly understood beyond periodic driving, where heating is exponentially slow in the drive frequency (Floquet Prethermalization). In contrast, non-periodic drives were found to exhibit widely different heating scalings with no unifying principle. This work identifies a reso

Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu

We present a three-level influence hierarchy for data valuation in stochastic LQR. At the \emph{model level}, the trajectory influence surrogate $\IFm_k := H^{-1}g_k$ approximates the leave-one-trajectory parameter shift. At the \emph{control level} with fixed covariance, the usual fixed-noise score is obtained by composing $\IFm_k$ with the Riccati gradient

Kazuhisa Ogawa, Yutaka Tabuchi, Makoto Negoro

Fixed-frequency transmon qubits, characterized by simple architectures and long coherence times, are promising platforms for large-scale quantum computing. However, the rapidly increasing frequency collisions, which directly reduce the fabrication yield, hinder scaling, especially in cross-resonance (CR) gate-based architectures, wherein the restricted drive

Kazuki Okamura

We study graph-directed conjugate functional equations on the unit interval indexed by the complete digraph with self-loops on two vertices. We focus on the singularity and regularity of the solutions for compatible systems of weak contractions. First, we show that both solutions are singular in the affine case unless the two systems coincide; second, we obt

Artur Kawalec

In this article, we derive a series expansion of the prime zeta function about the $s=1$ logarithmic singularity and prove general formula for its expansion coefficients, which is similar to the Stieltjes expansion coefficients for the Riemann zeta function. These results can also be viewed as a generalization of Mertens's Theorems to higher order. We also n

Jamie Mahowald, Tan Bui-Thanh

We investigate the generalization capabilities of In-Context Operator Networks (ICONs), a new class of operator networks that build on the principles of in-context learning, for higher-order partial differential equations. We extend previous work by expanding the type and scope of differential equations handled by the foundation model. We demonstrate that wh

Farbod Ekbatani, Rad Niazadeh, Mehdi Golari, Romain Camilleri

Ride-hailing platforms increasingly rely on non-exclusive notifications-broadcasting a single request to multiple drivers simultaneously-to mitigate inefficiencies caused by uncertain driver acceptance. In this paper, the first in a two-part collaboration with Lyft, we formally model the 'Notification Set Selection Problem' for a single decision cycle, where

Farbod Ekbatani, Rad Niazadeh, Mehdi Golari, Romain Camilleri

Ride-hailing platforms increasingly face uncertain driver acceptance, which makes traditional one-to-one 'exclusive dispatch (ED)' less efficient: rejections and timeouts force sequential retries and lengthen rider wait times, which in turn creates friction in the marketplace. 'Non-exclusive dispatch (NED)' mitigates this friction by broadcasting a request t

Yujia Chen, Yingli Zhou, Fangyuan Zhang, Cuiyun Gao

Database Management Systems (DBMSs) are fundamental infrastructure for modern data-driven applications, where thorough testing with high-quality SQL test cases is essential for ensuring system reliability. Traditional approaches such as fuzzing can be effective for specific DBMSs, but adapting them to different proprietary dialects requires substantial manua

Migyeong Kang, Jihyun Kim, Hyolim Jeon, Sunwoo Hwang

Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized anno

Gensheng Pei, Xiruo Jiang, Xinhao Cai, Tao Chen

Training-free open-vocabulary semantic segmentation (OVSS) promises rapid adaptation to new label sets without retraining. Yet, many methods rely on heavy post-processing or handle text and vision in isolation, leaving cross-modal geometry underutilized. Others introduce auxiliary vision backbones or multi-model pipelines, which increase complexity and laten

Bayezid Baten, M. Ayyan Iqbal, Sebastian Ament, Julius Kusuma

Modern concrete must simultaneously satisfy evolving demands for mechanical performance, workability, durability, and sustainability, making mix designs increasingly complex. Recent studies leveraging Artificial Intelligence (AI) and Machine Learning (ML) models show promise for predicting compressive strength and guiding mix optimization, but most existing

Ravi Ranjan, Utkarsh Grover, Mayur Akewar, Xiaomin Lin

Large Language Models (LLMs) are deployed in high-stakes settings but can show demographic, gender, and geographic biases that undermine fairness and trust. Prior debiasing methods, including embedding-space projections, prompt-based steering, and causal interventions, often act at a single stage of the pipeline, resulting in incomplete mitigation and brittl

Weizhe Xu, Mengyu Liu, Fanxin Kong

Large Language Models (LLMs), deep learning architectures with typically over 10 billion parameters, have recently begun to be integrated into various cyber-physical systems (CPS) such as robotics, industrial automation, and autopilot systems. The abstract knowledge and reasoning capabilities of LLMs are employed for tasks like planning and navigation. Howev

Lingzhe Zhang, Tong Jia, Mingyu Wang, Weijie Hong

Large Language Models (LLM)-based Multi-Agent Systems (MASs) have emerged as a new paradigm in software system design, increasingly demonstrating strong reasoning and collaboration capabilities. As these systems become more complex and autonomous, effective failure management is essential to ensure reliability and availability. However, existing approaches o

Qian Ma, Ze Gu, Zi Rui Feng, Qian Wen Wu

The evolution toward next-generation intelligent sensing requires microwave systems to move beyond static detection and achieve high-speed and adaptive perception of dynamic scenes. However, the existing microwave sensing systems have bottlenecks owing to their sequential digital processing chain, limiting the refresh rates to hundreds of hertz, while the ex

Guanbao Liang, Yuanchen Bei, Sheng Zhou, Yuheng Qin

Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time. In this pap

Bros Victor, Dufraisse Evan, Popescu Adrian, Gatica-Perez Daniel

Analyzing news coverage in multilingual societies can offer valuable insights into the dynamics of public discourse and the development of collective narratives, yet comprehensive studies that account for linguistic and cultural diversity within national media ecosystems remain limited, particularly in complex contexts such as Switzerland. This paper studies

Si-Yang Liu, Yilong Zhang

For a smooth projective variety $X\subseteq \mathbb P^N$ over an algebraically closed field of char $0$, we show that the discriminant locus of a generic projection of $X$ is projectively dual to a general linear section of the dual variety, and deduce a purity statement for the discriminant. Over $\mathbb C$, we also show that the fundamental group of the c

Ruihua Chen, Yisi Luo, Bangyu Wu, Deyu Meng

Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging, and non-destructive testing. Conventional FWI methods are limited by their notorious sensitivity to the accuracy of the initial models. Recent progress in continuous representation

Jiasheng Wang, Wenxian Li, Xianyong Bai, Yingzi Sun

The chromosphere is a complex solar atmosphere that hosts a variety of transients and transports significant free energy to heat the corona. However, due to the limited sensitivity of polarization measurement and the influence of spectral line broadening, the basic magnetic field configuration in the chromosphere has not yet been fully revealed to correspond

Zixu Feng, Hao Yuan

In this work, we establish that Nesterov's accelerated gradient method, applied to $C^2$ functions satisfying the Polyak--{\L}ojasiewicz inequality around local minimizers, achieves the optimal local linear convergence rate $\rho=\frac{\sqrt{3L+\mu}-2\sqrt{\mu}}{\sqrt{3L+\mu}}+\varepsilon$, where $\varepsilon$ is an arbitrarily small constant. Our analysis r

Nivedita Singh, Seyoung Jin, Hyoungshick Kim

To comply with data protection regulations such as the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), websites widely deploy cookie consent banners to collect users' privacy preferences. In practice, however, these interfaces often embed dark patterns that undermine informed and freely given consent. As regulator

Qirui Zheng, Dan Wu, Franz-Erich Wolter, Sijia Geng

The widespread adoption of renewable energy poses a challenge in maintaining a feasible operating point in highly variable scenarios. This paper demonstrates that, within a feasible region of a power system that meets practical stability requirements, the power flow equations define a smooth bijection between nodal voltage phasors (angle and magnitude) and n

Erik Garcia Neefjes, Stuart C. Hawkins, Mahadevan Ganesh

This work addresses the reconstruction of a scatterer's shape from phaseless far field-intensity data arising from multiple incident waves interacting with the scatterer. We formulate the reconstruction as a statistical inverse scattering problem and adopt a Bayesian inference framework, which can readily be used to compute statistical moments for quantifica

Yihang Ding, Wanke Xia, Yiting Zhao, Jinbo Su

Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the multifaceted nature of complex memory systems, such as dynamic state tracking and hierarchical reasoning in continuous interactions. To overcome these limitations, we propose MemGround, a rigorous long-term m

Liang Wu, Jun-Xian Wang, Luis C. Ho, Junfeng Wang

Measuring star formation in quasar host galaxies is crucial for understanding the coevolution of supermassive black holes (SMBHs) and galaxies, yet remains observationally challenging due to severe contamination from active galactic nucleus (AGN) emission. In this work, we present a new method to robustly isolate the AGN contribution to the [O II] $\lambda$3

Chen Gong, Zhenzhe Zheng, Yiliu Chen, Sheng Wang

Machine learning models are widely integrated into modern mobile apps to analyze user behaviors and deliver personalized services. Ensuring low-latency on-device model execution is critical for maintaining high-quality user experiences. While prior research has primarily focused on accelerating model inference with given input features, we identify an overlo

Zhicheng Deng, Zhaoya Gong, Jean-Claude Thill, Elizabeth C. Delmelle

Current studies on activity space are limited by the conceptualization of absolute physical space that fails to consider the heterogeneity of relational spaces reconstructed from spatial interactions of human movements between locations and falls short in incorporating the inherent hierarchical property of human mobility. Consequently, these approaches canno

Taiwang Deng, Malors Espinos

We extend to $\mathrm{GL}(3,\mathbb Q)$ the Poisson-summation method developed by Altuğ for $\mathrm{GL}(2,\mathbb Q)$ in Beyond Endoscopy. For a fixed prime and a family of factorizable test functions, we isolate the contribution of the trivial representation from the regular elliptic part of the trace formula and obtain an explicit expansion of \[ \mathrm{

Jayanie Bogahawatte, Sachith Seneviratne, Saman Halgamuge

Whole Slide Images (WSIs) are giga-pixel in scale and are typically partitioned into small instances in WSI classification pipelines for computational feasibility. However, obtaining extensive instance level annotations is costly, making few-shot weakly supervised WSI classification (FSWC) crucial for learning from limited slide-level labels. Recently, pre-t

Jian Liu, Hongsong Feng

Accurate prediction of protein-ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machine learning models critically depends on the quality of molecular descriptors, for which advanced mathematical frameworks provide powerful tools. In this work, we employ a novel mathematical theory, termed the persis

Hang-Cheng Dong, Pengcheng Cheng

Overparameterized shallow neural networks admit substantial parameter redundancy: distinct parameter vectors may represent the same predictor due to hidden-unit permutations, rescalings, and related symmetries. As a result, geometric quantities computed directly in the ambient Euclidean parameter space can reflect artifacts of representation rather than intr

Ryuichi Saito, Sho Tsugawa

Public perceptions and expectations of inflation shape household spending, wage bargaining, and policy support, making them key determinants of macroeconomic outcomes. However, current measures rely on infrequent surveys and offer limited insight into underlying narratives and sector-specific concerns. This paper presents a novel approach to measuring public

Yurii Nesterov

In this paper, we develop a new framework for constructing infeasible-start primal-dual methods for Conic Optimization. Our approach can be seen as a straightforward consequence of Gordan Theorem of Alternative. Given by the target upper bound $\epsilon > 0$ for the duality gap as the only input parameter, we form an auxiliary convex problem of minimizing ba

Hoda Ayad, Tanu Mitra

Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking. However, we identify a specific type of sought advice-a request for a moral judgment (i.e. "who was wrong?") in a social conflict-as an implicitly humanizing query which carries potentially harmful anthropomor

Kai Zhang, Dingchao Gao, Zhaohui Yang, Runshi Zhou

Quantum error correcting codes (QECC) are essential for constructing large-scale quantum computers that deliver faithful results. As strong competitors to the conventional surface code, quantum low-density parity-check (qLDPC) codes are emerging rapidly: they offer high encoding rates while maintaining reasonable physical-qubit connectivity requirements. Des

Yi Tao, Zhen Gao, Zhiao Zhu, De Mi

Against the backdrop of the global drive to advance the green transformation of the information and communications technology (ICT) industry and leverage technological innovation to facilitate the achievement of Net-Zero carbon goals, research into Rydberg atomic receivers (RAREs) is gaining significant interest. RAREs leverage the electron transition phenom

Min Li, Lixiang Meng, Gongxu Dong, Xiaobo Zhou

The rapid evolution of next-generation communications and the Internet of Things (IoT) has catalyzed an urgent demand for governing expansive spatial environments as functional electromagnetic (EM) entities. However, deterministically programming such open EM spaces remains a formidable challenge, as current methodologies are largely confined to localized in

Seou Choi, Sachin Vaidya, Caio Silva, Shiekh Zia Uddin

Robotic automation has transformed scientific workflows in domains such as chemistry and materials science, yet free-space optics, which is a high precision domain, remains largely manual. Optical systems impose strict spatial and angular tolerances, and their performance is governed by tightly coupled physical parameters, making generalizable automation par

Lingzhe Zhang, Tong Jia, Weijie Hong, Mingyu Wang

Modern software systems operate at unprecedented scale and complexity, where effective failure management is critical yet increasingly challenging. Metrics, traces, and logs provide complementary views of system runtime behavior, but existing failure management approaches typically rely on task-oriented pipelines that tightly couple modality-specific preproc

Mohamed Sobhi Jabal, Jikai Zhang, Dominic LaBella, Jessica L. Houk

The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for autom

Guowei Tang, Tianwen Qian, Huanran Zheng, Yifei Wang

Real-time, continuous understanding of visual signals is essential for real-world interactive AI applications, and poses a fundamental system-level challenge. Existing research on streaming video understanding, however, typically focuses on isolated aspects such as question-answering accuracy under limited visual context or improvements in encoding efficienc

Qixin Zhang, Wei Huang, Yan Sun, Yao Shu

Identifying the most representative subset for a close-to-submodular objective while satisfying the predefined partition constraint is a fundamental task with numerous applications in machine learning. However, the existing distorted local-search methods are often hindered by their prohibitive query complexities and the rigid requirement for prior knowledge

Zhipeng Zhang

Stable training is often treated as evidence that learning is succeeding, but stability characterizes optimization behavior rather than correctness relative to an external objective. We study what happens when the signal being optimized remains persistently biased. We define Stable but Wrong (SBW) as a learning state in which the learning process remains sta

Chiara Bellotti, Tim Trudgian, Andrew Yang

We prove a new explicit zero-free region for the Riemann zeta-function, drawing substantially on Heath-Brown's seminal work on Linnik's constant. Using these ideas we are able to prove that $\zeta(\sigma + it)\ne 0$ whenever $t\geq 3$ and $\sigma \geq 1- 1/(4.896\log t)$.

Jingnan Luo, Mingqi Gao, Jun Liu, Bin-Bin Gao

The prosperity of Multimodal Large Language Models (MLLMs) has stimulated the demand for video reasoning segmentation, which aims to segment video objects based on human instructions. Previous studies rely on unidirectional and implicit text-trajectory alignment, which struggles with trajectory perception when faced with severe video dynamics. In this work,

Ruiqi Xian, Jing Liang, He Yin, Xuewei Qi

We present \emph{GaussianSSC}, a two-stage, grid-native and triplane-guided approach to semantic scene completion (SSC) that injects the benefits of Gaussians without replacing the voxel grid or maintaining a separate Gaussian set. We introduce \emph{Gaussian Anchoring}, a sub-pixel, Gaussian-weighted image aggregation over fused FPN features that tightens v

Yuma Kawaguchi, Daria Smirnova, Filipp Komissarenko, Daria Kafeeva

Topological concepts have been at the forefront of materials research in recent years, driving a revolution in our understanding of the response of quantum materials and enabling new ways to manipulate light and sound in topological metamaterials. Topological defects and topological boundaries of different dimensions have driven a paradigm shift in photonics

Koichi Tanaka, Kazuki Kawamura, Takanori Muroi, Yusuke Narita

Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, requ

Rui Chai

Current AI alignment paradigms rely on behavioral correction: external supervisors (e.g., RLHF) observe outputs, judge against preferences, and adjust parameters. This paper argues that behavioral correction is structurally analogous to an economy without property rights, where order requires perpetual policing and does not scale. Drawing on institutional ec

Hyundong Jin, Dongyoon Han, Eunwoo Kim

Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests, while preserving general utility. However, sequential unlearning updates distort shared representations, creating spurious associations between vision-language pairs and refusal