March 2026 arXiv papers — page 125

Yang Ni, Fanli Jia

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening

Junyi Liu, Yi Lee, Yilun Xu, Gang Huang

Quantum error correction (QEC) is essential for realizing large-scale, fault-tolerant quantum computation, yet its practical implementation remains a major engineering challenge. In particular, QEC demands precise real-time control of a large number of qubits and low-latency, high-throughput and accurate decoding of error syndromes. While most prior work has

Ruiwu Liu, Yangjian Zhu

Electric vehicles (EVs) require substantially longer refueling times than gasoline vehicles, which can generate severe congestion at charging stations when demand concentrates. We propose a two-stage allocation framework for EV charging networks. In Stage 1, a central coordinator determines station-level admission quotas to control worst-station delay using

Kuan-Tang Huang, Chien-Chun Wang, Cheng-Yeh Yang, Hung-Shin Lee

The rapid proliferation of AI-Generated Content (AIGC) has necessitated robust metrics for perceptual quality assessment. However, automatic Mean Opinion Score (MOS) prediction models are often compromised by data scarcity, predisposing them to learn spurious correlations-- such as dataset-specific acoustic signatures-- rather than generalized quality featur

Yiming Zong, Jiashuo Jiang

We consider the dynamic resource allocation problem where the decision space is finite-dimensional, yet the solution must satisfy a large or even infinite number of constraints revealed via streaming data or oracle feedback. We model this challenge as an Online Semi-infinite Linear Programming (OSILP) problem and develop a novel LP formulation to solve it ap

N. Sowmya, H. C. Manjunatha, Roshini. K. N, R. S. Susheela

The prediction of nuclear half-lives is vital for understanding nuclear stability with significant applications in astrophysics, nuclear energy, and medical physics. This study investigates the $\alpha$-decay half-lives of 154 actinide nuclei in the atomic number range $89 \le Z \le 103$ using the Density-Dependent M3Y (DDM3Y) effective interaction potential

Lanhao Zhao, Yangzhou Chen

This paper investigates a decentralized design approach of leader-following consensus protocols for heterogeneous multiagent systems under a fixed communication topology with a directed spanning tree (DST) and asymmetric weight matrix. First, a control protocol using only the information of the neighbor on the DST of each agent is designed, which is called t

Eshwar Reddy M, Sourav Karmakar

Public leaderboards increasingly suggest that large language models (LLMs) surpass human experts on benchmarks spanning academic knowledge, law, and programming. Yet most benchmarks are fully public, their questions widely mirrored across the internet, creating systematic risk that models were trained on the very data used to evaluate them. This paper presen

Quanhao Ren, Yicheng Li, Nan Song

Motion forecasting is a core task in autonomous driving systems, aiming to accurately predict the future trajectories of surrounding agents to ensure driving safety. Existing methods typically process discrete driving scenes independently, neglecting the temporal continuity and historical context correlations inherent in real-world driving environments. This

Haodong Yan, Zhide Zhong, Jiaguan Zhu, Junjie He

Video action models (VAMs) have emerged as a promising paradigm for robot learning, owing to their powerful visual foresight for complex manipulation tasks. However, current VAMs, typically relying on either slow multi-step video generation or noisy one-step feature extraction, cannot simultaneously guarantee real-time inference and high-fidelity foresight.

Yi Lu, Jiaojiao Guan, Yang Shen, Jiayu Shang

Bacterial phase variation enables reversible, locus-specific phenotypic switching, often driven by DNA inversion (invertons). To identify these events, researchers commonly rely on sequencing reads that provide orientation-specific support. Metagenomic sequencing, which captures total genetic material independent of cultivation, offers a powerful platform fo

Vivek Bhabani Lama

In this article, we characterize the class of complementary edge ideals which satisfy the licci property in terms of the underlying graph. Using this characterization, we associate the licci property of a complementary edge ideal to its other algebraic properties. Finally, we provide two different probability regimes for which the complementary edge ideals o

Xiaobing Sun, Perry Lam, Shaohua Li, Zizhou Wang

Modern LLMs employ safety mechanisms that extend beyond surface-level input filtering to latent semantic representations and generation-time reasoning, enabling them to recover obfuscated malicious intent during inference and refuse accordingly, and rendering many surface-level obfuscation jailbreak attacks ineffective. We propose Structured Semantic Cloakin

ATLAS Collaboration

This paper presents the search for direct pair production of top squarks decaying into two on-shell top quarks and two neutralinos in final states with two oppositely charged leptons (electrons or muons), $b$-jets and large missing transverse momentum. The search uses the full Run 2 dataset, corresponding to an integrated luminosity of 140 fb$^{-1}$ of proto

Jie Xiong, Xu Yang, Xiaowen Zhou

In this paper, we study a two-dimensional process arising as the unique nonnegative solution to a system of two stochastic differential equations (SDEs) with mutually enhancing two-way interactions driven by independent Brownian motions and spectrally positive $\alpha$-stable random measures. Such a SDE system can be identified as a continuous-state Lotka-Vo

Haomin Wang, Qi Wei, Qianli Ma, Shengyuan Ding

With the rapid advancement of vision-language models, an increasing number of studies have explored their potential for SVG generation tasks. Although existing approaches improve performance by constructing large-scale SVG datasets and introducing SVG-specific tokens, they still suffer from limited generalization, redundant paths in code outputs, and a lack

Haozhe Jia, Jianfei Song, Yuan Zhang, Honglei Jin

We present ECHO, an edge--cloud framework for language-driven whole-body control of humanoid robots. A cloud-hosted diffusion-based text-to-motion generator synthesizes motion references from natural language instructions, while an edge-deployed reinforcement-learning tracker executes them in closed loop on the robot. The two modules are bridged by a compact

Zhonghao Liang, Dongmei Huang, Qunying Liao, Cuiling Fan

In recent years, the construction of non-GRS type linear codes has attracted considerable attention due to that they can effectively resist both the Sidelnikov-Shestakov attack and the Wieschebrink attack. Constructing linear complementary dual (LCD) codes and determining the hull of linear codes have long been important topics in coding theory, as they play

Andrew Qing He, Wei Cai

We extend the Weak Adversarial Neural Pushforward Method (WANPM) to the McKean--Vlasov mean-field Fokker--Planck equation, covering both the stationary and time-dependent cases. The key observation is that the mean-field nonlinearity -- an expectation under the solution distribution -- is naturally estimated by Monte Carlo sampling from the pushforward netwo

Camille Jimenez Cortes, Philippe Lalanda, German Vega

Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather than aiming to improve absolute in vitro prediction accuracy, this work examines whether explicitly separating representation learning from task supervision ena

Quy-Anh Dang, Chris Ngo

We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusively on publicly available speech corpora, using a balanced sampling strategy tha

Yangzhou Chen, Lanhao Zhao

This paper proposes a decentralized design approach of consensus protocols of multi-agent systems via a directed-spanning-tree(DST)-based linear transformation and the corresponding minimal communication links. First, the consensus problem of multi-agent systems is transformed into the decentralized output stabilization problem by constructing a linear trans

Viraj Panchal, Tanmay Talsaniya, Parag Patel, Meet Patel

We present KidsNanny, a two-stage multimodal content moderation architecture for child safety. Stage 1 combines a vision transformer (ViT) with an object detector for visual screening (11.7 ms); outputs are routed as text not raw pixels to Stage 2, which applies OCR and a text based 7B language model for contextual reasoning (120 ms total pipeline). We evalu

Zewen He, Yoshihiko Nakamura

Reinforcement learning (RL) has demonstrated substantial potential for humanoid bipedal locomotion and the control of complex motions. To cope with oscillations and impacts induced by environmental interactions, compliant control is widely regarded as an effective remedy. However, the model-free nature of RL makes it difficult to impose task-specified and qu

Huyen T. T. Tran, Van-Quang Nguyen, Farros Alferro, Kang-Jun Liu

Multimodal Large Language Models (MLLMs) have shown impressive abilities in understanding and reasoning over conventional images. However, their perception of 360° images remains largely underexplored. Unlike conventional images, 360° images capture the entire surrounding environment, enabling holistic spatial reasoning but introducing challenges such as geo

Chanakan Chaipan, Aueaphum Aueawatthanaphisut

Early identification of individuals at risk of stroke remains a major clinical challenge, as prodromal motor im- pairments are often subtle and transient. In this pilot study, a wearable sensor-based framework is proposed for early pre- stroke risk screening using a single inertial measurement unit mounted on the sacral region to capture pelvic motion during

Christina Baek, Ricardo Pio Monti, David Schwab, Amro Abbas

Real-world model deployments demand strong performance on narrow domains where data is often scarce. Typically, practitioners finetune models to specialize them, but this risks overfitting to the domain and forgetting general knowledge. We study a simple strategy, specialized pretraining (SPT), where a small domain dataset, typically reserved for finetuning,

Yan Xia, Sushmita Khan, Naiyah Lewis, Jinkyung Katie Park

Online LGBTQ+ communities face a persistent tension: remaining visible to welcome newcomers while protecting members from harassment. This challenge is particularly acute for lesbian communities on Reddit, which operate not as isolated groups but as an interconnected ecosystem. We examine how this tension is negotiated across the lesbian subreddit ecosystem

Deblina Dey, A. V. Jayanthan, Sarang Sane

In this article, we characterize all unmixed and Cohen-Macaulay parity binomial edge ideals of cactus and chordal graphs in terms of the structural properties of the graph.

David Nikitin, Nataliya Stankevich

Using the example of three-dimensional Mira map, it is shown that the destruction of a multi-turn invariant curve can occur through the appearance of local multiple bends. It was found that, depending on the precision of machine arithmetic, a complication of the multi-turn invariant curve can be observed, which is a numerical artifact. Artifact can be avoide

Susan Friedlander, Anthony Suen

We study an abstract family of advection-diffusion equations within the framework of the fractional Laplacian. The system involves two independent diffusion parameters: one introduced via a damping operator acting on the scalar unknown and the other as the coefficient of the fractional Laplacian. We establish existence and convergence results in specific par

Lizheng Sun

We present MemX, a local-first long-term memory system for AI assistants with stability-oriented retrieval design. MemX is implemented in Rust on top of libSQL and an OpenAI-compatible embedding API, providing persistent, searchable, and explainable memory for conversational agents. Its retrieval pipeline applies vector recall, keyword recall, Reciprocal Ran

Surya Vardhan Yalavarthi

Corrective Retrieval Augmented Generation (CRAG) improves the robustness of RAG systems by evaluating retrieved document quality and triggering corrective actions. However, the original implementation relies on proprietary components including the Google Search API and closed model weights, limiting reproducibility. In this work, we present a fully open-sour

Mikhail Gomoyunov

We study minimax (generalized) solutions of a Cauchy problem for a (first-order) path-dependent Hamilton--Jacobi equation with co-invariant derivatives under a right-end boundary condition. Under assumptions on the Hamiltonian that are more general than those previously considered in the literature and allow, in particular, a measurable dependence on the fir

Yongyi Wang, Jaeuk Kim, Yang Jiao, Izabella Stuhl

Hyperuniform many-particle systems, which encompass crystals, quasicrystals and certain exotic disordered systems, exhibit an anomalous suppression of density fluctuations on macroscopic length scales relative to those of conventional disordered systems. Here we investigate the percolation behaviors of disordered stealthy hyperuniform systems (SHU), a subcla

Jian Sun, Yuming Huang, He Li, Shuqi Xiao

Humans routinely leverage semantic hints provided by signage to navigate to destinations within novel Large-Scale Indoor (LSI) environments, such as hospitals and airport terminals. However, this capability remains underexplored within the field of embodied navigation. This paper introduces a novel embodied navigation task, SignNav, which requires the agent

Zanting Ye, Xuanbin Wu, Guoqing Zhong, Shengyuan Liu

Routine oncologic computed tomography (CT) presents an ideal opportunity for screening spinal instability, yet prophylactic stabilization windows are frequently missed due to the complex geometric reasoning required by the Spinal Instability Neoplastic Score (SINS). Automating SINS is fundamentally hindered by metastatic osteolysis, which induces topological

Yiming Wang

Visible-infrared person re-identification faces greater challenges than traditional person re-identification due to the significant differences between modalities. In particular, the differences between these modalities make effective matching even more challenging, mainly because existing re-ranking algorithms cannot simultaneously address the intra-modal v

Suvajit Patra, Soumitra Samanta

Continuous Sign Language Recognition (CSLR) is a crucial task for understanding the languages of deaf communities. Contemporary keypoint-based approaches typically rely on spatio-temporal encoding, where spatial interactions among keypoints are modeled using Graph Convolutional Networks or attention mechanisms, while temporal dynamics are captured using 1D c

Jeonghwan Ahn, Swarnava Ghosh, Seoung-Hun Kang, Dameul Jeong

Monolayer MnBi$_{2}$Te$_{4}$ (MBT) is an intrinsically magnetic topological insulator whose magnetic response is strongly affected by strain and electron correlation. In density functional theory with an on-site Hubbard correction (DFT+$U$), however, predictions vary substantially with the choice of Hubbard $U$, making it difficult to establish a reliable st

Long Li, Zhijian Zhou, Jiangxuan Long, Peiyang Liu

Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck is the credit assignment problem. In traditional paradigms, rewards are determined solely by the final-turn feedback, which ignores the intermediate process and leads to ambiguous credit evaluation. To

Junhyeok Lee, Han Jang, Heeseong Eum, Joon Jang

Multiplex immunofluorescence (mIF) enables simultaneous single-cell quantification of multiple biomarkers within intact tissue architecture, yet its high reagent cost, multi-round staining protocols, and need for specialized imaging platforms limit routine clinical adoption. Virtual staining can synthesize mIF channels from widely available brightfield immun

Davie Chen

The rapid advancement of generative AI has introduced a new class of tools capable of producing publication-quality scientific figures, graphical abstracts, and data visualizations. However, academic publishers have responded with inconsistent and often ambiguous policies regarding AI-generated imagery. This paper surveys the current stance of major journals

Abhijit Kumar, Natalya Kumar, Shikhar Gupta

Critic-free reinforcement learning with verifiable rewards (RLVR) improves code generation by optimizing unit-test pass rates, but GRPO-style updates suffer from coarse credit assignment: a single outcome signal is spread uniformly across long programs even when failure stems from a localized semantic error. We propose Execution-Grounded Credit Assignment (E

Hyunho Cha, Jungwoo Lee

The resource theory for nonnegativity of quantum amplitudes distinguishes completely positive completely positive (CPCP) quantum channels from the larger class of completely positive doubly nonnegative (CPDNN) quantum channels. Johnston and Sikora showed that all qubit-to-qubit quantum channels that are CPDNN are also CPCP. However, they left open the questi

Long Li, Zhijian Zhou, Tianyi Wang, Weidi Xu

While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard past rollouts. Existing experience replay methods address this by reusing accurate samples for direct policy updates, but this often incurs high computational costs and causes mode collapse via overfitting. We argue

Sahil Samar, Marc Vinyals, Vijay Ganesh

We prove that there exists a deterministic configuration of Conflict Driven Clause Learning (CDCL) SAT solvers using a variant of the VSIDS branching heuristic that solves instances of the Ordering Principle (OP) CNF formulas in time polynomial in n, where n is the number of variables in such formulas. Since tree-like resolution is known to have an exponenti

Junwen An, Kabilan Mahathevan, Manuel Rigger

SQL is a widely adopted language for querying data, which has led to the development of various SQL analysis and rewriting tools. However, due to the diversity of SQL dialects, such tools often fail when encountering unrecognized dialect-specific syntax. While Large Language Models (LLMs) have shown promise in understanding SQL queries, their inherent limita

Jiayi Tian, Jiaze Wang

Understanding 4D point cloud videos is essential for enabling intelligent agents to perceive dynamic environments. However, temporal scale bias across varying frame rates and distributional uncertainty in irregular point clouds make it highly challenging to design a unified and robust 4D backbone. Existing CNN or Transformer based methods are constrained eit

Yuxuan Zhu, Tengjun Jin, Chenghao Mo, Daniel Kang

Analytical join queries over unstructured data are increasingly prevalent in data analytics. Applying machine learning (ML) models to label every pair in the cross product of tables can achieve state-of-the-art accuracy, but the cost of pairwise execution of ML models is prohibitive. Existing algorithms, such as embedding-based blocking and sampling, aim to

Keru Chen, Jun Luo, Sen Lin, Yingbin Liang

Hierarchical Instruction Following (HIF) refers to the problem of prompting large language models with a priority-ordered stack of instructions. Standard methods like RLHF and DPO typically fail in this problem since they mainly optimize for a single objective, failing to explicitly enforce system prompt compliance. Meanwhile, supervised fine-tuning relies o

Yukun Zhao, Zichen Zhong, Yongshun Gong, Yilong Yin

Denoising generative models have recently become the dominant paradigm for dexterous grasp generation, owing to their ability to model complex grasp distributions from large-scale data. However, existing diffusion-based methods typically formulate generation as a stochastic differential equation (SDE), which often requires many sequential denoising steps and

Agnibha De Sarkar, Tanuman Ghosh

The Galactic plane survey conducted by the High Energy Stereoscopic System (H.E.S.S.) has revealed numerous teraelectronvolt (TeV) sources, many of which remain unidentified. HESS~J1832$-$085 is a point-like TeV source lacking a confirmed multiwavelength (MWL) counterpart. In this paper, we present evidence that HESS~J1832$-$085 is likely a gamma-ray binary.

Seunghwan Lee, Gisoo Lee, Seounghee Yun, Sumin Lee

3D-printed digital materials whose mechanical behavior travels between those from thermoplastic to rubbery polymers have become increasingly important. However, their mechanical functionalities have not been fully exploited due to intrinsic mechanical anisotropy resulting from microstructural heterogeneity. Here, we combine mechanical testing, microscopy ana

Zhengzheng Tang

We ask whether a pure spiking backbone can learn large-scale language modeling from random initialization, without Transformer distillation. We introduce NeuronSpark, a 0.9B-parameter SNN language model trained with next-token prediction and surrogate gradients. The model combines selective state-space spiking dynamics, leakage-current inter-layer communicat

Arno Strouwen, Sebastian Micluţa-Câmpeanu

Model-based design of experiments (MBDOE) is essential for efficient parameter estimation in nonlinear dynamical systems. However, conventional adaptive MBDOE requires costly posterior inference and design optimization between each experimental step, precluding real-time applications. We address this by combining Deep Adaptive Design (DAD), which amortizes s

Fu-He Hsiao, Yu-Jie Lin, Chia-Jung Tsai, Chia-Chen Li

We present a systematic design methodology, combining simulation and experimental validation, for high-speed 940 nm vertical-cavity surface-emitting lasers (VCSELs). A comprehensive simulation study was conducted to optimize the device structure, focusing on the number of oxide layers and the aperture size, which predicted a maximum modulation bandwidth of o

Mengyuan Li, Qianfan Lu, Jiachen Tian, Hongjun Hu

In near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, spherical wavefront propagation expands the traditional beam codebook into the joint angular-distance domain, rendering conventional beam training prohibitively inefficient, especially in complex 3-dimensional (3D) low-altitude environments. Furthermore, since near-field be

Peng Sun, Jun Xie, Tao Lin

Unified Multimodal Models (UMMs) are often constrained by the pre-training of their $\textbf{visual generation components}$, which typically relies on inefficient paradigms and scarce, high-quality text-image paired data. In this paper, we systematically analyze pre-training recipes for $\textbf{UMM visual generation}$ and identify these two issues as the ma

Michelle Huang, Agam Goyal, Koustuv Saha, Eshwar Chandrasekharan

Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across five systems---vanilla GPT, Search GPT, Perplexity Search with Grok, Google AI Overviews, and

Zhouwei Zhai, Mengxiang Chen, Anmeng Zhang

Large language models offer transformative potential for e-commerce search by enabling intent-aware recommendations. However, their industrial deployment is hindered by two critical challenges: (1) knowledge hallucination due to insufficient encoding of dynamic, fine-grained product knowledge, and (2) security vulnerabilities under jailbreak attacks that thr

Emily Chen, Alexander J. Bisberg, Dmitri Williams, Magy Seif El-Nasr

This paper examines how player flexibility -- a player's willingness to engage in a breadth of options or specialize -- manifests across two gaming environments: League of Legends (League) and Teamfight Tactics (TFT). We analyze the gameplay decisions of 4,830 players who have played at least 50 competitive games in both titles and explore cross-game dynamic

Kei Funano

We establish two universal inequalities for Neumann eigenvalues of the Laplacian on a Euclidean convex domain.

Shesh Narayan Gupta, Nik Bear Brown

Generative models are widely used to compensate for class imbalance in AI training pipelines, yet their failure modes under low-data conditions are poorly understood. This paper reports a controlled benchmark comparing three augmentation strategies applied to a fine-grained animal classification task: traditional transforms, FastGAN, and Stable Diffusion 1.5

Xiaoxu Meng, Zhongmin Chen, Bo Yang, Weikai Chen

Neural reconstructions often trade structure for fidelity, yielding dense and unstructured meshes with irregular topology and weak part boundaries that hinder editing, animation, and downstream asset reuse. We present DualPrim, a compact and structured 3D reconstruction framework. Unlike additive-only implicit or primitive methods, DualPrim represents shapes

Da Zhang, Bingyu Li, Feiyu Wang, Zhiyuan Zhao

Zero-shot object counting (ZSOC) aims to enumerate objects of arbitrary categories specified by text descriptions without requiring visual exemplars. However, existing methods often treat counting as a coarse retrieval task, suffering from a lack of fine-grained quantity awareness. Furthermore, they frequently exhibit spatial insensitivity and degraded gener

Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan

As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matc

Jiahua Hu, Hai L. Vu, Wynita Griggs, Hao Wang

The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging represent distinct charging regimes associated with different po

Kazuki Yano, Shun Kiyono, Sosuke Kobayashi, Sho Takase

We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence on downstream performance after supervised fine-tuning (SFT). Decay-based learning rate schedulers are widely used to minimize pre-training loss. However, despite their widespread use, how these schedulers affect performance

Hao Luo, Saeed R. Khosravirad, Ahmed Alkhateeb

Wireless digital twins can be leveraged to provide site-specific synthetic channel information through precise physical modeling and signal propagation simulations. This can help reduce the overhead of channel state information (CSI) acquisition, particularly needed for large-scale MIMO systems. For high-quality digital twin channels, the classical approach

Shuvam Banerji Seal, Aheli Poddar, Alok Mishra, Dwaipayan Roy

This paper introduces AgriIR, a configurable retrieval augmented generation (RAG) framework designed to deliver grounded, domain-specific answers while maintaining flexibility and low computational cost. Instead of relying on large, monolithic models, AgriIR decomposes the information access process into declarative modular stages -- query refinement, sub-qu

Jeonghwan Ahn, Seoung-Hun Kang, Panchapakesan Ganesh, Jaron T. Krogel

Rutile RuO$_2$ has been proposed as an altermagnet, but its bulk magnetic ground state is still under debate because density-functional calculations give conflicting predictions. Using fixed-node diffusion quantum Monte Carlo, we find that stoichiometric bulk RuO$_2$ is nonmagnetic in the pristine structure, lying 23(9) meV per formula unit below the lowest

Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen

Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often overlook the long tail topics and rely on popular repositories where Large Language Models (LLMs) can cheat via memorized knowledge. To address this, we introduce SWE-QA-Pro, a benchma

Sadia Ilyas, Annika Mütze, Klaus Friedrichs, Thomas Kurbiel

Out-of-distribution (OOD) object detection is an important yet underexplored task. A reliable object detector should be able to handle OOD objects by localizing and correctly classifying them as OOD. However, a critical issue arises when such atypical objects are completely missed by the object detector and incorrectly treated as background. Existing OOD det

Dan Shafir, Stanislav Burov

Time averages extracted from single-particle trajectories in complex media often vary strongly from one trajectory to another, even for long measurement times. Such persistent trajectory-to trajectory scatter is commonly observed in anomalous diffusion and signals weak ergodicity breaking driven by scale-free trapping. Here we identify conditional ergodicity

Nishant Balepur, Malachi Hamada, Varsha Kishore, Sergey Feldman

Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queries, but lack understanding of their users. We address this with MyScholarQA (MySQA), a personalized DR agent that: 1) infers a profile with a user's research interests; 2) proposes personalized actions for a user

Fengqian Guo, Yuhan Zhou, Longwei Jiang, Congcong Miao

Next-generation real-time communication (NGRTC) applications, such as cloud gaming and XR, demand consistently ultra-low latency. However, through our first large-scale measurement, we find that despite the deployment of edge servers, dedicated congestion control, and loss recovery mechanisms, cloud gaming users still experience long-tail latency in Wi-Fi ne

Gunhee Shin, Seungjae Lee, Jei Kong, Youngwoo Seo

In estimating odometry accurately, an inertial measurement unit (IMU) is widely used owing to its high-rate measurements, which can be utilized to obtain motion information through IMU propagation. In this paper, we address the limitations of existing IMU propagation methods in terms of motion prediction and motion compensation. In motion prediction, the exi

Alejandro Paredes La Torre

We study adversarial robustness of open-source vision-language model (VLM) agents deployed in a self-contained e-commerce environment built to simulate realistic pre-deployment conditions. We evaluate two agents, LLaVA-v1.5-7B and Qwen2.5-VL-7B, under three gradient-based attacks: the Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and a CLIP

Pratyush Acharya, Prasansha Bharati, Yokibha Chapagain, Isha Sharma Gauli

The integration of Large Language Models (LLMs) into educational ecosystems promises to democratize access to personalized tutoring, yet the readiness of these systems for deployment in non-Western, low-resource contexts remains critically under-examined. This study presents a systematic evaluation of four state-of-the-art LLMs--GPT-4o, Claude Sonnet 4, Qwen

Jiaqi Liu, Yuanyi Zhang, Fang-Wei Fu

We construct a lattice-based ciphertext-policy attribute-based encryption (CP-ABE) scheme for $\mathsf{NC}^1$ access policies with constant-size ciphertexts. Let $\lambda$ be the security parameter. For an $\mathsf{NC}^1$ circuit of depth $d$ and size $s$ on $\ell$-bit inputs, our scheme has the public-key and ciphertext sizes $O(1)$ (independent of $d$), an

Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger, Junil Choi

The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, traditional centralized artificial intelligence (AI) paradigms are ill-suited to the decentralized, resource-constrained, and dynamic nature of 6G ecosystems. This paper explores knowledge

Zi-Jie Yan, Zihao Wang, Bing Xia, Stephen Paolini

Iron-based superconductors are a fascinating family of materials in which multiple electronic bands and strong antiferromagnetic (AFM) correlations are key ingredients for competing ground states, including antiferromagnetism, electronic nematicity, and unconventional superconductivity. FeTe, unlike its superconducting isostructural counterpart FeSe, has lon

Milad Alipour Shahraki, Laurent Lessard

This paper presents a two-stage framework for constrained near-optimal feedback control of input-affine nonlinear systems. An approximate value function for the unconstrained control problem is computed offline by solving the Hamilton--Jacobi--Bellman equation. Online, a quadratic program is solved that minimizes the associated approximate Hamiltonian subjec

Minbing Chen, Zhu Meng, Fei Su

Vision-Language Models (VLMs) offer significant potential in computational pathology by enabling interpretable image analysis, automated reporting, and scalable decision support. However, their widespread clinical adoption remains limited due to the absence of reliable, automated evaluation metrics capable of identifying subtle failures such as hallucination

Tik Yu Yim, Wenting Tan, Sum Yee Chan, Tak-Wah Lam

Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have emerged, yet our experiments show that leading methods (GEPA and ACE) achieve only marginal gains on the FAMMA financial reasoning benchmark, exposing the limits of unstructure

Sarthak Ahuja, Neda Kordjazi, Evren Yortucboylu, Vishaal Kapoor

Enterprise IT support is constrained by heterogeneous devices, evolving policies, and long-tail failure modes that are difficult to resolve centrally. We present VIGIL, an edge-extended agentic AI system that deploys desktop-resident agents to perform situated diagnosis, retrieval over enterprise knowledge, and policy-governed remediation directly on user de

Kazuhide Matsuda

In this series of papers, we introduce higher level versions of the theta group $\Gamma_{\theta}.$ In this paper, we treat the theta group of level $5$, $\Gamma_{\theta,5},$ and construct modular forms on $\Gamma_{\theta,5}$. Moreover we compute their multiplier systems. For this purpose, we derive transformation formula of theta function with characteristic

Jaime Alberto Londoño

We develop a continuous-time general equilibrium framework for an infinite heterogeneous population whose household types are transported by a Brownian flow. Agents repeatedly solve short-horizon optimization problems over state-price-discounted consumption and wealth, which admit an endogenous relative-income criterion in equilibrium. For the resulting Radn

Peng Zhang

Repository-level code review requires reasoning over project structure, repository context, and file-level implementation details. Existing automated review workflows often collapse these tasks into a single pass, which can reduce relevance, increase duplication, and weaken prioritization. We present RepoReviewer, a local-first multi-agent system for automat

Ryan Flynn, Anders W. Sandvik

We introduce an SU($N$) symmetric two-dimensional quantum spin model, the X-Q model, which hosts a ground state transition between N\'eel antiferromagnetic and spontaneously dimerized states. The Q terms are products of two adjacent singlet projectors on first-neighbor sites, as in the often studied J-Q model (where J is the Heisenberg exchange), while the X

Wenqiang Huang, Xuhang Gu, Susu Fang, Shen'ao Xue

Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimizati

Noppanat Wadlom, Junyi Shen, Yao Lu

Agentic workflows are composed of sequences of interdependent Large Language Model (LLM) calls, and they have become a dominant workload in modern AI systems. These workflows exhibit extensive redundancy from overlapping prompts and intermediate results due to speculative and parallel exploration. Existing LLM serving systems, such as vLLM, focus on optimizi

Xiaoxuan Jiang, Yijie Mao

Integrated sensing, communication, and powering (ISCAP) has emerged as a promising solution for enabling multi-functionality in 6G networks. However, it poses a significant challenge in the design of multi-functional waveforms that must jointly consider communication, sensing, and powering performance. In this paper, we propose a novel rate-splitting multipl

Mehmet Yildirim, Fahrettin Ay, Laszlo B. Kish

The statistical fluctuations of the mean-square noise voltages measured at Alice's and Bob's ends in the KLJN scheme are used to implement a binary classifier for a new type of wire resistance-based attack. The data are plotted on a two-dimensional graph, where the x- and y- axes represent the mean-square voltages at Alice's and Bob's ends, respectively. Whe

Sensen Gao, Zhaoqing Wang, Qihang Cao, Dongdong Yu

Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space. Central to our approach is the

Elad Hirsch, Shubham Yadav, Mohit Garg, Purvanshi Mehta

We introduce LICA (Layered Image Composition Annotations), a large scale dataset of 1,550,244 multi-layer graphic design compositions designed to advance structured understanding and generation of graphic layouts. In addition to rendered PNG images, LICA represents each design as a hierarchical composition of typed components including text, image, vector, a

Zunwei Fu, Loukas Grafakos, Wei Wang, Qingyan Wu

This paper is devoted to the equivalence of various characterizations of holomorphic $H^1$ Hardy spaces on tube domains over polyhedral cones. We establish a new iterated Poisson integral formula which reproduces holomorphic functions on such domains. However, this formula shows that holomorphic $H^1$ functions have boundary values in a new type of Hardy spa

Alireza Fadakar, Yuchen Zhang, Hui Chen, Musa Furkan Keskin

Reconfigurable antennas (RAs) utilize the electromagnetic (EM) domain to provide dynamic control over antenna radiation patterns, which offers an effective way to enhance power efficiency in wireless links. Unlike conventional arrays with fixed element patterns, RAs enable on-demand beam-pattern synthesis by directly controlling each antenna's EM characteris

Yunfei Xue, Jiabin Wang, Li Chen, Chenwei Lv

Efimov effects arise from scale invariance, a fundamental symmetry with universal implications. While spatial Efimov physics has been extensively studied, realizing its temporal counterpart remains challenging, as it requires a dynamical system that breaks time-translation symmetry yet preserves the essential time-scaling symmetry. Analog cosmology offers a