April 2026 arXiv papers — page 40

Peng Liao, Peijia Zheng, Lingbo Li, Shangsong Liang

Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models (LLMs) with human values. However, achieving optimal performance with these methods typically involves additional hyperparameter tuning, resulting in substantial time overhead. Although prior work has proposed

Alireza Arastou, Ye Wang, Erik Weyer

This paper studies economic model predictive Control (EMPC) schemes, where the stage cost depends only on control inputs. Such problems arise in applications like water distribution networks and differ from standard EMPC since multiple steady states can correspond to the unique optimal steady input. We show that, under a strict dissipativity assumption relat

YuHao Yin, Zongji Wang, Yuanben Zhang, Biqing Li

Full 360$^\circ$ novel view synthesis under low-light conditions remains challenging. Insufficient illumination, noise amplification, and view-dependent photometric inconsistencies prevent existing methods from jointly preserving geometric consistency and photorealism. Unsupervised approaches often exhibit color drift under large viewpoint variations, while

Woojun Jung, Junyeong Kim

Video-to-text summarization remains underexplored in terms of comprehensive evaluation methods. Traditional n-gram overlap-based metrics and recent large language model (LLM)-based approaches depend heavily on human-written reference summaries, limiting their practicality and sensitivity to nuanced semantic aspects. In this paper, we propose QEVA, a referenc

Eungyu Woo, Yooshin Kim, Wonje Heo, Donghoon Shin

Industrial Control Systems (ICS) integrate computing, physical processes, and communication to operate critical infrastructures such as power grids, water treatment plants, and oil and gas facilities. As ICS become increasingly targeted by cyberattacks, timely and reliable anomaly diagnosis is essential for protecting operational safety. However, existing IC

Gaurav N. Gadbail, Kazuharu Bamba

We present a model-independent, sound-horizon-free measurement of the Hubble constant $H_0$ using baryon acoustic oscillation tracers from the Dark Energy Spectroscopic Instrument Data Release 2. The function reconstructions are performed using the artificial neural network method, which is a completely data-driven approach that avoids the mild $Λ$CDM prior

Yuhao Deng, Haoyu Wei, Zhongzhe Ouyang

Causal mediation analysis is a powerful tool for disentangling the total effect of a treatment into its direct effect on the outcome and its indirect effect mediated through an intermediate variable. However, in observational studies, confounding between treatment and potential outcomes typically renders the total and natural effects non-identifiable. In thi

Sijia Li, Min Gao, Zongwei Wang, Zhiyi Liu

Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns. Transformer-based recommenders demonstrate a strong capacity for learning long-range and interpretable dependencies, yet remain vulnerable to behavioral noise that is misaligned with users' true preferences. Recent la

Russell Tsuchida, Frank Nielsen

Bregman divergences play a pivotal role in statistics, machine learning and computational information geometry. Particularly in the context of machine learning, they are central to clustering, exponential families, parameter estimation and optimisation, among other things. Despite this, the full toolkit of Hilbert spaces and in particular reproducing kernel

Bingyi Liu, Chuanhui Zhu, Hongfei Xue, Jian Teng

Accurate 3D object detection is critical for autonomous driving, necessitating reliable, cost-effective sensors capable of operating in adverse weather conditions. Camera and millimeter-wave radar fusion has emerged as a promising solution; however, these methods often rely on finely annotated radar data, which is scarce and labor-intensive to produce. To ad

Bin Chen, Shouliang Zhu, Beidan Liu, Yong Zhao

Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large Language Model (LLM)-based Automated Heuristic Design (AHD) has shown promise in autonomously generating heuristic components with minimal human intervention. However, most existing

Stephen Wiggins

The Kerr-nonlinear parametric oscillator (KPO) provides a foundational semiclassical model for cat-state quantum hardware. Standard analyses of the KPO typically rely on autonomous, frozen-time approximations to describe the stabilization of macroscopic coherent states. However, state preparation and gate manipulation are driven by explicitly time-dependent

Wenjie Du, Yiyuan Yang, Tianxiang Zhan, Qingsong Wen

Partially-observed time series (POTS) is ubiquitous in real-world applications, yet most existing toolchains separate missing-value handling from downstream learning, which limits reproducibility and overall performance. This tutorial introduces PyPOTS, an open-source Python ecosystem for end-to-end data mining and machine learning on POTS. We present practi

Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao, Soham Dan

Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain unchanged. We show that semantically equivalent serializations, such as $\texttt{csv}$, $\texttt{tsv}$, $\texttt{html}$, $\texttt{markdown}$, and $\texttt{ddl}$, can produce subst

Hojoon Kim, Yuheng Wu, Thierry Tambe

Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this paper, we show that embodied tasks exhibit strong plan locality, where the next plan is largely predictable from the current one. Building on this, we introduce AgenticCache, a planning framework that reuses cached

Yuxuan Gao, Megan Wang, Yi Ling Yu

Static benchmarks measure what AI agents can do at a fixed point in time but not how they are adopted, maintained, or experienced in deployment. We introduce AgentPulse, a continuous evaluation framework scoring 50 agents across 10 workload categories along four factors (Benchmark Performance, Adoption Signals, Community Sentiment, and Ecosystem Health) aggr

Beomchan Park, Seongho Kim, Hyunjun Kim, Sungjune Park

While Multimodal Large Language Models (MLLMs) have enhanced grounding capabilities in general scenes, their robustness in crowded scenes remains underexplored. Crowded scenes entail visual challenges (i.e., occlusion and small objects), which impair object semantics and degrade grounding performance. In contrast, language expressions are immune to such degr

Ran Huang

Large monetary expansions do not necessarily generate consumer-price inflation, challenging scalar views of "money supply." Here we propose that monetary function is phase-dependent: newly issued base money can occupy distinct functional compartments with different coupling to prices. Starting from an accounting framework that separates reproduction, consump

Sheng Zhong, Junkai Niu, Guillermo Gallego, Kaizhen Sun

Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in high-speed maneuvering scenarios. Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from ma

Wanying Shao, Toshimitsu Hamasaki, Scott Evans, Guoqing Diao

Cluster randomized trials are widely used when individual randomization is logistically infeasible or when correlations between observations cannot be ignored, especially in fields such as ophthalmology, infectious disease, vaccine research, and sociology. The desirability of outcome ranking (DOOR) framework evaluates patient-centric benefit-risk using an or

Swadhin Das, Vivek Yadav

The encoder-decoder framework has become widely popular nowadays. In this model, the encoder extracts informative visual features from an input image, and the decoder employs a sequence-to-sequence formulation to generate the corresponding textual description from these features. The existing models focus more on the decision part. However, extracting meanin

Abdumauvlen Berdyshev, Dossan Baigereyev, Aibek Bakishev, Nurlana Alimbekova

We consider an incompressible magnetohydrodynamics (MHD) model in which the classical first-order time derivatives in the momentum and magnetic induction equations are replaced by variable-order Caputo time-fractional derivatives. This formulation allows the memory effect to vary during the evolution and represents a time-fractional generalization of the inc

Jiawei Wang, Ming Lei, Yaning Yang, Xinyan Lin

Identifying species in biology among tens of thousands of visually similar taxa while discovering unknown species in open-world environments remains a fundamental challenge in biodiversity research. Current methods treat identification and discovery as separate problems, with classification models assuming closed sets and discovery relying on threshold-based

Liyou Chen, Hailong Sun, Xiang Gao, Lin Shi

The utilization of third-party open-source libraries is widespread in modern software development. Due to the dependency relationships, vulnerabilities within open-source libraries pose significant security threats to downstream software. However, the library vulnerabilities are usually implicitly reported and patched, without explicit notification to depend

Qiliang Liang, Hansi Wang, Zhong Liang, Yang Liu

Large language model (LLM) agents increasingly rely on reusable skills: capability packages that combine instructions, control flow, constraints, and tool calls. In current agent systems, however, skills are still represented by text-heavy artifacts, mainly SKILL{.}md-style documents whose machine-usable evidence remains embedded largely in natural-language

Kevin Zelaya, Jonathan Friedman, Hector Rubio, Stefan Preble

Phase shifters are fundamental reconfigurable components in photonic circuits. In conjunction with passive elements, they control light flow and serve as foundational building blocks for diverse applications, including communication, sensing, analog signal processing, and quantum control. Conventional phase shifters achieve phase control by modulating the re

Takumi Kawano, Kohei Miura, Daisuke Iwai

Conventional multi-projector calibration requires projecting and capturing structured light patterns for each projector sequentially, causing calibration time and effort to increase linearly with the number of projectors. This scalability bottleneck has long limited the deployment of large-scale projection mapping systems. We present a new calibration framew

Ce Liu, Rui Meng, Yinqiu Liu, Xiaodong Xu

Radio Frequency Fingerprinting (RFF) is a key technology for identity authentication in wireless networks. However, due to the rapid dynamics of Autonomous Aerial Vehicles (AAVs) in low-altitude wireless networks, RFF models require parameter updates to maintain authentication performance, posing a major challenge to existing schemes. Conventional retraining

Jiaqi Li, Yang Zhao, Bin Sun, Yang Yu

Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet existing defences address only the platform perimeter, leaving the agent's own threat judgement entirely untrained. We present ClawdGo, a framework for endogenous security awareness training: we teach the agen

Zaid Mahboob, Yujia Chen, Bowen Weng

This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under severe constraints on physical experimentation. Such estimates are essential for benchmarking algorithms, comparing design alternatives, validating controllers, and supporting certification or regulatory deci

Bryan Park, Stefan Wager

We propose a framework, the Neyman Jackknife, for conservative variance estimation in finite-population causal inference under interference. Our approach provides a general, flexible blueprint that enables conservative variance estimation whenever we are able to recompute our target estimator with some treatment assignments omitted. In classical settings, ou

Zean Han, Ruihan Lin, Zezhen Ding, Jiheng Zhang

Many bandit systems are deployed with offline historical data, such as past logs from earlier policies. Using these data can reduce early online exploration when they remain informative for the online problem. When the offline and online environments differ, such data can be biased for the online problem. For linear (contextual) bandits, this bias is directi

Bella Hill, Miguel Morales-Trujillo

Quantum Computing (QC) is often challenging for beginners due to its abstract concepts and mathematical foundations. This paper explores the use of gamification to support the learning of introductory QC concepts. To investigate this, QubitQuest was developed as a set of three educational mini-games designed to teach key QC topics: the Bloch sphere, entangle

Molena Huynh

In low-depth implementations of the Quantum Approximate Optimization Algorithm (QAOA), the dominant cost is often the number of objective evaluations rather than circuit depth. We introduce a graph-conditioned trust-region method for reducing this query cost. A graph neural network predicts a Gaussian distribution N(mu, Sigma) over QAOA angles. The mean init

Jixia Li, Fengquan Wu, Shijie Sun, Yougang Wang

The Tianlai 21cm intensity mapping experiment is located at the Hongliuxia Observing Station, which is a remote site with excellent electromagnetic environment. To facilitate the operation of the Tianlai experiment while reducing the required human power and travel cost, we have designed the system to be remotely controllable from the start. In this paper, w

Rezaul Karim, Austin Wen, Wang Zongzuo, Weiwei Zhang

The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, TPUs, and NPUs. However, these parallelization strategies incur substantial data communication overhead significantly hindering computational efficiency. While communication-computation overlap presents a promis

Daniel Villalobos-Silva, Yerko Vásquez, Giovanni Otalora

We investigate inflation in modified teleparallel gravity within a scalar-tensor framework. We focus on two viable extensions of the Teleparallel Equivalent of General Relativity: a power-law model and an exponential model, which introduce controlled deviations from standard teleparallel gravity through a correction parameter $\alpha$. Inflation is driven by

Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson

This paper considers multiple extended object tracking based on Poisson multi-Bernoulli mixture (PMBM) filtering, which gives the closed-form Bayesian solution for standard multiple extended object models with Poisson birth. To efficiently address the challenging extended object data association problem in PMBM filtering, we develop implementations of the ex

Ahmed Mesfer Alkhudaydi, Bai Cui

The rapid growth of large, power-electronics-rich data center (DC) loads is creating new operational challenges for bulk power systems. A key risk arises when a DC uninterruptible power supply (UPS) disconnects the facility during voltage/frequency disturbances and then reconnects it while the bulk grid is still dynamically settling to a new equilibrium poin

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on which samples are chosen. We identify a failure mode in which calibration samples fail to activate outlier channels, hidden dimensions with unusually large activations, causing the quantizer to underestimate t

Fangqing Xiao, Dirk T. M. Slock

Line spectral estimation (LSE) involves estimating both spectral frequencies and their associated complex amplitudes. Existing Fisher-information-based benchmarks are local and therefore do not capture either the threshold behavior of frequency estimation or the propagation of frequency errors to subsequent amplitude reconstruction. This paper develops expli

George-Rafael Domenikos, Lock Yue Chew, Victoria Leong

We introduce a constructive framework for assigning thermodynamic structure to an arbitrary data system from its measured microstates. Starting from an empirical distribution over configurations, we first infer a data-driven energy function by fitting a Boltzmann-type model to the observed statistics, thereby defining an energy axis that is intrinsic to the

Jiaqi Wang, Wenhao Zhang, Weijie Shi, Yaliang Li

On-policy distillation (OPD) has shown strong potential for transferring reasoning ability from frontier or domain-specific models to smaller students. While effective on static single-turn tasks, its behavior in multi-turn agent settings remains underexplored. In this work, we identify a key limitation of vanilla OPD in such settings, which we term Trajecto

Tasnia Nabiha, Orthy Toor, Wakim Sajjad Sakib, Abdullah Bin Shams

Electrooculogram (EOG) is a non-invasive bio-signal generated by the potential difference between the retina and cornea during eye movement, and is widely utilized in Human-Computer Interaction (HCI) systems. Expanding the range of detectable eye movements enhances system capability. However, increasing the number of classes typically degrades classification

Han Wang, Xiaodong Yu, Jialian Wu, Jiang Liu

Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and verbose reasoning traces. While recent work on efficient reasoning reduces this overhead through length-based rewards or pruning, many approaches are post-trained under a much shorter context window than base-mo

Hamed Rahimi, Clemence Grislain, Adrien Jacquet Cretides, Olivier Sigaud

Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly critical in multimodal settings, where agents must integrate heterogeneous signals including text, visual cues to form a coherent interpretation of user intent. This paper present

Ruifeng Yuan, Wanxing Chang, Weiwei Cao, Bowen Shi

The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fine-grained, disease-oriented attributes. Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching and fail to r

Yuanhao Gong, Tan Tang, Qianyan Liu

The Laplacian operator transforms the image into its Laplacian field, which usually is sparse and satisfies a stable distribution. On the other hand, an image can be uniquely reconstructed from its Laplacian field via solving a Poisson equation with a proper boundary condition. Such uniqueness is mathematically guaranteed. Thanks to these properties, we prop

You Yang, Fei Wang

Randomized neural networks (RaNNs) are attractive for partial differential equations (PDEs) because they replace expensive end-to-end training with a linear least-squares solve over randomized hidden features. Their practical performance, however, depends strongly on the sampling distribution of the hidden-layer parameters, which is usually chosen heuristica

Bin Yin, Jie Wang, Qian Zhang, Deng Wang

Molecular cavity optomechanics (COM) leverages ultrastrong interactions between confined optical fields and high-frequency molecular vibration, providing a unique platform for exploring high-frequency phonon dynamics. In this work, we theoretically propose the use of a hybrid molecular COM system for realizing an ultra-low-threshold mid-infrared (MIR) phonon

Zi-Hao Bo, Yaqian Li, Anzhou Hou, Rinyoichi Takezoe

Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert routing remains under-explored. Existing routing strategies are either hand-crafted or modality-agnostic, relying on idealized priors that ignore the layer-dependent modality fusion patterns in MoE-VLMs and p

Danny Wang, Ruihong Qiu, Zi Huang

Discrete diffusion language models (dLLMs) enable parallel token updates with bidirectional attention, yet practical generation typically adopts blockwise semi-autoregressive decoding. This switch creates a training-inference mismatch: training denoises with full-sequence context, while inference commits tokens within a bounded block without future context.

Tomás Verdugo, Alberto Hernández-Almada, Miguel A. García-Aspeitia, Juan Magaña

We present a generalized phenomenological parameterization of the deceleration parameter $q(z)$ that incorporates an effective radiative component (ERC) in addition to a localized late-time contribution. The proposed framework extends previous two-parameter $q(z)$ reconstructions by explicitly regulating the high-redshift behavior while preserving the late-t

Christian Aebi, Grant Cairns

We show that for an integer $\ell$, there exists an acute integer lattice triangle of lattice perimeter $\ell$ such that its orthocenter is an integer lattice point, if and only if $\ell=6 $ or $\ell\ge 8$. Analogous results are obtained for the circumcenter and the centroid, and the results are contrasted with those for obtuse and right triangles.

Ethan Dickey, Sabre Kais

We study when block-coupled regular graphs can realize prescribed complex quantum-like (QL) bit states as exact synchronized eigenstates. Two regular subgraphs $G_A$ and $G_B$ supply normalized all-ones eigenvectors $V_A$ and $V_B$, and algebraically regular bipartite couplings reduce the full graph-supported operator exactly to a $2\times 2$ effective block

M. Meng

This paper presents PSA-Eval, a failure-centered runtime evaluation framework for deployed trilingual public-space agents. The central claim is that, when the evaluation object shifts from a static input-output mapping to a runtime system, the basic unit of analysis should shift from score to failure. PSA-Eval extends the conventional chain Question -> Answe

Yanwei Cui, Guanghui Wang, Xing Zhang, Peiyang He

Time series models predict numbers; decision-makers need advisory -- directional signals with reasoning, actionable suggestions, and risk management. Training language models for such predictive advisory faces a fundamental challenge: quality depends on outcomes unknown at prediction time. We bridge two ideas from reinforcement learning -- using information

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because uncertainty reliability can degrade earlier and more sharply than top-1 performance. We study this empirically by measuring conformal coverage and calibration error on sequentially

Longma Du, Xinyu Hu, Ruilong Liu, Guanghui Wang

The Ramsey number $r_k(s,n)$ is the smallest integer $N$ such that every $N$-vertex $k$-graph contains either a copy of $K_s^{(k)}$ or an independent set of size $n$. We prove that $r_4(5,n)\ge 2^{2^{cn^{1/7}}}$, where $c>0$ is an absolute constant. As a consequence, we determine the tower growth rate of $r_k(k+1,n)$, which completely solves the problem of e

Jack King, Evelina Fedorenko, Eghbal A. Hosseini

In autoregressive large language models (LLMs), temporal straightening offers an account of how the next-token prediction objective shapes representations. Models learn to progressively straighten the representational trajectory of input sequences across layers, potentially facilitating next-token prediction via linear extrapolation. However, a direct link b

Qi Li, Weining Wang, Shuangjun Du, Bo Peng

Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion models.Despite these advances, existing methods remain fragmented across different paradigms, and their evaluation is highly inconsistent due to the lack of standardized datasets and protocols. Moreover, prior surv

Aleksandr Koshkarov, Nadia Tahiri

Comparative analyses of phylogenetic trees typically require identical taxon sets, however, in practice, trees often include distinct but overlapping taxa. Pruning non-shared leaves discards phylogenetic signal, whereas tree completion can preserve both taxa and branch-length information. This work introduces a polynomial-time algorithm for set-wide completi

Janusz Milek

We study multivariate tail-dependence compatibility for complete and partial signed tail families, treating lower-tail, upper-tail, and mixed configurations in one geometric witness representation indexed by active coordinate sets and sign patterns. For a complete signed tail family, witness generator weights w = (w_{I,sigma}) give a linear incidence paramet

Xuemei Qiu, Dawei Fan, Yebin Huang, Yanping Chen

Digital pathology has fundamentally altered diagnostic workflows by enabling the computational analysis of gigapixel Whole Slide Images (WSIs), yet effectively deciphering their complex tumor microenvironments remains a formidable challenge. Existing Multiple Instance Learning (MIL) frameworks typically treat Whole Slide Images as unstructured bags of patche

Yuanyuan Feng, Lei Li, Jian-Guo Liu, Xiaoqian Xu

In this paper, we accelerate Langevin Monte Carlo sampling from Gibbs measures $\pi\propto \exp(-U)$ by adding a large drift that preserves the invariant measure. For warm-start initial data, we characterize the sharp asymptotic decay rate of the relative entropy and introduce asymptotic relaxation enhancing flows: sequences that achieve arbitrarily fast dec

Hengrui Zhang, Boao Kong, Jiahe Geng, Zhengyang Huang

Fully decentralized Muon is difficult because its nonlinear matrix-sign operator does not commute with linear gossip averaging. This makes decentralized Muon a structural design problem: in designing the algorithm, one must distinguish modular components from non-modular ones. We propose \sudamuon{}, which realizes this separation through a unified primal--d

Shaofeng Yang, Yunting Wang, Yingying Cheng, Fan Zhang

The solution of sparse linear systems constitutes the dominant computational bottleneck in interior point methods (IPMs), frequently consuming over 70% of the total solution time. As optimization problems scale to millions of variables, direct solvers encounter prohibitive fill-in, excessive memory consumption, and limited parallel scalability. We present SD

Michael F Xu, Qiyao Yang, Heather Kirkorian, Bilge Mutlu

Family-school partnerships (FSP) are critical to children's development, yet families often face barriers such as time constraints, fragmented communication, and limited opportunities for meaningful engagement. As a step toward facilitating broader family-school partnerships, we explore a novel approach that integrates a social robot into family settings, sp

Xiaoliu Luo, Minxue Xiao, Ting Xie, Mengzhu Wang

Accurate biomedical image classification under low-resource conditions remains challenging due to limited annotations, subtle inter-class visual differences, and complex disease semantics. While vision--language models offer a promising foundation for mitigating data scarcity, their effective adaptation in biomedical settings is constrained by the need for p

Michael F Xu, Bengisu Cagiltay, Yaxin Hu, Anjun Zhu

The sense of family connectedness may support positive outcomes including individual well-being, resilience, and healthy family functioning. However, as technologies advance, they often replace human-human interactions instead of nurturing them. In this work, we investigate how robot-facilitated communication tools might instead create new opportunities for

Thomas Carmichael

Autoregressive transformers make confident errors that output-confidence monitoring cannot catch. Activation monitors catch them only when training leaves a decision-quality signal beyond what the output already exposes. This signal is an architectural property of the trained model, fixed upstream of any monitor. Controlling for output confidence removes 60.

Ryuji Hashimoto, Ryosuke Takata, Masahiro Suzuki, Yuki Tanaka

Agent-based models provide a constructive approach to studying emergent dynamics in life-like systems composed of interacting, adaptive agents. Financial markets serve as a canonical example of such systems, where collective price dynamics arise from individual decision-making. In this modeling tradition, investor behavior has typically been captured by two

Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou

Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily learn the semantic features from news content or integrate structural features from propagation. However, in practical scenarios, due to the semantic ambiguity of informal language

Zhenyu Mao, Jacky Keung, Xiangyu Li, Yicheng Sun

Online scams often unfold gradually through interaction, yet existing detection systems predominantly rely on snapshot-based signals and interruptive warnings, revealing two research gaps in the lack of signals that represent scam risk within conversational dynamics and the underexplored design of non-interruptive interaction. To address these gaps, we intro

Yao Wang, Zixu Geng, Jun Yan

Knowledge graphs (KGs) are increasingly used to support large lan guage model (LLM) reasoning, but standard triplet-based KGs treat each relation as globally valid. In many settings, whether a relation should count as evidence depends on the context. We therefore formulate triplet validity as a triplet-specific function of context and refer to this formulati

Zhiming Feng

This paper develops a decomposition methodology for common agency games in which each principal's payoff depends on her own outcome and the agent's type, but not on rivals' outcomes. The key step reduces each principal's best-response problem to a standard screening problem defined over the agent's indirect utility -- the upper envelope of her payoff over ri

Aydin Ayanzadeh, Tim Oates

Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present an agentic framework that converts a single floor plan image into a structured, retrievable knowledge base to generate safe, accessible navigation instructions with lightweig

Haohao Sheng, Sheng Zhang, Zhong Fang, Hongming Weng

Three-dimensional (3D) topological ferroelectric (FE) insulators, in which topological and FE orders naturally coexist, enable field-controlled spintronic devices. In this work, we predict a new structure of bismuth monohalides Bi4Br4 and Bi4I4, denoted $\gamma$ phase, and demonstrate that it is an ideal 3D topological FE insulator. Systematic first-principl

Naveen Mysore

Accurate time series forecasting in scientific domains such as climate modeling, physiological monitoring, and energy systems benefits from both competitive predictions and model transparency. This work proposes DecompKAN, a lightweight attention-free architecture that combines trend-residual decomposition, channel-wise patching, learned instance normalizati

Yifan Jia, Heer Tern Koh, Bakh Khoussainov

Term algebras are important objects in computer science and are correspondingly well-studied. A natural generalization is to quotient these algebras by finitely many ground term equations, obtaining what we call almost free algebras. One of the earliest results on almost free algebras is that their word problem is polynomial time decidable. In this paper, we

DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud

DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge

Jianfeng Lin, Hongbin Sun, Zhongzi Wang

A classical question in quantitative topology is to bound the mapping degree $\operatorname{deg}(f)$ in terms of its Lipchitz constant $\text{Lip}(f)$. For a closed, orientable, Riemannian manifold $M$, the flexible exponent $\alpha(M)$ is the infimum of $\alpha\geqslant 0$ such that $|\text{deg}(f)|\leqslant C\cdot (\text{Lip}(f))^\alpha$ holds for any Lips

Xiaoyu Zheng, Xu Tian, Bin Jiao, Kunbo Cui

Patients with dementia typically exhibit cognitive impairment, which is routinely assessed using the Mini-Mental State Examination (MMSE). Concurrently, their underlying neurophysiological abnormalities are reflected in Electroencephalography (EEG), providing a basis for joint modeling. However, traditional multi-task approaches suffer from feature entanglem

Xianjin Shen, Zehao Lin, Nobuyuki Sakai, Ye Xu

Accurate astrometric measurements for star-forming regions located on the far side of the Milky Way remain scarce. In this work, we present the astrometric results for a 22\,GHz water maser associated with star-forming region G040.96+02.48 located on the far side of the Milky Way, using the East Asian VLBI Network. The target water maser's proper motion was

Y. Sungtaek Ju

Using direct numerical simulation of forced isotropic turbulence at $\text{Re}_\lambda \approx 1300$ and $\approx 433$, together with two independent Markov-by-construction null surrogates, we measure the Markov--Einstein coherence length of the turbulent energy cascade to be $\Delta r \approx 3.2$-$3.6$ in log-scale cascade coordinates, approximately three

Akitoshi Kimura

This paper proposes an Extended State-Dependent Hawkes Process (ExsdHawkes) to model the intricate dynamics of Limit Order Books (LOBs). Our theoretical contribution lies in relaxing traditional constraints by allowing for state disappearances -- a phenomenon frequently observed in high-frequency trading. We mathematically prove, using Karush--Kuhn--Tucker (

Marco Carmosino, Ngu Dang, Tim Jackman

Gate elimination is the primary technique for proving explicit lower bounds against general Boolean circuits, including Li and Yang's state-of-the-art $3.1n - o(n)$ bound for affine dispersers (STOC 2022). Every circuit lower bound is implicitly existential: every circuit that is too small to compute $f$ must err on some input. This raises a natural question

Bokang Zeng, Zheng Gao, Xiaoyu Li, Xiaoyan Feng

Proactive watermarking offers a promising approach for deepfake tamper detection and localization in short-form videos. However, existing methods often decouple audio and visual evidence and assume that watermark signals remain reliable under real-world degradations, making tamper localization vulnerable to multimodal misalignment and compression distortions

Krista Smith

Measurements of quarkonia production in peripheral and ultraperipheral heavy-ion collisions are sensitive to photon-photon and photon-nucleus interactions, the partonic structure of nuclei, and the mechanisms of vector-meson production. In this contribution, quarkonium measurements with the highest precision currently accessible will be compared with the lat

Jiebin Yan, Kangcheng Wu, Jingwen Hou, Jiayu Zhang

Blind omnidirectional image quality assessment (BOIQA) presents a great challenge to the visual quality assessment community, due to different storage formats and diverse user viewing behaviors. The main paradigm of BOIQA models includes two steps, ie, viewport generation, and quality prediction, which brings an extra computational burden and is hard to gene

Daniel Cederberg, Stephen Boyd

Recent research has focused on developing GPU-accelerated first-order solvers for linear programming (LP). This line of work, however, has largely overlooked the role of presolving, and thus prior results do not fully reflect the speedups achievable through GPU acceleration in a realistic end-to-end solver pipeline. At the same time, LP presolving has tradit

Rinyoichi Takezoe, Yaqian Li, Zihao Bo, Anzhou Hou

Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant computational burdens due to long visual sequence inputs. Recent works address this issue by pruning unimportant visual tokens, achieving substantial computational reduction while maintaining model performan

Rhea Makkuni, Ananya Joshi

Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization trends during large-scale healthcare disruptions (e.g., operational failures or pandemics). Forecasting models can assist in this task by analyzing large volumes of resource-related data at the facility level,

SungHo Kim, Juhyeong Park, Yeachan Kim, SangKeun Lee

The Korean writing system, \textit{Hangeul}, has a unique character representation rigidly following the invention principles recorded in \textit{Hunminjeongeum}.\footnote{\textit{Hunminjeongeum} is a book published in 1446 that describes the principles of invention and usage of \textit{Hangeul}, devised by King Sejong \cite{Hunminjeongeum_Guide}.} However,

Shiven Agarwal, Yash Shah, Ashish Raj Shekhar, Priyanuj Bordoloi

We introduce GamEDAI, a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. Built on phase-based LangGraph sub-graphs, deterministic Quality Gates, and structured Pydantic schemas, GamEDAI supports two template families enco

Anirban Bhattacharjee, Luis H. Hatashita, Suhas S. Jain

The reliability of machine learning in multiscale physical systems depends on how physical structure is embedded into the learning process. We investigate this in the context of turbulent multiphase flows, focusing on the prediction of subgrid interfacial area density, a key quantity governing interphase transport that remains unresolved in large-eddy simula

Tenglong Luo, Yiqing Guo, Shanshan Su, Qiaoyu Yang

Long-term stability of neural interfaces is frequently compromised by mechanical mismatch and chronic neuroinflammation, often leading to electrode detachment and signal failure. While hydrogel coatings offer a solution, conventional designs typically rely on exogenous conductive fillers that can sacrifice mechanical flexibility or induce toxicity. Here, we

Yiming Li, Katya Mikhailova, Lerato Takana, Daisy O'Mahoney

Low-loss magnetic insulating thin films are promising for information transport via magnons, where isotropic in-plane magnon propagation is desirable. We report nonlocal measurements of electrically and thermally generated magnons in epitaxial (001) lithium aluminum ferrite Li$_{0.5}$Al$_{0.7}$Fe$_{1.8}$O$_4$ thin films with pronounced fourfold in-plane magn

Qiming Yuan, Linyi Han, Nam Ling, Cihan Ruan

People increasingly turn to large language models (LLMs) to interpret ambiguous social situations: a delayed text reply, an unusually cold supervisor, a teacher's mixed signals, or a boundary-crossing friend. Yet in many such cases, no stable interpretation can be verified from the available evidence alone. We study how LLMs respond to these situations acros

Hongxin Li, Yuntao Chen, Zhaoxiang Zhang

Graphical User Interface (GUI) element grounding (precisely locating elements on screenshots based on natural language instructions) is fundamental for agents interacting with GUIs. Deploying this capability directly on resource-constrained devices like mobile phones is increasingly critical for GUI agents requiring low latency. However, this goal faces a si