April 2026 arXiv papers — page 84

Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou

Temporal Knowledge Graph (TKG) extrapolation aims to predict future events based on historical facts. Recent studies have attempted to enhance TKG extrapolation by integrating TKG's evolving structural representations and textual event chains into Large Language Models (LLMs). Yet, two main challenges limit these approaches: (1) The loss of essential spatial

Xianxin Song, Xianghao Yu, Jie Xu, Derrick Wing Kwan Ng

Reliable surveillance and communication for unmanned aerial vehicles (UAVs) are crucial for enabling and sustaining the accelerated growth of the low-altitude economy. Integrated sensing and communications (ISAC) offers a cost-effective and scalable framework for target sensing by leveraging existing wireless communication systems. This paper investigates a

Rongjia Zheng, Shangwei Huang, Lei Zhu, Wei-Shi Zheng

We present a generative method for texture filtering, which exhibits surprisingly good performance and generalizability. Our core idea is to empower texture filtering by taking full advantage of the strong learned image prior of pre-trained generative models. To this end, we propose to fine-tune a pre-trained generative model via a two-stage strategy. Specif

David Billington

Defeasible statements are statements that are likely, or probable, or usually true, but may occasionally be false. Plausible reasoning makes conclusions from statements that are either facts or defeasible statements without using numbers. So there are no probabilities or suchlike involved. Seventeen principles of logics that do plausible reasoning are sugges

Zhengtong Li, Chentao Yue, Jiafu Hao, Branka Vucetic

Traditional wireless communications rely solely on bit-level channel coding for error correction, without exploiting the inherent linguistic structure of the data source. This paper proposes a large language model (LLM) Viterbi decoder that integrates LLM priors into the Viterbi decoding for text transmission over AWGN channels. The proposed decoder maintain

Xu Chen, Shichao Xie, Zhining Gu, Lu Jia

Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory construction predominantly relies on a decoupled, two-stage paradigm: agents first aggregate environmental data through exploration, followed by the offline reconstruction of spatial memory. However, this post-hoc and g

Arsalan Sharifnassab, Mohamed Elsayed, Kris De Asis, A. Rupam Mahmood

In gradient-based learning, a step size chosen in parameter units does not produce a predictable per-step change in function output. This often leads to instability in the streaming setting (i.e., batch size=1), where stochasticity is not averaged out and update magnitudes can momentarily become arbitrarily big or small. Instead, we propose intentional updat

Arka Majhi, Satish B. Agnihotri

POSHAN Abhiyan envisages capacity building of AWWs or frontline health workers through 21 training modules of ILA (Incremental Learning Approach), modularising the net learning content into smaller learning topics to help them perform their daily activities. It envisions building skilled AWWs, strengthening supervisory hierarchies, and improving coordination

Zhengyang Shan, Xu Qian, Jiayun Xin, Minghui Xu

Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detection, the emergence of Large Language Models (LLMs) has introduced a transformative paradigm driven by superior semantic reasoning and cross-environment generalization. However, in t

Mulundano Machiya, Matt Menickelly, Paul Hovland, Ji Liu

Hamiltonian simulation is one of the most promising paths toward quantum advantage. Most prior approaches to Hamiltonian simulation circuit synthesis focus on local rewrite rules and low-level optimizations, and give limited attention to high-level scheduling of Pauli terms under varying constraints. In practice, different simulation algorithms require diffe

Jeongwhan Choi, Jongwoo Kim, Woosung Kang, Noseong Park

One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each new graph, preventing universal generalization across diverse graph datasets. A critical challenge facing GNNs lies in their reliance on label

Jinkyo Han, Payam Poorsolhjouy, Bahador Bahmani

Micromechanics-based granular models are widely used to predict the failure behavior of porous and particulate materials, including concrete, soils, foams, and biological tissues. Although these models offer considerable flexibility through microstructural parametrization and statistical representation, their mapping to macroscopic responses, particularly fa

Shaoyu Li, Chaoyu Zhang, Hexuan Yu, Y. Thomas Hou

Autonomous AI agents live or die by the API tokens they consume: without paid inference capacity they cannot reason, act, or delegate. Compute-token cost has become the binding resource of the emerging agent economy, yet it is non-transferable: it is account-bound, vendor-specific, and absent from on-chain ledgers. Existing payment rails such as x402 move fi

Seok Joon Kim, Dinh Duc Cao, Federica Spinola, Se Jin Lee

Widespread RGB-Depth (RGB-D) sensors and advanced 3D reconstruction technologies facilitate the capture of indoor spaces, improving the fields of augmented reality (AR), virtual reality (VR), and extended reality (XR). Nevertheless, current technologies still face limitations, such as the inability to reflect minor scene changes without a complete recapture,

Qiang Liu, Adrienne Kline, Ermin Wei

Safe Reinforcement Learning from Human Feedback (Safe RLHF) has recently achieved empirical success in developing helpful and harmless large language models by decoupling human preferences regarding helpfulness and harmlessness. Existing approaches typically rely on fitting fixed horizon reward models from human feedback and have only been validated empirica

Rhorom Priyatikanto, Gerhana P. Putri, Clara Y. Yatini, Isfahani Rusyda

Understanding the seeing conditions is crucial for astronomical observations using a ground-based telescope. This study analyzes long-term atmospheric data (2002-2021) from the ERA5 dataset to assess the seeing conditions at the new Timau National Observatory in Indonesia, which will house a 3.8-meter optical telescope. While the ERA5 dataset shows good agre

Santosh Ganji

A major challenge for niche scientific and technical domains in leveraging coding agents is the lack of access to up-to-date, domain- specific knowledge. Foundational models often demonstrate limited reasoning capabilities in specialized fields and cannot inherently incorporate knowledge that evolves through ongoing research and experimentation. Materials sc

Pingwei Sun, Yuxuan Hu, Jianchao Tan, Xue Wang

Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advances such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA) have demonstrated that the delta rule, an online gradient descent update, enables superior associative recall compared to simple additive updates

Jiguang Bao, Rongli Huang, Qinfeng Jiang

In this paper, we consider the existence of mean curvature type hypersurfaces with prescribed gradient image. Let $\Omega$ and $\tilde{\Omega}$ be uniformly convex bounded domains in $\mathbb{R}^n$ with smooth boundary. We show that there exists unique convex solutions for the second boundary value problem of mean curvature type equations.

Julian Skifstad, Xinyue Annie Yang, Glen Chou

Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying activations during generation. Existing methods, however, often rely on non-anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, ope

Astrid J. M. Bergman, Yunxiang Liao, Jing Yang

We study quantum sensing with Floquet chaotic dynamics generated by Haar random unitary gates. The metrological resources consist of three ingredients: A given initial state, a set number of Haar random unitary gates and the sensing gates. There are two natural ways of organizing the resources: the first one is the "control" protocol, where the random unitar

Gautam Siddharth Kashyap, Mark Dras, Usman Naseem

Cultural alignment in Large Language Models (LLMs) is essential for producing contextually aware, respectful, and trustworthy outputs. Without it, models risk generating stereotyped, insensitive, or misleading responses that fail to reflect cultural diversity w.r.t Helpful, Harmless, and Honest (HHH) paradigm. Existing benchmarks represent early steps toward

Tao Fan, Guoqiang Ma, Yuanfeng Song, Lixin Fan

Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring client privacy, and mitigating performance loss on heterogeneous data. Existing methods like Offsite-Tuning (OT) secure the LLMs IP by having clients train only lightweight adapters, yet our analysis reveals th

Bai Xue, C. -H. Luke Ong

This paper addresses the quantitative verification of finite-time constrained occupation time for stochastic continuous-time systems governed by stochastic differential equations (SDEs). Unlike classical reachability analysis, which focuses on single-event properties such as entering a target set, many autonomous tasks-including surveillance, wireless chargi

Gayatri Thik, Amit Loyal, Srinivasan K, Raghavan G

We present a compact, automated, high-brightness entangled photon source capable of generating all four Bell states with high fidelity. The system utilizes a type-0 quasi-phase-matched PPKTP crystal embedded within a polarization Sagnac interferometer. We introduce a switching scheme based on the controlled, motorized translation of the nonlinear crystal. Th

Yongchao Wang, Zhiqiu Huang

Deep learning for vulnerability detection has shown promising results on early benchmarks, but recent evaluations reveal catastrophic degradation: models achieving F1 > 0.68 on legacy datasets collapse to 0.031 under strict deduplication. We identify the root cause as the semantic ambiguity problem: identical code can be secure or vulnerable depending on pro

Aimin Tang, Shuhan Wang, Yin Xu

High-mobility uncrewed aerial vehicle (UAV) communications in low-altitude wireless networks (LAWN) demand reliable beamforming, while conventional feedback-based schemes suffer from excessive overhead and severe misalignment under rapid trajectory variations. To address this challenge, this paper proposes an SSB-based sensing-assisted predictive robust beam

Linwei Dong, Ruoyu Guo, Ge Bai, Zehuan Yuan

Diffusion distillation, exemplified by Distribution Matching Distillation (DMD), has shown great promise in few-step generation but often sacrifices quality for sampling speed. While integrating Reinforcement Learning (RL) into distillation offers potential, a naive fusion of these two objectives relies on suboptimal raw sample evaluation. This sample-based

Yunfan Zhang, Yuxuan Han, Hongyu Shan, Jose Blanchet

Selecting which products to display and at what prices is a central decision in retail and e-commerce operations. In many applications, these two choices must be made jointly under limited display capacity and uncertain customer demand. In this paper, we study the joint assortment and pricing problem under a price-based contextual multinomial logit model, wh

Chia-Hsiang Lin, Zi-Chao Leng

Mainstream optical satellites often acquire multispectral multi-resolution images, which have limited material identifiability compared to the HSIs. Thus, spectrally super-resolving the MSI into their hyperspectral counterparts greatly facilitates remote material identification and the downstream tasks. However, spectrally super-resolving the MSI into an HSI

Jiguang Bao, Qinfeng Jiang

This article is concerned with the second boundary value problem of the Lagrangian mean curvature type equation arising from special Lagrangian geometry. By the parabolic method, we consider a fully nonlinear parabolic equation with oblique derivative boundary condition, and show the long time existence and convergence of the flow. It follows that the existe

Yifan Li, Giulia Guidi

In computational science and data analytics, many workloads involve irregular and sparse computations that are inherently difficult to optimize for modern hardware. A key kernel is Sparse General Matrix-Matrix Multiplication (SpGEMM), which underpins simulations, graph analytics, and machine learning applications. SpGEMM exhibits irregular memory access patt

Adiljan Sawut, Ying-Jun Li, Hong-Hao Fan, Bai-Song Xie

We investigate the spin resolved vortex properties of electron positron pairs created from vacuum in time delayed, two color electromagnetic fields. By treating the temporal delay G as a continuous tuning parameter, we reveal a dynamic transition from interference-dominated domain patterns at G=0 to the nucleation of quantized vortex lattices at G=0.5. These

Ishita Kakkar, Enze Zhang, Rheeya Uppaal, Junjie Hu

Large reasoning models (LRMs) produce complex, multi-step reasoning traces, yet safety evaluation remains focused on final outputs, overlooking how harm emerges during reasoning. When jailbroken, harm does not appear instantaneously but unfolds through distinct behavioral steps such as suppressing refusal, rationalizing compliance, decomposing harmful tasks,

Kaile Chen, Qi Lu, Yuan Zhong, Jingchi Li

We report a systematic methodology to obtain supermodes with equidistant effective index distribution and to excite arbitrary target supermodes with high precision. By employing a multi-well optical potential realized by a judiciously designed waveguide array, the supported supermodes achieve maximal spacing and an equidistant distribution in effective index

Qifeng Li

This paper introduces a new modeling framework for optimization under uncertainty, called Probable Event Constrained Optimization (PECO). Unlike conventional chance-constrained formulations, which only limit the probability of constraint violation, PECO also explicitly requires feasibility for all events whose probability exceeds a prescribed threshold. This

Andrew Pocklington, Aashish A. Clerk

The fate of non-trivial many-body states subject to decoherence is of both fundamental and practical interest. Here, we demonstrate a new analytic technique that allows for an exact treatment of dynamics of observables in matchgate circuits subject to arbitrary Pauli noise. We use this to obtain new insights on how decoherence influences critical ground stat

Jiagao Hu, Daiguo Zhou, Danzhen Fu, Fuhao Li

Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-world video data in adverse weather. Existing weather generation approaches struggle to balance visual quality and annotation reusability. We present AutoAWG, a controllable Adverse Weather video Generation fram

Yang Jiang, Qing-Guo Huang

The stochastic gravitational wave background in the mHz band is a key target for future spaceborne interferometers. Detecting such a signal presents multiple challenges for data processing, especially complicated by the presence of numerous compact binaries in our galaxy. The superposition of gravitational waves from their inspiral stages creates a confusion

Kunling Zhou, Zihe Yang, Bowen Zeng, Yong Hu

The non-Hermitian skin effect can lead to directional amplification of response, with the associated end-to-end Green's function generally exhibiting size dependence. Any deviation in length or local disorder can drastically alter the amplification factor, rendering the response fragile in practical implementations. In this work, we identify a new type of sc

Shihe Liu, Yunfeng Shi, Zhifei Zhang

In this paper, we prove Anderson localization for a hierarchical Anderson-Bernoulli model on lattice with arbitrary dimension, where the potential is characterized by a geometric hierarchical structure combined with fluctuations induced by independent and identically distributed (i.i.d.) Bernoulli random variables. Our method is also applicable to proving a

Liping Wang, Cheng Ye, Weidong Chen, Peipei Song

Multimodal empathetic response generation (MERG) aims to generate emotionally engaging and empathetic responses based on users' multimodal contexts. Existing approaches usually rely on an implicit one-pass generation paradigm from multimodal context to the final response, which overlooks two intrinsic characteristics of MERG: (1) Human perception of emotiona

Changjun He, Li Zhang, Qi Liu, Rui Zou

This paper investigates the transient synchronization stability in power systems hybridized with virtual synchronous generators (VSGs) and synchronous generators (SGs). A relative swing equation model is established to capture the transient synchronization dynamics between the VSG and the SG. Based on this model, both static and dynamic characteristics are s

Konstantinos Ntontin, Symeon Chatzinotas

Contrary to the vast majority of works on simultaneous wireless information and power transfer that provide information-theoretic limits for the separate receiver architecture, in this work we focus on the integrated receiver and provide a channel-capacity lower bound. Towards this, we provide a closed-form tight approximation for the probability transition

Jian-Chao Sun, Yong-Wei Dong, Jiang He, Jiang-Tao Liu

The Gamma-Ray Monitor (GRM) is a key scientific payload onboard the Space-based Multi-band Variable Object Monitor (SVOM) satellite, designed specifically for the detection and study of gamma-ray bursts (GRBs). Launched into a 625 km low-Earth orbit on 22 June 2024, GRM serves as a large-area, wide-field-of-view instrument capable of observing the hard X-ray

Severino V. Gervacio, Hiroshi Maehara, Phoebe Chloe Ramos

For a graph $G$, let $\mathscr{C}_5(G)$ denote the graph whose vertices are the induced $5$-cycles of $G$, where two vertices are adjacent whenever the corresponding cycles share an edge. We investigate the iterative behavior of the pentagon graph operator $\mathscr{C}_5(G)$ , positioning it as the natural continuation of the quadrangle graph operator and th

Han-Yun Tu, Xiang Yang, Si-Yao Wei

Firms' positions in innovation networks determine their access to external knowledge, yet how these positions shape technological search behavior and influence productivity remains underexplored. We propose that central network positions systematically reconfigure firms' innovation strategies by promoting exploratory search across emerging technological doma

Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin

Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learning requires solving the credit assignment problem: determining how individual utterances contribute to multi-turn dialogue outcomes. Existing approaches directly employ language mo

Tamás Darvas

We survey selected developments in the metric geometry of the space of Kähler metrics, emphasizing results from the past decade, highlighting open problems along the way.

Joongho Jo, Hyerin Lim, Hanjun Choi, Jongsun Park

Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs. However, the importance difference existing among Gaussian-tile pairs has never been considered in the previous works. In this paper, we propose AdaGScale, a novel viewpoint-adaptive Gaussian scaling technique f

Shao-Hua Liu

Our first main result shows that, for words with a fixed multiset of weak right-to-left minima, the statistics within each of the following three classes are equidistributed: 1. Mahonian statistics: $\textsf{inv}$, $\textsf{maj}$, $\textsf{den}$, $\textsf{mak}$, $\textsf{mad}$, $\textsf{inv}_{r}$, $r\textsf{maj}$, and $r\textsf{den}$; 2. Euler--Mahonian stat

Yuan Zhuang, Yuexin Bian, Sihong He, Jie Feng

Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and instability in replay-based bootstrapped training. In this paper, we propose using Low-Rank Adaptation (LoRA) as a structural regularizer for critic learning. Our approach freezes r

Emily B. Dryden, Carolyn Gordon, Javier Moreno, Julie Rowlett

The extent to which the geometry of an object is determined by some associated spectral data is a longstanding problem. We investigate this problem in the context of the Steklov spectrum, focusing on convex polygons. We prove that almost all triangles are uniquely determined by their Steklov spectra within the class of all triangles; further results dependin

MinJae Jung, YongTaek Lim, Chaeyun Kim, Junghwan Kim

While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. This paper introduces STAR-Teaming, a novel black-box framework for automated red teaming that effectively generates such prompts. STAR-Teaming integrates a Multi-Agent System (MAS) with a Strategy-Response Mult

HuaDong Jian, Chenghao Li, Haoyu Wang, Jiajia Shuai

In long-horizon open-world multi-agent systems, existing methods often treat local anomalies as automatic triggers for communication. This default design introduces coordination noise, interrupts local execution, and overuses public interaction in cases that could be resolved locally. To address this issue, we propose a partitioned information architecture f

Jorge Herbert Soares de Lira, Rafael Rocha de Farias

In this paper we prove existence and classification results for translating solitons defined as initial conditions for higher order mean curvature flows that are invariant by translations in warped product manifolds $\mathbb{P}\times_\chi \mathbb{R}$. Here, $\mathbb P$ is a Cartan-Hadamard manifold endowed with a rotationally symmetric metric and $\chi$ is a

Zachary R. Fox, Janet O. Agbaje, Dakotah Maguire, Javier E. Santos

Air pollution is a worldwide public health threat that can cause or exacerbate many illnesses, including respiratory disease, cardiovascular disease, and some cancers. However, epidemiological studies and public health decision-making are stymied by the inability to assess pollution exposure impacts in near real time. To address this, developing accurate dig

Yaowei Zheng, Richong Zhang, Shenxi Wu, Shirui Bian

We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value surface solves a backward parabolic equation, but the Bellman baseline obtained from one-step recursion is only first-order in the grid width. We estimate the time-dependent generator from multi-step transitions

Lin Jiang, Jihui Sun, Qiao Zhang, Jincheng Cui

Physical random number generators based on chaotic microcombs, with their complex nonlinear dynamics and multi-channel parallel capability, have attracted considerable research attention. However, key technical challenges for chaotic microcombs are the high correlation between symmetric teeth and the low bandwidth of single-channel teeth, which seriously aff

Hirotaka Obo, Atsushi Tsuchiya, Tadashi Ebihara, Naoto Wakatsuki

The self-noise of capacitive sensors, primarily caused by thermal noise from the gate-bias resistor in the preamplifier, imposes a fundamental limit on measurement sensitivity. In electret condenser microphones (ECMs), this resistor simultaneously determines the noise low-pass cutoff frequency and the signal high-pass cutoff frequency through a single RC tim

E. Novais, A. H. Castro-Neto

Standard quantum error correction (QEC) models typically assume discrete, Markovian noise, obscuring the continuous quantum nature of physical environments. In this manuscript, we investigate the fundamental limits of an actively corrected surface code coupled to a continuous, un-reset quantum environment at zero and finite temperature. Using the generalized

Aaron Nicolson, Elizabeth J. Cooper, Hwan-Jin Yoon, Claire McCafferty

Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress on automated metrics, yet their clinical utility remains uncertain due to limited qualitative evaluation by radiologists. We present CXRMate-2, a state-of-the-art CXR RRG model that enables tractable reinforcement learning (RL) through structured multimodal temporal embeddin

Minsu Kim, Walid Saad, Kui Wang, Zongdian Li

Next-generation wireless networks are expected to leverage multi-modal data sources to execute various wireless communication tasks such as beamforming and blockage prediction with situational-awareness. To do so, multi-modal transformers emerged as an effective tool, however, existing transformer-based approaches suffer from high inference latency and large

Ahmed G. A. H Ahmed, C. Okan Sakar

This paper introduces DW-Bench, a new benchmark that evaluates large language models (LLMs) on graph-topology reasoning over data warehouse schemas, explicitly integrating both foreign-key (FK) and data-lineage edges. The benchmark comprises 1,046 automatically generated, verifiably correct questions across five schemas. Experiments show that tool-augmented

Lu Yu, Xiang Li

Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present. This project studies how much labor is required to manage capital across those waves by tracking a simple productivity me

Weixiao Zhan, Yongcheng Jing, Leszek Rutkowski, Dacheng Tao

Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. Our analysis revealed several distillation traps: tail noise, off-policy instability, and, most fundamentally, the teacher-student gap, that distort training signals. These traps manif

Huiyu Zhao, Jiahao Yan, Catherine Dawson, Haitao Yang

Modern scanning tunneling microscopy (STM) experiments increasingly generate multidimensional datasets that require coordinated data management, visualization, and analysis. We present AngstromPro, an open-source Python-based software platform designed to support these activities within a common interactive environment. Its application architecture organizes

Zhi Ma, Xiao Liang, Cheng Wen, Rui Chen

In the development and verification of safety-critical aero-space software, Linear Temporal Logic (LTL) has been widely used to specify complex system properties derived from requirements. However, a significant gap remains in industrial practice: translating natural language (NL) requirements into formal LTL properties is a labor-intensive and error-prone p

Shayan Sepahvand, Farrokh Janabi-Sharifi, Farhad Aghili

This paper presents an AI-enabled cascaded hybrid vision/force control framework for tendon-driven aerial continuum manipulators based on constant-strain modeling in $SE(3)$ as a coupled system. The proposed controller is designed to enable autonomous, physical interaction with a static environment while stabilizing the image feature error. The developed str

M. F. V. Oliveira, F. A. B. F. de Moura, M. L. Lyra, G. M. A. Almeida

Quantum entanglement in systems of identical particles is often obscured by the interplay between exchange-induced correlations and the operational framework used to define entanglement. To study the role of exchange statistics, we propose a scheme using two \textit{distinguishable} particles where an exchange symmetry is artificially engineered via a relati

Arka Majhi, Satish B. Agnihotri, Aparajita Mondal

Recent health surveys in India highlight the alarming child malnutrition levels and lower rates of complete child immunization in many parts of India. Previous researches report that the conventional training pedagogy of the CHWs (Community Healthcare Workers) or the ASHAs (Accredited Social Health Activists) in India is ineffective in enhancing their capaci

Tao Xiong, Younes El Haddaoui, Hwankoo Kim, Qiang Zhou

In this paper, we study the small finitistic dimension of a commutative ring from the viewpoint of finitistic flat homological algebra. Using the class $FPR(R)$ of modules admitting finite projective resolutions, we investigate the finitistic flat ($FT$-flat) dimension and establish several of its basic properties. We prove change-of-rings results for the $F

Wei-Ting Tang, Akshay Kudva, Calvin Tsay, Joel A. Paulson

We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compact closed-form representation, its global optimization is challenging because it remains nonlinear and nonconvex. Existing exact deterministic approaches become increasingly difficu

Abdul Mueez, Shruti Vyas

Extracting standardized metallurgical metrics from microscopy images remains challenging due to complex grain morphology and the data demands of supervised segmentation. To bridge foundational computer vision with practical metallurgical evaluation, we propose an automated pipeline for dense instance segmentation and grain size estimation that adapts Cellpos

Andrew Hassell, Qiuye Jia, Ethan Sussman

These are lecture notes from the Austral Winter School on Microlocal Analysis and Non-elliptic Fredholm Theory, held at the Australian National University, Canberra, June 30 -- July 11, 2025.

Ramtin Davoudi, Kartik Thakkar, Nazanin Donyapour, Tyler Derr

In this study, we present the first comprehensive evaluation of modern LLMs - including GPT-4, GPT-4o, GPT-3.5-Turbo, Gemini 1.5 Pro, DeepSeek-V3, Llama 3.2, and BERT - across three core social media analytics tasks on a Twitter (X) dataset: (I) Social Media Authorship Verification, (II) Social Media Post Generation, and (III) User Attribute Inference. For t

Xiaowen Zhang, Ziming Zhou, Fengnian Zhao, David L. S. Hung

Deep learning surrogates for CFD flow-field prediction often rely on large, complex models, which can be slow and fragile when data are noisy or incomplete. We introduce FlowForge, a staged local rollout engine that predicts future flow fields by compiling a locality-preserving update schedule and executing it with a shared lightweight local predictor. Rathe

Toan T. Nguyen, Chanjin You

In this paper, we establish nonlinear Landau damping and asymptotic stability of a large class of translation-invariant steady solutions to the time-dependent Hartree--Fock equations in the presence of an {\em off-diagonal exchange operator}, which arises naturally in the meanfield theory of a large fermionic system, in the whole space $\mathbb{R}^d$, $d\ge

Namyoung So, Seokgyu Jang, Taeuk Kim

Adaptive multi-agent systems (MAS) are increasingly adopted to tackle complex problems. However, the narrow task coverage of their optimization raises the question of whether they can function as general-purpose systems. To address this gap, we conduct an extensive empirical study of adaptive MAS, revealing two key findings: (1) topological overfitting -- th

Seppo Laine, Emmanuel Momjian, Emilia Järvelä, Thomas P. Krichbaum

We have obtained very high angular resolution (a few milliarcseconds or sub-parsec scale) Very Long Baseline Array (VLBA) and European Very Long Baseline Interferometry (VLBI) Network (EVN) radio continuum images of the nucleus in the barred spiral galaxy NGC 7479, to search for possible nuclear emission on parsec scales. The observations were taken using ph

Dohoon Kim, Eungyu Woo, Donghoon Shin

This paper investigates a special variant of a pursuit-evasion game called lions and contamination. In a graph where all vertices are initially contaminated, a set of lions traverses the graph, clearing the contamination from every vertex they visit. However, the contamination simultaneously spreads to any adjacent vertex not occupied by a lion. We analyze t

Nicholas Gardella, Matthew L. Bolton, Sara L. Riggs

Objective. To explore how novice programmers' trust in Artificial Intelligence-driven Development Environments (AIDEs) relates to their coding performance and AI compliance while programming under time pressure. Background. Computer programming has undergone rapid upheaval due to state-of-the-art AIDEs, which provide clever automation for many aspects of sof

Duncan A. Forbes, Bas van Heumen, Yimeng Tang

Two studies that utilised the same HST/WFC3 imaging of NGC5846_UDG1 have reported quite different total counts for its globular cluster (GC) system, i.e. 54 $\pm$ 9 vs 33 $\pm$ 3 GCs. In both cases they counted all GCs, that met their selection criteria, down to the faintest magnitudes. They also disagree as to whether NGC5846_UDG1 lies in the NGC 5846 group

Yeonjun In, Wonjoong Kim, Sangwu Park, Chanyoung Park

Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the underlying cause of these safety risks and shows that the issue lies in the reasoning structure itself. Based on this insight, we claim that effective safety alignment can be achieved

Wenshuai Hu, Guanghua Ji, Xiao Li

The Smectic-A (SmA) phase is modeled by a modified Landau-de Gennes (mLdG) model proposed by Xia et al. [Phys. Rev. Lett., 126 (2021), 177801], in which a tensor order parameter Q for the orientational order is coupled with a real scalar $u$ characterizing the positional order. In this paper, we propose and analyze a novel, highly efficient, and unconditiona

Jiang Xiaobo, Dinghong Lai, Song Qiu, Yadong Deng

Named Entity Recognition (NER) models trained on clean, high-resource corpora exhibit catastrophic performance collapse when deployed on noisy, sparse User-Generated Content (UGC), such as social media. Prior research has predominantly focused on point-wise symptom remediation -- employing customized fine-tuning to address issues like neologisms, alias drift

Cristina Garbacea, Heran Wang, Chenhao Tan

With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences has become an important challenge. Current benchmarks average preferences across all users to compute aggregate ratings, overlooking individual user preferences when establishing model rankings. Since users h

Charikleia Moraitaki, Sarah Pan, Skyler Pulling, Gwendolyn Flusche

Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ("X is present") even when the correct description contains negation ("no X"). While prior work has documented this failure mode in English and proposed solutions, negation manifests differently across languages through varying morphology, word order, an

Goutam Das, Takashi Tanaka

Path integral control in Gaussian belief space requires a structural matching condition between the observation-driven diffusion of the belief mean and the actuation authority, which a fixed observation matrix cannot enforce. We treat the observation matrix as a control variable and show that constraining the sensing control to a measurable selector from the

Jiawei Yong, Deyuan Qu, Qi Chen, Kentaro Oguchi

Autonomous driving systems often degrade under adverse visibility conditions-such as rain, nighttime, or snow-where online scene geometry (e.g., lane dividers, road boundaries, and pedestrian crossings) becomes sparse or fragmented. While high-definition (HD) maps can provide missing structural context, they are costly to construct and maintain at scale. We

Ehsan Hoseinzade, Ke Wang, Anandharaju Durai Raju

Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically rich token or sentence embeddings often suffice, tables are structured combinations of columns wherein useful representations must jointly capture column's semantics and the inter-column relationships. Existi

Joshua Ancheta, Caeli Benyacko, Fanghao Zhang, Stephen D. Wilson

Magnetocaloric materials are typically limited by a trade-off between magnetic entropy and field responsiveness. Here we show that magnetic polarons provide an intermediate regime that mitigates this constraint and enables an exceptional magnetocaloric response. Using EuB$_6$ as a model system, we combine thermodynamic and magnetic measurements to demonstrat

Shao Qi Lim, Alexander A. Wood, Brett C. Johnson, Qiang Sun

The negatively charged nitrogen-vacancy center (NV$^-$) in diamond is a versatile platform for quantum magnetometry under ambient conditions. Recently, laser threshold magnetometry (LTM) has been proposed as a means to significantly enhance the sensitivity of NV-based magnetometers by incorporating a diamond hosting NV$^-$ centers within a laser cavity and o

Nathaniel S. Woodward, Zhiqi Gao, Yurii Kvasiuk, Kendrick M. Smith

Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability develops while training these models. To investigate this, we perform the first academic fine-tuning study of small (7B-parameter) reasoning models dedicated specifically to theoretica

Owen A. Vail, Shu-Wei Wang, Yasen Hou, Dinura Hettiarachchi

Magnetic topological insulators and their heterostructures provide great opportunities in coupling band topology with nontrivial spin configuration for enhanced spintronic device performance as well as designing totally new magnetoelectric systems and functionalities. We find that Mn interdiffusion from MnTe when interfaced with (Bi,Sb)2Te3 stabilizes as sel

Naohiro Yoshida

This paper proposes a simple and parsimonious discrete-time simulation model to describe the endogenous formation and periodic collapse of financial bubbles. While existing literature has extensively explored the statistical properties of locally explosive bubble dynamics, capturing the micro-level interplay of investor herd behavior and panic selling within

Daniel Shepard, Robin Salimans

Existing AI benchmarks for software automation rarely combine cross-application coordination, autonomous API discovery, and policy adherence. Real business workflows demand all three: a single task may span a CRM, inbox, calendar, and messaging platform - requiring the agent to find the right endpoints, follow a policy document, and write correct data to eac

Faisal Alherran

Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present Tadabur, a large-scale Quran audio dataset. Tadabur comprises more than 1400+ hours of recitation audio from over 600 distinct reciters, providing substantial variation in recitation styles, vocal characterist

Abdoulaye Thiam

This Part develops structural consequences of the thermodynamic formalism for Axiom A diffeomorphisms. The Pesin Entropy Formula equates the metric entropy of the SRB measure to the sum of positive Lyapunov exponents, with complete proofs of absolute continuity of conditional measures along unstable manifolds; the individual results are due to Sinai, Ruelle,

Hong-Yi Wang, Yu-Qi Li, Qian Wu, Zhu-Fang Cui

Proton-boron-11 (p-$^{11}$B) fusion is a highly attractive aneutronic pathway for clean energy production, offering abundant fuel, negligible neutron activation, and the potential for direct energy conversion of charged $\alpha$ particles. However, its practical implementation is severely hindered by the extremely high Coulomb barrier, necessitating ignition