April 2026 arXiv papers — page 94

P. C. Vinaya

In this paper, we introduce a new sequence of operators based on the Gr\"unwald interpolation operators on Chebyshev nodes on the space $L^p[0,{\pi}]$. The operators we consider are integral variants of the Gr\"unwald interpolation operators, inspired from the classical Kantorovich operators. Unlike the original Gr\"unwald interpolation operators, our constr

Gautier-Edouard Edouard Filardo

We introduce a traceable operator-algebraic framework for incompressible transport on M= T3 (and, more generally, compact Riemannian manifolds endowed with a smooth invariant probability measure). Given an autonomous divergence-free velocity field u, the time-1 map $\Phi$ induces the Koopman unitary U on L2(M) and the crossed-product finite von Neumann algeb

Yiwei Zhang, Xuesong Chen, Jin Gao, Hanshi Wang

Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured outputs and heterogeneous decoding behaviors, such as autoregressive language generation, parallel object detection and trajectory regression. To accommodate these differences, existing systems typically introdu

Yingjie Feng, Yi Wang, Jiaze Wang, Anfeng Liu

Self-supervised contrastive learning has emerged as a powerful paradigm for skeleton-based action recognition by enforcing consistency in the embedding space. However, existing methods rely on binary contrastive objectives that overlook the intrinsic continuity of human motion, resulting in fragmented feature clusters and rigid class boundaries. To address t

Sara Nicoletti, Giacomo Belli, Omar Morandi, Emanuele Marchetti

We present a physics based numerical model that estimates the seismic radiation generated by water sediment flows in gravel-bed rivers. The model reproduces the trajectories of individual particles, evaluates impact and rolling forces from grain scale dynamics, and accounts for broadband turbulence and vortex shedding in the water column. Synthetic seismic s

Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Samet Oymak

State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this work, we explore a long CoT setting where the model makes up to K successive attempts at solving a problem, in which each attempt is allowed to build on earlier ones after the model receives a hard verifier fe

Ross J. Kang, Clément Legrand-Duchesne

Let $G$ be a graph on an even number $n$ of vertices and let ${\cal M}_G$ be the collection of perfect matchings in $G$. Dirac's theorem says that if the minimum degree $\delta(G)$ of $G$ is at least $n/2$, then ${\cal M}_G$ is guaranteed to be non-empty, while this is not necessarily the case if $\delta(G) \le n/2-1$. Given an integer $k\ge 2$, let $\mathca

Chuhan Qiao

Off-policy learning in constrained MDPs with large binary state spaces faces a fundamental tension: causal identification of transition dynamics requires structural assumptions, while sample-efficient policy learning requires state-space compression. We introduce PI-CMDP, a framework for CMDPs whose constraint dependencies form a layered DAG under a Lifecycl

Yuli Cheng, Xiaoyu Zhang, Jiongchi Yu, Shiqing Ma

GitHub plays a critical role in modern software supply chains, making its security an important research concern. Existing studies have primarily focused on CI/CD automation, collaboration patterns, and community management, while abuse behaviors on GitHub have received little systematic investigation. In this paper, we systematically review and summarize re

Sravanth Kodavanti, Sowmya Vajrala, Srinivas Miriyala, Utsav Tiwari

Deploying large language models (LLMs) on smartphones poses significant engineering challenges due to stringent constraints on memory, latency, and runtime flexibility. In this work, we present a hardware-aware framework for efficient on-device inference of a LLaMA-based multilingual foundation model supporting multiple use cases on Samsung Galaxy S24 and S2

Wei Chen, Jun Hu, Limin Ma, Mingyan Zhang

This paper proposes a finite element method that couples mixed and Lagrange finite elements to efficiently capture stress concentrations in elasticity problems. The method employs conforming mixed finite elements in regions with stress concentration, while standard Lagrange elements are used elsewhere, achieving a balance between stress accuracy and computat

Jie Ma, Quanyu Tang, Yuchang Wang, Zhiheng Zheng

Let $G$ be an $n$-vertex graph with Laplacian eigenvalues $0=\lambda_1(G)\le \lambda_2(G)\le\cdots\le \lambda_n(G)$. Motivated by the Alon-Boppana bound and the Ramanujan phenomenon for regular graphs, Spielman conjectured that, for every graph $G$ with fixed average degree $d\ge 1$, its Laplacian eigenratio satisfies $$ \frac{\lambda_2(G)}{\lambda_n(G)} \le

Sekar Nugraheni, Paolo Giordano

This paper studies the equivalence between generalized holomorphic functions (GHF) and complex analytic functions in the framework of Robinson-Colombeau generalized numbers. In every non-Archimedean ring, the use of ordinary series is severely restricted by the topological property that a series converges (in a topology of infinitesimal neighborhoods) if and

Jiahao Wu, Ming Zeng, Hui Liu, Daren Yu

The micro-newton cusped field Hall thruster is an electric propulsion device that employs microwave-assisted ionization control. It serves as an actuator in drag-free control systems, ensuring control accuracy and stability by providing continuously adjustable thrust over a wide range. However, a mode transition occurring during the regulation process can le

Bai Xue, Peixin Wang, C. -H. Luke Ong

This paper addresses the quantitative verification of constrained occupation time in stochastic discrete-time systems, focusing on the probability of visiting a target set at least $k$ times while maintaining safety. Such cumulative properties are essential for certifying repeated behaviors like surveillance and periodic charging. To address this, we present

Kohtaro Yamaguchi, Shushi Harashita

In this paper, we determine the reduced automorphism groups of hyperelliptic curves of a small genus in characteristic $2$, when they are of $2$-rank $0$. Such a curve is an Artin-Schreier curve defined in the form $y^2-y=f(x)$ for a polynomial $f(x)$. After we clarify semidirect-product structures of the automorphism groups for an arbitrary genus, we derive

Mazhar Ali

We construct a family of positive but not completely positive linear maps acting on four dimensional space. We employ these maps to detect bound entanglement in high dimensional quantum systems.

Zixu Li, Yupeng Hu, Zhiwei Chen, Qinlei Huang

With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing attention from researchers. Unlike unimodal video retrieval methods, the CVR task takes a multi-modal query consisting of a reference video and a piece of modification text as input. The modification text conveys

Hongyu Zhan, Qixin Wang, Yusen Tan, Haitao Yu

The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies, graph learning can benefit from LLMs. However, we observe limited benefits when we directly utilize LLMs to make predictions for graph-related tasks within GraphLLM paradigm, whic

Yubai Wei, Chen Wu, Hashem Haghbayan

Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. While this paradigm has shown strong performance, prevailing VLA training procedures do not explicitly supervise hard physical constraints such as obstacle avoidance or kinematic feasibility. As a result, the g

Kun Zhou, Jiakai He, Wenmian Yang, Zhensheng Wang

Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks. We therefore define "Dynamic Slide Update via Natural Language Instructio

M. Alizada, M. Suleymanov

The influence of the QED-analog of the inverse Compton effect on the transverse momentum spectra of particles produced in proton-proton collisions at energies of \sqrt{s}=14 TeV has been investigated. The analysis is based on the quark-gluon scattering process g + q --> g + q, which is the QCD analogue of Compton scattering of a photon on an electron and can

Tokio Uchida, Ko Watanabe, Andrew Vargo, Shoya Ishimaru

"Learning by Teaching (LbT)" helps learners deepen their understanding by explaining concepts to others, with questions playing a vital role in identifying knowledge gaps and reinforcing comprehension. However, existing systems for generating such questions often rely on rigid templates and are expensive to build. To overcome these limitations, we developed

Kirill Petrovnin, Jiaming Wang, Gheorghe Sorin Paraoanu

Microwave photon detection is a key technology for low-temperature superconducting electronics and quantum information processing. A promising possibility is to use switching processes in parametric superconducting devices at criticality, which can be triggered by small perturbations. Here we demonstrate the unique sensing properties of the superconducting K

Francesco Urdih, Theodoros Theodoropoulos, Uwe Zdun

Continuous Integration and Deployment (CI/CD) facilitate rapid software delivery, making fast feedback and minimal downtime essential. While caching has been shown to be an effective technique for tackling pipeline performance and reliability issues, existing works have primarily focused on missing dependency caches, ignoring other types of caches and cache

Wensheng Wang, Chuanjun Guo, Wei Wei, Tong Wu

Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes. Most existing methods are evaluated in relatively unoccluded settings and typically do not explicitly model the distinc

Kerui Li, Zhe Jing, Xiaofeng Wang, Zheng Zhu

Inverse Dynamics Models (IDMs) map visual observations to low-level action commands, serving as central components for data labeling and policy execution in embodied AI. However, their performance degrades severely under manipulator truncation, a common failure mode that makes state recovery ill-posed and leads to unstable control. We present StableIDM, a sp

Yejin Yoon, Minseo Kim, Taeuk Kim

Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental challenge for tool-augmented agents, as API execution typically requires complete arguments, highlighting the need for personalized tool calling. To study this problem, we introduce MPT, a benchmark comprising 2

Lloyd Allison

Conway's surreal numbers were aptly named by Knuth. This note examines how far one can get towards implementing surreals and the arithmetic operations on them so that they execute efficiently. Lazy evaluation and recursive data structures yield a considerable speed up.

Xuan Wang, Yu Ming, Xinhao Zhong, Xinyu Yu

Large Language Models (LLMs) are prone to logical hallucinations and stochastic drifts during long-chain reasoning. While Classifier-Free Guidance (CFG) can improve instruction adherence, standard static implementations often cause semantic dilution and linguistic degradation. We propose SPREG (Structured Plan-guided Real-time Entropy Gating), a lightweight

Tianfu Wang, Zhezheng Hao, Yin Wu, Wei Wu

Vibe coding produces correct, executable code at speed, but leaves no record of the structural commitments, dependencies, or evidence behind it. Reviewers cannot determine what invariants were assumed, what changed, or why a regression occurred. This is not a generation failure but a control failure: the dominant artifact of AI-assisted development (code plu

Fen Zou, Shu-Xian Quan, Yong Li, Hui Dong

Molecular optomechanical cavities have recently emerged as a promising platform for frequency upconversion, enabling the quantum coherent conversion of infrared signal into the visible range. In a recent work [F. Zou et al., Phys. Rev. Lett. 132, 153602 (2024)], we proposed an amplification mechanism that can enhance the intensity of the upconverted infrared

Yuji Takeuchi

In curved spacetime, deformable bodies can undergo a displacement of their center of mass through cyclic internal motions without the use of propellant, as shown by Wisdom. In this paper, we explicitly construct static structures with internal stress in Schwarzschild spacetime that generate a buoyancy-like force (in the sense of a pressure-imbalance-induced

Chuanhao Ma, Hanyu Zhou, Shihan Peng, Yan Li

Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal manipulation. Typically, existing methods mainly embed spatiotemporal knowledge into visual and action representations, and directly perform a cross-modal mapping for step-level action prediction. However, such sp

Song Yu, Yang Hu, Haokang Ding, Zhifang Liao

Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current COD methods mainly rely on edge extraction in the spatial domain and local pixel-level information, neglecting the importance of global structural features. Additionally, they fail to effectively leverage the

Jin Chen, Shangyu Zhang, Bin Hu, Chao Zhou

The scaling laws for recommender systems have been increasingly validated, where MetaFormer-based architectures consistently benefit from increased model depth, hidden dimensionality, and user behavior sequence length. However, whether representation capacity scales proportionally with parameter growth remains unexplored. Prior studies on RankMixer reveal th

Zhou Rui, Zhi-Tian Zou, Ya Li, Ying Li

Motivated by the first observation of CP violation in $b$-baryon decays, the search for baryonic decays exhibiting large CP violation will be a primary focus in the coming years. We propose that significant CP-violating effects exist in the decay $Λ_b \to ΛD$, where $D$ denotes a CP eigenstate of the $D^0 - \bar{D}^0$ system. The predicted CP asymmetries for

Kuanning Wang, Ke Fan, Chenhao Qiu, Zeyu Shangguan

Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models face two limitations. They typically act only on the current frame, while future prediction and object-aware reasoning are often learned in separate latent spaces. We propose OFlo

Haonan Chen, Kaiwen Xiao, Bin Tian, Jun Fu

Autonomous parking remains a critical yet challenging task in intelligent driving systems, particularly within constrained urban environments where maneuvering space is limited and precise control is essential. While recent advances in end-to-end learning have shown great promise, the lack of high-quality, structured datasets tailored for parking scenarios r

Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleadi

Menghao Tang, Zimin Liang, Miqing Li

Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the scalability of multi-objective evolutionary algorithms (MOEAs) has predominantly focussed on continuous problems. However, multi-objective combinatorial optimisation problems (MOCOPs)

Cengiz Ozel, Waleed Nadeem, Samuel Potter, Yahya Bokhari

Post-traumatic stress disorder (PTSD) is highly prevalent yet chronically underreported among combat-exposed military personnel. This paper presents Molhim, a culturally adapted multimodal conversational AI platform that supports purpose-specific interactions through a configurable conversational pipeline consisting of session setup, real-time dialogue with

Tianle Xia, Lingxiang Hu, Yiding Sun, Ming Xu

Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve performance -- focused sets of 2-3 skills outperform comprehensive documentation, and excessive skills actually hurt. The bottleneck has shifted from skill availability to skill orchestration: agents need not

Xiaoyong Chu, Yue-Lin Sming Tsai, Mei-Wen Yang

Astrophysical dark matter particles with masses well below GeV-scale can be difficult to detect using conventional nuclear recoil experiments due to their low velocities in our Milky Way halo. Elastic scattering with high-energy cosmic rays or thermal production inside core-collapse supernovae can accelerate sub-GeV DM to (semi-)relativistic velocities, prod

Tousif Islam, Adhrit Ravichandran, Peter James Nee, Scott E. Field

\texttt{gwNRHME} is a framework that converts multi-modal (i.e., containing several spherical harmonic modes) quasi-circular waveforms into their eccentric counterparts, provided the quadrupolar eccentric mode is known, by exploiting universal eccentric modulation functions. Leveraging this framework, we combine the quasi-circular NR surrogate model \texttt{

Limin Gao, Chenxiao Wang

We derive a tight and saturable monogamy relation for three-qubit pure states that bounds the sum of concurrence and concurrence of assistance by the entanglement with an external qubit. The bound decreases strictly with increasing external entanglement, establishing a precise trade-off between internal and environment-induced entanglement. Equivalent formul

Ha Lan N. T, Minh-Anh Nguyen, Dung D. Le

Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallucinations, and improving factuality. However, existing systems rely on generating natural language queries at each hop and maintaining a strict architectural separation between retriever and generator, preven

Shivanshu Agnihotri, Snehashis Majhi, Deepak Ranjan Nayak

Automated polyp segmentation is critical for early colorectal cancer detection and its prevention, yet remains challenging due to weak boundaries, large appearance variations, and limited annotated data. Lightweight segmentation models such as U-Net, U-Net++, and PraNet offer practical efficiency for clinical deployment but struggle to capture the rich seman

Haojie Gu, Zhihao Zhu, Jun Zhang

The Schur square of linear codes over a finite field has emerged as a fundamental operation in both classical and quantum coding theory. In this paper, we investigate the Schur square problem of Hyperderivative Reed-Solomon (HRS) codes. By solving certain special determinants, we first give a lower bound and an upper bound for the dimensions of Schur squares

Lei Liu, Haonan Zhang, Huahang Xu, Zefan Zhang

Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics with frictional contacts and boundary constraints. To address these challenges, we first design an intuitive dexterous wrist based on a parallel anti-parallelogram tendon-driven structure, which achieves 90 d

Yan Xie, Changkui Mao, Changsong Wu, Chao Lu

As deep learning-based AI technologies gain momentum, the demand for general-purpose AI computing architectures continues to grow. While GPGPU-based architectures offer versatility for diverse AI workloads, they often fall short in efficiency and cost-effectiveness. Various Domain-Specific Architectures (DSAs) excel at particular AI tasks but struggle to ext

Yiwei Yang, Xiangyu Gao, Yuan Zhou, Yuhang Gan

Modern deep learning workloads often consist of many small tensor operations, especially in inference, attention, and micro-batched training. In these settings, kernel launch overhead can become a major bottleneck, sometimes exceeding the actual computation time. We present GPUOS, a GPU runtime JIT system that reduces launch overhead using a persistent kerne

Michael A. Frazer, Eriita G. Jones, Katarina Miljkovic, Gretchen K. Benedix

Thermal infrared data (TIR; 8 - 15 $\mu m$) has a wide range of applications in Earth and planetary remote sensing. On Mars, this includes deriving thermal inertia (TI), which describes surface physical characteristics (e.g. particle size, degree of cementation) and is key for understanding geologic processes, assessing in-situ resource utilisation (ISRU) en

You Xu, Huaijin Zhang, Lixia Xiao, Guanghua Liu

In practical affine frequency division multiplexing (AFDM) systems, the intricate coupling of oscillator phase noise (PN) and off-grid fractional shifts traps conventional estimators in a severe high-SNR error floor. To address these challenges, we propose a joint PN and channel estimation method based on sparse Bayesian learning (JPNCE-SBL). Specifically, a

Zichao Wei

Can syntactic processing emerge spontaneously from purely local interaction? We present a concrete instance on a minimal system: an 18,658-parameter two-dimensional neural cellular automaton (NCA), supervised by nothing more than a 1-bit boundary signal, is trained on the membership problem of an arithmetic-expression grammar. After training, its internal $L

Masaharu Miyazaki, Yurie Otake, Koichi Ito, Wataru Makino

Plankton monitoring is essential for assessing aquatic ecosystems but is limited by the labor-intensive nature of manual microscopic analysis. Automating the segmentation of plankton from crowded images is crucial, however, it faces two major challenges: (i) the scarcity of pixel-level annotated datasets and (ii) the difficulty of distinguishing plankton fro

Pavel Exner, Ayman Kachmar

We study resonances for the semiclassical magnetic Laplacian in the full plane with a compactly supported magnetic field in the framework of semiclassical complex scaling and black box scattering theory. Assuming that the magnetic field is locally constant, we prove the existence of semiclassical resonances near the Landau levels with exponentially small ima

Navid Reyhanian, Parisa Ramezani, Emil Björnson

Millimeter-wave (mmWave) technology is a crucial enabler for next-generation networks because it offers substantially greater available bandwidth. mmWave multiple-input multiple-output (MIMO) systems cannot rely solely on fully digital precoding due to hardware costs. As a result, hybrid precoding, which combines digital baseband processing with RF precoding

Ho-Lam Chung, Yiming Chen, Hung-yi Lee

Neural audio codecs are widely used as tokenizers for spoken language models, but they are optimized for waveform reconstruction rather than autoregressive prediction. This mismatch injects acoustically driven uncertainty into the discrete token space and increases language-model perplexity. We propose \ours, which augments codec training with language-model

Naia Suzuki, André V. G. Cavalieri, Daniel M. Edgington-Mitchell, Petrônio A. S. Nogueira

Coherent structures in aspect ratio 2, axis-switching elliptical jets are studied using direct numerical simulation (DNS). Three different datasets are studied with varying near-nozzle forcing levels. Increasing the forcing level causes the jet to axis switch at an earlier streamwise location. Spectral proper orthogonal decomposition was applied to the datas

Gonzalo Gonzalez-Pumariega, Saaket Agashe, Jiachen Yang, Ang Li

Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task. This raises a fundamental question: if an agent can succe

Yudai Hamada, Tatsuhiro Misumi

We numerically investigate whether the center-symmetric confined phase of large-$N$ $SU(N)$ gauge theory with one adjoint Dirac fermion persists under spatial compactification on $\mathbb{R}^3 \times S^1$. To this end, we employ a partially reduced twisted Eguchi-Kawai (TEK) model on a $1^3 \times L_4$ lattice with an adjoint Wilson fermion, and measure both

Aradhya Goel

We study the set $S(q)$ of residue classes $r$ modulo the Pisano period $\pi(q)$ for which $q \mid \varphi(F_m)$ for every $m \equiv r \pmod{\pi(q)}$. We prove that if $q$ is a Sophie Germain prime and $z(2q+1) \mid \pi(q)$, then $S(q)$ is a nonempty arithmetic progression, and for $q > 5$ its cardinality is odd and $q \equiv 8 \pmod{15}$. Conversely, we sho

Nathasha Naranpanawa, Maree T. Izatt, Robert D. Labrom, Geoffrey N. Askin

MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction, which continues to rely on CT. MRI-based 3D reconstruction remains impractical due to manual workflows and the scarcity of labelled full-spine datas

Sourabh Solanki, Abuzar Babikir Mohammad Adam, Chandan Kumar Sheemar, Zaid Abdullah

Terahertz (THz) communications have emerged as a key technology for escalating data rates in future generation wireless networks. However, severe propagation losses at THz frequencies pose significant challenges, which can be mitigated via ultra-massive multiple-input multiple-output (UM-MIMO) systems employing highly directional transmissions. To this end,

Nimisha Karnatak, Mohamad Chatila, Daniel Alejandro Pinzón Hernández, Reza Yazdanfar

General-purpose LLMs pose misinformation risks for development and policy experts, lacking epistemic humility for verifiable outputs. We present AVA (AI + Verified Analysis), a GenAI platform built on a curated library of over 4,000 World Bank Reports with multilingual capabilities. AVA's multi-agent pipeline enables users to query and receive evidence-based

Taylor Lundy, Narun K. Raman, Kevin Leyton-Brown

LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effectively unlimited number of question variants. This flexibility is valuable, but it makes evaluation expensive -- especially when the goal is not just determining an average score, but reliably identifying a m

Hao Cheng, Yanbo Jiang, Wenhao Yu, Rui Zhou

Most autonomous driving safety benchmarks use time-to-collision (TTC) to assess risk and guide safe behaviour. However, TTC-based methods treat risk as a one-dimensional closing problem, despite the inherently two-dimensional nature of collision avoidance, and therefore cannot faithfully capture risk or its evolution over time. Here, we report evasive accele

Tsurugi Takata, Kanji Mori, Ko Nakamura, Kei Kotake

We study how rotation modifies the constraints on MeV-scale axion-like particles (ALPs) coupled to photons derived from SN 1987A. We constrain the ALP parameter space based on both the energy-loss argument and the gamma-ray limits, and examine how these constraints are affected by stellar rotation. Adopting initial angular velocities of $Ω_{0} = 0.0 and 1.0

Emily Adlam

Recently, Hausmann and Renner have pointed out that several famous paradoxes relating to black holes have a similar character to various Extended Wigner's Friend paradoxes. In this paper I consider what the connection between these things could teach us about the Wigner's Friend scenarios. I argue that if we take the analogy between these cases seriously, th

Charles Ye, Bo Yuan, Lee Sharkey

An LLM's residual stream is both state and instruction: it encodes the current context and determines the next transformation. We introduce a parameter-free decomposition for Mixture-of-Experts models that splits each layer's hidden state into a control signal that causally drives routing and an orthogonal content channel invisible to the router. Across six

Oleg Solozobov

Risk decision systems in fraud detection and credit scoring operate under structural label absence: ground truth arrives weeks to months after decisions are made. During this blind period, model performance may degrade silently, eroding the governance evidence that justifies automated decisions. Existing drift detection methods either require labels (supervi

Tao Chen, Shilian Zheng, Jiepeng Chen, Zhangbin Pei

Multi-antenna receiving systems have become a prevalent technical solution in communication systems. Meanwhile, deep learning has achieved significant progress in automatic modulation recognition tasks in single-antenna systems. However, the application of deep learning in multi-antenna modulation recognition (MAMR) tasks is still limited. In this paper, we

Liang Li

The properties of the spectral evolution during the prompt emission phase of gamma-ray bursts (GRBs), which are closely related to the radiation mechanism (synchrotron or photosphere), are still a subject of debate. Two spectral evolution patterns (``hard-to-soft'' and ``intensity-tracking'') have been commonly observed in GRB prompt emission spectra. Here w

Jie Liu, Huanzhi Pu, Zhiru Zhang

Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental kernel across scientific computing and machine learning. While prior work accelerates SpMM using Tensor Cores, no existing sparse kernel exploits the asynchronous features of modern GPU architectures, such as NVIDIA's Tensor Memory Accelerator (TMA) and warp specialization. This work systematically

Autrio Das, Shreya Bollimuntha, Madala Venkata Renu Jeevesh, Keshab Patra

What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult, and the complexity is amplified in dual-arm settings. Such tasks are highly relevant to service robotics in domains such as hotels and hospitality, where robots must transport and reposition diverse objects w

Krishnaswami Alladi, Sroyon Sengupta

In 1977, the first author observed a duality between the largest and smallest prime factors of integers, and established as a consequence some new results on the M\"obius function $\mu(n)$ using the Prime Number Theorem for Arithmetic Progressions. In that 1977 paper, higher order dualities were observed involving the $k$-th largest and $k$-th smallest prime

Shravan Venkatraman, Rakesh Raj Madavan, Pavan Kumar Sathya Venkatesh

Neural surface reconstruction methods typically treat camera poses as fixed values, assuming perfect accuracy from Structure-from-Motion (SfM) systems. This assumption breaks down with imperfect pose estimates, leading to distorted or incomplete reconstructions. We present PCM-NeRF, a probabilistic framework that augments neural surface reconstruction with p

Sami Azirar, Zlatan Ajanovic, Hermann Blum

Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Although physics model are often available, symbolic models (consisting of symbolic state interpretation and action models) must be meticulously handcrafted or learned from labeled data. This process is both resourc

Ashish Nair

Whether the Indus Valley sign system (c. 2600-1900 BCE) encodes spoken language has been debated for decades. This paper introduces a multi-metric discrimination framework that tests the observed Indus corpus against two kinds of computer-generated non-linguistic baseline -- one mimicking a heraldic emblem system, the other an administrative coding system --

Hang Zeng, Xiangyu Liu, Yong Hu, Chaoyue Niu

Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient and private local inference yet suffer from limited capacity. To synergize the complementary strengths, we introduce a dynamic collaboration framework, where an SLM learns to proactively decide how to request a

Jeremy Ellis

This paper presents webmcu-vision-web, a single-file, zero-install browser application for end-to-end TinyML vision model training and deployment on the Seeed Studio XIAO ESP32-S3 Sense (XIAO ML Kit, $15--40 USD). Acting as a browser-based companion to the on-device Arduino firmware of Paper 1 [1], it provides a private, fully local machine learning pipeline

Xin An, Shaosui Xu, Vassilis Angelopoulos, Terry Z. Liu

Inferring electric potentials from electron phase space density measurements in the lunar wake is complicated by two challenges: the asymmetry between the sunward and anti-sunward sides of the wake driven by the solar wind strahl, and the presence of ion acoustic shocks in the central wake. We develop the Hamiltonian inversion method, which infers the full s

Kalpesh M. Popat, Kunal R. Shingala

The graph $G_\sigma$ is obtained from graph $G$ by attaching self loops on $\sigma$ vertices. The energy $ E(G_\sigma)$ of the graph $G_\sigma$ with order $n$ and eigenvalues $\lambda_1,\lambda_2,\dots,\lambda_n$ is defined as $ E(G_\sigma)= \displaystyle \sum_{i=1}^n\left|\lambda_i-\dfrac{\sigma}{n}\right| $. It has been proved that if $\sigma=0\; or\; n$ t

Li Ya, Chen Wei, Li Xiulai, Yu Lei

In this paper, we propose a novel approach for generating music based on an artificial intelligence (AI) system. We analyze the features of music and use them to fit and predict the music. The fractional Fourier transform (FrFT) and the long short-term memory (LSTM) network are the foundations of our method. The FrFT method is used to extract the spectral fe

Tianqi Wang, Jingcai Guo

Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Language-Image Pre-trained (CLIP) models have shown strong potential for CIL due to their powerful generalization ability. However, existing methods still face two key challenges: shared-parameter adaptation tends

Lingfeng Zhang, Yongan Sun, Jinpeng Hu, Hui Ma

Recent advancements in large language models (LLMs) have empowered autonomous web agents to execute natural language instructions directly on real-world webpages. However, existing agents often struggle with complex tasks involving dynamic interactions and long-horizon execution due to rigid planning strategies and hallucination-prone reasoning. To address t

Donglin Li, Daming Li, Hanyuan Shi, Jialu Zhang

Block-based programming environments such as Scratch are widely used in introductory computing education, yet scalable and reliable automated assessment remains elusive. Scratch programs are highly heterogeneous, event-driven, and visually grounded, which makes traditional assertion-based or test-based grading brittle and difficult to scale. As a result, ass

Wang Bill Zhu, Qiutong Tony Yi, Robin Jia, Jesse Thomason

Large language models (LLMs) perform substantially below human level on existing theory-of-mind (ToM) benchmarks, even when augmented with chain-of-thought prompting or probabilistic belief updates. We argue that these failures primarily arise from unreliable implicit state tracking rather than limitations in high-level reasoning. We introduce PDDL-Mind, a n

Hongjie Li, Heng Yu, Jiaman Li, Hong-Xing Yu

Reconstructing 3D human motion and human-object interactions (HOI) from Internet videos is a fundamental step toward building large-scale datasets of human behavior. Existing methods struggle to recover globally consistent 3D motion under dynamic cameras, especially for motion types underrepresented in current motion-capture datasets, and face additional dif

Shiquan Zhang, Tianyi Zhang, Le Fang, Simon D'Alfonso

With the rapid advancement of large language models (LLMs), mobile agents have emerged as promising tools for phone automation, simulating human interactions on screens to accomplish complex tasks. However, these agents often suffer from low accuracy, misinterpretation of user instructions, and failure on challenging tasks, with limited prior work examining

Shozo Saeki, Minoru Kawahara, Hirohisa Aman

A nearest-neighbor framework is a fundamental tool for various applications involving Large Language Models (LLMs) and Visual Language Models (VLMs). Vectors used for nearest-neighbor searches have richer information for similarity searches. This information leads to security risks, such as embedding inversion and membership attacks. Therefore, Privacy-Prese

Andre Ye, Jenny Y. Huang, Alicia Guo, Rose Novick

When language models answer open-ended problems, they implicitly make hidden decisions that shape their outputs, leaving users with uncontextualized answers rather than a working map of the problem; drawing on multiverse analysis from statistics, we build and evaluate the conceptual multiverse, an interactive system that represents conceptual decisions such

Meifang Chen, Zhe Yang, Huang Nianchen, Yizhan Huang

Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Code Large Language Models (CLLMs), CLLMs are shown to inadvertently leak such secrets due to a notorious memorization phenomenon. This study first reveals that Byte-Pair Encoding (BP

Tomona Kinugawa, Tetsuo Hyodo

We investigate the internal structure of near-threshold $s$-wave eigenstates in a two-body system with Coulomb plus short-range interactions. Using a nonrelativistic effective field theory, we derive the expression for the compositeness in terms of the energy derivative of the self-energy, which is applicable to the present system with the non-separable Coul

Yushi Yamada, Tatsuya Kaneko, Masataka Kakoi, Ryota Ueda

We investigate the ground-state properties of correlated three-leg ladders near 1/3 filling. We apply the density-matrix renormalization group method to the three-leg t-J ladder with strong interchain couplings and evaluate its pairing nature. When holes are doped into the spin-gapped state at 1/3 filling, we find that pair correlations develop with power-la

Liraz Mudrik, Yaakov Oshman

Classical guidance laws aim at minimizing the miss distance, thus implicitly determining the minimum warhead lethality radius required against nominal targets. However, nonnominal targets or scenarios might render the designed warhead insufficient, causing a significant degradation in the single-shot kill probability (SSKP). We propose a guidance methodology

Chengyang Li, Shuai Wang, Kejiang Ye, Weijie Yuan

This paper considers multi-agent embodied question answering (MA-EQA), which enables robot teams to answer queries based on their long-horizon observations. In contrast to existing edge resource management methods that optimize sensing, communication, or computation performance metrics, MA-EQA focuses on the quality of aggregated memory. To address this para

Aissa Bouhali, Issam Louhichi, Abdelrahman Yousef

We investigate the commutant problem for Toeplitz operators on the Bergman space of the unit disk whose symbols belong to a subclass of biharmonic functions. We obtain a complete characterization of when two such Toeplitz operators commute. As a consequence, we derive a full description of normal Toeplitz operators with symbols in this class.

Shintaro Suzuki

We consider a generalized Takagi function for beta-expansions with the base $1<\beta\leq2$, motivated by multifractal analysis for digit frequency sets of beta-expansions [20]. We show that it is pointwise $\alpha$-H\"older continuous for any $\alpha\in(0,1)$ but not pointwise Lipschitz continuous on the unit interval except a Lebesgue null set. Our proof re