April 2026 arXiv papers — page 96

Libo Sun, Peixiong He, Po-Wei Harn, Xiao Qin

KV cache memory is the dominant bottleneck for long-context LLM inference. Existing compression methods each act on a single axis of the four-dimensional KV tensor -- token eviction (sequence), quantization (precision), low-rank projection (head dimension), or cross-layer sharing -- but apply the same recipe to every layer. We show that this homogeneity leav

Nicholas Williams, Alejandro Schuler

Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stability via a concentration condition and prove that subbagging guarantees stability for any bounded-outcome regression algorithm. We introduce a new cross-fitting procedure, adaptive c

Shripad Deshmukh, Jayakumar Subramanian, Raghavendra Addanki, Nikos Vlassis

In cooperative teams where agents act in a fixed order and share a single team-level reward (multi-agent language systems, sequential robotic tasks), per-agent credit assignment is under-determined. Critic-based approaches scale poorly as the number of agents grows owing to the costly maintenance of joint/factored critic(s), whereas the existing critic-free

Chenhao Xue, Yukun Wang, An Guo, Yuhui Shi

SRAM-based compute-in-memory (CIM) offers high computational density and energy efficiency for deep neural network (DNN) accelerators, but its limited capacity causes on/off-chip data movement overhead for large DNN models. Existing CIM accelerator studies typically assume that DNN models fit entirely on-chip, leaving efficient dataflow design largely untapp

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Safety alignment in large language models is remarkably shallow: it is concentrated in the first few output tokens and reversible by fine-tuning on as few as 100 adversarial examples. This fragility becomes critical in real-world deployment, where models undergo sequential adaptation across domains such as medicine, law, and code, causing safety guardrails t

Noha Hassan, Xavier Fernando, Halim Yanikomeroglu

Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specifi

Jihye Kim, Deok-Sun Lee, K. -I. Goh

We explore the role of intrinsic structural properties of hypergraphs in governing group-driven social dynamics with social reinforcement. First, we analyze simplicial contagion dynamics on random hypergraphs in which the level of hyperedge nestedness is systematically controlled. By developing the facet-based approximate master equation (FAME) method, we de

Gang Chen, Qing Ren, Ilia Ponomarenko

Ternary coherent configurations are, on the one hand, a special case of multidimensional coherent configurations introduced by L. Babai (2016), and, on the other hand, a natural generalization of association schemes on triples introduced by D. M. Mesner and P. Bhattacharya (1990). A ternary coherent configuration X is said to be circulant if the automorphism

Magdalena Toda, Erhan Güler

We study explicit conformal minimal immersions into $\mathbb{R}^5$ obtained from holomorphic null curves in $\mathbb{C}^5$. Although the general correspondence between conformal minimal immersions in $\mathbb{R}^n$ and holomorphic null data in $\mathbb{C}^n$ is classical, our aim here is different. We isolate the five-dimensional case and develop a concrete,

Woody Haosheng Gan, William Held, Diyi Yang

The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate whether minimal subsets can reliably evaluate LAMs while reducing costs and data redundancy. Analyzing 10 subset selection methods with 18 audio models across 40 tasks covering major

Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh, Cédric Langbort

We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of minimizing the total cost incurred. A recent line of literature in ONC develops algorithms that enjoy sublinear regret with respect to a benchmark based on the set of steady-states

Phan Nhut Huan

The impact of Coulomb core polarization on the neutron density distribution of $^{48}\mathrm{Ca}$ is investigated. The Coulomb boundary radius is located before the neutron skin region, leading to proton admixture and establishing neutron skin impurity as an intrinsic feature of the density profile. Using the $(^3\mathrm{He},t)$ isobaric analog state (IAS) r

Christopher Duffy, Benjamin Fok, Gary MacGillivray

We define and develop preliminary theoretical results for the $\Gamma$-switch Ramsey number, a variation on the classical $m$-colour Ramsey number for which we allow permuting the colours incident with a vertex using elements of a group $\Gamma \leq S_m$. We find bounds for the $\Gamma$-switch Ramsey number for groups with various properties as a function of

Mu Gao, Jun Li, Huicheng Yin

For the 3D cubic quasilinear wave system $\square_{c_i} u^i=G^i(u,\partial u,\partial^2u)=\displaystyle\sum_{\substack{0\le|\alpha|,|\beta|,|\gamma|\le1 \\ 1\le j,k,l \le m}}g_{\alpha\beta\gamma}^{ijkl}\partial^{\alpha}u^j\partial^{\beta}u^k\partial^{\gamma}u^l$, it is well known that global solution $u$ exists when the small smooth initial data $(u,\partial

Miguel Cruz, Diego da Silva, Simón González, Samuel Lepe

We investigate the thermodynamic and phenomenological implications of a cosmological model governed by fractional entropy applied to the apparent horizon of a flat Friedmann-Lemaître-Robertson-Walker (FLRW) universe. By utilizing the unified first law of thermodynamics alongside the Kodama-Hayward temperature, we derive a generalized set of Friedmann equatio

Lei Guo, Ting Yang, Xu Yu, Xiaohui Han

As user behavior data becomes increasingly scattered across different platforms, achieving cross-domain knowledge fusion while preserving privacy has become a critical issue in recommender systems. Existing PPCDR methods usually rely on overlapping users or items as a bridge, making them inapplicable to non-overlapping scenarios. They also suffer from limita

Hanyang Hu, Cameron Siu, Mo Chen

Autonomous navigation requires planning to reach a goal safely and efficiently in complex and potentially dynamic environments. Graph search-based algorithms are widely adopted due to their generality and theoretical guarantees when equipped with admissible heuristics. However, the computational complexity of graph search grows rapidly with the dimensionalit

Nick Loghmani

Retrieval-Augmented Generation (RAG) systems depend on the geometric properties of vector representations to retrieve contextually appropriate evidence. When source documents interleave multiple topics within contiguous text, standard vectorization produces embedding spaces in which semantically distinct content occupies overlapping neighborhoods. We term th

Yukai Yang, Rickard Sandberg

This paper studies a structural failure of subsample-based estimation in dynamic time series models. Even under oracle knowledge of contamination locations, removing contaminated observations does not restore the uncontaminated objective. In such settings, contamination propagates through the residual filter and distorts the estimation criterion. As a result

Vincent E. Coll,, Alan Hylton

Seaweed (biparabolic) subalgebras form a large and structurally rich class of subalgebras of simple Lie algebras. We determine their adjoint cohomology. If $\mathfrak{s}$ is an indecomposable seaweed subalgebra of a complex simple Lie algebra, then \[ H^\ast(\mathfrak{s},\mathfrak{s})=0, \] and hence $\mathfrak{s}$ is absolutely rigid. If $\mathfrak{s}$ is d

Moinul Hossain, Sourav Rabi Das, Zikrul Shariar Ayon, Sadia Afrin Promi

Legal practitioners and judicial institutions face an ever-growing volume of case-law documents characterised by formalised language, lengthy sentence structures, and highly specialised terminology, making manual triage both time-consuming and error-prone. This work presents a lightweight yet high-accuracy framework for citation-treatment classification that

Joon Hyeok Kim, Yong-Hyun Park, Mattis Dalsætra Østby, Jiatao Gu

Despite their empirical success, how diffusion models generalize remains poorly understood from a mechanistic perspective. We demonstrate that diffusion models trained with flow-matching objectives exhibit grokking--delayed generalization after overfitting--on modular addition, enabling controlled analysis of their internal computations. We study this phenom

Ali Goksu, F. Gozde Kardes, Mustafa Yaylali

This article examines the enduring epistemic and methodological crisis of traditional legal practice in light of the opportunities and constraints introduced by artificial intelligence. It proposes an ontologically grounded framework termed the Mecellem semantic protocol as a response to this crisis. The analysis focuses on the structural tension within law

Alekos Cecchin, Luca Di Persio, Nicola Fraccarolo

We study a class of continuous-time mean field games on a finite state space with transition rates depending on the population distribution, leading to a non-separable Hamiltonian. In this setting, classical Lasry--Lions monotonicity arguments do not apply directly. We establish a new uniqueness result on arbitrary time horizons under a combination of strong

Yu-Jui Huang, Liviu Ignat, Traian A. Pirvu, Reihaneh Vafadar

Major life events can significantly increase individuals' risk aversion over a sustained period of time, as empirical studies reveal. How such an event-triggered shift of risk preferences impacts optimal investment is the focus of this paper. On a finite time horizon where a major life event may occur independently of the financial market, an investor ai

Kris Pan

Identifying where to innovate in a dense technical domain - such as operating systems or hardware/software co-design - is fundamentally a search problem in a high-dimensional knowledge space. Existing approaches rely on keyword search, citation proximity, or human intuition, none of which formalise the notion of an unexplored region that is simultaneously re

Zhichao Geng, Yang Yang

While advanced foundation models like ModernBERT significantly outperform older architectures in dense retrieval, they surprisingly lag behind the aging BERT-base baseline in learned sparse retrieval (LSR). We identify the root cause as the \textit{Vocabulary Gap}: modern tokenizers utilize raw, case-sensitive vocabularies designed for lossless reconstructio

Trystan Surawy-Stepney, Stephen L. Cornford

Second order derivatives of model outputs with respect to input parameters are key to several applications in ice sheet modelling. For example, the ability to compute Hessian-vector products broadens the list of available optimisation methods, and facilitates certain kinds of parametric uncertainty quantification. Some modern ice sheet models are built on fr

Merim Dzaferagic

The convergence of multiple network domains, including radio access, optical transport, and core networks, under unified intelligent control is a fundamental requirement for future 6G systems. This is important because existing network controllers remain largely domain-specific, such as the O-RAN RIC for radio, or they lack native support for AI-driven autom

Tingchao Fu, Wenkai Wang, Fanxiao Li, Huadong Zhang

Although Knowledge Editing provides an efficient mechanism for updating the knowledge of Multimodal Large Language Models (MLLMs), we find that current paradigms still suffer from an important yet remain underexplored issue : editing decoupling failure, where entity-related knowledge can be updated when the model is triggered by multimodal inputs (text--imag

Zhonghao Zhan, Yefan Zhang, Hamed Haddadi

Edge-resident AI agents increasingly span home servers, IoT hubs, laptops, and phones, yet their coordination stacks still assume cloud-style transports or a central relay. We present EdgeCitadel, an edge multi-agent orchestration platform built around a single NATS 2.10 server with the built-in MQTT adapter. The design combines MQTT connectivity for heterog

Xuzhi Wang, Xinran Wu, Ziping Zhao, Jianhua Tao

Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health settings. In recent years, deep learning has demonstrated impressive success across various domains, including affective computing and mental health assessment. Most existing approaches r

Arther Tian, Alex Ding, Frank Chen, Simon Wu

Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework that trains dedicated judge models to score query-output pairs without ground-truth references. We study three architectures across the quality-cost tradeoff: a TextCNN judge, a MiniLM cross-encoder, and a DeB

Barbara Sienkiewicz, Bipin Indurkhya

This paper explores how {\textit{Calm Technology}} can be integrated into Human-Robot Interaction (HRI), with a particular focus on the household environment. It offers comprehensive guidelines for designing assistive robots that prioritize and enhance the human need for {\textit{equanimity}}, ensuring interactions are calm, non-intrusive, and harmonious. Th

Derya Akbaba, Camilla Svensson, Claudia Torelli, Martin Callmeryd

Visualization researchers utilize workshops both for applied research and to engage different populations with visualization-based activities. While there are many benefits to running visualization workshops, their utility and impact rely on the presence of a researcher who has deep knowledge about visualization theory and practice. In this work, we introduc

Masanori Ibara, Yuichi Hiroi, Takushi Kamegai, Yusuke Masubuchi

Digital twin technology creates bidirectional synchronization between physical and virtual environments, yet current implementations fail to provide authentic environmental experiences that enhance user presence in shared virtual spaces. While digital twin environments using commercial metaverse platforms for IoT sensor data visualization have been proposed,

Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram, Shaik Gayaz Ali

Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requi

Michael Hassid, Yossi Adi, Roy Schwartz

The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL. In this position paper, we argue that this methodology effectively marks a reversion to the ``pre-train then fine-tune'' approach of the BERT era, explicitly tailoring models to the desired behaviors and specific benchmarks on whic

Lin Mu, Guoji Wang, Li Ni, Lei Sang

Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the textual semantics modeled by LLMs with the collaborative signals remains a key challenge. Existing methods either translate collaborative information into textual prompts or injec

Liesbeth Allein, Marie-Francine Moens

Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph

Zirui Wang, Yusen Hou, Shaofeng Liang, Bowen Tian

The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are

Samir Wagle, Abiral Adhikari, Reewaj Khanal, Batsal Bhandari

Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering. However, data scarcity in low resource languages such as Nepali has limited the training of large language models on Nepali legal texts. This study presents the first application of a Retrieval Augmented Generation based model for N

Johannes Conrad, Martin Oberlack

We formulate elastic and elasto-inertial turbulence in the Martin-Siggia-Rose path-integral formalism and develop a systematic source-extended symmetry algorithm to derive Ward identities directly from the Euler-Lagrange equations. These identities provide nonperturbative constraints and a principled foundation for constructing closure schemes. As a dimensio

Pitchai Muthu M

Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early demonstrations, but it breaks down when the deployment target is an embedded system with long product lifecycles, vendor-specific kernels, heterogeneous accelerators, safety constraints, and nontrivial I/O paths.

Moiz Imran, Sahan Bulathwela

Intelligent tutoring systems increasingly provide automated feedback on student work, but robust feedback requires assessing reasoning, not only final answers. We study a failure mode we call the correct answer trap (CAT): models under-detect misconceptions when students reach a correct answer via flawed reasoning. Analysing real student responses from the E

Yue Liu, Zhiyuan Cheng, Longying Lai

Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of economic activities across operating units. However, segment information is often presented in both qualitative and quantitative forms, dispersed across tables and narrative sections of Form 10-K filings. Emp

Gabriel Becquet, Sébastien Lallé, Vanda Luengo, Ali Abou-Hassan

Educational videos are a cornerstone of remote and blended learning. However, learners' fluctuating attention remains a significant barrier to effective information retention. Prior research has attempted to mitigate this by detecting and reacting to attention loss at runtime using eye tracking. Such detection has been based so far on classical machine l

Zhantao Wang

Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage constraints that govern real business processes. We present SDOF, a framework that treats multi-agent execution as a constrained state machine. SDOF operates through two primary defensive layers, implemented by

Sandip Ghoshal, Anshul Mittal, Jyotika Singh, Miguel Ballesteros

Large language model (LLM) agents augmented with external tools often struggle as number of tools grow large and become domain-specific. In such settings, ambiguous tool descriptions and under-specified agent instructions frequently lead to tool mis-selection and incorrect slot/value instantiation. We hypothesize that this is due to two root causes: generic,

S. D. Odintsov, V. K. Oikonomou, Pyotr Tsyba, Olga Razina

In this article we present a systematic observational verification of the ghost-free string-inspired $f(R,\mathcal{G})$ model, where the Gauss-Bonnet invariant is non-minimally coupled to an auxiliary scalar field $χ$ through the coupling function $h(χ)$. Previous studies confirmed the theoretical viability of this framework using phenomenological parameter

S. A. Rappaport, R. Jayaraman, G. Handler, D. Kurtz

Aims. We are attempting to better understand how stellar pulsations in close binary systems are affected, and possibly induced, by tidal, Coriolis, and centrifugal forces. Methods. We analyzed TESS data for some 50,000 potential eclipsing binaries selected by machine learning algorithms in order to search for pulsation multiplets split by integer multiples o

Kun Hu, Matthew Fritts, Daniel Becker, Daniel Schmidt

The 511 keV electron-positron annihilation feature near the galactic center has been detected for more than half a century, yet its origin remains a mystery. In this paper, we describe a concept for a balloon-borne 511 keV $γ$-ray mission called the 511-Spectrometer Mission. The mission will use Transition-Edge Sensor (TES) arrays with thick metal absorbers

Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge

Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In particular, causal scores produced by regularized neural autoregressive models are often treated as analogues of regression coefficients, leading to misleading claims of statistica

Marcus Berg, Andrea Cappelli, Riccardo Villa

Ordinary, s-wave superconductors have been recognized as being topological phases of matter, in which the dynamical gauge field implies less understood global features. Using the tools of topological field theories and generalized symmetries, we provide an updated description of these systems. At very low energies, the Higgs model reduces to the BF theory, w

Ian Smail, Steven Gillman, Ugne Dudzeviciute, A. M. Swinbank

We investigate the properties of 870-um selected galaxies at z~1-4 with FIR luminosities of LIR~1e11-1e13Lo, encompassing systems that dominate obscured activity at the peak of cosmic star formation, to identify variations in star-formation processes as a function of dust mass and redshift. We revisit ALMA 870-um continuum maps from the ALMA/SCUBA-2 UDS (AS2

Janez Bonča, Andrea Damascelli, Mona Berciu

We use variational exact diagonalization (VED) to calculate the two-electron removal spectral weight for the Hubbard-Holstein model, starting from the ground-state with two electrons on a one-dimensional chain. We argue that this spectral weight provides a valuable proxy for the intensity of 2eARPES processes. Our results show that when contrasted to the pre

Jincheng Shi, Yicheng Pan, Yue Ban, Xi Chen

Shortcuts to adiabaticity (STA) were first developed in quantum dynamics to realize rapid transformations with suppressed residual excitations. Here we show how the same idea can be implemented in classical nonlinear dissipative Lagrangian systems. Using a coupled $r$-$θ$ manipulator as an illustrative model, we perform inverse engineering on the Euler-Lagra

C. S. Luo, X. D. Tang, C. Henkel, Y. Sun

The outer Galaxy presents an optimal setting for investigating molecular clouds and star formation in environments with low metallicity. A total of 72 Galactic edge clouds were surveyed using the CO\,(2--1) line with the IRAM\,30\,m telescope, leading to the identification of 112 CO clumps within molecular clouds with linear resolutions of 0.5--0.9\,pc. Para

Christiaan P. A. van Buchem, Rojita Buddhacharya, Mantas Zilinskas, Sebastian Zieba

The atmospheres of hot rocky exoplanets are among the first primary targets of the JWST. Interpreting their atmospheric spectra requires understanding the link between silicate lava compositions and overlying atmospheres. We investigate the sensitivity of simulated emission spectra of dry lava planets to variations in oxide abundances in silicate melt. Our g

Tadahiro Kimura, Masahiro Ikoma

Exoplanet surveys have revealed a wide diversity of planetary systems, requiring integrated models of planet formation to explain their origin. Planet population synthesis (PPS) modelling is a key tool for linking theory with the statistical properties of observed exoplanets. In the coming decade, the number of known exoplanets is expected to increase ten-fo

Tobias Grantner, Emanuel Sallinger, Martin Flechl

Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propose recurrent architectures as an efficient alternative, introducing a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length o

Ghiglino Davide, Foglino Caterina, Wykowska Agnieszka

Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static or one-off formats that cannot capture how stakeholder perspectives evolve over time. This limitation is especially evident in clinical contexts, where families and patients face heavy burdens and cannot easil

Muhammad Bilal Khan, Florian Hofmann, Kilian Schäfer, Matthias Lutzi

Functional magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft robots. Here, we present muscle-inspired magnetic actuators (MMA), additively manufactured from a thermoplastic/permanent magnet polyurethane/Nd2Fe14B (TPU/MQP-S) composite using laser powder bed fusion (LPBF).

Diogo J. L. Rodrigues, Evelio Francisco, Ángel Martín Pendás

In this work, we investigate the reliability of information-theoretic measures based on the electron-density and shape-function, specifically Shannon and Rényi entropies, as descriptors of electronic correlation. By establishing a rigorous decomposition of these entropic measures into additive and nonadditive contributions, supported on a Mulliken-like atomi

Topi Halme, Visa Koivunen

This paper provides an overview of recent developments in quickest change detection (QCD) for high-dimensional multi-sensor systems, with an emphasis on settings involving structural constraints and limited sensing resources. Classical QCD methodologies, while well understood in low-dimensional and fully observed regimes, face significant challenges when ext

Julien Lavalle, Vivian Poulin, Pierre Salati

In a mixed dark matter scenario in which primordial black holes (PBHs) would co-exist with thermally produced self-annihilating particles, one expects the former to be surrounded by extremely dense halos made of the latter, built up during radiation domination. Here, as a continuation of previous work, we derive observational limits on such a scenario from a

Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin, Ibrahim Hokelek

Reconfigurable intelligent surface (RIS) technology is a promising enabler for next-generation (NextG) wireless systems, capable of dynamically shaping the propagation environment. Integrating RIS within the open radio access network (O-RAN) architecture enables flexible and intelligent control of wireless links. However, practical RIS-assisted operation req

Ryunosuke Takahashi, Kaede Yamada, Harjinder Singh, Kanata Watanabe

Rare-earth-ferrimagnetic oxides are emerging as attractive platforms for investigating ultrafast spin dynamics. Here, we study the photoinduced magnetization dynamics of epitaxial NiCo2O4 (NCO) thin films by time-resolved magneto-optical Faraday effect using two independent pump-probe configurations: 1030/515 nm and 800/400 nm. In both measurements, photoexc

Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui

While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained global optimization problem. Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: (1) failing to retrieve relevant passages in semantic

Tristan Ehlert, Arne Sachtler, Annika Schmidt, Davide Calzolari

Nature suggests that exploiting the elasticities and natural dynamics of robotic systems could increase their locomotion efficiency. Prior work on elastic snake robots supports this hypothesis, but has not fully exploited the nonlinear dynamic behavior of the systems. Recent advances in eigenmanifold theory enable a better characterization of the natural dyn

J. S. Morgan, B. Das, H. E. Bignall

A subset of magnetic stars exhibit periodic radio pulses produced by the coherent electron cyclotron maser mechanism. These pulses are known to exhibit both temporal and spectral variations, which have been attributed to phenomena intrinsic to the stellar magnetosphere. However, in order to fully characterise the radio pulses and use them as magnetospheric p

Jesús Miguel Jáquez-Domínguez, Carlos Carrasco-González, Daniel Guirado, Olga Muñoz

Polarization at millimeter wavelengths provides a powerful diagnostic of dust grain properties in protoplanetary disks. Standard models based on solid spherical grains often struggle to reproduce the observed polarization fractions and morphologies in systems where self-scattering is expected to dominate. We investigate the impact of grain morphology on pola

Jiawen Duan, Jian Xiang, Zhiqiang Li, Linlin Xue

3D human pose estimation is a classic and important research direction in the field of computer vision. In recent years, Transformer-based methods have made significant progress in lifting 2D to 3D human pose estimation. However, these methods primarily focus on modeling global temporal and spatial relationships, neglecting local skeletal relationships and t

Md Toyaha Rahman Ratul, Zhiqian Chen, Kaiqun Fu, Taoran Ji

The explosive growth of AI and machine learning literature -- with venues like NeurIPS and ICLR now accepting thousands of papers annually -- has made comprehensive citation coverage increasingly difficult for researchers. While citation recommendation has been studied for over a decade, existing systems primarily focus on broad relevance rather than identif

Anne S. Freise, Jamie I. McDonald, Kirill Riabtsev, Samuel J. Witte

The high-frequency gravitational-wave band is often discussed primarily in the context of new physics, but realistic Standard-Model foregrounds remain incompletely characterized. We investigate pulsar polar caps as a physically motivated astrophysical source of high-frequency gravitational waves, generated by repeated discharge cycles in compact near-surface

Mrinmoy Basak, Debsubhra Chakraborty, Nilmani Mathur

We propose an effective Hamiltonian formulation of quantum field theories using a Daubechies wavelet basis in position space. Combined with flow-equation methods of the similarity renormalization group (SRG), this approach provides an efficient framework for analyzing quantum field theories by reducing the dimensionality of the Hamiltonian and systematically

Yanyi Su, Hongshuai Wang, Zhifeng Gao, Jun Cheng

Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully capture this pathway. Current approaches typically model only isolated segments of the olfactory pathway, overlooking the complete chain from molecule to receptors to linguistic descriptions. Such fragmentati

Stefanie M. Kampa, Florian Sammüller, Matthias Schmidt

We investigate and exploit consequences of the recent neural metadensity functional theory [Kampa et al., Phys. Rev. Lett. 134, 107301 (2025), 10.1103/PhysRevLett.134.107301] for describing the physics of inhomogeneous fluids. The metadensity dependence on the pair potential is relevant for soft matter design and Henderson inversion and it allows one to chan

Xin Ren, Wei Yan, Ruining Zhao, Shu Wang

China's Tianwen-1 Mars orbiter successfully imaged the third interstellar object, 3I/ATLAS, during its close encounter with Mars using the onboard HiRIC CMOS camera. This is China's first deep-space observation of an astronomical object. These observations constitute the first imaging of this object from a vantage point significantly out of its orbit

Michael Hardy, Yunsung Kim

LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on benchmarks, downstream tasks, and, importantly the intended impact of those tasks. We evaluate the performance of leading LLMs (i.e., generative pre-trained base models) on difficult-to-verify tasks of the teaching an

Slava G. Turyshev

Terraforming Mars can be evaluated with a set of system-level constraints linking (i) target pressures & compositions to required atmospheric inventories, (ii) target surface temperatures to the required radiative control, (iii) inventories & climate agents to sustained industrial throughput & power over a build time, (iv) persistence against collapse, escap

Ulf-G. Meißner, Akaki Rusetsky, Ajay S. Sakthivasan, Gerrit Schierholz

The calculation of resonance form factors in effective field theory as well as on the lattice is a highly challenging task. In a recent paper, we proposed a novel method based on the introduction of a background field and the Feynman-Hellmann theorem to address the problem, and applied it to a toy model. In the present work we use this method for the electro

D. A. Matters, A. M. Hurst, T. Kawano

Westcott $g$ factors are used in Neutron Activation Analysis (NAA) and Prompt Gamma-ray Activation Analysis (PGAA) to evaluate the impact of non-$1/v$ behavior in the neutron-capture cross sections of certain nuclei on activation product yields. This non-$1/v$ behavior arises from the presence of neutron resonances in the neutron-capture cross sections that

Federico Naldini, Fabio Oddi, Leo D'Amato, Grégory Marlière

Improving traffic management in case of perturbation is one of the main challenges in today's railway research. The great majority of the existing literature proposes approaches to make centralized decisions to minimize delay propagation. In this paper, we propose a new paradigm to the same aim: we design and implement a modular process to allow trains t

Haodong Chen, Qiang Huang, Jiaqi Zhao, Qiuping Jiang

Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demographic cues. A central challenge in measuring such social bias is attribution under visual confounding: real-world images entangle race and gender with correlated factors such as background and clothing, obscuring attr

Jaewoo Kim, Taehyun Sung, Woonam Hwang, Jaemyung Ahn

This paper proposes a maintenance strategy for a satellite constellation that utilizes on-orbit servicing (OOS). Under this strategy, the constellation operator addresses satellite failures in two ways: by deploying new satellites and by recovering failed satellites through OOS. We develop an inventory management model with a parametric replenishment policy

Zurab K. Silagadze

The teaching of special relativity still follows Einstein's original two-postulate approach and thus recreates the relativistic revolution in the minds of students again and again, with all its attendant shocking and mysterious aspects. As Hermann Bondi long ago noted, such an approach, which emphasizes the revolutionary aspects of a theory rather than i

Siyi Yang, Yi-Ming Ding, Zheng Yan

As a powerful theoretical construct, the entanglement Hamiltonian (EH) encapsulates the essential entanglement properties of a quantum many-body system. From the EH, one can extract a variety of entanglement quantities, such as entanglement entropies, negativity, and the entanglement spectrum. However, its general analytical form remains largely unknown. Whi

Sinan Deger, Hiranya V. Peiris, Stephen Thorp, Daniel J. Mortlock

We study star formation over 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of 420,000 COSMOS2020 galaxies (IRAC Ch.1 $<26$). The model learns distributions over 16 SPS parameters via score-based diffusion, matching observed colours and magnitudes. We compute the star formation rate density (SFRD) to $z=3.5$ by directly integrating

Etienne Granet, Henrik Dreyer

We consider the problem of preparing thermal equilibrium states at finite temperature on quantum computers. Assuming thermalization, we show that states that are locally at thermal equilibrium can be prepared by evolving adiabatically an initial thermal Gibbs state of a simple Hamiltonian with an interpolating time-dependent Hamiltonian, identically to adiab

Ataru Tanikawa, Shuai Liu, WeiWei Wu, Michiko S. Fujii

GW231123 is a merger of two black holes (BHs) with estimated masses exceeding $100\;{\rm M}_{\odot}$, making them the most massive BHs discovered to date via gravitational wave (GW) observations. We investigate whether GW231123-like events can originate from isolated Population (Pop) III binary stars using binary population synthesis calculations. Our findin

Md. Zihad Bin Jahangir, Muhammad Ashad Kabir, Sumaiya Akter, Israt Jahan

Automated radiology report generation holds significant potential to reduce radiologists&#39; workload and enhance diagnostic accuracy. However, generating precise and clinically meaningful reports from chest radiographs remains challenging due to the complexity of medical language and the need for contextual understanding. Existing models often struggle wit

Zhu Li, Yuqing Zhang, Xiyuan Gao, Shekhar Nayak

Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, existing detection systems often rely on multimodal data, limiting their applicability in contexts where only speech is available. To address this, we propose an annotation pipeline that leverages large language mod

Eduardo Casas, Mariano Mateos

We analyze a bilinear control problem governed by a semilinear parabolic equation. The control variable is the Robin coefficient on the boundary. First-order necessary and second-order sufficient optimality conditions are derived. A sequential quadratic programming algorithm is then proposed to compute local solutions. Starting the iterations from an initial

Frankie Higgs

We analyse the aggregate Loewner evolution (ALE), introduced in 2018 by Sola, Turner and Viklund to generalise versions of diffusion limited aggregation (DLA) in the plane using complex analysis. They showed convergence of the ALE for certain parameters to a single growing slit. Started from a non-trivial initial configuration of $k$ needles and the same par

Xian-Peng Zhang, Yan-Qing Feng, Haiwen Liu, Yugui Yao

Microscopic theories of magnetoresistance have traditionally focused on momentum relaxation and the plasma frequency of itinerant electrons. Here, we uncover a distinct mechanism in which magnetoresistance originates from quantum decoherence throughout the whole Fermi sea, specifically the decay of the off-diagonal components of the density matrix. The resul

Kenichiro Kimura

Goncharov defined for each field $F$ and an integer $n$ greater than 1 a certain group $B_n(F)$. We consider the possibility of defining a linear map from $B_n(F)$ to the co-Lie algebra of the category of mixed Tate motives defined by Bloch and Kriz, in terms of motivic polylogarithms. We give results which support this possibility assuming part of the conje

César Ojeda, Niklas Hartung, Wilhelm Huisinga, Tim Jahn

We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent vi

Michael Robinson, Sajal Halder, Muhammad Ejaz Ahmed, Muhammad Ikram

Understanding vulnerability propagation is essential for assessing how vulnerabilities spread across components of a software package. This supports more accurate impact analysis and enhances threat detection and mitigation. In this paper, we investigate how a small number of vulnerable JavaScript packages contribute to the creation of a disproportionately l

Ali Ghorbanpour, Soroush Sadeghian, Alireza Daghighfarsoodeh, Sajad Ebrahimi

Peer review is central to scientific publishing, yet reviewers frequently include claims that are subjective, rhetorical, or misaligned with the submitted work. Assessing whether review statements are factual and verifiable is crucial for fairness and accountability. At the scale of modern conferences and journals, manually inspecting the grounding of such c