October 2025 arXiv papers — page 153

Antoine Mouchamps, Arthur Malherbe, Adrien Bolland, Damien Ernst

This paper presents Gym-TORAX, a Python package enabling the implementation of Reinforcement Learning (RL) environments for simulating plasma dynamics and control in tokamaks. Users define succinctly a set of control actions and observations, and a control objective from which Gym-TORAX creates a Gymnasium environment that wraps TORAX for simulating the plas

Deepeka Garg, Sihan Zeng, Annapoorani L. Narayanan, Sumitra Ganesh

Learning to autonomously execute long-horizon procedures from natural language remains a core challenge for intelligent agents. Free-form instructions such as recipes, scientific protocols, or business workflows encode rich procedural knowledge, but their variability and lack of structure cause agents driven by large language models (LLMs) to drift or fail d

Patricia Piedade

This is the Proceedings of the Access InContext Workshop, which was held at the CHI'25 Conference on Human Factors in Computing Systems, in Yokohama, Japan, on April 26th 2025.

Mingzhi Wang, Zhichao Zhang

Graph signal processing (GSP) advances spectral analysis on irregular domains. However, existing two-dimensional graph fractional Fourier transform (2D-GFRFT) employs a single fractional order for both factor graphs, thereby limiting its adaptability to heterogeneous signals. We proposed the two-dimensional graph bi-fractional Fourier transform (2D-GBFRFT),

Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi, Jeffrey Molendijk

We present Entropic Mutual-Information Geometry Large-Language Model Alignment (ENIGMA), a novel approach to Large-Language Model (LLM) training that jointly improves reasoning, alignment and robustness by treating an organisation's policies/principles as directions to move on a model's information manifold. Our single-loop trainer combines Group-Relative Po

Guangyu Wei, Ke Han, Yueming Lyu, Yu Luo

Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily on external evidence and therefore struggle to generalize under evidence scarcity. To address this issue, we propose Evaluation-Aware Selection of Experts (EASE), a novel framewor

Juan De Gregorio, David Sánchez, Raúl Toral

Understanding the temporal dependence of precipitation is key to improving weather predictability and developing efficient stochastic rainfall models. We introduce an information-theoretic approach to quantify memory effects in discrete stochastic processes and apply it to daily precipitation records across the contiguous United States. The method is based o

Ana Sofia Carmo, Lourenço Abrunhosa Rodrigues, Ana Rita Peralta, Ana Fred

The lack of standardization in seizure forecasting slows progress in the field and limits the clinical translation of forecasting models. In this work, we introduce a Python-based framework aimed at streamlining the development, assessment, and documentation of individualized seizure forecasting algorithms. The framework automates data labeling, cross-valida

Jianzhe Zhao, Hailin Zhu, Yu Zhang, Ziqi Chen

Federated efficient fine-tuning has emerged as an approach that leverages distributed data and computational resources across nodes to address the challenges of large-scale fine-tuning and privacy preservation. The Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large-scale pre-trained models by introducing trainable low-rank matrices into weight

Vera Djordjilović, Tamar Sofer, Jonathan M. Dreyfuss

Directional replicability addresses the question of whether an effect studied across $n$ independent studies is present with the same direction in at least $r$ of them, for $r \geq 2$. When the expected direction of the effect is not specified in advance, the state of the art recommends assessing replicability separately by combining one-sided $p$-values for

Timothée Marquis, Bernhard Mühlherr

To any generalised Cartan matrix (GCM) $A$ and any ring $R$, Tits associated a Kac-Moody group $\mathfrak{G}_A(R)$ defined by a presentation \`a la Steinberg. For a domain $R$ with field of fractions $\mathbb{K}$, we explore the question of whether the canonical map $\varphi_R\colon\thinspace \mathfrak{G}_A(R)\to \mathfrak{G}_A(\mathbb{K})$ is injective. Thi

Anastasiia Quarz, Angelica De Gregorio, Gaia Franciosini, Angelo Schiavi

Monte Carlo (MC) simulations provide gold-standard accuracy for carbon ion therapy dose calculations but are computationally intensive. Analytical pencil beam algorithms offer speed but reduced accuracy in heterogeneous tissues. We developed the first AI-based dose engine capable of predicting absorbed dose, the alpha and beta parameters for relative biologi

Junyan Ye, Hoi Ying Wong

We propose \textit{DeepMartingale}, a deep-learning framework for the dual formulation of discrete-monitoring optimal stopping problems under continuous-time models. Leveraging a martingale representation, our method implements a \emph{pure-dual} procedure that directly optimizes over a parameterized class of martingales, producing computable and tight \emph

Fabian Rennecke

The phase structure of QCD remains an open fundamental problem of standard model physics. In particular at finite density, our knowledge is limited. Yet, numerous model studies point towards a rich and complex phase diagram at large density. Functional methods like the functional renormalization group and Dyson-Schwinger equations offer a way to study hot an

Antonio Montieri, Alfredo Nascita, Antonio Pescapè

GenAI chatbots are now pervasive in digital ecosystems, fundamentally reshaping user interactions over the Internet. Their reliance on an always-online, cloud-centric operating model introduces novel traffic dynamics that challenge practical network management. Despite the critical need to anticipate these changes in network demand, the traffic characterizat

Jaeik Kim, Jaeyoung Do

Image classifiers play a critical role in detecting diseases in medical imaging and identifying anomalies in manufacturing processes. However, their predefined behaviors after extensive training make post hoc model editing difficult, especially when it comes to forgetting specific classes or adapting to distribution shifts. Existing classifier editing method

Eugen Rogozinnikov

We introduce the symplectic group $\mathrm{Sp}_2(G, \sigma)$ associated to a Lie subgroup $G$ of a (possibly noncommutative) associative algebra $A$ equipped with an anti-involution $\sigma$. Our construction recovers several classical Lie groups as special cases, and in particular provides new realizations of spin groups as instances of $\mathrm{Sp}_2(G, \s

Kalen Patton

Online resource allocation problems are central challenges in economics and computer science, modeling situations in which $n$ items arriving one at a time must each be immediately allocated among $m$ agents. In such problems, our objective is to maximize a monotone reward function $f(\mathbf{x})$ over the allocation vector $\mathbf{x} = (x_{ij})_{i, j}$, wh

Ahtsham Ul Haq, Muhammad Usman Rashid, Muhammad Ishaq

This work establishes combinatorial bounds on the Castelnuovo-Mumford regularity of edge ideals for trees and their multi-whiskered variants. For a tree \( T \), we give bounds for the Castelnuovo-Mumford regularity of \( I(T) \) in terms of the order, diameter, and number of pendant vertices. Furthermore, we present an upper bound for multi-whiskered trees

Conglin Ma, Jiatong Li, Sen-Zhe Xu, Ju Dai

This paper introduces a novel VR-based system that redefines the acquisition of Hanzi character literacy by integrating traditional mortise-tenon joinery principles (HVRMT).Addressing the challenge of abstract character memorization in digital learning,our system deconstructs Hanzi components into interactive "structural radicals"akin to wooden joint modules

Tianyi Tao, Junchi Zhang, Wentao Zhang, Alex Toole

A graph \( G \) is said to be (vertex) non-repetitively colored if no simple path in \( G \) has a sequence of vertex colors that forms a repetition. Formally, a coloring \( c: V(G) \to \{1, 2, \dots, k\} \) is non-repetitive if, for every path \(\langle v_1, v_2, \dots, v_{2m} \rangle\) in \( G \), the sequence of colors \( c(v_1), c(v_2), \dots, c(v_{2m})

D. Rochman, A. Koning, S. Goriely, S. Hilaire

In this work, we are presenting a new database of astrophysical interest, based on calculations performed with the nuclear reaction code TALYS. Four quantities are systematically calculated for over 8000 nuclides: cross sections, reaction rates, Maxwellian Averaged Cross Sections (or MACS) at 30 keV and partition functions. For cross sections and reaction ra

Yuxuan Chen, Ruotong Yang, Zhengyang Zhang, Mehreen Ahmed

Microscopic characterizations, such as Scanning Electron Microscopy (SEM), are widely used in scientific research for visualizing and analyzing microstructures. Determining the scale bars is an important first step of accurate SEM analysis; however, currently, it mainly relies on manual operations, which is both time-consuming and prone to errors. To address

Weixuan Li, Quanjun Li, Guang Yu, Song Yang

In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limited structural representation and insufficient contextual modeling, affecting generalization in complex clinical scenarios. We propose the DTEA model, featuring a new skip connection

Yuhui Fu, Feiyang Xie, Chaoyi Xu, Jing Xiong

Loco-manipulation is a fundamental challenge for humanoid robots to achieve versatile interactions in human environments. Although recent studies have made significant progress in humanoid whole-body control, loco-manipulation remains underexplored and often relies on hard-coded task definitions or costly real-world data collection, which limits autonomy and

Davide Borghini, Davide Marchi, Angelo Nardone, Giordano Scerra

As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-e

D. Rochman, A. Koning, S. Goriely, S. Hilaire

Comparisons between predicted and benchmark k$_{\rm eff}$ values from criticality-safety systems are often used as metrics to estimate the quality of evaluated nuclear data libraries. Relevant nuclear data for these critical systems generally come from a mixture of expert knowledge and phenomenological predictions. In the present work, we use solely microsco

Ashwin Goyal, Drashthi Doshi, Swaprava Nath

Motivated by the fact that the worth of a coalition may depend on the order in which agents arrive, Nowak and Radzik (1994) (NR) introduced cooperative games with generalized characteristic functions. We study such temporal cooperative games (TCGs), where the worth function v is defined on sequences of agents {\pi} rather than sets S. This order sensitivity

Jana Jung, Marlene Lutz, Indira Sen, Markus Strohmaier

Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). However, it remains unclear whether these tests -- originally developed for humans -- yield meaningful results when applied to LLMs. In this study, we systematically evaluate the reliability and validity of human psychometric tests on 17 LLMs for thre

Sayantika Mandal, Harman Agrawal, Swaprava Nath

Firms (businesses, service providers, entertainment organizations, political parties, etc.) advertise on social networks to draw people's attention and improve their awareness of the brands of the firms. In all such cases, the competitive nature of their engagements gives rise to a game where the firms need to decide how to distribute their budget over the a

Tomohiro Yamada

We prove that the sum of reciprocals $1/x$ of integer solutions of $(x^m-1)/(x-1)=N$ with $x, m\geq 2$ for a given integer $N$ except the smallest $x$ is smaller than $5.9037$. If we limit $x$ to be prime, then the sum is smaller than $0.73194$.

Rui Xu, Jiawei Chen, Weizhi Liu, Zhaoxia Yin

The proliferation of open-source code and large language models (LLMs) for code generation has amplified the risks of unauthorized reuse and intellectual property infringement. Source code watermarking offers a potential solution, yet existing methods typically encode watermarks through identifiers, local code patterns, or limited handcrafted edits, leaving

Bruno Sicardy, Heikki Salo, Maryame El Moutamid, Stefan Renner

Three ring systems have been discovered to date around small irregular objects of the solar system (Chariklo, Haumea and Quaoar). For the three bodies, material is observed near the second-order 1/3 Spin-Orbit Resonance (SOR) with the central object, and in the case of Quaoar, a ring is also observed near the second-order resonance 5/7 SOR. This suggests tha

Marion Zannese, Jacques Le Bourlot, Evelyne Roueff, Emeric Bron

The James Webb Space Telescope enabled the first detection of several rovibrational emission lines of HD in the Orion Bar, a prototypical photodissociation region. This provides an incentive to examine the physics of HD in dense and strong PDRs. Using the latest data available on HD excitation by collisional, radiative and chemical processes, our goal is to

Ivette Rodríguez, Josep Maria Duró, Ernest Mestres, Ming Teng

We conducted high-resolution large-eddy simulations over a real urban district in Barcelona to examine the impact of wind direction on near-ground flow. The computational mesh resolves over 500 million degrees of freedom, with a spatial resolution on the order of 1 m at pedestrian level. This allows a detailed analysis of mean velocity and turbulence pattern

Pengyu Zhu, Lijun Li, Yaxing Lyu, Li Sun

LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks remains largely underexplored. However, existing research has focused exclusively on single-agent backdoor attacks, overlooking the novel attack surfaces introduced by agent collaboration in MAS. To bridge this gap, w

Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa, Paolo Di Lorenzo

Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. They play a key role in applications such as synchronization, Riemannian signal processing, and neural sheaf diffusion. In this work, we address the inverse problem of learning CGs

Birat Poudel, Satyam Ghimire, Sijan Bhattarai, Saurav Bhandari

Sign languages serve as essential communication systems for individuals with hearing and speech impairments. However, digital linguistic dataset resources for underrepresented sign languages, such as Nepali Sign Language (NSL), remain scarce. This study introduces the first benchmark dataset for NSL, consisting of 36 gesture classes with 1,500 samples per cl

Katarina Dyreby, Francisco Caldas, Cláudia Soares

In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Star

Jaesun Kim, Jinmu You, Yutack Park, Yunsung Lim

Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that joint

Rafael Cabral, Tuan Manh Do, Xuejun Yu, Wai Ming Tai

Proof autoformalization, the task of translating natural language theorems and proofs into machine-verifiable code, is a critical step for integrating large language models into rigorous mathematical workflows. Current approaches focus on producing executable code, but they frequently fail to preserve the semantic meaning and logical structure of the origina

J. Fransson

The chirality induced spin selectivity effect remains a challenge to capture with theoretical modeling. While at least a decade was spent on independent electron models, which completely fail to reproduce the experimental results, the lesson to be drawn out of these efforts is that a correct modeling of the effect has to include interactions among the electr

Charlie Sire, Mike Pereira, Thomas Romary

Spline interpolation is a widely used class of methods for solving interpolation problems by constructing smooth interpolants that minimize a regularized energy functional involving the Laplacian operator. While many existing approaches focus on Euclidean domains or the sphere, relying on the spectral properties of the Laplacian, this work introduces a metho

Michael Schlichtkrull

When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous research has studied indirect prompt injection, where the attacker injects malicious instructions. We argue that injection of instructions is not necessary to manipulate agents - attackers could instead supply bias

Malena Sabaté Landman, Yuji Nakatsukasa

The computation of sparse solutions of large-scale linear discrete ill-posed problems remains a computationally demanding task. A powerful framework in this context is the use of iteratively reweighted schemes, which are based on constructing a sequence of quadratic tangent majorants of the $\ell_2$-$\ell_1$ regularization functional (with additional smoothi

Haoqi Yang, Yao Yao, Zuchao Li, Baoyuan Qi

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. However, their extensive memory requirements, particularly due to KV cache growth during long-text understanding and generation, present significant challenges for deployment in resource-constrained environments. Quantization has emerged a

Leonard Dung, Florian Mai

AI alignment research aims to develop techniques to ensure that AI systems do not cause harm. However, every alignment technique has failure modes, which are conditions in which there is a non-negligible chance that the technique fails to provide safety. As a strategy for risk mitigation, the AI safety community has increasingly adopted a defense-in-depth fr

Jinyuan Xu, Tian Lan, Xintao Yu, Xue He

Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce and largely focus on binary classification. To address this limitation, we release CNSocialDepress, a benchmark dataset for depression risk detection on Chinese social media. The dataset contains 44,178 posts from

Neilansh Chauhan, Piyush Kumar Gupta, Faraz Doja

Effective pneumonia diagnosis is often challenged by the difficulty of deploying large, computationally expensive deep learning models in resource-limited settings. This study introduces LightPneumoNet, an efficient, lightweight convolutional neural network (CNN) built from scratch to provide an accessible and accurate diagnostic solution for pneumonia detec

Judhajeet Basu, G. C. Anupama, Jan-Uwe Ness, Kulinder Pal Singh

We report on UV and X-ray observations of the 2024 eruption of the recurrent nova LMCN 1968-12a, a rapidly recurring extragalactic system with a $\sim$4.3 year recurrence period and a massive white dwarf (WD). The eruption was discovered on 2024 August 1.8 by \textit{Swift}, and subsequently monitored using \textit{AstroSat}'s UVIT and SXT, along with Swift/

Bikash Chandra Paul, Sahil Saini

We investigate uniform rate inflationary universe in the framework of loop quantum cosmology (LQC) and find that this seemingly simple inflationary model is interlinked with various concepts such as cyclic evolution, HNI inflation and polymer quantized scalar fields, when the background spacetime is loop quantized. The potential for an \textit{exactly} unifo

Dimitrios G. Konstantinides

We consider the multivariate risk model with common renewal process among the lines of business, and Brownian perturbations. Assuming that the integrated tail distribution of claims is multivariate subexponential, we establish an asymptotic relation for the infinite-time ruin probability. A more explicit expression is given in case of claim distribution from

Ahmed Rashwan, Keith Briggs, Chris Budd, Lisa Kreusser

Many machine learning applications require outputs that satisfy complex, dynamic constraints. This task is particularly challenging in Graph Neural Network models due to the variable output sizes of graph-structured data. In this paper, we introduce ProjNet, a Graph Neural Network framework which satisfies input-dependant constraints. ProjNet combines a spar

Ib Thorsgaard Jensen, Jean-François Coeurjolly, Rasmus Waagepetersen

Multi-type Markov point processes offer a flexible framework for modelling complex multi-type point patterns where it is pertinent to capture both interactions between points as well as large scale trends depending on observed covariates. However, estimation of interaction and covariate effects may be seriously biased in the presence of unobserved spatial co

Hayate Funakura, Hyunsoo Kim, Koji Mineshima

Graph-matching metrics such as Smatch are the de facto standard for evaluating neural semantic parsers, yet they capture surface overlap rather than logical equivalence. We reassess evaluation by pairing graph-matching with automated theorem proving. We compare two approaches to building parsers: supervised fine-tuning (T5-Small/Base) and few-shot in-context

Sebastian Bitzer, Michele Battagliola, Antonia Wachter-Zeh, Violetta Weger

Threshold-Computation-in-the-Head (TCitH) and VOLE-in-the-Head (VOLEitH), two recent developments of the MPC-in-the-Head (MPCitH) paradigm, have significantly improved the performance of digital signature schemes. This work embeds the restricted decoding problem within these frameworks: we propose a structurally simple modeling that achieves competitive sign

Masoumeh Chapariniya, Pierre Vuillecard, Jean-Marc Odobez, Volker Dellwo

This work investigates whether individuals can be identified solely through the pure dynamical components of their facial expressions, independent of static facial appearance. We leverage the FLAME 3D morphable model to achieve explicit disentanglement between facial shape and expression dynamics, extracting frame-by-frame parameters from conversational vide

Battemuulen Naranbat, Seyed Sahand Mohammadi Ziabari, Yousuf Nasser Al Husaini, Ali Mohammed Mansoor Alsahag

Ensuring fairness in natural language processing for moral sentiment classification is challenging, particularly under cross-domain shifts where transformer models are increasingly deployed. Using the Moral Foundations Twitter Corpus (MFTC) and Moral Foundations Reddit Corpus (MFRC), this work evaluates BERT and DistilBERT in a multi-label setting with in-do

Tao Li, Jinlong Hu, Yang Wang, Junfeng Liu

LLM-brained web agents offer powerful capabilities for web automation but face a critical cost-performance trade-off. The challenge is amplified by web agents' inherently complex prompts that include goals, action histories, and environmental states, leading to degraded LLM ensemble performance. To address this, we introduce WebRouter, a novel query-specific

Shijie Qin, Kun Xu, Shijun Liao

The Navier-Stokes (NS) equations as a turbulence model have been widely applied in lots of fields. The NS equations contain such a fundamental assumption that all small physical/artificial disturbances could be neglected. Is this assumption correct? In this paper a two-dimensional Rayleigh-B\'{e}nard convection governed by the NS equations is predicted by tr

S. M. Troshin, N. E. Tyurin

Transition to the reflective scattering mode results in the increasing role of the multiplicity fluctuations of quantum origin and its asymptotic dominance. We note here the feasibility to experimentally detect presence of quantum fluctuations of multiplicity at finite energies.

Saad Obaid ul Islam, Anne Lauscher, Goran Glavaš

Large language models (LLMs) can correctly answer "When was Einstein born?" yet fail to provide the same date when writing about Einstein's life revealing a fundamental inconsistency in how models access factual knowledge across task complexities. While models display impressive accuracy on factual question-answering benchmarks, the reliability gap between s

Chris Xing Tian, Weihao Xie, Zhen Chen, Zhengyuan Yi

Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external retrieval to enable domain-grounded responses. Effectively adapting RAG systems to domain-specific settings requires specialized, context-rich training data beyond general-purpose question-answering. Here, we propose RAGe

Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu

This paper presents a unified and system-agnostic analysis of the ambiguity function (AF) characteristics of four representative multicarrier waveforms, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), affine frequency division multiplexing (AFDM), and chirp-permuted AFDM (CP-AFDM), which are considered as key candid

Jiwon Shin, C. Y. Hui, Sangin Kim, Kwangmin Oh

Using 16 years of data collected by Fermi Large Area Telescope and 1523 days of survey data from High Altitude Water Cherenkov (HAWC) Observatory, we discovered the long-sought second GeV-TeV connection towards the globular cluster (GC) UKS 1 (Shin et al. 2025). Gamma-ray spectroscopy suggests that the GeV emission can be attributed to both the pulsar magnet

Téo Guichoux, Théodor Lemerle, Shivam Mehta, Jonas Beskow

Human communication is multimodal, with speech and gestures tightly coupled, yet most computational methods for generating speech and gestures synthesize them sequentially, weakening synchrony and prosody alignment. We introduce Gelina, a unified framework that jointly synthesizes speech and co-speech gestures from text using interleaved token sequences in a

Songqinghao Yang, Haomu Yuan, Crispin H. W. Barnes

We present an experimental implementation of the Pusey-Barrett-Rudolph (PBR) no-go theorem on IBM's 156-qubit Heron2 Marrakesh superconducting quantum processor. By preparing qubits in a set of non-orthogonal states and evolving them under carefully compiled unitary circuits, we test whether one can interpret the hidden variable model for quantum states as m

Joshua A. Grochow, Abhiram Natarajan

Motivated by better understanding the bideterminant (=product of minors) basis on the polynomial ring in $n \times m$ variables, we develop theory \& algorithms for Gr\"obner bases in not only algebras with straightening law (ASLs or Hodge algebras), but in any commutative algebra over a field that comes equipped with a notion of "monomial" (generalizing the

Sheikh Azizul Hakim, Saem Hasan

Large language models (LLM) are advanced AI systems trained on extensive textual data, leveraging deep learning techniques to understand and generate human-like language. Today's LLMs with billions of parameters are so huge that hardly any single computing node can train, fine-tune, or infer from them. Therefore, several distributed computing techniques are

Yisong Miao, Min-Yen Kan

Which components in transformer language models are responsible for discourse understanding? We hypothesize that sparse computational graphs, termed as discursive circuits, control how models process discourse relations. Unlike simpler tasks, discourse relations involve longer spans and complex reasoning. To make circuit discovery feasible, we introduce a ta

Nicola Alboré, Gabriele Di Antonio, Fabrizio Coccetti, Andrea Gabrielli

We propose a new reservoir computing method for forecasting high-resolution spatiotemporal datasets. By combining multi-resolution inputs from coarser to finer layers, our architecture better captures both local and global dynamics. Applied to Sea Surface Temperature data, it outperforms standard parallel reservoir models in long-term forecasting, demonstrat

Donghyeon Kim

In this paper, we define the notion of asymptotically flat divisor on a normal variety over $\C$, and prove that if $X$ is a strongly $F$-regular type variety and $K_X$ is asymptotically flat, then $X$ is of klt type.

Margherita Bertè, Tommaso Gili

Based on recent advances in fibration symmetry theory, we investigate how structural symmetries influence synchronization in systems with higher-order interactions (HOI). Using bipartite graph representations, we identify a node partition in fibres, based on equivalent incidence relations in hypergraphs. We study how identical nodes with an isomorphic input

Yacine Chitour, Zhengping Ji, Emmanuel Trélat

We propose global surjectivity theorems of differentiable maps based on second order conditions. Using the homotopy continuation method, we demonstrate that, for a $C^2$ differentiable map from a Hilbert space to a finite-dimensional Euclidean space, when its second-order differential has uniform upper and lower bounds, it has a global path-lifting property

Yujie Wu

We explore the notion of m-intermediate Ricci curvature assumption introduced by Brendle-Hirsch-Johne further. If a manifold has non-negative m-intermediate Ricci curvature and stable weighted slicing of order m-1, then the last slice has almost non-negative Ricci curvature in the spectral sense. We prove comparison theorems on the diameter and in-radius bou

Rohit Gupta, Anirban Roy, Claire Christensen, Sujeong Kim

The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational content for young learners. This paper presents an approach for detecting educational content in online videos. We focus on two widely used educational content classes: literacy and ma

Jiahao Liu, Bonan Ruan, Xianglin Yang, Zhiwei Lin

LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and malicious instructions, that compromise their execution flow and can lead to serious consequences like data breaches and financial loss. Existing studies typically attempt to miti

Marco Pintore, Giorgio Piras, Angelo Sotgiu, Maura Pintor

To address the extremely concerning problem of software vulnerability, system security is often entrusted to Machine Learning (ML) algorithms. Despite their now established detection capabilities, such models are limited by design to flagging the entire input source code function as vulnerable, rather than precisely localizing the concerned code lines. Howev

Benoit Kaczmarczuk, Yannick Bidel, Alexandre Bresson, Nassim Zahzam

We study two hybridization algorithms used for the combination of a quantum inertial sensor based on atom interferometry with a classical inertial sensor for onboard acceleration measurements. The first is based on the direct extraction of the interferometer phase, and was previously used in seaborne and airborne gravity measurement campaigns. The second is

Kaustuv Chaudhuri, Daniele Nantes-Sobrinho

These are the contributed papers presented at the 20th International Workshop on Logical Frameworks and Meta-Languages: Theory and Practice (LFMTP 2025), at Birmingham, UK on 19 July as a satellite event of the FSCD conference. The program committee for this edition of LFMTP was chaired by Kaustuv Chaudhuri and Daniele Nantes-Sobrinho. More information about

Georgios Smpokos, Dionysis Xenakis, Marios Kountouris, Nikolaos Pappas

This study investigates a cognitive shared access network with energy harvesting capabilities operating under Age of Information (AoI) constraints for the primary user. Secondary transmitters are spatially distributed according to a homogeneous Poisson Point Process (PPP), while the primary user is located at a fixed position. The primary transmitter handles

Gregory Ashton

The LIGO, Virgo, and KAGRA (LVK) gravitational-wave observatories have opened new scientific research in astrophysics, fundamental physics, and cosmology. The collaborations that build and operate these observatories release the interferometric strain data as well as a catalogue of observed signals with accompanying Bayesian posterior distributions. These po

Johannes Moll, Markus Graf, Tristan Lemke, Nicolas Lenhart

Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-stakes clinical use. Existing evaluations rarely catch this misalignment, prioritizing answer accuracy or adherence to formats. We present a clinically grounded framework for chest

Markus Klein, Enrico Reiss, Elke Rosenberger

We prove a sharp Weyl estimate for the number of eigenvalues belonging to a fixed interval of energy of a self-adjoint difference operator acting on $\ell^2(\epsilon\mathbb{Z}^d)$ if the associated symplectic volume of phase space in ${\mathbb R}^d \times {\mathbb T}^d$ accessible for the Hamiltonian flow of the principal symbol is finite. Here $\epsilon$ is

João Paulo Cardoso de Lima, Marc Dietrich, Jeronimo Castrillon, Asif Ali Khan

Structured sparsity enables deploying large language models (LLMs) on resource-constrained systems. Approaches like dense-to-sparse fine-tuning are particularly compelling, achieving remarkable structured sparsity by reducing the model size by over 6.7x, while still maintaining acceptable accuracy. Despite this reduction, LLM inference, especially the decode

Gang Chen, Zihan Yang, Peng Sun, Chenglong Wang

Biomass-derived hard carbon has become the most promising anode material for sodium-ion batteries (SIBs) due to its high capacity and excellent cycling stability. However, the effects of synthesis parameters and structural features on hard carbon's (HC) electrochemical performance are still unclear, requiring time-consuming and resource-intensive experimenta

Zhi Qi

Let $\phi$ be a fixed Hecke--Maass form for $\mathrm{SL}_3 (\mathbb{Z})$ and $u_j $ traverse an orthonormal basis of Hecke--Maass forms for $\mathrm{SL}_2 (\mathbb{Z}) $. Let $1/4+t_j^2$ be the Laplace eigenvalue of $u_j $. In this paper, we prove the mean Lindel\"of hypothesis for the second moment of $ L (1/2+it_j, \phi \times u_j) $ on $ T < t_j \leqslant

Shengming Yuan, Xinyu Lyu, Shuailong Wang, Beitao Chen

Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity, as different tasks require varying degrees of associative reasoning. However, existing methods lack the flexibility to modulate this reasoning strength, limiting MLLMs' adaptability across factual and creative scenarios. To bridge this gap, we propose equ

Yangyang Wen, Paul Townend, Per-Olov Östberg, Abel Souza

As microservice-based systems scale across the cloud-edge continuum, traditional centralized scheduling mechanisms increasingly struggle with latency, coordination overhead, and fault tolerance. This paper presents a new architectural direction: leveraging service mesh sidecar proxies as decentralized, in-situ schedulers to enable scalable, low-latency coord

Xinhui Chen, Zuchao Li, Mengqi Gao, Yufeng Zhang

Deciphering the function of unseen protein sequences is a fundamental challenge with broad scientific impact, yet most existing methods depend on task-specific adapters or large-scale supervised fine-tuning. We introduce the "Protein-as-Second-Language" framework, which reformulates amino-acid sequences as sentences in a novel symbolic language that large la

Stephan Wulfinghoff

The computational homogenization of elastoplastic polycrystals is a challenging task due to the huge number of grains required, their complicated interactions and due to the complexity of crystal plasticity models per se. Despite a few successes of reduced order models, mean field and simplified homogenization approaches often remain the preferred choice. In

Yaman Yu, Mohi, Aishi Debroy, Xin Cao

AI companions are increasingly popular among teenagers, yet current platforms lack safeguards to address developmental risks and harmful normalization. Despite growing concerns, little is known about how parents and developmental psychology experts assess these interactions or what protections they consider necessary. We conducted 26 semi structured intervie

Zhengyu Chen, Jinluan Yang, Teng Xiao, Ruochen Zhou

Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and tool utilization. However, the generalization of tool-augmented reinforcement learning (RL) across diverse domains remains a significant challenge. Standard paradigms often treat tool usage as a linear or isolated event, which becomes brittle when trans

Ali Alhejab, Tomas Zelezny, Lamya Alkanhal, Ivan Gruber

This paper explores the application of T5 models for Saudi Sign Language (SSL) translation using a novel dataset. The SSL dataset includes three challenging testing protocols, enabling comprehensive evaluation across different scenarios. Additionally, it captures unique SSL characteristics, such as face coverings, which pose challenges for sign recognition a

Ole-Johan Skrede, Manohar Pradhan, Maria Xepapadakis Isaksen, Tarjei Sveinsgjerd Hveem

Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological images and examine its performance in different cancer types. The model was developed using over 20 000 whole-slide images from over 4 000 patients with colorectal, endometrial, lung,

Tamme Emunds, Paul Brunzema, Sebastian Trimpe, Nils Nießen

The efficiency of railway infrastructure is significantly influenced by the mix of trains that utilize it, as different service types have competing operational requirements. While freight services might require extended service times, passenger services demand more predictable schedules. Traditional methods for addressing long-term traffic assignment proble

Shilei Ji, Jianping Yang, Li Gao, Xing'ao Li

Orbitronics, which leverages the angular momentum of atomic orbitals for information transmission, provides a novel strategy to overcome the limitations of electronic devices. Unlike electron spin, orbital angular momentum (OAM) is strongly influenced by crystal field effects and band topology, making its orientation difficult to manipulate with external fie

David Georg Reichelt, Shinhyung Yang, Wilhelm Hasselbring

The observability framework Kieker provides a range of analysis capabilities, but it is currently only able to instrument a smaller selection of languages and technologies, including Java, C, Fortran, and Python. The OpenTelemetry standard aims for providing reference implementations for most programming languages, including C# and JavaScript, that are curre

Bryan Chen Zhengyu Tan, Zheng Weihua, Zhengyuan Liu, Nancy F. Chen

As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static recall or isolated visual grounding, leaving unanswered whether VLMs possess robust and transferable cultural understanding. We introduce BLEnD-Vis, a multimodal, multicultural be

Yesung Cho, Sungmin Lee, Geongyu Lee, Minkyung Lee

Recent studies in pathology foundation models have shown that scaling training data, diversifying cancer types, and increasing model size consistently improve their performance. However, giga-scale foundation models, which are trained on hundreds of thousands of slides covering tens of cancer types and contain billions of parameters, pose significant challen