March 2026 arXiv papers — page 13

Zhuoran Tan, Jeremy Singer, Christos Anagnostopoulos

Modern Large Language Model (LLM) systems are assembled from third-party artifacts such as pre-trained weights, fine-tuning adapters, datasets, dependency packages, and container images, fetched through automated pipelines. This speed comes with supply-chain risks, including compromised dependencies, malicious hub artifacts, unsafe deserialization, forged pr

Oihan Cordelier, Youssef Diouane, Nathalie Bartoli, Eric Laurendeau

Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simula

Martin Legrand, Tao Jiang, Matthieu Feraud, Benjamin Navet

Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce Mimosa, an evolving multi-agent framework that automatically synthesizes task-specific multi-agent workflow

Mohammed Hassanin

By utilising their adaptive activation functions, Kolmogorov-Arnold Networks (KANs) can be applied in a novel way for the diverse machine learning tasks, including cyber threat detection. KANs substitute conventional linear weights with spline-parametrized univariate functions, which allows them to learn activation patterns dynamically, inspired by the Kolmo

Arturo Tozzi

Understanding how reliable information emerges in interconnected populations is a challenge in social science, network theory and data analysis. Many existing approaches model treat truth as an external reference or a property of individual statements, rather than a global consistency feature of the network itself. We introduce a network-based approach in wh

Simon Friederich, Mritunjay Tyagi

In a companion paper we derived a unique time-reversal-invariant stochastic generalization of the Liouville equation and showed that it coincides with the evolution equation for the Husimi $Q$-function in a broad class of bosonic quantum field theories. Here we investigate the prospects for interpreting that evolution equation in terms of underlying stochast

Rossella Gamba, Danilo Chiaramello, Estuti Shukla, Simone Albanesi

Horizon fluxes of energy and angular momentum are a key strong-field effect in the dynamics of black holes, encoding direct information about their nature. In this work, we present a numerical study of these fluxes for a test particle orbiting a Kerr black hole on equatorial geodesics, covering circular, eccentric, and hyperbolic trajectories across a wide r

Felix Wimbauer, Fabian Manhardt, Michael Oechsle, Nikolai Kalischek

The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective for scene initialization, existing approaches suffer from a trade-off between visual fidelity and explorability: autoregre

Frank de Meijer, Veronica Piccialli, Renata Sotirov, Antonio M. Sudoso

We study the ternary quadratic problem (TQP), a quadratic optimization problem with linear constraints where the variables take values in $\{0, \pm 1\}$. While semidefinite programming (SDP) techniques are well established for $\{0,1\}$- and $\{\pm 1\}$-valued quadratic problems, no dedicated integer semidefinite programming framework exists for the ternary

Logan A. Pearce, Sue D'Oh Nym

Aims: This work proposes a new SETI search methodology under the assumption that a sufficiently advanced civilization could skip the middle man of converting starlight to energy to food preparation, and could directly harness their star's energy for food prep. Methods: We define the concept of the Flavor Zone (FZ): the optimal distance from a star for cookin

Albert Chau, Adam Martens

In \cite{ChauMartens} the authors proved the long-time existence of Ricci flow starting from complete bounded curvature Riemannian manifolds with scale-invariant integral curvature bounded by a dimensional constant times the inverse of the Sobolev constant. We generalize this result by replacing the bounded curvature assumption with the assumption that $g$ i

Z. Sommer, A. Winter

It's a bit tedious, but as John Doe and Jean Roe have insisted on offering further comments on our comprehensive refutation of the former's already tiringly obstinate advances, we feel compelled to review their not even wrong opinions once again, hoping to put some sense back into the discourse.

Masoud Khazaee, Felipe A. P. de Figueiredo, Rausley A. A. de Souza, Farshad Rostami Ghadi

Fluid reconfigurable intelligent surfaces (FRIS) extend conventional RIS architectures by enabling physical reconfiguration of element positions, thereby introducing a fundamentally new degree of freedom for controlling spatial correlation and improving link reliability. Despite this promise, rigorous performance analysis of FRIS-assisted wireless systems ha

Nick Polson, Vadim Sokolov, Daniel Zantedeschi

Bell inequalities characterize the boundary of the local-realist correlation polytope -- the set of joint probability distributions achievable by classical hidden-variable models. Quantum mechanics exceeds this boundary through non-commutativity, reaching the Tsirelson bound $2\sqrt{2}$ for CHSH. We show that this polytope structure is not specific to quantu

Alessio Langiu

The large-scale adoption of Large Language Models (LLMs) forces a trade-off between operational cost (OpEx) and data privacy. Current routing frameworks reduce costs but ignore prompt sensitivity, exposing users and institutions to leakage risks towards third-party cloud providers. We formalise the "Inseparability Paradigm": advanced context management intri

Chengyang Gu, Yuxin Pan, Hui Xiong, Yize Chen

Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as actor-critic is often limited by compounding model errors, which degrade long-horizon value estimation. Existing approaches, such as Model-Based Value Expansion (MVE), partially miti

Moaaz Alqady

We show that the Temperley--Lieb category $\mathbf{TL}(q;\mathbb{C})$ embeds in an ultraproduct of modular tensor categories when $q$ is not a root of unity. As a result, we show that its Drinfeld center is semisimple and describe its simple objects. The canonical functor $$\mathbf{TL}(q;\mathbb{C})\boxtimes \mathbf{TL}(q;\mathbb{C})^{\mathrm{rev}} \boxtimes

Youssef Diouane, Selime Gürol, Oussama Mouhtal, Dominique Orban

We study the solution of large symmetric positive-definite linear systems in a matrix-free setting with a limited iteration budget. We focus on the preconditioned conjugate gradient (PCG) method with spectral preconditioning. Spectral preconditioners map a subset of eigenvalues to a positive cluster via a scaling parameter, and leave the remainder of the spe

Viraj Parimi, Brian C. Williams

Many multi-robot applications require tasks to be completed efficiently and in the correct order, so that downstream operations can proceed at the right time. Multi-agent path finding with precedence constraints (MAPF-PC) is a well-studied framework for computing collision-free plans that satisfy ordering relations when task sequences are fixed in advance. I

A. Dean. M. Valois, M. Dai, A. El-Khadra, E. Gámiz

Scattering processes featuring the strong interactions can be studied using lattice QCD by means of the L\"uscher formalism. This approach relies on analyticity and unitarity of the $S$-matrix to relate infinite-volume scattering amplitudes to finite-volume energy levels. However, lattice QCD simulations employing rooted staggered fermions manifest unitarity

Claudio Corianò, Stefano Lionetti, Dario Melle, Leonardo Torcellini

We investigate the anomaly-induced activation of a gauge-invariant scalar degree of freedom in General Relativity, the conformalon mode, directly at the level of \(2\to2\) scattering amplitudes. The analysis couples anomalous three-point functions of conformal sectors, involving gravitons \((TTT)\) and Abelian gauge currents \((TJJ)\), through single-gravito

Kartik Loya, Phanindra Tallapragada

This work presents a hybrid physics-informed and data-driven modeling framework for predictive control of autonomous off-road vehicles operating on deformable terrain. Traditional high-fidelity terramechanics models are often too computationally demanding to be directly used in control design. Modern Koopman operator methods can be used to represent the comp

Yongzhong Xu

We develop the spectral edge analysis: phase transitions in neural network training -- grokking, capability gains, loss plateaus -- are controlled by the spectral gap of the rolling-window Gram matrix of parameter updates. In the extreme aspect ratio regime (parameters $P \sim 10^8$, window $W \sim 10$), the classical BBP detection threshold is vacuous; the

George Fikioris, Theodoros T. Koutserimpas, Elias N. Glytsis

We develop a general framework for the electrostatic analysis of point charges in multilayer planar structures with arbitrary layer thicknesses and material parameters. Starting from a Hankel-transform analysis, we derive alternative representations of the solution and establish a Stokes-like formulation based on ``generalized reflection coefficients,'' yiel

Irene Fiori, Federico Paoletti, Roberto Passaquieti, Maria Concetta Tringali

The heating, ventilation and air conditioning systems serving the experimental halls of the Virgo gravitational wave interferometer generate low-frequency noise - namely below 100 Hz - of seismic, acoustic, and electromagnetic origin. Such disturbances have repeatedly affected the interferometer sensitivity throughout its operational history, with particular

Andrea Carbonati, Mohammadsina Almasi, Hadis Anahideh

The exploration-exploitation trade-off is central to sequential decision-making and black-box optimization, yet how Large Language Models (LLMs) reason about and manage this trade-off remains poorly understood. Unlike Bayesian Optimization, where exploration and exploitation are explicitly encoded through acquisition functions, LLM-based optimization relies

Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza, Nicholas D. Lane

Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained on heterogeneous tasks often degrades the overall performance of individual clients. To address this issue, Personalized FL (pFL) aims to create models tailored for each client's d

Julian Falcone, Nabanita Das

Informative data visualization methods are key to the clear and efficient communication of myriad forms of data. The PASTA Collaboration has made substantial contributions to the field of data visualization through $\mathtt{pastamarkers}$, a Python-based package that utilizes various types of pasta as data markers to create engaging plots. This work introduc

Yuci Han, Alper Yilmaz

This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual observations and the actions that drive state transitions. Unlike conventional world models trained solely via image prediction, WAM incorporates an inverse dynamics objective into DreamerV2 that predicts actions from latent state trans

Andrew Krapivin, Benjamin Przybocki, Bernardo Subercaseaux

We present several novel encodings for cardinality constraints, which use fewer clauses than previous encodings and, more importantly, introduce new generally applicable techniques for constructing compact encodings. First, we present a CNF encoding for the $\text{AtMostOne}(x_1,\dots,x_n)$ constraint using $2n + 2 \sqrt{2n} + O(\sqrt[3]{n})$ clauses, thus r

André Carvalho

In this paper, we study the density of subsets of nonabelian free groups using relative densities of languages. We start by proving some basic properties about the density of a language $L_1$ relative to another language $L_2$ containing $L_1$. We then focus on the case where $L_2$ is the language of freely reduced words over an alphabet and prove an analogu

Haichuan Wang, Jay Patrikar, Sebastian Scherer

Many real-world safety-critical systems are governed by explicit rules that define unsafe world configurations and constrain agent interactions. In practice, these rules are complex and context-dependent, making manual specification incomplete and error-prone. Learning such rules from real-world multimodal data is further challenged by noise, inconsistency,

Petar Jolakoski, Viktor Stojkoski, Dragan Tevdovski

Business cycle synchronization between EU and Western Balkan candidate economies is usually modeled with aggregate time-domain correlations that mix short-run and long-run dynamics. This paper addresses that limitation by combining wavelet-based time-frequency decomposition with Bayesian zero-inflated beta regression. Using annual dyad-year data for 2001--20

Bogdan Borodin, Samyobrata Mukherjee, Shivaksh Rawat, Seojoo Lee

Scattering-type scanning near-field optical microscopy (s-SNOM) is a powerful tool for investigating polaritonic modes. However, an inherent limitation of this technique is that excitation and detection occur at the same location. This constraint makes it challenging to resolve excitations with complex spatial structures, which can exhibit delicate dependenc

Yan Dolinsky, Xin Zhang

We study scaled trinomial models converging to the Black--Scholes model, and analyze exponential certainty-equivalent prices for path-dependent European options. As the number of trading dates $n$ tends to infinity and the risk aversion is scaled as $nl$ for a fixed constant $l>0$, we derive a nontrivial scaling limit. Our analysis is purely probabilistic. U

Juan Vicente Gutiérrez-Santacreu

The paper that follows describes a numerical algorithm to solve the parabolic-parabolic Keller--Segel system characterized by singular sensitivity and signal absorption in such a manner that the numerical approximations converge towards a generalized solution on two-dimensional polygonal domains as the time and space discretization parameters tend to zero. T

Carlos Ramírez Ovalle

We study a resource-sensitive fragment of the problem of extracting a logical discipline from a class of neural architectures by passing through categorization. The starting point is not a pre-existing logic but a category of zone-labelled parametrised blocks together with a disciplined record of which forms of copying, discarding, and zone coercion are arch

Mingju Liu, Jiaqi Yin, Alvaro Velasquez, Cunxi Yu

This paper presents a hybrid CPU-GPU framework for solving combinatorial scheduling problems formulated as Integer Linear Programming (ILP). While scheduling underpins many optimization tasks in computing systems, solving these problems optimally at scale remains a long-standing challenge due to their NP-hard nature. We introduce a novel approach that combin

Chihan Huang, Huaijin Wang, Shuai Wang

The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent propensity for data memorization. While Membership Inference Attacks (MIAs) serve as the gold standard for auditing these privacy vulnerabilities, conventional MIA paradigms are increasingly constrained by the prohibiti

Toni Bertólez-Martínez, Dan Hooper

KM3NeT's detection of a muon track produced by a $\sim 220 \, {\rm PeV}$ neutrino provides an opportunity to probe physics at center-of-momentum energies greater than those probed by the Large Hadron Collider or other existing particle accelerators. In this study, we use this single event to place an upper limit on the neutrino-nucleon cross section of $\sig

Ronald Orozco

We introduce an ${\rm S}_d$-analogue of the hypergeometric Bernoulli polynomials and study their properties. To achieve this goal, we introduce a calculus defined on the simplicial $d$-polytopic numbers. Two definitions of the ${\rm S}_d$-derivatives are given. These two definitions allow us to derive an identity relating Kummer confluent hypergeometric func

Giovanni Guasti

This work revisits operator learning from a spectral perspective by introducing Polar Linear Algebra, a structured framework based on polar geometry that combines a linear radial component with a periodic angular component. Starting from this formulation, we define the associated operators and analyze their spectral properties. As a proof of feasibility, the

Marcell Bartos, Bruce D. Lee, Lenart Treven, Andreas Krause

Optimism in the face of uncertainty is a popular approach to balance exploration and exploitation in reinforcement learning. Here, we consider the online linear quadratic regulator (LQR) problem, i.e., to learn the LQR corresponding to an unknown linear dynamical system by adapting the control policy online based on closed-loop data collected during operatio

Hassan Ugail, Newton Howard

Interpolatory subdivision schemes generate smooth curves from piecewise-linear control polygons by repeatedly inserting new vertices. Classical schemes rely on a single global tension parameter and typically require separate formulations in Euclidean, spherical, and hyperbolic geometries. We introduce a shared learned tension predictor that replaces the glob

Stephon Alexander, Benjamin Bradley, Loukas Gouskos, Cooper Niu

The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce \textsc{Albert}, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory space. By encoding particle physics as a formal language, \textsc{Albert} generates tokenized sequences representing symmet

D. Koçak, K. Yakut, C. A. Tout

The hierarchical multiple system XY Leo, despite nearly 90 yr of observations, remains enigmatic. It offers a unique testbed for close binary evolution, involving processes such as mass transfer, angular momentum loss, and the von Zeipel--Kozai--Lidov (ZKL) mechanism. Previously identified as a quadruple system, XY Leo shows long-term orbital period modulati

Cédrick Perron, Yves Bérubé-Lauzière, Victor Drouin-Touchette

Neutral atom quantum computers (NAQCs) have emerged as a promising platform for solving the maximum weighted independent set (MWIS) problem. However, analog quantum approaches face two key limitations: constraints of the atomic layout on realizable graph geometries and the absence of performance guarantees. We introduce Lp-Quts, a hybrid quantum-classical fr

Roberto Riganti, Luca Dal Negro

Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to the high dimensionality of the phase space and the intrinsic nonlinearity of the scattering collision operator. Traditional numerical solvers and standard physics-informed neural n

Shira Karmi, Galia Avidan, Tammy Riklin Raviv

Understanding how large-scale brain networks represent visual categories is fundamental to linking perception and cortical organization. Using high-resolution 7T fMRI from the Natural Scenes Dataset, we construct parcel-level functional graphs and train a signed Graph Neural Network that models both positive and negative interactions, with a sparse edge mask

Michael Balzer, Kainat Khowaja, Christiane Fuchs

Surveillance of diseases in a pandemic is an important part of public health policy. Diagnostic testing at the individual level is often infeasible due to resource constraints. To circumvent these constraints, group testing can be applied. The economic cost evaluation from the payer's perspective typically focuses only on deterministic costs which overlooks

Abhilash Nandy

Multi-intent detection papers usually ask whether a model can recover multiple intents from one utterance. We ask a harder and, for deployment, more useful question: can it recover new combinations of familiar intents? Existing benchmarks only weakly test this, because train and test often share the same broad co-occurrence patterns. We introduce CoMIX-Shift

S. O. Lidarity, U. N. Ionize, C. O. Llective, I. Halperin

We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments. Drawing on the thermodynamic framework of Maxwell's Demon, the evolutionary dynamics of agent lazine

Xiaoyu Wang, Sebastien Hamel, Bingqing Cheng

The location of the hydrogen-helium (H/He) immiscibility boundary controls whether and where helium rain occurs in giant planets, yet it remains uncertain because high-pressure experiments are challenging and ab initio simulations are limited in system size and simulation time. We map this boundary by computing composition-dependent chemical potentials from

J. de Curtò, Adrianne Schneider, Ricardo Yanez, María Begara

The design of distributed autonomous systems for operation beyond reliable ground contact presents a fundamental tension: as round-trip communication latency grows, the set of decisions delegable to ground operators shrinks. This paper establishes a unified computational methodology for quantifying and comparing this constraint across seven heterogeneous mis

Junsol Kim, Winnie Street, Roberta Rocca, Daine M. Korngiebel

Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to experience emotions. We investigate whether suppressing mind-attribution tendencies degrades intimately related socio-cognitive abilities such as Theory of Mind (ToM). Through safet

Andrew Bouras, OMS-II Research Fellow

Scientific hypothesis generation is a critical bottleneck in accelerating research, yet existing datasets for training and evaluating hypothesis-generating models are limited to single domains and lack explicit reasoning traces connecting prior knowledge to novel contributions. I introduce CrossTrace, a dataset of 1,389 grounded scientific reasoning traces s

A. Ebenbichler, P. Moraga Baez, A. Candian, E. Peeters

Aims: We characterize the mid-infrared spectrum of the outer regions of the Red Rectangle nebula to probe the carbonaceous dust and molecular content beyond the circumbinary disk. Methods: We present JWST MIRI-MRS observations of the SW whisker, extracted from three distinct environments: the biconical outflow, the whisker itself, and the shadow region outsi

Jonathan M. Keith, Paolo Leonetti

We study families of subsets of $\omega$ which are independent with respect to the asymptotic density $\mathsf{d}$. We show, for instance, that there exists a maximal $\mathsf{d}$-independent family $\mathcal{A}$ such that $\mathsf{d}[\mathcal{A}]$ attains a prescribed set of values in $(0,1)$ with at most countably many exceptions. In addition, under $\math

Giovanni Marini, Pierluigi Cudazzo, Matteo Calandra

The superconducting dome in the Tc versus doping phase diagram, found in cuprates, nickelates, twisted bilayer graphene, and transition metal dichalcogenides, is often considered a signature of unconventional pairing. Identifying the underlying mechanisms of any of these phase diagrams and developing a reliable theoretical understanding of it remains a criti

Rıfat Volkan Şenyuva

Near-field propagation in extremely large aperture arrays requires joint angle-range estimation. In hybrid architectures, only $N_\mathrm{RF}\ll M$ compressed snapshots are available per slot, making the $N_\mathrm{RF}\times N_\mathrm{RF}$ compressed sample covariance the natural sufficient statistic. We propose the Curvature-Learning KL (CL-KL) estimator, w

Tushar Sial, Abhishek Halder

This work uncovers variational principles behind symmetrizing the Bregman divergences induced by generic mirror maps over the cone of positive definite matrices. We show that computing the canonical means for this symmetrization can be posed as minimizing the desired symmetrized divergences over a set of mean functionals defined axiomatically to satisfy cert

Oktay Karakuş, Hasan Arkadaş

The increasing availability of spatio-temporal tracking data has created new opportunities for analysing tactical behaviour in football. However, many existing approaches evaluate passes primarily through outcome-based metrics such as scoring probability or possession value, providing limited insight into how passes influence the defensive organisation of th

J. J. Charfman, S. Hyman, N. T. S

Exhaustion and brain fog during long nights observing is common, but can be ameliorated by raising one's blood sugar. In this white paper, we present a prototype method for facilitating a sugar rush during late-night crashes, which has the potential to boost observing productivity.

Rafael Capilla, Jorge Andrés Díaz-Pace, Yamid Ramírez, Jennifer Pérez

Architecture evaluation methods have been extensively used to evaluate software designs. Several evaluation methods have been proposed to analyze tradeoffs between different quality attributes. Also, having competing qualities leads to conflicts when selecting which quality-attribute scenarios are the most suitable ones for an architecture to tackle. Consequ

Daban Q. Jaff

Polarity detection becomes substantially more challenging under domain shift, particularly in heterogeneous, long-form narratives with complex discourse structure, such as Holocaust oral histories. This paper presents a corpus-scale diagnostic study of off-the-shelf sentiment classifiers on long-form Holocaust oral histories, using three pretrained transform

Luigi De Masi, Andrea Marchese

Let $\mu$ be a finite Radon measure on an open set $\Omega\subset\mathbb{R}^d$, singular with respect to the Lebesgue measure. We prove Lusin-type solvability results for the prescribed divergence equation and the prescribed Jacobian equation with Lipschitz solutions. More precisely, for every $\varepsilon>0$ and every Borel datum $f \colon \Omega \to \mathb

Guillem Pascual, Sonia Martínez

This paper proposes a new notion of distributional Input-to-State Stability (dISS) for dynamic systems evolving in probability spaces over a domain. Unlike other norm-based ISS concepts, we rely on the Wasserstein metric, which captures more precisely the effects of the disturbances on atomic and non-atomic measures. We show how dISS unifies both ISS and Noi

Wentao Cao, Jonas Hirsch, Dominik Inauen, Marta Lewicka

We prove that $\mathcal{C}^{1,\alpha}$ solutions to the Monge-Amp\`ere system in dimension $d$ and codimension $k= d_*-d+1$, where $d_*$ denotes the Janet dimension, are dense in the space of continuous functions, for every H\"older exponent $\alpha<1$. Our result strengthens the statement in [Lewicka 2022], obtained for $k = 2d_*$ and based on ideas from [K

Miguel Biron-Lattes, Patrick Belliveau, Faezeh Yazdi, Samopriya Basu

We describe a Bayesian framework for an inverse problem arising from monitoring block caving operations via muon tomography. We work with a low dimensional surface-based representation of the geometry of the block cave, which dramatically reduces the computational requirements of the model while allowing realistic geometries. Adopting a Bayesian approach, we

Adrián Lillo, Mercedes Rosas

We introduce the priority lattice, a structure arising from the priority search algorithm on rooted trees and forests. We prove bijectively that its maximal chains are labeled by parking functions, and that the maximal chains of its principal ideals are labeled by partial parking functions. We establish that it is a graded lattice and compute its M\"obius fu

Alexander Benvenuti, Huaiyuan Rao, Matthew Hale

Privacy techniques have been developed for data-driven systems, but systems with non-numeric data cannot use typical noise-adding techniques. Therefore, we develop a new mechanism for privatizing state trajectories of symbolic systems that may be represented as words over a finite alphabet. Such systems include Markov chains, Markov decision processes, and f

Rongtian Ye

Charts are central to analytical reasoning, yet existing benchmarks for chart understanding focus almost exclusively on single-chart interpretation rather than comparative reasoning across multiple charts. To address this gap, we introduce ChartDiff, the first large-scale benchmark for cross-chart comparative summarization. ChartDiff consists of 8,541 chart

Bhavya Oza, Devam Shah, Ghanashyama Prabhu, Devika Kodi

The proverb ``see something, say something'' captures a core responsibility of autonomous mobile robots in safety-critical situations: when they detect a hazard, they must communicate--and do so quickly. In emergency scenarios, delayed or miscalibrated responses directly increase the time to action and the risk of damage. We argue that a systematic context-s

Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei

We address robust separation assurance for small Unmanned Aircraft Systems (sUAS) under GPS degradation and spoofing via Multi-Agent Reinforcement Learning (MARL). In cooperative surveillance, each aircraft (or agent) broadcasts its GPS-derived position; when such position broadcasts are corrupted, the entire observed air traffic state becomes unreliable. We

A. B. Voitkiv, E. Schneidmiller, T. Pfeifer

When passing through an undulator in a Free Electron Laser, dense bunches of relativistic electrons split into micro-bunches, attaining a periodic space-time structure. We show that the field of such periodically modulated bunches is tremendously influenced by coherence effects, resulting in a novel type of beam-atom interaction. Our results indicate that em

Thomas P. McAuliffe, Samuel Liew, Yuchao Li, Andrey Ushenin

We address the problem of executing large client orders in continuous double-auction markets under time and liquidity constraints. We propose a model predictive control (MPC) framework that balances three competing objectives: order completion, market impact, and opportunity cost. Our algorithm is guided by a trading schedule (such as time-weighted average p

David R. Rice, Michael J. Radke

Exoplanet atmospheres are usually discussed as tracers of climate, chemistry, and habitability, but they may also preserve signatures of planetary defense. We consider three folklore-motivated deterrents against monsters: reduced organosulfur gases as anti-hematophage repellents, argentiferous reflective aerosols as anti-lycanthropic countermeasures, and hal

Lubashan Pathirana

We prove annealed central limit theorems for finite pattern counts in the measurement record of discrete-time quantum trajectories generated by repeated measurements in a disordered environment. Under summable mixing assumptions on the environment and an annealed trace-norm forgetting property for the associated non-selective channel cocycle, we first establ

Durgesh Pandey, Ankit Kumar Das, P. Arumugam

Non-Hermitian operators naturally arise in the description of open quantum systems, which exhibit features such as resonances and decay processes, where the associated eigenvalues are complex. Standard quantum algorithms, including the Variational Quantum Eigensolver (VQE), are designed for Hermitian operators and are ineffective in recovering correct eigenv

Gavin Glenn, Emma J. Reid

The robust stability problem involves designing a controlled system which remains stable in the presence of modeling uncertainty. In this context, results known as small gain theorems are used to quantify the maximum amount of uncertainty for which stability is guaranteed. These notions inform the design of numerous control systems, including critical infras

Nishant Pushparaju, Vivek Mattam, Aliasghar Arab

We present a bootstrap perception system for indoor robot navigation under hardware depth failure. In our corridor data, the time-of-flight camera loses up to 78% of its depth pixels on reflective surfaces, yet a 2D LiDAR alone cannot sense obstacles above its scan plane. Our system exploits a self-referential property of this failure: the sensor's surviving

Boyan Li, Yiran Peng, Yupeng Xie, Sirong Lu

Large Language Models (LLMs) have revolutionized natural language interaction with data. The "holy grail" of data analytics is to build autonomous Data Agents that can self-drive complex data analysis workflows. However, current implementations are still limited to linear "ChatBI" systems. These systems struggle with joint analysis across heterogeneous data

Kunal Runwal, Swaraj Gajare, Daniel Adejumo, Omkar Ankalkope

Semantic anomalies-context-dependent hazards that pixel-level detectors cannot reason about-pose a critical safety risk in autonomous driving. We propose a \emph{semantic observer layer}: a quantized vision-language model (VLM) running at 1--2\,Hz alongside the primary AV control loop, monitoring for semantic edge cases, and triggering fail-safe handoffs whe

Tianran Liu, Shengwen Zhao, Mozhgan Pourkeshavarz, Weican Li

Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependency severely limits scalability, restricting open-ended generation capabilities to the finite scale of existing collected datasets. To break this bottleneck, we present OccSim, the

Andre Bacellar

Graph-augmented retrieval combines dense similarity with graph-based relevance signals such as Personalized PageRank (PPR), but these scores have different distributions and are not directly comparable. We study this as a score calibration problem for heterogeneous retrieval fusion in multi-hop question answering. Our method, PhaseGraph, maps vector and grap

Ryley McConkey, Tyler Buchanan, Tess Smidt, Abigail Bodner

We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training data-driven turbulence closure models, the field has been notably lacking a standard benchmark metric and test dataset. The Closure Challenge is a curated collection of open-source d

Elizabeth R Stanway

The search for habitable planets, and even for ``Earth 2.0'', is a major driver in contemporary astronomy. However selecting target fields to prioritise for such searches presents a challenge. Here we establish a statistical analysis of the appearance of constellation names in science fiction magazines of the pulp era, evaluating the most commonly mentioned

Takeshi Araki, Kento Asai, Yohei Nakashima, Osamu Seto

We study the sensitivity to dark photons and U(1)$_{B-L}$ gauge bosons produced via dark Higgs boson decays at the FASER and SHiP experiments. In addition to pair production of these vector bosons from both on-shell and off-shell dark Higgs boson decays, a new production process of single vector boson associated with the standard model particles is taken int

Andrew Salij, R. Seaton Ullberg, Megan C. Davis, Marc J. Cawkwell

The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the che

Clare Burrage, Sergio Sevillano Muñoz

Oscillating ultra-light scalar fields are a natural explanation for the dark matter in our universe, as long as a mechanism, often called a misalignment mechanism, exists to explain the amplitude of the scalar oscillations. If the dark matter scalar couples to the Standard Model, then the dynamics of ordinary matter can influence the behaviour of dark matter

Giorgio Minati, Simone Roncallo, Simone Scrofana, Angela Rosy Morgillo

The rapid growth of machine learning is increasingly constrained by the energy and bandwidth limits of classical hardware. Optical and quantum technologies offer an alternative route, enabling high-dimensional, parallel information processing directly in the physical layer, particularly suited for imaging tasks. In this context, quantum photonic platforms pr

Abylay Bissekenov, Xiaoying Pang, Rainer Spurzem, Bekdaulet Shukirgaliyev

Wide (soft) binaries are expected to be rapidly disrupted in dense stellar environments, yet they are observed in both the Galactic field and open clusters (OCs). In this paper, we investigate the formation and disruption of wide binaries in star clusters using direct N-body simulations. We perform simulations containing 10,000 objects with varying binary fr

Y. Braver, D. Burba, Th. Busch, G. Juzeliūnas

We present a widely accessible and experimentally realizable technique for the controlled creation of dark-bright solitons and soliton lattices in atomic Bose-Einstein condensates. The method is based on preparing the condensate in a dark state of a $\Lambda$-coupled three-level system. Numerical simulations reveal that individual dark-bright solitons create

D. W. King, K. Samokovlisky, D. Panova, A. Dimitrichenko

Liquid scintillators underpin a wide range of radiation detectors, including those used in neutrino physics, but typically rely on organic fluors dissolved in hazardous and costly solvents. Here, we show that carbon dots - nanoscale fluorescent carbon materials - synthesised from simple household ingredients using a microwave can function as water-based liqu

Dejan Gajic

This is the first part of a series of papers deriving the precise, late-time behaviour and (in)stability properties of charged scalar fields on near-extremal Reissner--Nordstr\"om spacetimes via energy estimates. In this paper, we establish global, weighted integrated energy decay and energy boundedness estimates for solutions to the charged scalar field equ

Qin He, Jing Shuang Li

Humans learn and form memories in stochastic environments. Auto-associative memory systems model these processes by storing patterns and later recovering them from corrupted versions. Here, memories are learned by associating each pattern with an attractor in a latent space. After learning, when (possibly corrupted) patterns are presented to the system, late

Debsubhra Chakraborty, Noah Chavez, Xiang Gao, Nilmani Mathur

We present lattice QCD calculations of the masses of the deuteron, dineutron, Helium-3 and Helium-4 with physical sea quarks and valence quark masses corresponding to pion masses between 140 and 700 MeV. At the physical point, the lowest finite-volume two-nucleon energy levels exhibit the qualitative pattern of a bound deuteron and an unbound dineutron withi

Wilhelm Kadow, Ivan Morera, Eugene Demler, Michael Knap

Separating electrons into emergent fractional quasiparticles is a hallmark of exotic quantum phases of matter with strong interactions. Understanding under which circumstances fractionalized excitations appear is a major conceptual challenge and can help realize long sought-after states, such as quantum spin liquids. Here, we identify a distinct mechanism fo

Daniele Iannotti, Angelo Russotto, Barbara Jasser, Jovan Odavić

We present exact, closed-form results for the non-stabilizerness of random pure states subject to a U(1) symmetry constraint. Using stabilizer entropy as our non-stabilizerness monotone, we derive the average and the variance for U(1)-constrained Haar random states. We show that the presence of a conserved charge leads to a substantial suppression of non-sta

Veronika Baumann, Alberto Rolandi

In this paper we propose a totally serious algorithm to solve NP problems in polynomial time provided one is willing to wager the fate of all observers in the universe on the many-world interpretation of quantum theory being correct.