March 2026 arXiv papers — page 127

Aaron David Fairbanks, Michael Shulman

We propose a definition of double categories whose composition of 1-cells is weak in both directions. Namely, a doubly weak double category is a double computad -- a structure with 2-cells of all possible double-categorical shapes -- equipped with all possible composition operations, coherently. We also characterize them using "implicit" double categ

Debarshi Majumder, Binod Sreenivasan

This study investigates, analytically and numerically, the role of an equatorially anti-symmetric lateral variation in heat flux at the outer boundary in polarity transitions in rapidly rotating dynamos. In an unstably stratified fluid, the frequencies of vertical and horizontal (lateral) buoyancy complement each other such that a polarity transition is indu

Connery Chen, Yihan Wang, Bing Zhang

Neutron star (NS) mergers, including both binary NS mergers and black hole-NS mergers, are multimessenger sources detectable in both gravitational wave (GW) and electromagnetic (EM) radiation. The expected EM emission signatures depend on the source's progenitor, merger remnant, and observer's line of sight (LoS). Widely discussed EM counterparts of

Vera Koponen

We consider continuous relational structures with finite domain $[n] := \{1, \ldots, n\}$ and a many valued logic, $CLA$, with values in the unit interval and which uses continuous connectives and continuous aggregation functions. $CLA$ subsumes first-order logic on ``conventional'' finite structures. To each relation symbol $R$ and identity constrai

S. Carin Gavin, Moumita Kar, Jianguo Wen, Anushka Dasgupta

Understanding the mechanism of single photon emission (SPE) in two-dimensional (2D) material is an unsolved problem important for quantum optical materials and the development of quantum information applications. In 2D transition metal dichalcogenides (TMDs) such as tungsten diselenide (WSe2), quantum emission has been broadly attributed to exciton localizat

Aleix Bou-Comas, Carlos Ramos Marimón, Jan T. Schneider, Stefano Carignano

We propose a novel experimental protocol to measure generalized temporal entropies in many-body quantum systems. Our approach involves using local operators as probes to characterize the out-of-equilibrium dynamics induced by a geometric double quench on a replicated system. Such protocol mimics the path-integral on the corresponding Riemann surface encoding

Stavros Garoufalidis, Shana Yunsheng Li

Recently, Kashaev and the first author constructed an $R$-matrix from a Nichols algebra with an automorphism, that leads, via the Reshetikhin--Turaev functor, to a multivariable polynomial invariant of knots. Applying this to a rank 2 Nichols algebra, results in a sequence $V_n$ of 2-variable knot polynomials with integer coefficients, the first polynomial b

Kevin Aguyar Brix, Chris Bruce, Adam Dor-On

We show that a normal coaction of a discrete group on an operator algebra extends to a normal coaction on the C*-envelope. This resolves an open problem attempted by several experts in the area, and provides a more direct proof of a prominent result of Sehnem. As an application, we resolve a question of X. Li, where we identify the C*-envelopes of the operat

Nikolas Cruz-Camacho, Carlos Conde-Ocazionez, Veronica Dexheimer, Jacquelyn Noronha-Hostler

The equation of state of matter at supranuclear densities governs the astrophysical observables of neutron stars. A realistic, though complex, description is provided by the Chiral-Mean-Field model, which depends on many microscopic nuclear-physics parameters. We present a Fisher-information-inspired analysis of the sensitivity of neutron-star observables to

Ryan Younger

Packet analysis tools conventionally present capture data through tabular packet lists, constraining the analyst to a sequential view that obscures the relational structure of network communication. This paper presents Galaxy Tracer, a browser-native packet capture exploration system in which the default interface is an interactive three-dimensional network

Fan Huang, Haewoon Kwak, Jisun An

Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across reasoning steps remains underexplored. We introduce \textit{moral reasoning trajectories}, sequences of ethical framework invocations across intermediate reasoning steps, and analyze their dynamics across six models and

Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar, Pranay Tankala

Recent work has highlighted the centrality of smooth calibration [Kakade and Foster, 2008] as a robust measure of calibration error. We generalize, unify, and extend previous results on smooth calibration, both as a robust calibration measure, and as a step towards omniprediction, which enables predictions with low regret for downstream decision makers seeki

Aleksei G. Sorokin, Pieterjan Robbe, Yen-Chun Liu, Simon Mak

Gaussian processes (GPs) are broadly used for the surrogate modeling of computer experiments with reliable uncertainty quantification. Our motivating application comes from the study of the quark-gluon plasma (QGP), an extreme state of nuclear matter that filled the universe shortly after the Big Bang. To reliably infer properties of the QGP, multiple surrog

Ewan Tempero, Paul Ralph

Most engineers use measurements to make decisions. However, measurements are rarely used for decisions about constructing software products. While many approaches to measuring attributes of software (``metrics'') have been developed, they are rarely used to answer useful questions such as ``Do I need to refactor this class?'' or ``Are these integration tests

Pedro Linck, Graeme Pleasance, Francesco Petruccione, Nadja K. Bernardes

Open quantum walks (OQWs) constitute a class of quantum walks whose dynamics are entirely driven by interactions with the environment. It is well known that OQWs provide a general framework for implementing dissipative quantum computation. In this work, we demonstrate the feasibility of running the previously proposed quantum distance-based classifier within

S. M. M. Rasouli

We consider a minimal fractional deformation of Newtonian gravity characterized by a single parameter $\alpha$. In the limit $\alpha \to 1$, the theory reduces to standard Newtonian gravity. Previous works showed that the $\Lambda$CDM cosmology consistently emerges from this framework. Using a single potential, the model reproduces the full sequence of cosmi

Zhaoxi Zhang, Ruolin Wu, Feiyang Ren, Sridevi Turaga

Public participation has become increasingly important in collaborative urban design; yet, existing processes often face challenges in achieving efficient and scalable citizen engagement. To address this gap, this study explores how large language models (LLMs) can support cooperation among community members in participatory design. We introduce CoDesignAI,

David Schunck, Lucia McCallum, Jamie McCallum, Tiege McCarthy

Interest in the topic of geodetic co-location in space and space ties has recently intensified within the geodetic community, particularly following the approval of the European Space Agency's (ESA) Genesis mission. From the perspective of Very Long Baseline Interferometry (VLBI), observations of Earth-orbiting satellites are not standard practice yet. To en

Alberto Gonzalez-Sanz, Marco Avella Medina

Two recent works, Avella-Medina and Gonz\'alez-Sanz (2026) and Passeggeri and Paindaveine (2026), studied the robustness of the optimal transport map through its breakdown point, i.e., the smallest fraction of contamination that can make the map take arbitrarily aberrant values. Their main finding is the following: let $P$ and $Q$ denote the target and refer

Henning Ulfarsson

We study the impartial game PAP (``permutations avoiding patterns''), in which players take turns choosing patterns to avoid. We define a set of length $k$ patterns, $B_k$, and show that it is the unique minimal monotone-forcing subset of $S_k$: every sufficiently long permutation that avoids $B_k$ is monotone, and every monotone-forcing subset of $S_k$ must

Joseph Adamo, Grace Gibbins, Anne Moore, Tim Eifler

We present neural networks to generate redshift-space galaxy power spectrum multipoles for multiple tracer and redshift bins simultaneously given a set of input cosmology and galaxy bias parameters. This emulator utilizes a combination of fully-connected layers and transformer architecture to accurately predict galaxy power spectrum multipoles $900$ times fa

Saisha Pradeep Shetty, Roger Eric Goldman, Vladimir Filkov

Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based framework that studies retrieval-augmented synthetic reports and confidence-based selective automation to reduce expert effort for labeling in RadGraph. We study RadGraph-style entity labeling (graph nodes) and leave relatio

Sijie Li, Biao Qian, Jungong Han

Network pruning is an effective technique for enabling lightweight Large Vision-Language Models (LVLMs), which primarily incorporates both weights and activations into the importance metric. However, existing efforts typically process calibration data from different modalities in a unified manner, overlooking modality-specific behaviors. This raises a critic

Erika N. Lima, Teldo A. S. Pereira, Elisangela S. Barboza, Dominike Pacine

Understanding and controlling interfacial electronic coupling in two-dimensional (2D) heterostructures is essential for designing functional materials for electronic, optoelectronic, and catalytic applications. Here, we investigate vertical heterobilayers constructed from two distinct XP3 monolayers (X = As, Ge, Sb, Bi, Sn, Al, Ga, and Pb) using first-princi

Yue-Yan Dong, Ji-Yu Song, Jing-Fei Zhang, Xin Zhang

Intermediate-mass black holes (IMBHs) bridge the gap between stellar-mass and supermassive black holes, but remain challenging to detect electromagnetically. Gravitational-wave observations provide a direct means of detecting IMBHs and their mergers. We simulate the gravitational-wave signals of IMBH binaries under different population models and assess thei

David Nordfors

Occupations form and evolve faster than classification systems can track. We propose that a genuine occupation is a self-reinforcing structure (a bipartite co-attractor) in which a shared professional vocabulary makes practitioners cohesive as a group, and the cohesive group sustains the vocabulary. This co-attractor concept enables a zero-assumption method

Zehua Cheng, Wei Dai, Wenhu Zhang, Thomas Lukasiewicz

A user pointing their phone at a supermarket shelf and asking "Which soda has the least sugar?" poses a difficult challenge for current visual Al assistants. Such queries require not only object recognition, but explicit set-based reasoning such as filtering, comparison, and aggregation. Standard endto-end MLLMs often fail at these tasks because they lack an

Jack Hirschman, Randy Lemons, Hao Zhang, Razib Obaid

Leveraging the full scientific capabilities of next-generation high-repetition-rate free-electron lasers requires programmable control over electron-beam properties at their source. The photoinjector drive laser defines the electron beam's initial six-dimensional phase-space distribution, yet has historically been limited to Gaussian or static flat-top profi

Devansh Zurale, Iris Lorente, Michael Lester, Alex Mitchell

In this work, we present a deep learning-based automatic multitrack music mixing system catered towards live performances. In a live performance, channels are often corrupted with acoustic bleeds of co-located instruments. Moreover, audio-visual synchronization is of critical importance thus putting a tight constraint on the audio latency. In this work we pr

Oliver Zahn, Simran Chana

Retrieval-augmented generation stores all content indiscriminately, degrading accuracy as noise accumulates. Parametric approaches compress knowledge into weights, precluding selective updates. Neither mirrors biological memory, which gates encoding based on salience and archives rather than deletes superseded information. We introduce write-time gating that

Solomon Goldgraber Casspi, Daniel Zelazo

This letter presents a geometric input-output analysis of distance-based formation control, focusing on the phenomenon of steady-state signal blocking between actuator and sensor pairs. We characterize steady-state multivariable transmission zeros, where fully excited rigid-body and deformational modes destructively interfere at the measured output. By analy

Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao, Giacomo Falcucci

We present a physics-constrained, data-driven subgrid-scale (SGS) stress closure for large-eddy simulation (LES) in the lattice Boltzmann method (LBM). Trained on filtered-downsampled (FD) data from LBM direct numerical simulation (DNS) of forced homogeneous isotropic turbulence (FHIT) spanning multiple filter widths, a compact neural network maps nine macro

Mohammad Dastgheib, Fatemeh Pourmahdian

Extended Reality (XR) interfaces impose both ergonomic and cognitive demands, yet current systems often force a binary choice between hand-based input, which can produce fatigue, and gaze-based input, which is vulnerable to the Midas Touch problem and precision limitations. We introduce the xr-adaptive-modality-2025 platform, a web-based open-source framewor

Xiaolong Han, Ferrante Neri, Zijian Jiang, Fang Wu

Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without run

Shuge Zeng, Hsiang-nan Li, Fanrong Xu

We extend our dispersive analyses on meson static properties to those of light baryons. The formalism treats the dispersion relation, which a baryonic correlation function obeys, as an inverse problem, solve for the involved spectral density with available operator-product-expansion (OPE) inputs directly, and extract baryon static properties from the spectra

Jing Yan, Kang You, Zhezhi He, Yaoyu Zhang

Achieving deterministic computation results in asynchronous neuromorphic systems remains a fundamental challenge due to the inherent temporal stochasticity of continuous-time hardware. To address this, we develop a unified continuous-time framework for spiking neural networks (SNNs) that couples the Law of Charge Conservation with minimal neuron-level constr

Chao Wu

We study the Cauchy problem for generalized electron magnetohydrodynamics (EMHD). We establish the local existence and uniqueness of solutions in critical Sobolev spaces, as well as global existence and uniqueness for small initial data. In addition, we prove an instantaneous smoothing effect for the corresponding solutions. Finally, we derive time decay rat

Maarten Golterman, Yigal Shamir

The symmetric mass generation (SMG) approach to the construction of lattice chiral gauge theories attempts to use interactions to render mirror fermions massive without symmetry breaking, to obtain the desired chiral massless spectrum (before the gauge field is turned on). If the zeros that often replace the mirror poles of fermion two-point functions in an

Pushpendra Gupta, Peter Meisenheimer, Xinyan Li, Sajid Husain

BiFeO3 is a model multiferroic in which the ferroelectric polarization is coupled to ferroelastic lattice distortions, yet deterministic control of its domain structure remains limited by high switching fields and competing polarization variants. Here, we identify a mechanically assisted polarization switching pathway in epitaxial BiFeO3 thin films that fund

Guido Cavraro, Andrey Bernstein, Emiliano Dall'Anese

This paper focuses on price-based residential demand response implemented through dynamic adjustments of electricity prices during DR events. It extends existing DR models to a stochastic framework in which customer response is represented by price-dependent random variables, leveraging models and tools from the theory of stochastic optimization with decisio

Ujjwal Shekhar, Einar Iversen, Florin A. Radu, Inga Berre

Accurate characterization of microseismic events during fluid injection in sedimentary formations is essential to mitigate environmental risks. The source mechanism for microseismic events related to a slip on a fault plane is given by a double-couple. Waveform inversion has emerged as a promising technique for estimating the moment tensor and the position v

Jingxiang Chen, Minseok Kim, Seong-Gyun Leem, Yin Huang

Speech large language models (LLMs) observe paralinguistic cues such as prosody, emotion, and non-verbal sounds--crucial for intent understanding. However, leveraging these cues faces challenges: limited training data, annotation difficulty, and models exploiting lexical shortcuts over paralinguistic signals. We propose multi-task reinforcement learning (RL)

Callen MacPhee, Yiming Zhou, Koichiro Kishima, Bahram Jalali

Deep learning has achieved remarkable success in medical image analysis, yet its performance remains highly sensitive to the heterogeneity of clinical data. Differences in imaging hardware, staining protocols, and acquisition conditions produce substantial domain shifts that degrade model generalization across institutions. Here we present a physics-based da

Cosimo Spera

Customer service automation is undergoing a structural transformation. The dominant paradigm is shifting from scripted chatbots and single-agent responders toward networks of specialised AI agents that compose capabilities dynamically across billing, service provision, payments, and fulfilment. This shift introduces a safety gap that no current platform has

Sunday David Ubur, Eugenia Ha Rim Rho, Denis Gracanin

Real-time captioning is vital for Deaf and Hard of Hearing (DHH) and neurodivergent learners (e.g., those with ADHD), yet it often omits emotional and non-verbal cues essential for comprehension. This omission is particularly consequential in STEM education, where cognitively demanding material can exacerbate the challenges faced by caption users across dive

Hong Zhang, Barry Smith, Satish Balay, Le Chen

While LLMs have accelerated scientific code generation, comprehensively evaluating generated code remains challenging. Many benchmarks emphasize functional correctness or task completion, which is insufficient for code built on production HPC libraries, where solver selection, API conventions, memory management, parallel awareness, and performance also matte

Xiaoyan Cong, Zekun Li, Zhiyang Dou, Hongyu Li

Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse cross-modal and

Anders G Frøseth

A proportional wealth tax - a levy on the stock of wealth - preserves portfolio neutrality by acting as a uniform drift shift in the Fokker-Planck equation for wealth dynamics. We extend this result to the full system of ownership taxes (eierkostnader) that a shareholder faces: a corporate tax on gross profits, a capital income tax on the risk-free return, a

Cosimo Spera

This paper contains the first formal proof that safety is non-compositional in the presence of conjunctive capability dependencies: two agents each individually inca- pable of reaching any forbidden capability can, when combined, collectively reach a forbidden goal through an emergent conjunctive dependency.

Xu-Chen Yang, Botao Wang, Jianpeng Liu, Bing Yang

Particle statistics impose fundamental constraints on nonequilibrium quantum dynamics, yet it remains an open question whether anyonic statistics can lead to emergent dynamical scaling beyond the conventional Bose-Fermi paradigm. Here we investigate the far-from-equilibrium many-body relaxation of anyons in a one-dimensional lattice, uncovering a statistics-

Yeounoh Chung, Rushabh Desai, Jian He, Yu Xiao

Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evaluated by LLMs, thereby broadening significantly the kinds of queries one can express over the combination of structured and unstructured data. LLMs offer remarkable semantic reasoni

Charlotte Model, Sina Ahmadi, Jannis Vamvas

The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also be able to recognize Rumantsch Grischun

Rushil Thareja, Gautam Gupta, Francesco Pinto, Nils Lukas

Constitutional AI is a method to oversee and control LLMs based on a set of rules written in natural language. These rules are typically written by human experts, but could in principle be learned automatically given sufficient training data for the desired behavior. Existing LLM-based prompt optimizers attempt this but are ineffective at learning constituti

Harishwar Reddy Kasireddy, Patricio S. La Rosa, Akshita Gupta, Anindya S. Paul

Histopathology foundation models (HFMs), pretrained on large-scale cancer datasets, have advanced computational pathology. However, their applicability to non-cancerous chronic kidney disease remains underexplored, despite coexistence of renal pathology with malignancies such as renal cell and urothelial carcinoma. We systematically evaluate 11 publicly avai

Marina Godinho, Dave Murphy

We introduce the notion of extended admissible dissections of a marked surface, building upon the notion of an admissible dissection of a marked surface by Amiot--Plamondon--Schroll. For each extended admissible dissection we construct a differential graded algebra, called a piano algebra, which may be viewed in some sense as a differential graded analogue o

Shrey Shah, Justin Wagle

Multi-adapter serving systems route entire sequences to a single adapter, forcing a choice when requests span multiple domains. This assumption fails in two important settings: (1) multimodal generation, where text and image tokens require different adapters within the same sequence, and (2) mixed-capability requests like "write code to solve this equation,"

Víctor Bayona

We present a unified framework for the construction of localized exponential integrators that bypasses the traditional trade-off between the accuracy of global spectral methods and the efficiency of sparse finite differences. By evaluating the matrix exponential of a discrete operator strictly within a local stencil of size $n$, we "harvest" integration weig

Ana Čolović, Xinyu Gao

In analogy with bilinear Riesz potentials, we introduce bilinear Bessel potentials and characterize their boundedness from $L^p\times L^q$ into Lebesgue and Lorentz spaces $L^{r,\alpha}.$ In several cases we identify the optimal Lorentz indices by constructing explicit counterexamples.

Jungbae Chun, Sengiyumva Kisole, Matthew M. Peet, Peter Seiler

It is difficult to analyze the stability of systems with time-varying delays. One approach is to construct a time-transformation that converts the system into a form with a constant delay but with a time-varying scalar appearing in the system matrices. The stability of this transformed system can then be analyzed using methods to bound the effect of the time

Sadaf Tabatabaee, Hicham El Baz, Mohammed Khalil Ghali, Nagendra N. Nagarur

The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing a time series prediction model to forecast patient arrival rates. Using historical data

Egor Shulgin, Dimitri von Rütte, Tianyue H. Zhang, Niccolò Ajroldi

Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules informed by insights from timescale preservation, quadratic proxies, and continuous-ti

Swadesh Jana, Cansu Sancaktar, Tomáš Daniš, Georg Martius

Asymmetric self-play has emerged as a promising paradigm for post-training large language models, where a teacher continually generates questions for a student to solve at the edge of the student's learnability. Although these methods promise open-ended data generation bootstrapped from no human data, they suffer from one major problem: not all problems that

Jun Sakuma, Hisashi Asanuma, Naoto Takahata, Akira Yamaguchi

Precise and accurate ages for asteroidal crusts are fundamental for reconstructing the timeline of magmatic, metamorphic, and impact events in the early Solar System. Zirconolite (CaZrTi2O7) is an accessory mineral found in a wide range of crustal rocks on both the Earth and Moon, and has proven to be a potentially useful U Pb chronometer. However, this mine

Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman

Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce near-real-time responses and exhibit broad hardware compatibility, maximizing user reach. We present a methodology for designing such models using hardware-in-the-loop architecture se

Aleph Alpha, :, Adnen Abdessaied, Artur Baranowski

Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although learned tokenizers are widely adopted, they exhibit notable limitations, including their large, fixed vocabulary sizes and poor adaptability to new domains or languages. We present a

Linlin Cheng, Koen Hindriks, Artem V. Belopolsky

In human-robot interaction (HRI), detecting a human's gaze helps robots interpret user attention and intent. However, most gaze detection approaches rely on specialized eye-tracking hardware, limiting deployment in everyday settings. Appearance-based gaze estimation methods remove this dependency by using standard RGB cameras, but their practicality in HRI r

Pedro Bento, Arthur Buzelin, Arthur Chagas, Yan Aquino

Most intrinsic association probes operate at the word, sentence, or corpus level, obscuring author-level variation. We present POLAR (Per-user On-axis Lexical Association Re-port), a per-user lexical association test that runs in the embedding space of a lightly adapted masked language model. Authors are represented by private deterministic to-kens; POLAR pr

Tanvir Ahmed Sijan, S. M Golam Rifat, Pankaj Chowdhury Partha, Md. Tanjeed Islam

Large Language Models have demonstrated strong multilingual fluency, yet fluency alone does not guarantee socially appropriate language use. In high-context languages, communicative competence requires sensitivity to social hierarchy, relational roles, and interactional norms that are encoded directly in everyday language. Bangla exemplifies this challenge t

Sengiyumva Kisole, Jungbae Chun, Peter Seiler, Matthew M. Peet

Abel's classic transformation shows that any well-posed system with time-varying delay is equivalent to a parameter-varying system with fixed delay. The existence of such a parameter-varying constant delay representation then simplifies the problems of stability analysis and optimal control. Unfortunately, the method for construction of such transformations

Greg Serbarinov

We study cubic cardinality-constrained portfolio optimization, a higher-order extension of the standard Markowitz formulation where three-way sector co-movement terms augment the quadratic risk-return objective. Classical heuristics like simulated annealing (SA) and tabu search require Rosenberg quadratization of these cubic interactions. This inflates the v

Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni, William Yeoh

Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and extensive feature engineering. In contrast, LLMs excel at processing unstructured text, yet their opaque nature makes their reasoning difficult to evaluate and trust. We argue that th

Yihang Cao, Milena Radenkovic

Delay Tolerant Networks (DTNs) can provide emergency communication support when conventional infrastructure is disrupted during disasters. This paper evaluates the performance of opportunistic routing protocols in a realistic disaster scenario based on the 2016 Central Italy earthquake, modelled as an emergency occurring during a football match at Stadio Adr

Kyle M. Jordan, Yingwen Zhang, Frédéric Bouchard, Duncan England

Quantum spectroscopy seeks to probe chemical systems using nonclassical light, which has properties that are qualitatively and quantitatively different than conventional light sources. One promising technique uses intensity-correlated twin beams of light to reduce the noise sources inherent to absorption spectroscopy. However, measurements of the phase shift

Makoto Fukushima

Neural circuits must balance local connectivity constraints against the need for global integration. Here we introduce a minimal wiring rule motivated by synaptic crowding: as a neuron accumulates incoming connections, each additional synapse becomes progressively harder to form. This single-parameter model admits an exact finite-size solution for the induce

Sebastian Micluta-Campeanu, Avinash Subramanian, Anas Abdelrehim, Ranjan Anantharaman

Calibration of dynamic models to data is an important step in building building digital twins of HVAC equipment, thermal loads and control systems. Sometimes, when a model fails to calibrate to data, a possible cause is that the model has made too many sim- plifying assumptions and is missing physics. In this paper we propose a semi-automated approach, calle

Jean Douçot

We give a mathematical interpretation of the dualities between type $A$ Argyres-Douglas theories recently obtained by Beem, Martone, Sacchi, Singh and Stedman, building on work of Xie. Using the fact that, via the wild nonabelian Hodge correspondence, the data defining such a theory amount to singularity data for irregular connections on $\mathbb P^1$ of a s

Samuel Johnny, Blessed Guda, Goodness Obasi, Aaron Emmanuel

Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in clinical deployment: distribution shift across acquisition sites and performance disparity across demographic subgroups. We address both simultaneously across two complementary tasks: binary COVID-19 classification from multi-site CT volumes (Task 1) and four-cla

Amira Guesmi, Muhammad Shafique

Vision-language models (VLMs) have recently shown remarkable capabilities in visual understanding and generation, but remain vulnerable to adversarial manipulations of visual content. Prior object-hiding attacks primarily rely on suppressing or blocking region-specific representations, often creating semantic gaps that inadvertently induce hallucination, whe

Emil Hardarson, Luka Biedebach, Ómar Bessi Ómarsson, Teitur Hrólfsson

Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learni

Daniel J. Gracias, Thomas J. Ugras, Richard D. Robinson

Achieving large optical rotation with minimal ellipticity and absorption, 'pure' optical rotation, remains a central challenge in chiral photonics. Solution-processed self-assembled materials can exhibit exceptional chiroptical responses (g-factors > 1), yet their circular birefringence (CB) typically overlaps with circular dichroism (CD) and resonant loss (

Jean-Christophe Pain

The so-called Ahmed integral $$ \int_{0}^{1}\frac{\arctan\left(\sqrt{2+x^{2}}\right)}{(1+x^{2})\sqrt{2+x^{2}}}\,\mathrm{d} x=\frac{5\pi^{2}}{96}, $$ has attracted considerable interest since its appearance in the "American Mathematical Monthly" in 2001. Several proofs and extensions have been proposed, including a probabilistic multivariate approach introduc

Jeffery L. Painter, François Haguinet, Andrew Bate

ClinicalTrials .gov (CT .gov) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) terminology limit systematic pharmacovigilance (PV) analytics. AEs are typically recorded as investigator-reported text rather than standardized identifiers, requiring manual reconciliation

Hui Sun, Yinan Wu, Wesley K. G. Assunção, Kathryn T. Stolee

Test code is indispensable in software development, ensuring the correctness of production code and supporting maintainability. Nonetheless, errors or omissions in the test code can conceal production defects. While code review is widely adopted to assess code quality and correctness, little research has examined how test code is reviewed. Spadini et al.'s r

Utkarsh Grover, Ravi Ranjan, Mingyang Mao, Trung Tien Dong

Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size, weight, and power constraints, where memory traffic, compute latency, timing variability, and safety margins interact directly. The Deployment Gauntlet organizes these constraints

Shaked Regev, Evan J. R. Brody, Charles Foltz, Eve Tsybina

The US energy system is increasingly under pressure to serve expanding data loads and to accommodate a larger number of generating units with varying technologies and own- ership structures. Therefore, developing new unit commitment methods remains a priority for reliable and affordable grid operations. We expand our novel computational method for unit commi

Allan Alonzo-Artiles, Manfred Kraus

We employ scattering amplitudes in Einstein-Maxwell theory to compute the classical Hamiltonian of a binary system of two charged, non-spinning compact objects. The effective Hamiltonian is valid to all orders in velocity and up to second post-Minkowskian order (2PM), i.e. $\mathcal{O}(G^2)$. The classical interaction potential is extracted via matching the

Forrest Mozer, Kyungeun Choi, Richard Sydora, Andrii . Voshchepynets

A major goal of solar physics is understanding the transition of the medium from the closed-loop magnetic configuration of the corona to the open structure of the heliospheric current sheet. The evolution of solar wind streamers, an essential component of this transition, has been observed in-situ for the first time by measuring a pass through a streamer sta

Vasily Ilin

We present a complete Lean 4 formalization of the equilibrium characterization in the Vlasov-Maxwell-Landau (VML) system, which describes the motion of charged plasma. The project demonstrates the full AI-assisted mathematical research loop: an AI reasoning model (Gemini DeepThink) generated the proof from a conjecture, an agentic coding tool (Claude Code) t

Francisco Mourao, David Hajage, Daria Bystrova, Bertrand Bouvarel

Prior-Data Fitted Networks (PFNs) represent a paradigm shift in tabular data prediction. We present the principles of this new paradigm and evaluate two PFNs for estimating the average treatment effect (ATE) of a binary treatment on a binary outcome, using simulated clinical scenarios based on real-world data. We assessed TabPFN combined with causal inferenc

Giacomo Albi, Alessandro Alla, Elisa Calzola

We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data, without any a priori knowledge of the underlying interaction structure. Starting from a discrete stochastic binary-interaction model,

Nitish Nagesh, Elahe Khatibi, Thomas Hughes, Mahdi Bagheri

Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical records data. We evaluate the Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference alg

Harold Tankpinou Zoumenou, Simon Ferreira, Charles Assaad, Nathanael Lapidus

In target trial emulation, time partitioning enables researchers to handle time-varying confounders and immortal time bias with appropriate methods. Based on two clinical scenarios, this study aimed to explore issues related to time partitioning and to provide guidance for trial emulation. After formalizing the research question within the framework of struc

Nuri Mert Vural, Alberto Bietti, Mahdi Soltanolkotabi, Denny Wu

Modern large language models (LLMs) excel at tasks that require storing and retrieving knowledge, such as factual recall and question answering. Transformers are central to this capability because they can encode information during training and retrieve it at inference. Existing theoretical analyses typically study transformers under idealized assumptions su

Sui He

This paper examines user reactions to the launch of the machine translation (MT) feature on Xiaohongshu, a Chinese social media and e-commerce platform, in January 2025. Drawing on a dataset of 6,723 comments collected from 11 official posts promoting the translation function, this paper combines sentiment analysis with thematic analysis to investigate how u

Srijan Bansal, Jiao Fangkai, Yilun Zhou, Austin Xu

As Large Language Models shift the programming toward human-guided ''vibe coding'', agentic coding tools increasingly rely on models to self-diagnose and repair their own subtle faults -- a capability central to autonomous software engineering yet never systematically evaluated. We present \name{}, the first empirical decomposition that jointly evaluates two

Pan Du, Yongqi Li, Mingqi Xu, Jian-Xun Wang

Differentiable programming has emerged as a structural prerequisite for gradient-based inverse problems and end-to-end hybrid physics--machine learning in computational fluid dynamics. However, existing differentiable CFD platforms are confined to structured Cartesian grids, excluding the geometrically complex domains where body-conforming unstructured discr

Siyu Zhang

Sparse neural networks are often hypothesized to be more interpretable than dense models, motivated by findings that weight sparsity can produce compact circuits in language models. However, it remains unclear whether structural sparsity itself leads to improved semantic interpretability. In this work, we systematically evaluate the relationship between weig

Hooman Danesh, Henning Wessels

From a pool of admissible designs, we aim to identify a structure that achieves a target macroscopic stress response. For each candidate, the response is obtained from a high-fidelity oracle, such as expensive computational homogenization or experiments. We consider cases in which (i) the geometry cannot be conveniently parameterized, rendering gradient-base

Xiaoyi Li

When LLM agents autonomously design ML experiments, do they perform genuine architecture search -- or do they default to hyperparameter tuning within a narrow region of the design space? We answer this question by analyzing 10,469 experiments executed by two LLM agents (Claude Opus and Gemini 2.5 Pro) across a combinatorial configuration space of 108,000 dis

Han Huang, Jinghang Dai, Joyce Christiansen-Salameh, Jiyoung Kim

How ordered phases melt in low-dimensional quantum materials remain difficult to resolve because the relevant fluctuations are dynamic and charge neutral. In this work, we show that thermal transport provides a sensitive probe of these hidden fluctuations in the layered transition metal dichalcogenide 2H-TaSe2. We observe a striking V-shaped temperature depe