March 2026 arXiv papers — page 51

Qingchen Li, Pavel A. Nosov, Taige Wang, Eslam Khalaf

The question of anyon interactions and their possible binding plays a key role in the physics of fractional quantum Hall states. Here, we introduce a controlled and scalable approach to study anyon binding by working entirely within the Hilbert space of anyons. The resulting theory is characterized by an effective potential, which captures the electrostatic

Aidan P. Cloonan, Katherine E. Whitaker, Sinclaire M. Manning, Christina C. Williams

Among the most remarkable results from JWST is the discovery of abundant, compact, and very red sources in the early Universe known as "Little Red Dots" (LRDs). The relative degree to which starlight and active galactic nuclei (AGN) drive the rest-frame UV and optical emission from LRDs remains unclear. With a large sample of LRDs selected photometrically fr

Manoj Velmurugan, Phillip Brush, Colin Balfour, Richard J. Przybyla

Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle av

Emilio Pisanty, Nicola Mayer, Andrés Ordóñez, Alexander Löhr

We develop a family of chiral measures to quantify the chirality of a distribution and assign it a handedness. Our measures are built using the tensorial moments of the distribution, which naturally encode its spatial character, not only via its angular shape consistently with existing multipolar-moment approaches, but also its radial dependence. We combine

Navya Gupta, Emil Mathew, Saurabh V. Kadam, Jesse R. Stryker

String breaking is at the core of hadronization models of relevance to particle colliders. Yet, studies of string-breaking dynamics rooted in quantum chromodynamics remain fundamentally challenging. Tensor networks enable sign-problem-free studies of static and dynamical properties of lattice gauge theories. In this work, we develop and apply a tensor-networ

Marie Zinnkann, Tereza Jerabkova, Zhiqiang Yan, Pavel Kroupa

The relation between the maximum stellar mass in a very young cluster (mmax) and the total stellar mass of the cluster (Mecl), known as the mmax-Mecl relation, remains debated in the literature. To test the validity of this relation, we modelled young star clusters with masses between 102.5 and 105.0 M_sun and ages of 1-4 Myr using the galIMF code, in which

Gokce Inal, Pouyan Navard, Alper Yilmaz

Recent advances in multimodal vision-language models (VLMs) have enabled joint reasoning over visual and textual information, yet their application to planetary science remains largely unexplored. A key hindrance is the absence of large-scale datasets that pair real planetary imagery with detailed scientific descriptions. In this work, we introduce LLaVA-LE

Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt, Stephan Günnemann

Random cropping is one of the most common data augmentation techniques in computer vision, yet the role of its inherent randomness in training differentially private machine learning models has thus far gone unexplored. We observe that when sensitive content in an image is spatially localized, such as a face or license plate, random cropping can probabilisti

Sonia, Tanmay Maji, Hemant Kumar

The Wigner distributions (WDs) in boost-invariant longitudinal space for unpolarized, longitudinally polarized and linearly polarized gluons in a proton are presented in the framework of the light-front gluon spectator model inspired by AdS/QCD. The boost-invariant longitudinal space defined by the coordinate $\sigma=\frac{1}{2}b^- P^+$, can be accessed thro

Wuque Cai, Hongze Sun, Quan Tang, Shifeng Mao

Spiking neural networks (SNNs) are promising for neuromorphic computing, but high-performing models still rely on dense multilayer architectures with substantial communication and state-storage costs. Inspired by autapses, we propose time-delayed autapse SNN (TDA-SNN), a framework that reconstructs SNNs with a single leaky integrate-and-fire neuron and a pro

Bentao Song, Jun Huang, Qingfeng Wang

In mixed domain semi-supervised medical image segmentation (MiDSS), achieving superior performance under domain shift and limited annotations is challenging. This scenario presents two primary issues: (1) distributional differences between labeled and unlabeled data hinder effective knowledge transfer, and (2) inefficient learning from unlabeled data causes

Tapas Chatterjee, Priya Jain

In this paper, we construct a special family of cyclic codes, known as quadratic residue codes of prime length \( p \equiv \pm 1 \pmod{44} ,\) \( p \equiv \pm 5 \pmod{44} ,\) \( p \equiv \pm 7 \pmod{44} ,\) \( p \equiv \pm 9 \pmod{44} \) and \( p \equiv \pm 19 \pmod{44} \) over $\mathbb{Z}_{121}$ by defining them using their generating idempotents. Furthermo

Connor Hainje, Glennys R. Farrar

We report on a new method for incorporating interactions between dark matter (DM) and baryons in cosmological simulations, capable of handling the challenging regime in which the dark matter particle mass is comparable to or lighter than the baryon mass. The method hybridizes two distinct approaches: gas particles receive momentum and energy transfer accordi

Dennis Delali Kwesi Wayo, Chinonso Onah, Leonardo Goliatt, Sven Groppe

We quantify decoder dependence in surface-code threshold studies under two matched regimes: Pauli noise and native GKP-style Gaussian displacement digitization. Using LiDMaS+ v1.1.0, we benchmark MWPM, Union-Find (UF), Belief Propagation (BP), and neural-guided MWPM with fixed seeds, identical sweep grids, and unified reporting across runs 06--14. At $d=5$ a

Francesco Fournier-Facio, Xiaolei Wu, Matthew C. B. Zaremsky

Given a group $G$ acting faithfully on a set $S$, one gets a simple group denoted $SV_G$, called a twisted Brin--Thompson group. In this paper we drop the faithfulness assumption, and get an abstract version of a twisted Brin--Thompson group $SV_G$. While the resulting group is not simple, since $SV_G$ surjects onto $SV_{G/\ker(G \curvearrowright S)}$, we pr

Mohd Rehan, Md Sabir Ali, Sushant G. Ghosh

Black hole thermodynamics and topology have emerged as a strong foundation for a coordinate-independent understanding of phase transitions. Using both Duan's topological current theory and a novel complex residue method, we perform a topological study of the Kerr-Sen AdS black hole arising in heterotic string theory. In turn, we find the zero points correspo

Quanyun Wu, Kyle Gao, Daniel Long, David A. Clausi

Embodied AI training and evaluation require object-centric digital twin environments with accurate metric geometry and semantic grounding. Recent transformer-based feedforward reconstruction methods can efficiently predict global point clouds from sparse monocular videos, yet these geometries suffer from inherent scale ambiguity and inconsistent coordinate c

Alyana Jusino, Kishalay De, Andrea Antoni

Black hole X-ray binaries (BHXRBs) have traditionally been discovered by X-ray surveys with cadences of hours to days. However, large optical time-domain surveys now provide novel avenues for early detection and insights into their elusive outburst triggering mechanisms. We present early-time light curves of the BHXRB AT 2019wey serendipitously observed by t

Maxwell A. Kroft, Thomas G. Beatty, Joseph M. Salzer, Claire Zwicker

We use WIYN/NEID radial velocity measurements to confirm the planetary nature and measure the mass of the TESS transiting exoplanet candidate around the mid-K dwarf GJ 523 ($V=9.23$, $K=6.525$). We find that GJ 523b is on a 17.75 day orbit and has a radius of $2.55\pm0.15\,R_\oplus$, a mass of $23.5\pm3.3\,M_\oplus$, and a zero-albedo equilibrium temperature

Maciej Kolanowski, Donald Marolf

We resolve a puzzle associated with the spherically-symmetric sector of the AdS$_4$ Einstein-Maxwell partition function with inverse temperature $\beta$. Since charge is quantized, the semiclassical limit of the partition function is expected to be given by a sum over complex black hole solutions obtained by shifting the associated chemical potential $\mu$ b

An Yu, Ting Yu Tsai, Zhenfei Zhang, Weiheng Lu

Recent multimodal large language models are computationally expensive because Transformers must process a large number of visual tokens. We present ReDiPrune, a training-free token pruning method applied before the vision-language projector, where visual features remain rich and discriminative. Unlike post-projection pruning methods that operate on compresse

Emil K. F. Donkersloot, René Sondenheimer, Jan Sperling

Quantum walks constitute a rich area of quantum information science, where multipartite entanglement plays a central role in the dynamics and scalability of quantum advantage over classical simulators. In this work, we study the multipartite entanglement of quantum walks in optical settings. We present methods for computing a geometric measure of entanglemen

Pau Amaro Seoane, Alessandra Mastrobuono Battisti, Chingis Omarov, Denis Yurin

We establish the multi-messenger mechanics of episodic mass transfer in supermassive black hole binaries stalled within circumbinary discs. Utilizing continuous wavelet transforms, we isolate localized gas clumps at the cavity edge and track their evolution. By regularizing the forced fluid equations at Lindblad resonances via the inhomogeneous Airy differen

Hidenori Tanaka

Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective reasoning, systematic bias, or mere chance. Recent work has sharpened this question with naming games, showing that even when no in

XRISM Collaboration, Marc Audard, Hisamitsu Awaki, Ralf Ballhausen

Galaxies with intense star formation often host multiphase, galaxy-scale winds powered by supernovae and fast stellar winds. These are strong enough to disrupt the star-forming interstellar medium, and they chemically enrich the surrounding circumgalactic medium. However, their launching mechanism remains unknown. Here we show that thermal gas pressure is su

Madison Reich, W. N. Brandt, Bin Luo, Richard Plotkin

We present the results of a multi-cycle Chandra program to systematically monitor the X-ray variability of 10 weak-line quasars (WLQs) that previously had limited multi-epoch X-ray observations. Three new Chandra 2.8 to 8.2 ks observations were obtained for each WLQ with C$\,$IV rest-frame equivalent widths (REWs) $\lesssim 10$ \r{A}, substantially improving

Rui-Heng Liu, Jiangping Hu, Chen Fang

Electronic flat bands have localized Wannier-like orbitals as zero modes. In the Lieb or the kagome models, the localized orbitals satisfy a topological condition that entails two non-contractible loop eigenstates along $x/y$-axis in real space, and one topological band touching point with other bands in momentum space. In these topological-flat bands, the B

Abhijat Sarma, Cenke Xu

Under decoherence, an initial Gaussian (free-fermion) state evolves into a non-Gaussian mixed state, so the resulting decohered fermionic state is not exactly solvable in general. We show through an inequality that a class of R\'{e}nyi-2 correlators of the decohered fermion state are upper-bounded by the R\'{e}nyi-2 correlator serving as a proximate diagnost

Karim Benakli

A charged massive spin-$\tfrac32$ field in a constant electromagnetic background admits two familiar realizations. In supergravity, where it is identified with a gravitino, its mass and charge are tied in Planck units. In the decoupled-gravity regime, the two-derivative Fierz--Pauli system describing the first massive open-string modes instead allows arbitra

Richard M. Feder, Liam Parker, Uroš Seljak

We present a probabilistic autoencoder (PAE) framework for galaxy spectral energy distribution (SED) modeling and redshift estimation, applied to synthetic SPHEREx 102-band spectrophotometry. Our PAE learns a compact latent representation of rest-frame galaxy SEDs transformed to a simple Gaussian base density using a normalizing flow, combined with an explic

Chiara Altavista, Edoardo Anastasi, Roberta Angius, Angel M. Uranga

We describe the explicit construction of configurations of several 10d string theories joining at a 9d junction, providing a dynamical realization of cobordisms between multiple 10d string theories, predicted by the Cobordism Conjecture. We provide the microscopic worldsheet description of the configuration in a generalization of the `going up and down the R

Antonella Grassi, Rick Miranda, Kapil Paranjape, Vasudevan Srinivas

We prove that the Mordell-Weil group of a higher dimensional elliptic fibration naturally embeds into that of a suitable elliptic surface. We give sufficient conditions for the existence of such surfaces. We apply our result to obtain explicit and uniform bounds for the Mordell-Weil rank of elliptic threefolds of Kodaira dimension zero, including Calabi-Yau

Filippo Cutrona, Francesco Rescigno, Alberto Salvio

Classically scale-invariant (and perturbative) theories provide a way to understand large hierarchies, as scales are generated through dimensional transmutation. They always lead to first-order phase transitions, since symmetries are radiatively broken, and they generically feature quasi-flat potentials, which are suitable for inflation. We construct a simpl

M. B. Lam, J. M. Vos, G. Suárez, C. -C. Hsu

We present the first full JWST NIRSpec Prism and MIRI LRS 0.6 - 14 $\mu$m (R ~ 100) spectra and analysis of five ~ 133 Myr L dwarf members of the AB Doradus moving group and one probable $\sim 500$ Myr T dwarf of the Oceanus moving group with known inclination angles between ~ $23 - 90^{\circ}$: W0047+68, 2M0355+11, 2M0642+41, W1741-46, 2M2206-42, and 2M2244

Ioannis Matthaiakakis

Current literature lacks consensus on how to theoretically describe spin polarization and its transport in matter. The underlying reason is the presence of the Belinfante-Rosenfeld (BR) ambiguity in the form of the energy-momentum and spin tensors. I re-examine the BR ambiguity in Einstein-Cartan spacetimes, using the bi-form formalism in a manner applicable

Tullia Sbarrato, Silvia Belladitta, Julien Wolf, Pietro Baldini

We present BLAZ4R, the first living catalog of confirmed $z>4$ blazars, with a focus on the contribution of eROSITA, on board of the Spectrum Roentgen Gamma (SRG) spacecraft. Blazars at $z>4$ are rare but powerful probes of how active supermassive black holes evolve in connection to relativistic jets, in the first 2 billion years of cosmic history. At these

Upamanyu Moitra

We study duality-invariant higher-derivative corrections to the charged black hole geometry in two-dimensional heterotic string theory. We illustrate how the conventional perturbative approach to determine the corrected geometry breaks down. Using a non-perturbative (in $\alpha'$) parametrization of the solution, we find the corrected charge-to-mass ratio fo

Shounak De, Hayden Lee

We express the scalar four-point function of minimal Vasiliev higher-spin gravity in de Sitter space as an integral over the orthogonal Grassmannian OGr(4,8). The full crossing-symmetric Vasiliev Grassmannian correlator is given by $(S^2+T^2+U^2)/(STU)$, where $S$, $T$, $U$ are the Grassmannian Mandelstam variables. Remarkably, this has the same form as the

Mainak Mukhopadhyay, Shigeo S. Kimura, Indrek Vurm, Brian D. Metzger

Superluminous supernovae (SLSNe) are rare transients that are $\sim 10 - 100$ times more luminous than ordinary stellar explosions, reaching peak optical luminosities $\sim 10^{44} - 10^{45}$ erg s$^{-1}$. The energy source powering SLSNe remains uncertain. In this work, we explore the multi-wavelength and multi-messenger signatures of the scenario in which

Vasilis Belis, Joseph Bowles, Rishabh Gupta, Evan Peters

This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine learning model, are often natural for quantum computers. For example, if a generative machine learning model is represe

Francesco Gentile, Nicola Dall'Asen, Francesco Tonini, Massimiliano Mancini

As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on activations, making them dataset-dependent, vulnerable to data bias, and often restricted to coarse head-level explanations. We introduce SITH (Semantic Inspection of Transformer Heads

Arthur Jacot

We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators $f^1,\dots,f^k$ to the drift $f$ with increasing accuracy and computational cost, only requiring a few evaluations of the most accurate $f^k$ and many evaluations of the less costly $f^1,\dots,f^{k-1}$. If the drift lies in the so-calle

Fabio Briscese, Gianluca Calcagni

Quantum field theory (QFT) on fractal spacetimes is a program aiming at quantizing the gravitational interaction consistently at all energy scales thanks to an intrinsically or dynamically induced multiscale or multifractal-like spacetime geometry that regularizes the infinities of standard QFT. We reach the goal of this program and formulate a field theory

Oriana K. Diessel, Subir Sachdev, Pietro M. Bonetti

We obtain the phase diagrams of field theories of intertwined orders in the presence of periodic driving by an external field which preserves all symmetries. We consider both a conventional Landau theory of competing orders, and a fractionalized theory in which the order parameters are distinct composites of an underlying multi-component Higgs field. We work

Ruofei Du, Benjamin Hersh, David Li, Nels Numan

While large language models (LLMs) have accelerated 2D software development through intent-driven "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains a major challenge. The fundamental barrier is not just the steep learning curve for human creators, but that low-level sensor APIs and complex game engine hierarchies are ill-suited

Mateo Londoño, Jesús Pérez-Ríos

We present a theoretical investigation on the electronic structure and properties of radium monochalcogenides, with chalcogens O, S, and Se, as well as the ionic species RaO +/-. Our approach combines fully relativistic and partially relativistic quantum-chemistry methods. Electronic properties are obtained using the exact two-component Hamiltonian-based cou

Kenta Kasai

We construct asymptotically good nested Calderbank-Shor-Steane (CSS) code pairs from Hsu-Anastasopoulos codes and MacKay-Neal codes. In the fixed-degree regime, we prove that the coding rate stays bounded away from zero and that the relative distances on both sides stay bounded away from zero with probability tending to one as the blocklength grows. Moreover

Pengxuan Yang, Yupeng Zheng, Deheng Qian, Zebin Xing

We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 - achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing p

Prajwal Niraula, Benjamin V. Rackham, Julien de Wit, Daniel Apai

HAT-P-11 is a well-studied, active K dwarf hosting an eccentric, misaligned transiting sub-Neptune. As part of the HST Stellar Treasure Trove program (HST-AR-17551), we analyze absolutely calibrated out-of-transit \HST{} spectra from \texttt{STIS} and \texttt{WFC3} across the \textsc{G430L}, \textsc{G750L}, \textsc{G102}, and \textsc{G141} bandpasses to cons

Jiaying Zhou, Zhihao Zhan, Ruifeng Zhai, Qinhan Lyu

Vision--Language--Action (VLA) policies have shown strong progress in mapping language instructions and visual observations to robotic actions, yet their reliability degrades in cluttered scenes with distractors. By analyzing failure cases, we find that many errors do not arise from infeasible motions, but from instance-level grounding failures: the policy o

Tomasz Krajewski, Marek Lewicki, Marco Merchand, Ignacy Nałęcz

We present a unified description of first-order cosmological phase transition dynamics that links the phenomenological friction model employed in hydrodynamic simulations to the microscopic treatment based on Boltzmann equations. We derive an approximate analytical expression for the chemical potential and demonstrate that the resulting friction parameter $\

Biplab Pal, Santanu Bhattacharya

Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies over actions and tool calls, the key question is not whether a next step appears plausible, but whether the resulting trajectory remains statistically supported,

Linbo Wang, Yupeng Zheng, Qiang Chen, Shiwei Li

We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations. Existing world-model-based planners suffer from inadequately compressed representations, limited spatial understanding, and underutilized temporal dynamics, resultin

Shwai He, Guoheng Sun, Haichao Zhang, Yun Fu

Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non-generative tasks but frequently fail in generative settings. To understand this discrepancy, we analy

Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galván, Fernando Sánchez-Vega

Automatic depression detection from doctor-patient conversations has gained momentum thanks to the availability of public corpora and advances in language modeling. However, interpretability remains limited: strong performance is often reported without revealing what drives predictions. We analyze three datasets: ANDROIDS, DAIC-WOZ, E-DAIC and identify a sys

Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff

Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Go

Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song

Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent confirmation bias,

Imran Mehmood, Imad Ali Shah, Ming Ronnier Luo, Brian Deegan

Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability encourage automated approaches. We investigate whether Vision Language Models (VLMs) can approximate human perceptual judgments across three image quality scales: contrast, colorfulness and overall preference. Six

Falong Fan, Yi Xie, Arnis Lektauers, Bo Liu

Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highlights, and instrument occlusions often fragment geometric continuity, posing a challenge for existing fixed-topology approaches. To address this, we propose EndoVGGT, a geometry-centric framework equipped with a Def

Qijia He, Xunmei Liu, Hammaad Memon, Ziang Li

Scalable Vector Graphics (SVG) are essential for technical illustration and digital design, offering resolution independence and semantic editability. In practice, original vector files are frequently lost, leaving only rasterized versions (e.g., PNG, JPEG) that resist modification, while manual reconstruction is prohibitively expensive. Progress on automati

Philip Kaminsky, Rachitesh Kumar, Roger Lederman

The freight industry is undergoing a digital revolution, with an ever-growing volume of transactions being facilitated by digital marketplaces. A core capability of these marketplaces is the fulfillment of demand for truckload movements (loads) by procuring the services of carriers who execute them. Notably, these services are procured both through long-term

Shival Dasu, Ben Criger

We provide a recursively defined sequence of flag circuits which will detect logical errors induced by non-fault-tolerant $R_{\overline{Z}}(\frac{\pi}{2^l})$ gates on CSS codes with a fault distance of two. As applications, we give a family of circuits with $O(l)$ gates and ancillae which implement fault-tolerant logical $R_{Z}(\frac{\pi}{2^l})$ or $R_{ZZ}(\

Quentin Cohen-Solal

In this article, we focus on search algorithms for two-player perfect information games, whose objective is to determine the best possible strategy, and ideally a winning strategy. Unfortunately, some search algorithms for games in the literature are not able to always determine a winning strategy, even with an infinite search time. This is the case, for exa

Yubo Li, Xugong Qin, Peng Zhang, Hailun Lin

Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene text editing framework that integrates the strengths of Att

Duc Vu, Anh Nguyen, Chi Tran, Anh Tran

Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and text prompts. Recent efforts have focused on adversarial attacks that introduce crafted perturbations to protect images from diffusion-based models. However, most ex

Ovidiu Racorean

Time reversal symmetry occupies a distinctive role in quantum mechanics, fundamentally requiring an anti-unitary operator to ensure a physically consistent representation. As such, the time reversal operator combines a unitary transformation with complex conjugation, enabling the necessary inversion of the imaginary unit that appears in quantum commutation r

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman

Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications, such assumptions often do not hold. Visual information may be missing due to occlusions, camera failures, or privacy constraints, while multilingual speakers int

Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan

Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions. In this work, we propose a Bayesian optimization method that combines a penalty formulation, a surrogate model, and a trust region strategy. The constrained problem is converted to an un

Adam K. Kiss, Ersin Das, Tamas G. Molnar, Aaron D. Ames

Time delays in feedback control loops can cause controllers to respond too late, and with excessively large corrective actions, leading to unsafe behavior (violation of state constraints) and controller infeasibility (violation of input constraints). To address this problem, we develop a safety-critical control framework for nonlinear systems with input dela

Noya Ruth Itzhak, Kate Reidy, Maya Levy-Greenberg, Paul Anthony Miller

Chiral crystals exhibit useful handedness-dependent properties, including spin selectivity and circularly polarized light sensitivity, yet controlling which enantiomer forms during synthesis remains a central challenge. Existing approaches utilize molecules in solution to template crystal growth, which restricts processing conditions and introduces organic c

Mengyuan Li, Lei Gao, Haoxuan Xu, Jiate Li

Every API token you spend is your accumulated wealth; once you can prove its value and the effort behind it, you can resell it. As autonomous agents repeatedly call models and tools, they accumulate memories that are your intellectual property. But today these memories remain private and non-transferable, as there is no way to validate their value. We argue

Philip Tosteson

We show that the homology of strata of abelian differentials stabilizes in a range where the number of simple zeros is large relative to the homological degree. In this range, we show that the rational cohomology agrees with the restriction of the tautological classes to the stratum, and that the rational Picard group is trivial for unprojectivized strata. O

Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu, Shih-Lun Huang

While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individ

H. I. Cohen, N. Islam, R. H. D. Corbet, A. Lange

Superorbital modulations has been detected in the supergiant High-Mass X-ray binary 4U 1538-52 using long-term monitoring with the Neil Gehrels Swift Observatory Burst Alert Telescope (BAT). The source also exhibits a long-term pulse period evolution as seen with Rossi X-ray Timing Explorer (RXTE), INTEGRAL, and Fermi Gamma-Ray Burst Monitor (GBM) that appea

Bo Wang, Ming Deng, Mingda Chen, Chengran Yang

LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutations as few-shot examples or none at all. This can result in generic low-quality mutations, missed context-specific mutation patterns, substantial numbers of redundant and uncompilable mutants, and limited semantic similarity to real bu

Martin Jaraiz

We introduce the Free-Market Algorithm (FMA), a novel metaheuristic inspired by free-market economics. Unlike Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing -- which require prescribed fitness functions and fixed search spaces -- FMA uses distributed supply-and-demand dynamics where fitness is emergent, the search space is open-ende

Yoav Alon, Cristina David

Determining whether a program terminates is a core challenge in program analysis with direct implications for correctness, verification, and security. We investigate whether transformer architectures can recognise termination patterns directly from source code and how their strengths can be amplified through ensembles. To overcome the extreme scarcity of non

Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu

The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited by the inherent disconnect between reasoning and perception: they rely on static, pre-processed information and cannot actively seek raw evidence from video as the

Sebastian Nowak, Jann-Frederick Laß, Narine Mesropyan, Babak Salam

Purpose: To design, implement, evaluate, and report on the regulatory requirements of a self-hosted LLM infrastructure for radiology adhering to the principle of least privilege, emphasizing technical feasibility, network isolation, and clinical utility. Materials and Methods: The isolation-first, containerized LLM inference stack relies on strict network se

Eric R. Bittner

Classical thermodynamics admits a geometric formulation in which work is associated with areas enclosed by cycles in state space. Whether an analogous structure persists in driven, dissipative quantum systems remains an open question. Here we show that quasistatic work in open quantum steady states is governed by an emergent geometric curvature in control-pa

Samuel Taiwo, Mohd Amaluddin Yusoff

Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking - an often-overlooked determinant of its quality. This paper presents an empirical study quantifying performance differences across four chunking strategies: fixed-size sl

Frederick Rajasekaran, Oren Yakir, Yanxin Zhou

Given any compact connected matrix Lie group $G$ and any lattice dimension $d\ge 2$, we construct a massive Gaussian scaling limit for the $G$-valued lattice Yang-Mills-Higgs theory in the "complete breakdown of symmetry" regime. This limit arises as the lattice spacing tends to zero and the (inverse) gauge coupling constant tends to infinity sufficiently fa

Kenechi Omeke, Michael Mollel, Lei Zhang, Qammer H. Abbasi

Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth, energy-intensive, and often unable to support direct sensor-to-surface communication. Standard flat federated learning therefore faces two coupled limitations in underwater deploy

Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar, Frank Hutter

The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare classical HPO algorithms against LLM-based methods on tuning the hyperparameters of a small language model under a fixed compute budget. When defining a fixed search space over autoresearch, classical methods such

Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman

We solve the orientation recovery of a tumbling protein in the gas phase from single-event measurements of the spatial positions of its ions after an X-ray laser induced explosion. We simulate diffracted X-ray signal and ion dynamics under experimental conditions and compare our method to conventional orientation recovery in single-particle imaging with X-ra

Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas

Organic farming is a key element in achieving more sustainable agriculture. For a better understanding of the development and impact of organic farming, comprehensive, spatially explicit information is needed. This study presents an approach for the discrimination of organic and conventional farming systems using intra-annual Sentinel-2 time series. In addit

Imtiaz Khan, Muzamil Shah, Reza Asgari, Gao Xianlong

We investigate Floquet-driven topological phase transitions in an AB-stacked bilayer Haldane lattice with tunable intralayer hopping anisotropy. By combining interlayer hybridization, Haldane flux, and off-resonant circularly polarized light, we obtain controlled transitions among Dirac, semi-Dirac, and higher-Chern insulating phases. As the hopping anisotro

Dana Serditova, Kevin Tang

Automatic Speech Recognition (ASR) systems are widely used in everyday communication, education, healthcare, and industry, yet their performance remains uneven across speakers, particularly when dialectal variation diverges from the mainstream accents represented in training data. This study investigates ASR bias through a sociolinguistic analysis of Newcast

Gennadiy A. Kalyabin

The unconditional, i.e. without assuming validity of RH, sharp limit relationship (as p tends to infinity) is found between the remainder in the modified Mertens asymptotic formula for the sums of primes' reciprocals and maximal values of Gronwall numbers G(N) among all integers whose greatest prime factor is p and which are divided by any prime q<p. The pro

Viyom Vivek, David Martin de Diego, Ravi N. Banavar

We develop a geometric framework for the numerical integration of mechanical systems evolving on manifolds. After briefly reviewing classical numerical methods and highlighting their limitations and shortcomings in non-flat (non-Euclidean) spaces, we include a short interlude reviewing the differential geometric notions required in the sequel. We then introd

Hasna Chnafa, Clarence Cortes, David Laroze, Ahmed Jellal

We investigate the impact of an induced mass term $\Delta$ on the current density in graphene subjected to a space- and time-dependent periodic potential $U(x,t)$. By solving the Dirac equation and deriving both the quasi-energy spectrum and the corresponding eigenspinors, we obtain explicit analytical expressions for the current density, which exhibits a cl

Z. Abreu, J. Lieb, R. Pinto, R. Simoes

In this paper, we analyze $m$-dimensional ($m$D) convolutional codes with finite support, viewed as a natural generalization of one-dimensional (1D) convolutional codes to higher dimensions. An $m$D convolutional code with finite support consists of codewords with compact support indexed in $\mathbb{N}^m$ and taking values in $\mathbb{F}_{q}[z_1,\ldots,z_m]^

Jinho Bok, Shuangping Li, Sophie H. Yu

We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric graph on $\mathbb{S}^{d-1}$, while all remaining edges follow the Erd\H{o}s--R\'enyi model $\mathcal{G}(n, p)$. The random geometric graph is generated by thresho

Brianna Aguilar-Solis, Brittany P. Harding, Aurora Pribram-Jones

Density functional theory at finite temperatures often relies on the zero-temperature approximation, which uses a ground-state exchange-correlation functional with thermalized densities. This approach, however, neglects the explicit temperature dependence of the exchange-correlation free energy -- a key factor in regimes such as warm dense matter, where both

Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas, Bardh Prenkaj

Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and unreliability are well-documented, its safety implications remain underexplored. In this work, we present a systematic safety audit of steering vectors obtained with Contrastive Activation Addition (CAA), a widely u

Kyrill Ho, Axel Klawonn, Martin Lanser

Nonlinear Schwarz methods are a type of nonlinear domain decomposition method used as an alternative to Newton's method for solving discretized nonlinear partial differential equations. In this article, the first parallel implementation of a two-level nonlinear Schwarz method leveraging the GDSW-type coarse spaces from the Fast and Robust Overlapping Schwarz

Fanjun Bu, Chenyang Yuan, Hiroshi Yasuda

Generative world models offer a compelling foundation for augmented-reality (AR) applications: by predicting future image sequences that incorporate deliberate visual edits, they enable temporally coherent, augmented future frames that can be computed ahead of time and cached, avoiding per-frame rendering from scratch in real time. In this work, we present S

Kevin Jamsahar, Adrian Wiltz, Maria Charitidou, Dimos V. Dimarogonas

This work presents a modular, Python-based simulator that simplifies the evaluation of novel vehicle control and coordination algorithms in complex traffic scenarios while keeping the implementation overhead low. It allows researchers to focus primarily on developing the control and coordination strategies themselves, while the simulator manages the setup of

Florian Stilz, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks. To address this challenge, we introduce Cl

Deyu Ming, Daniel Williamson

Gaussian process (GP) emulators have become essential tools for approximating complex simulators, significantly reducing computational demands in optimization, sensitivity analysis, and model calibration. While traditional GP emulators effectively model continuous and Gaussian-distributed simulator outputs with homogeneous variability, they typically struggl