November 2025 arXiv papers — page 137

Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar

We present a hybrid variational framework for quantum optimal control aimed at high-fidelity state transfer in spin chains. The system dynamics are discretized and compiled into a parameterized circuit, where deterministic two-qubit blocks implement the drift interactions, while trainable on-site RZ rotations encode the control inputs. We study two parameter

Arda Aydin, Nicolas Delfosse, Edwin Tham

Hypergraph product (HGP) codes are one of the most popular family of quantum low-density parity-check (LDPC) codes. Circuit-level simulations show that they can achieve the same logical error rate as surface codes with a reduced qubit overhead. They have been extensively optimized by importing classical techniques such as the progressive edge growth, or thro

Tiansheng Huang, Virat Shejwalkar, Oscar Chang, Milad Nasr

Instilling reasoning capabilities in large models (LMs) using reasoning training (RT) significantly improves LMs' performances. Thus Audio Reasoning Models (ARMs), i.e., audio LMs that can reason, are becoming increasingly popular. However, no work has studied the safety of ARMs against jailbreak attacks that aim to elicit harmful responses from target model

Tairan Huang, Yulin Jin, Junxu Liu, Qingqing Ye

Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existing black-box attacks focus on vector-based or discrete-action RL, and their effectiveness on image-based continuous control is limited by the large action space and excessive envir

Shunyuan Shang, Ziyuan Shi, Mohamed-Slim Alouini

This paper presents a unified analytical framework for a two phase underwater wireless optical communication (UWOC) system that integrates Simultaneous Lightwave Information and Power Transfer (SLIPT) using a photovoltaic (PV) panel receiver. The proposed architecture enables self powered underwater sensor nodes by leveraging wide area and low cost PV panels

Samantha J. Fournier, Pierfrancesco Urbani

We study the dynamical properties of a broad class of high-dimensional random dynamical systems exhibiting chaotic as well as fixed point and periodic attractors. We consider cases in which attractors can co-exists in some regions of the phase diagrams and we characterize their nature by computing the maximal Lyapunov exponent. For a specific choice of the d

Cameron. Crabb, Zachary. T. Kloenne, Samuel. R. Rogers, Chi-Hang. D. Kwok

Understanding how protective oxide scales evolve over time is necessary for improving the long term resistance of superalloys. This work investigates the time-dependent oxidation behavior of an ingot-processable Co/Ni-based superalloy oxidized in air at $800~^\circ\mathrm{C}$ for $20$, $100$, and $1000~\mathrm{h}$ . Mass-gain and white-light interferometry m

Pedro Dall'Antonia, Tiago da Silva, Daniel Augusto de Souza, César Lincoln C. Mattos

Generative Flow Networks (GFlowNets) are powerful samplers for compositional objects that, by design, sample proportionally to a given non-negative reward. Nonetheless, in practice, they often struggle to explore the reward landscape evenly: trajectories toward easy-to-reach regions dominate training, while hard-to-reach modes receive vanishing or uninformat

Milenne Ávila-Bravo, Carolina Charalambous, Claudia Aguilera-Gómez

Context. The presence of a stellar companion can strongly influence the architecture and long-term stability of planetary systems. Motivated by the discovery of exoplanets exhibiting extremely high eccentricities (e >= 0.8) in systems with a binary companion, we investigate how planetary orbits around one star (S-type configuration) evolve under the gravitat

Felix B. Mueller, Jan F. Meier, Timo Lueddecke, Richard Vogg

Non-human primates are our closest living relatives, and analyzing their behavior is central to research in cognition, evolution, and conservation. Computer vision could greatly aid this research, but existing methods often rely on human-centric pretrained models and focus on single datasets, which limits generalization. We address this limitation by shiftin

Hannah Lu, Lluıs Salo-Salgado, Ruben Juanes

Fault zones exhibit complex and heterogeneous permeability structures influenced by stratigraphic, compositional, and structural factors, making them critical yet uncertain components in subsurface flow modeling. In this study, we investigate how lithological controls influence fault permeability using the PREDICT framework: a probabilistic workflow that cou

S. K. Mercourakis, G. Vassiliadis

We study some touching properties of the three-dimensional Petty space $X=(\ell_2^2 \oplus \mathbb{R})_1$. In particular we give an estimation of its Hadwiger number and also show that its equilateral subsets $A$ of maximum cardinality (i.e. $|A|=e(X)$) do not have a center.

Samuel Maddock, Shripad Gade, Graham Cormode, Will Bullock

State-of-the-art differentially private synthetic tabular data has been defined by adaptive 'select-measure-generate' frameworks, exemplified by methods like AIM. These approaches iteratively measure low-order noisy marginals and fit graphical models to produce synthetic data, enabling systematic optimisation of data quality under privacy constraints. Graphi

Zeyuan Sun, Mengting Sun, Rajiv Giridharagopal, Robert C. Hamburger

Coupled ionic and electronic transport underpins processes as diverse as electrochemical energy conversion, biological signaling, and soft adaptive electronics. Yet, how chemical environments such as pH modulate this coupling at the molecular scale remains poorly understood. Here, we show that the protonation state of carboxylated polythiophenes provides pre

Chanchal Sharma, Shuvayu Roy, Sudipta Sarkar

Black holes in four-dimensional, asymptotically flat general relativity have vanishing static tidal Love numbers (TLNs), a property tied to a hidden symmetry of the perturbation equations. Within the Konoplya-Rezzolla-Zhidenko (KRZ) parametrization, a subclass of spacetimes was previously shown to admit such Ladder symmetry, which enforces the absence of sta

Song He, Xuhang Jiang, Xiang Li, Jiahao Liu

We revisit the symbol bootstrap program for the seven-particle MHV and NMHV amplitudes in planar $\mathcal{N}=4$ super-Yang-Mills (SYM) based on the alphabet associated with the $E_6$ cluster algebra. After imposing integrability, cluster adjacency (or extended Steinmann), first- and last-entry conditions, the solution space is already highly restrictive: e.

Zhe-Yu Daniel Lin, Alycia J. Weinberger, Evgenij Zubko, Jessica A. Arnold

Light scattering by dust particles is often modeled assuming the dust is spherical for numerical simplicity and speed. However, real dust particles have highly irregular morphologies that significantly affect their scattering properties. We have developed glitterin, a neural network trained to predict light scattering from irregularly shaped dust grains, off

Zekun Wu, Mayank Jobanputra, Vera Demberg, Jessica Hullman

The growing use of AI-generated responses in everyday tools raises concern about how subtle features such as supporting detail or tone of confidence may shape people's beliefs. To understand this, we conducted a pre-registered online experiment (N = 304) investigating how the detail and confidence of AI-generated responses influence belief change. We introdu

Damian Suski, Maria Cywinska, Julianna Winnik, Michal Jozwik

Advanced geometrical nanometrology is critical for process control in semiconductor manufacturing, supporting applications in, e.g., photonic integrated circuits, nanoelectronics, and emerging quantum and optoelectronic technologies. Widefield interferometric approach provide a cost-effective, non-destructive solution for characterizing semiconductor optical

Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi

Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of $(x,y)$ pairs as context and predicts labels for new inputs without weight updates. We challenge the prevailing view that broad generalization here requires pre-training on large synthetic corpora (e.g., TabPFN priors) or a large collection of

Quentin Marsal, Hui Liu, Emil J. Bergholtz, Annica M. Black-Schaffer

Spatially resolved local quantum geometric markers play a crucial role in the diagnosis of topological phases without long-range translational symmetry, including amorphous systems. Here, we focus on the nonlocality of such markers. We demonstrate that they behave as correlation functions independently of the material's structure, showing sharp variations in

Cumi Oyemike, Elizabeth Akpan, Pierre Hervé-Berdys

Frontier LLMs are optimised around high-resource assumptions about language, knowledge, devices, and connectivity. Whilst widely accessible, they often misfit conditions in the Global South. As a result, users must often perform additional work to make these systems usable. We term this alignment debt: the user-side burden that arises when AI systems fail to

Lina Sartinska

This study explores the properties of nanopowders synthesized under high-temperature, non-equilibrium conditions in a high-flux optical furnace in a nitrogen flow. Boron powders served as the starting material, and the intense thermal gradients during synthesis led to incomplete chemical reactions. As a result, the surface of the resulting nanoparticles is c

Elias Milios, Kim P. Wabersich, Felix Berkel, Felix Gruber

Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learning a computationally cheaper surrogate policy. Common approaches focus on imitation learning (IL) via behavioral cloning (BC), minimizing a mean-squared-error loss on a collection

Ethan Tregidga, David Harvey, Luca Biggio, Felix Vecchi

We have developed a machine learning algorithm capable of detecting ``out-of-domain data'' for trustworthy cosmological inference. By using data from two separate suites of cosmological simulations, we show that our algorithm is able to determine whether ``observed'' data is consistent with its training domain, returning confidence estimates as well as accur

Ujjaini Das, Shreya Kappala, Meng Chen, Mina Huh

Videos make exercise instruction widely available, but they rely on visual demonstrations that blind and low vision (BLV) learners cannot see. While audio descriptions (AD) can make videos accessible, describing movements remains challenging as the AD must convey what to do (mechanics, location, orientation) and how to do it (speed, fluidity, timing). Prior

Matthew Barber, Stefano Pirandola

Bipartite entanglement purification is the conversion of copies of weakly entangled pairs shared between two separated parties into a smaller number of strongly entangled shared pairs using only local operations and classical communication. Choosing between different entanglement purification protocols generally involves weighing up a trade-off between the r

Ethan Payne, Lee McCuller, Katerina Chatziioannou

Gravitational waves emitted after neutron star binary coalescences and the information they carry about dense matter are a high-priority target for next-generation detectors. Even though such detectors are expected to observe millions of signals, detectable postmerger emission will remain rare. In this work, we explore postmerger detectability and inference

Yu. Yu. Dubenskaya, S. P. Polyakov, A. P. Kryukov, A. P. Demichev

The aim of extensive air shower (EAS) analysis is to reconstruct the physical parameters of the primary particle that initiated the shower. The TAIGA experiment is a hybrid detector system that combines several imaging atmospheric Cherenkov telescopes (IACTs) and an array of non-imaging Cherenkov detectors (TAIGA-HiSCORE) for EAS detection. Because the signa

Filipp Chernikov, Simon Ekhammar, Nikolay Gromov, Benjamin Smith

We propose a Quantum Spectral Curve for planar string theory on AdS3*S3*S3*S1 supported by pure Ramond-Ramond flux. Our proposal is built on symmetry considerations and integrability-based functional relations. To test our construction, we consider the large volume limit and successfully reproduce the cross- ing equations and the correct structure of the Bet

Mohammad Alipour-Vaezi, Huaiyang Zhong, Kwok-Leung Tsui, Sajad Khodadadian

Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective function, which is critical in various fields, including healthcare and finance. A popular approach to incorporate risk sensitivity is to optimize a specific quantile of the cumulative

Mohamed Assili, Panagiotis Kotetes

We bring forward a Green function approach for the prediction of Floquet topological phases in driven superconductor-semiconductor hybrids. Although it is common to treat the superconducting component as a mere Cooper-pair reservoir, it was recently pointed out that such an approximation breaks down in the presence of driving, due to the emergence of level b

Joey Braspenning, Joop Schaye, Annalisa Pillepich, Dylan Nelson

Galaxy groups and clusters are excellent probes of large-scale structure and are shaped by some of the most energetic physical processes in the Universe. They follow a tight scaling relation of X-ray luminosity with halo mass. However, predicting the dependence of the scatter in this relation on mass and redshift is challenging, due to the statistical requir

Pau Amaro Seoane

We investigate the application of fractional calculus to model stellar dynamics, focusing on Resonant Relaxation (RR) near a supermassive black hole (SMBH). Standard theories use the local Fokker-Planck (FP) equation, restricted to Gaussian processes under the Central Limit Theorem (CLT). We argue this is inadequate for RR. We demonstrate that gravitational

Franz J. Schreiber, Maximilian J. Kramer, Alexander Nietner, Jens Eisert

The Boolean satisfiability problem (SAT) is of central importance in both theory and practice. Yet, most provable guarantees for quantum algorithms rely exclusively on Grover-type methods that cap the possible advantage at only quadratic speed-ups, making the search for approaches that surpass this quadratic barrier a key challenge. In this light, this work

Nur E. M. Rifat, David A. Nichols, Kent Yagi

Gravitational waves from comparable-mass binary-black-hole mergers are often described in terms of three stages: inspiral, merger and ringdown. Post-Newtonian and black-hole perturbation theories are used to model the inspiral and ringdown parts of the waveform, respectively, while the merger phase has been modeled most accurately using numerical relativity

Michel Pannier

This paper proposes a definition of what has previously been coined a Wilson Spool in the case of three-dimensional gravity with vanishing cosmological constant. The definition builds upon a construction of the one-loop partition function of a massive, spinning field from a fixed background holonomy. While the background is taken to be a flat-space cosmology

Elizaveta Sazonova, Cameron R. Morgan, Michael Balogh, Matías Blaña

Quantitative morphology provides a key probe of galaxy evolution across cosmic time and environments. However, these metrics can be biased by changes in imaging quality - resolution and depth - either across the survey area or the sample. To prepare for the upcoming Rubin LSST data, we investigate this bias for all metrics measured by statmorph and single-co

Oscar Bouverot-Dupuis, Vincent Grison, Nicolas Paris

We investigate the phase diagram of a one-dimensional dissipative Bose-Hubbard model using the nonperturbative functional renormalization group (FRG). Each lattice site is coupled to an independent bath, generating long-range temporal interactions that encode non-Markovian dissipation. For a broad class of bath spectra - ohmic, sub-ohmic, and super-ohmic - w

Minghao Xia, Liang Ma, Yi Pang, H. Lu

We present a comprehensive analysis of the full spectrum of tidal Love numbers for Reissner-Nordstr\"om (RN) black holes in general spacetime dimensions. By perturbing the Einstein-Maxwell theory around the $D$-dimensional RN background, we derive an effective two dimensional quadratic action encompassing tensor, vector, and scalar-type perturbation sectors.

Megan R. Sturm, Amy E. Reines, Anne M. Lohfink, Akos Bogdan

We present Chandra X-ray Observatory and Hubble Space Telescope (HST) follow-up observations of 12 dwarf galaxies from Reines et al. (2020) that are potential hosts of radio-selected active galactic nuclei (AGNs), eight of which are non-nuclear and possible ``wandering" black holes (BHs). Our multi-wavelength analysis indicates a heterogeneous sample with fi

Lewis R. Prole, John A. Regan, Daxal Mehta, Rudiger Pakmor

Here we introduce the SEEDZ simulations, a suite of cosmological hydrodynamic simulations exploring the formation and growth of the first massive black holes in the Universe. SEEDZ includes models for Population III star formation, supernovae explosions and the resulting formation of light seed black holes, metal enrichment and subsequent Population II star

Bastien Lapierre, Per Moosavi, Blagoje Oblak

Much of our understanding of gapless quantum matter stems from low-energy descriptions using conformal field theory. This is especially true in 1+1 dimensions, where such theories have an infinite-dimensional parameter space induced by their conformal symmetry. We reveal the underlying quantum geometry by considering finite many-body systems driven by time-d

Nejc Čeplak, Hong Liu, Andrei Parnachev, Samuel Valach

The analytic structure of holographic correlation functions at finite temperature contains information about curvature singularities of black holes in AdS. We compute the Operator Product Expansion (OPE) coefficients of the holographic two-point function of scalar operators at finite temperature and finite chemical potential. We show that the stress-tensor a

Ethan Baker, Hongwan Liu

This is the second part in a pair of papers forecasting the sensitivity of the Square Kilometre Array (SKA) to dark photons, a highly motivated, simple extension of the Standard Model. Through a kinetic mixing term, visible photons from the cosmic microwave background can resonantly convert into dark photons, generating new temperature anisotropies in the sk

Ishaan Madan, Ben K. D. Pearce

Saturnian moon Titan presents a compelling testbed for probing prebiotic chemistry beyond early Earth. Impact-generated melt pools provide transient aqueous habitats in an otherwise cryogenic environment. We use Cantera equilibrium models to assess whether mixtures of hydrogen cyanide (HCN), acetylene (C2H2), and ammonia (NH3) can drive amino acid synthesis

Andrea Cavaglià, Rouven Frassek, Nicolò Primi, Roberto Tateo

In this paper, we put forward and discuss a proposal for a Quantum Spectral Curve (QSC) describing the planar spectrum of the holographic CFT dual to strings on AdS$_3\times$ S$^3\times$ S$^3\times$ S$^1$, a theory with global symmetry $\mathfrak{d}(2,1;\alpha)^{\oplus 2}$. We focus mainly on the case when the radii of the two spheres are the same, i.e. $\al

Alexandre M. Pombo, Lorenzo Pizzuti, Alessandra di Giacomo

Can a dynamically robust (\textit{aka} stable) $Q$-ball reproduce the rotation curve of a disk galaxy? In an astrophysical environment, $Q$-balls are non-topological solitons that are transparent and only perceived by their gravitational effects. Traditionally, scalar $Q$-balls are modelled with a polynomial potential, but axion-like periodic potentials are

Madhumita Sarkar, Ben Zindorf, Bhaskar Mukherjee, Sougato Bose

Periodic driving enables the engineering of complex quantum matter, yet in interacting systems it generically leads to energy absorption, which limits the lifetime of the engineered states. To address this challenge, dynamical freezing has been proposed as a mechanism for stabilizing non-equilibrium states over parametrically long timescales. While theory pr

Bogdan A. Dobrescu, Max H. Fieg

We study properties of a hypothetical scalar particle, $\Theta$, which is a color octet and an electroweak singlet. At hadron colliders, $\Theta$ is pair produced through its QCD coupling to gluons, so that its mass determines the cross section. It decays at tree level into $q\bar q$ through dimension-5 operators, and at one loop into gluons. Thus, the main

Christopher Dessert, Soubhik Kumar, Joshua T. Ruderman

The presence of multiple light axions in the infrared is a generic feature of many ultraviolet (UV) scenarios. In many cases the number of axions ${\cal N}$ is ${\cal O}(10-100)$ or more. Even in the scenario where these axions interact very weakly with the Standard Model (SM), the presence of ${\cal N}$ light axions poses a challenge to the stringent constr

Ethan Baker, Hongwan Liu

Mixing between dark photons and visible photons leads to substantial anisotropies in the cosmic microwave background due to resonant conversions of visible photons into dark photons in baryonic matter found in dark matter halos. In this Letter, we forecast the sensitivity of the Square Kilometre Array (SKA) to this signal. We find that SKA could be the first

Shaofeng Huang, Yu-Peng Wang, Jie Ren, Chen Fang

Superdiffusion is an anomalous transport behavior. Recently, a new mechanism, termed the ``nodal mechanism," has been proposed to induce superdiffusion in quantum models. However, existing realizations of the nodal mechanism have so far been proposed on fine-tuned, artificial Hamiltonians, posing a significant challenge for experimental observation. In this

Charles J. Law, Romane Le Gal, Karin I. Öberg, Ke Zhang

The sulfur chemistry in protoplanetary disks influences the properties of nascent planets, including potential habitability. Although the inventory of sulfur molecules in disks has gradually increased over the last decade, CS is still the most commonly-observed sulfur-bearing species and it is expected to be the dominant gas-phase sulfur carrier beyond the w

Tyler Gorda, Pablo Navarrete, Risto Paatelainen, Leon Sandbote

We demonstrate that at finite density and sufficiently high temperatures, phase-quenched (PQ) lattice simulations combined with perturbation theory provide a new precision approach to determining the thermodynamics of QCD across a wide arc of the phase diagram where the strong coupling constant $α_s$ remains small. In this regime, nonperturbative pairing eff

Nicholas Kaaz, Matthew Liska, Charlotte Ward, Jordy Davelaar

Changing-look active galactic nuclei (CLAGN) feature order-of-magnitude variability in both the continuum and broad line luminosities on months-to-years long timescales, and are currently unexplained. Simulations have demonstrated that rotating black holes sometimes tear apart tilted accretion disks. These tearing events violently restructure the disk on tim

Masaki Yamada

The Lorentzian path integral for the wave function of the Universe is only conditionally convergent and thus requires a well-defined prescription. The Picard-Lefschetz approach ensures convergence through contour deformation, but it has been argued that this leads to unsuppressed perturbations due to relevant saddle points residing in the region ${\rm Im}N>0

Thomas Schuster, Bryce Kobrin, Vincent P. Su, Hugo Marrochio

We analyze a simple and efficient experimental protocol to cool the Sachdev-Ye-Kitaev (SYK) model to low temperatures. The protocol utilizes local couplings between two copies of an SYK model to create a gapped adiabatic path, between a high temperature product state and a low temperature thermofield double state. By smoothly varying the coupling strength be

Seunghwan Lim, Sandro Tacchella, Roberto Maiolino, Christopher C. Lovell

We use the highest-resolution FLAMINGO hydrodynamical simulation to quantify cosmic variance and large-scale coherence in the evolution of massive galaxies at high redshift. FLAMINGO combines a $(1\,\mathrm{cGpc})^3$ volume with baryonic resolution sufficient to identify ${\gtrsim}\,10^3$ independent JWST-like survey volumes of $(100\,\mathrm{cMpc})^3$, prov

J. S. G. Mombarg, V. Vanlaer, S. B. Das, M. Rieutord

Asymmetries in the observed rotational splittings of a multiplet contain information about the star's rotation profile and internal magnetic field. However, to exploit this information, highly accurate theoretical predictions are needed. We aim to quantify the difference in the predicted mode asymmetries between a 1D perturbative method, and a 2D method that

Matthew Dodelson, Cristoforo Iossa, Robin Karlsson

A sharp signature of the black hole singularity in holography is a divergence in the boundary thermal two-point function at a specific point in the complex time plane. This divergence arises from a null geodesic that bounces off the black hole singularity. At finite 't Hooft coupling, stringy corrections to the bulk dynamics cannot be neglected, and the fate

Achilleas Gitsis, Falk Hassler

Compatibility with T-duality severely constrains higher-derivative corrections to the low-energy supergravity limits of string theory. For example, it suggests that Lorentz transformations for heterotic strings are modified in precisely the way required for the Green-Schwarz anomaly cancellation mechanism. A systematic procedure to construct the resulting ge

Peter W. Graham, Harikrishnan Ramani, Olivier Simon, Erwin H. Tanin

We showcase cosmology's ability to constrain long-range forces between dark matter particles. Specifically, we consider a fermionic dark matter interacting via a Yukawa-coupled light scalar, focusing on regimes where the dark forces are stronger than gravitational and yet unconstrained. We show that the dark sector dynamics, both at the background and pertur

Zihni Kaan Baykara, Markus Dierigl, Hee-Cheol Kim, Cumrun Vafa

We place bounds on the order of enhanced discrete gauge symmetries that act on massless fields and thus arise at subloci of the moduli space in supergravity theories. We focus on supersymmetric theories with 8 or more supercharges which in some cases lead to sharp upper bounds realized by specific string constructions.

Ray Muxin Liu, Mingxuan Li, Kenneth Shaw, Deepak Pathak

Large Vision Models trained on internet-scale data have demonstrated strong capabilities in segmenting and semantically understanding object parts, even in cluttered, crowded scenes. However, while these models can direct a robot toward the general region of an object, they lack the geometric understanding required to precisely control dexterous robotic hand

Alexey Cheskidov, Mimi Dai, Stan Palasek

For any smooth, divergence-free initial data, we construct a solution of the Navier--Stokes equations that exhibits Type~I blow-up of the $L^\infty$ norm at time $T_*>0$, while remaining smooth in space and time on $\mathbb T^d\times([0,T]\setminus\{T_*\})$. An instantaneous injection of energy from infinite wavenumber initiates a bifurcation from the classi

Hao Shi, Bin Xie, Yingfei Liu, Yang Yue

Robotic manipulation requires precise spatial understanding to interact with objects in the real world. Point-based methods suffer from sparse sampling, leading to the loss of fine-grained semantics. Image-based methods typically feed RGB and depth into 2D backbones pre-trained on 3D auxiliary tasks, but their entangled semantics and geometry are sensitive t

Isaac Robinson, Peter Robicheaux, Matvei Popov, Deva Ramanan

Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that

Ye Tian, Ling Yang, Jiongfan Yang, Anran Wang

While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modal

Germán Stefanich

We discuss a systematic procedure for categorifying presentable six-functor formalisms. Our main result produces, given the input of a representation of the $\infty$-category of correspondences of an $\infty$-category with finite limits $\mathcal{C}$, a compatible sequence of representations of the $(\infty,n)$-category of correspondences of $\mathcal{C}$ fo

Abhirup Das, Pranav Dudani, Shruti Sharma, Ravi Kumar C.

In today's digital world, which has many different types of media, steganography, the art of secret communication, has a lot of problems to deal with. Traditional methods are often fixed and only work with one type of carrier media. This means they don't work well with all the different types of media that are out there. This system doesn't send data to "wea

John Bostanci, Jonas Haferkamp, Chinmay Nirkhe, Mark Zhandry

We construct a classical oracle proving that, in a relativized setting, the set of languages decidable by an efficient quantum verifier with a quantum witness (QMA) is strictly bigger than those decidable with access only to a classical witness (QCMA). The separating classical oracle we construct is for a decision problem we coin spectral Forrelation -- the

Alexey A. Sokolik, Azat F. Aminov, Evgenii E. Vdovin, Yurii N. Khanin

Tunneling conductance between two bilayer graphene (BLG) sheets separated by 2 nm-thick insulating barrier was measured in two devices with the twist angles between BLGs less than 1{\deg}. At small bias voltages, the tunneling occurs with conservation of energy and momentum at the points of intersection between two relatively shifted Fermi circles. Here, we

Daniel Platnick, Dawson Tomasz, Eamon Earl, Sourena Khanzadeh

Greedy search methods like Greedy Best-First Search (GBFS) and Enforced Hill-Climbing (EHC) often struggle when faced with Uninformed Heuristic Regions (UHRs) like heuristic local minima or plateaus. In this work, we theoretically and empirically compare two popular methods for escaping UHRs in breadth-first search (BrFS) and restarting random walks (RRWs).

Johan Asplund, Yash Deshmukh, Alex Pieloch

An exact Lagrangian submanifold $L \subset X^{2n}$ in a Weinstein sector is called a nearby Lagrangian cocore if it avoids all Lagrangian cocores and is equal to a shifted Lagrangian cocore at infinity. Let $k$ be the dimension of the core of the subcritical part of $X$. For $n \geq 2k+2$ we prove that that the inclusion of $L$ followed by the retract to the

George Turner, Vojtěch Pravda, Alena Pravdová

In this paper, we study asymptotic properties of static spherically symmetric black holes in quadratic gravity with a cosmological constant $\Lambda$. We find that for sufficiently large values of $|\Lambda|$ these black holes are generically asymptotically (A)dS and form a three-parameter family of black holes, with free parameters being the horizon radius,

Xing-Yu Zhang, Qi Yang, Philippe Corboz, Jutho Haegeman

We revisit gradient-based optimization for infinite projected entangled pair states (iPEPS), a tensor network ansatz for simulating many-body quantum systems. This approach is hindered by two major challenges: the high computational cost of evaluating energies and gradients, and an ill-conditioned optimization landscape that slows convergence. To reduce the

Etienne Dallaire

This paper addresses the guessing game in building production RAG. Classical rank-centric IR metrics (nDCG/MAP/MRR) are a poor fit for RAG, where LLMs consume a set of passages rather than a browsed list; position discounts and prevalence-blind aggregation miss what matters: whether the prompt at cutoff K contains the decisive evidence. Second, there is no s

Shiming Gu, Ludovic van Waerbeke, Francis Bernardeau, Sébastien Fabbro

The Bernardeau-Nishimichi-Taruya (BNT) transform provides a powerful framework for analysing tomographic cosmic shear data by improving the localization of shear correlations in physical scale. It operates by performing a linear combination of the shear data vector in $\ell$-space, yielding a transformed vector that is better localized in both redshift and $

Justin Kin Jun Hew, David N. Hosking, Christoph Federrath, James R. Beattie

Hosking & Schekochihin (2021, Phys. Rev. X 11, 041005) have proposed that statistically isotropic decaying MHD turbulence without net magnetic helicity conserves the mean square fluctuation level of magnetic helicity in large volumes -- or, equivalently, the integral over space of the two-point correlation function of the magnetic-helicity density, denoted $

Daniyal Ganiuly, Nurzhau Bolatbek

The increasing virtualization of fifth generation (5G) networks expands the attack surface of the user plane, making spoofing a persistent threat to slice integrity and service reliability. This study presents a slice-aware lightweight machine-learning framework for detecting spoofing attacks within 5G network slices. The framework was implemented on a repro

Francisco J. Herranz, Alfonso Blasco, Rutwig Campoamor-Stursberg, Ivan Gutierrez-Sagredo

A superintegrable generalization of the classical and quantum Zernike systems is reviewed. The corresponding Hamiltonians are endowed with higher-order integrals and can be interpreted as higher-order superintegrable perturbations of the 2D spherical (Higgs), hyperbolic, and Euclidean harmonic oscillators. As a new result, the complete polynomial Higgs-type

Yini Li, Louis Forster, David Bull, Nantheera Anantrasirichai

The acquisition of paired low-light video sequences remains challenging due to issues associated with poor temporal consistency, varying illumination characteristics and camera parameters. This has driven significant interest in unsupervised low-light enhancement approaches. In this context, we propose TempRetinex, an unsupervised Retinex-based video enhance

Minye Shao, Sihan Guo, Xinrun Li, Xingyu Miao

Recent advances in context optimization (CoOp) guided by large language model (LLM)-distilled medical semantic priors offer a scalable alternative to manual prompt engineering and full fine-tuning for adapting biomedical CLIP-based vision-language models (VLMs). However, prompt learning in this context is challenged by semantic misalignment between LLMs and

Mohamed Elaraby, Jyoti Prakash Maheswari

Large Language Models (LLMs) with extended context windows promise direct reasoning over long documents, reducing the need for chunking or retrieval. Constructing annotated resources for training and evaluation, however, remains costly. Synthetic data offers a scalable alternative, and we introduce SynClaimEval, a framework for evaluating synthetic data util

Felix Pogorzelski, Elias Zimmermann

The paper is devoted to equipartition of measured information for finite state processes over regular trees whose laws are invariant under all parity preserving tree automorphisms. We show almost everywhere equipartition for ergodic processes along spheres and balls in every horosphere. Moreover, under a quantitive mixing condition we obtain a Shannon-McMill

Mamadou K. Keita, Christopher Homan, Huy Le

We introduce negative space learning machine translation (NSL-MT), a training method for underresourced languages, that augments limited parallel data with synthetically generated violations of the target language's grammar and explicitly penalizes the model when it assigns high probability to these linguistically invalid outputs. NSL-MT delivers improvement

Rémi Cardon, A. Seza Doğruöz

Readability is a key concept in the current era of abundant written information. To help making texts more readable and make information more accessible to everyone, a line of researched aims at making texts accessible for their target audience: automatic text simplification (ATS). Lately, there have been studies on the correlations between automatic evaluat

Niklas Lauffer, Ameesh Shah, Micah Carroll, Sanjit A. Seshia

Adversarial optimization algorithms that explicitly search for flaws in agents' policies have been successfully applied to finding robust and diverse policies in multi-agent settings. However, the success of adversarial optimization has been largely limited to zero-sum settings because its naive application in cooperative settings leads to a critical failure

Adam Gargasson, Julien Bouvard, Carine Douarche, Peter Mergaert

Bacteria can adjust their swimming behaviour in response to chemical variations, a phenomenon known as chemotaxis. This process is characterised by a drift velocity that depends non-linearly on the concentration of chemical species and its "local" gradient. To study this process more effectively, we optimised a 3-channel microfluidic device to generate a sta

Farhad Rezazadeh, Pegah Bonehgazy

This paper investigates how individual entrepreneurs can turn creative ideas into successful solo businesses in an era increasingly shaped by Artificial Intelligence (AI) agents. It highlights the key steps that connect personal vision, structured experimentation, and lasting value creation, and shows how AI agents can act as digital co-founders throughout t

I. E. Ochs

Wave interactions with magnetized particles underly many plasma heating and current drive technologies. Typically, these interactions are modeled by bounce-averaging the quasilinear Kennel-Engelmann diffusion tensor over the particle orbit. However, as an object derived in a two-dimensional space, the Kennel-Engelmann tensor does not fully respect the conser

Gregory Kehne, Thomas Kesselheim

Many online problems are studied in stochastic settings for which inputs are samples from a known distribution, given in advance, or from an unknown distribution. Such distributions model both beyond-worst-case inputs and, when given, partial foreknowledge for the online algorithm. But how robust can such algorithms be to misspecification of the given distri

Márton Balázs, Edward Crane, Alexander Tallis

We give a rigorous solution of an optimisation problem of minimizing the expected delay caused by encountering a red traffic light on a road journey. The problem incorporates simple constraints on maximum speed, acceleration and braking rates, and depends on the assumed distribution of the remaining time until the traffic light will turn green, after it is f

Samyak Sanghvi, Nishant Ranjan, Tarak Karmakar

Structure-based drug design (SBDD) faces a fundamental scaling fidelity dilemma: rich pocket-aware conditioning captures interaction geometry but can be costly, often scales quadratically ($O(L^2)$) or worse with protein length ($L$), while efficient sequence-only conditioning can miss key interaction structure. We propose SiDGen, a structure-informed discre

Sudhakar Sah, Nikhil Chabbra, Matthieu Durnerin

Deep Convolutional Neural Networks (CNNs) are increasingly difficult to deploy on microcontrollers (MCUs) and lightweight NPUs (Neural Processing Units) due to their growing size and compute demands. Low-rank tensor decomposition, such as Tucker factorization, is a promising way to reduce parameters and operations with reasonable accuracy loss. However, exis

Evan Miller

In this paper, we show that the positive multiples of a particular function $F$ -- which is singular with a jump discontinuity at the origin -- are finite-time global attractors in $L^2$ for generic odd, smooth solutions of the one dimensional inviscid Burgers equation. Furthermore, the identity that leads to this result provides to an alternative proof of f

Tian Lan, Rishad Shafik, Alex Yakovlev

Machine learning fits model parameters to approximate input-output mappings, predicting unknown samples. However, these models often require extensive arithmetic computations during inference, increasing latency and power consumption. This paper proposes a digital-time-domain computing approach for Tsetlin machine (TM) inference process to address these chal

Yunfei Shen, Zhongcheng Wu

As autonomous driving technology advances, the critical challenge evolves beyond collision avoidance to the \textbf{adjudication of liability} when accidents occur. Existing datasets, focused on detection and localization, lack the annotations required for this legal reasoning. To bridge this gap, we introduce the \textbf{C}hinese \textbf{A}ccident \textbf{D

Margarita Geleta, Hong Sodoma, Hannes Gamper

Language barriers in virtual meetings remain a persistent challenge to global collaboration. Real-time translation offers promise, yet current integrations often neglect perceptual cues. This study investigates how spatial audio rendering of translated speech influences comprehension, cognitive load, and user experience in multilingual meetings. We conducted