April 2026 arXiv papers — page 14

Truman Yu Ng, Yuzhu Wang, Wei Jie Chan, Ruizhe Shen

Knots and links represent a fundamental motif of non-local connectivity that permeates the physical sciences from string theory to protein folds. While spectral braiding has been explored in two-band non-Hermitian models across various platforms, its direct simulation and characterization on programmable quantum hardware, particularly beyond two strands, rem

Jonas Bresch

Optimization of quadratic functions and their quotients is relevant in subspace and iterative optimization methods. In this paper, we consider the matrix-free computation of the generalized operator norm and the maximization of a generalized Rayleigh quotient when only forward evaluations of two linear operators $A$ and $B$ are available. The proposed method

Nishil Mehta, Vivien Parmentier, Xianyu Tan, Elspeth K. H. Lee

JWST has shown a large diversity in warm Jupiter spectra, despite only small variations in the planetary parameters. However, the main driver of this diversity remains unclear. We aim to identify the mechanisms responsible for the spectral difference of three warm Jupiter-size exoplanets observed by JWST: whereas WASP-80b appears mostly cloud-free, both WASP

Maksym Nechepurenko

Prediction-market price moves are widely treated as informationally equivalent: a price jump is read the same way regardless of whether it reflects durable Bayesian updating, transient liquidity pressure, strategic position adjustment, or genuine disagreement. This paper formalizes the Signal Credibility Index (SCI) introduced in Nechepurenko (2026) as a sta

Ruben Malacarne, Ioannis Tsikelis, Enrico Mingo Hoffman, Michele Focchi

This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulated as a bi-level optimization scheme that addresses a mixed-integer problem: selecting feasible terrain regions for landing while simultaneously optimizing the control inputs, namely rope tensions and leg forc

Rikuto Fukumori, Chengyi Luo, Alexey Tiranov, Karolina Waszkowska

Long-range interactions between emitters give rise to collective phenomena, including superradiance, spin squeezing, and coherence protection, that are important to both fundamental physics and quantum technologies. Despite progress in cold atoms, coherent cavity-mediated all-to-all interactions have not yet been realized in a solid-state ensemble. Here we d

Nikita Solonovich, Chaoliang Ding, Polina P. Kuzhir, Tero Setälä

Ghost imaging uses two light beams correlated in the transverse position, time, or frequency to create an image of a spatial, temporal, or spectral object. We propose a scheme of time-to-space ghost imaging for creating a spatial image of a temporal object, enabled by two spatio-temporally correlated light beams. Assuming a spatio-temporal Gaussian Schell mo

Sankha Subhra Bakshi, Brandon B. Le, Seung-Hun Lee, Gia-Wei Chern

We investigate the effect of spatial exchange anisotropy on the spin-$1/2$ kagome antiferromagnet using Schwinger-boson mean-field theory. The anisotropy is introduced by strengthening the Heisenberg exchange along one set of nearest-neighbor bonds relative to the other two, and is controlled by a parameter $\delta$ that measures the deviation from the isotr

Yuming Paul Zhang

This paper is a continuation of the work in \cite{kimzhang2024} concerning Hele-Shaw flow with both drift and source terms. We prove that, in a local neighborhood, if the free boundary is Lipschitz continuous with a sufficiently small Lipschitz constant, then the free boundary is $C^{1}$. As a corollary, we also consider the 2D vertical Hele-Shaw (or one-pha

Bjarne Bergh, Marco Parentin

Exploiting permutation invariance to reduce the exponential scaling of semidefinite programs in quantum information has emerged as a powerful computational technique. In this work, we develop a systematic framework for using this reduction via Schur-Weyl duality for optimization problems, and establish methods that allow one to work fully inside the permutat

Pengxue Xiang, Yuebo Cao, Hongying Yang

This paper considers the homogeneous Neumann initial-boundary value problem for Alopecia Areata chemotaxis model with weakly singular sensitivity. For any appropriately regular initial conditions,it is shown that the problem admits a global boundedness of classical solutions in two spatial dimensions. Moreover, through the explicit construction of Lyapunov f

Baktash Behmanesh, Ahmad Rezvanitabar

This paper provides a concise yet comprehensive review of recent advancements in millimeter-wave (mm-wave) oscillators below 100 GHz and sub-terahertz (sub-THz/THz) oscillators above 100 GHz for next-generation computing and communication systems, including 5G, 6G, and beyond. Various design approaches, including CMOS, SiGe, and III-V semiconductor technolog

Michael Greinecker, Karolina Vocke

We study stability notions for networked many-to-many matching markets with individually insignificant agents in distributional form. Outcomes are formulated as joint distributions over characteristics of agents and contract choices. Characteristics can lie in an arbitrary Polish space. We provide a mechanical method for transferring existence results for fi

Omanshu Thapliyal, Malarvizhi Sankaranarayanasamy, Ravigopal Vennelakanti

Safe navigation in cluttered environments is an important challenge for autonomous systems. Robots navigating through obstacle ridden scenarios need to be able to navigate safely in the presence of obstacles, goals, and ego objects of varying geometries. In this work, reachable set representations of the robot's real-time capabilities in the state space can

Andrea Agazzi, Giuseppe Bruno, Eloy Mosig García, Samuele Saviozzi

We prove pathwise convergence of the layerwise evolution of tokens in a finite-depth, finite-width transformer model with MultiLayer Perceptron (MLP) blocks to a continuous-time stochastic interacting particle system. We also identify the stochastic partial differential equation describing the evolution of the tokens' distribution in this limit and prove pro

Arne Eichholtz, Yongkang Li, Jutte Vijverberg, Tobias Groot

The Hypencoder, proposed by Killingback et al., is a retrieval framework that replaces the fixed inner-product scoring function used in standard bi-encoders with a query-specific neural network (the $q$-net), whose weights are generated by a hypernetwork from the contextualized query embeddings. This design enables more expressive relevance estimation while

Aytekin Çıbık, Rui Fang

Continuous data assimilation (CDA) nudges observational data into governing equations to recover the underlying flow and improve predictions. Existing rigorous CDA analyses focus primarily on incompressible flows, yet no physical flow is perfectly incompressible. Approximating a slightly compressible flow with an incompressible model introduces non-negligibl

Sherzod R. Otajonov, Uktambek R. Eshimbetov, Bakhram A. Umarov, Fatkhulla Kh. Abdullaev

We investigate, both analytically and numerically, the scattering of quasi-one-dimensional quantum droplets from Pöschl-Teller potential wells and barriers. For attractive wells, we find a sharp transition between complete reflection and transmission at a critical incident velocity for both small and large flat-top droplets. The scattering interactions diffe

José G. Mijares

We prove a generalization of the infinite quantum Ramsey theorem of Kennedy et al. (arXiv:1711.09526), showing that it follows from an archetypical "selective" pattern satisfied by certain families of projections in an infinite-dimensional Hilbert space.

Fangqiang Fan, Zhicheng Zhao, Xiaoliang Ma, Chenglong Li

Fine-grained RGBT image semantic segmentation is crucial for all-weather unmanned aerial vehicle (UAV) scene understanding. However, UAV RGBT image semantic segmentation faces two coupled challenges: cross-modal spatial misalignment caused by sensor parallax and platform vibration, and severe semantic confusion among fine-grained ground objects under top-dow

Carol Hanna, Karine Even-Mendoza, W. B. Langdon, Mar Zamorano López

Despite the operational importance of hot fixes, large-scale evidence on how they reshape routine maintenance workflows, particularly in the era of autonomous coding agents, remains limited. We analyse hot fixes present in over 61,000 GitHub repositories from the Hao-Li/AIDev dataset and find consistent patterns of urgency: reduced collaboration (typically a

Geoff G. Murphy, Philip Bull, Mario G. Santos, Zheng Zhang

Foreground removal remains an ongoing challenge in radio cosmology, and increasingly sensitive experiments necessitate more robust analysis techniques. In this work, we model simulated data from a single-dish intensity mapping experiment, and use the Gibbs sampling and Gaussian constrained realisation (GCR) techniques to draw samples from the posterior proba

Yuang Yan, Ian Karlin, Ryan Grant

For NVIDIA GPUs, CUDA is the primary interface through which applications orchestrate GPU execution, yet much of the logic that realizes CUDA operations resides in NVIDIA's closed-source userspace driver. As a result, the translation from high-level CUDA APIs to low-level hardware commands remains opaque, limiting both software understanding and performance

Janis Mohr, Jörg Frochte

This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework, utilizing nearly shallow neural networks instead of decision trees as base learners. This innovative approach leverages neural network architectures, notably Convolutional Neural Networks (CNNs) and Capsule

Felix Eder, Zeno Maesen, Yurii Skourski, Enrico Giannini

The layered delafossite-like antiferromagnet AgCrSe$_2$ is a superionic conductor at high temperatures and has been reported to exhibit anomalous Hall behavior and Kondo physics at low temperatures. These extraordinary transport properties have been established almost exclusively on single crystals grown by chemical vapor transport, raising questions about t

Yingjie Dai, Zhuoyuan Liu, Tong Li, Xiujie Deng

Storage ring-based steady-state microbunching (SSMB) is a promising approach for generating high-average-power coherent radiation, while the instabilities driven by coherent undulator radiation in the laser modulator (LM) is important for the ring performance. In this paper we investigate the longitudinal single-bunch multi-turn LM instability using cavity m

Xu Wang, Zexian Li, Litong Gong, Tiezheng Ge

Diffusion models offer superior generation quality at the expense of extensive sampling steps. Distillation methods, with Distribution Matching Distillation (DMD) as a popular example, can mitigate this issue, but performance degradation remains pronounced when sampling steps are limited. Reinforcement learning (RL) has been leveraged to improve the few-step

Sohini Dutta, Philip Bull, Jacob Burba, Michael J. Wilensky

Observing the Epoch of Reionisation using 21cm radio interferometry has proven to be a challenging task. Extraction of the extremely faint redshifted signal is complicated by the presence of bright foregrounds, radio frequency interference (RFI), and systematic artefacts. We discuss the challenge of accounting for systematic effects, particularly cable refle

Changhyun Roh, Yonghyun Jeong, Jonghyun Lee, Chanho Eom

Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for sticker personalization where prompts often require explicit attribute edits. With only one reference, test-time fine-tuning (TTF) methods tend to overfit, producing \textit{visual entanglement}, where backgrou

Max Klimm, Marc E. Pfetsch, Martin Skutella, Lea Strubberg

We develop efficient algorithms for a fundamental network design problem arising in potential-based flow models, which are central to many energy transport networks (e.g., hydrogen and electricity). In contrast to classical network flow problems, the nonlinearities inherent in potential-based networks introduce significant new challenges. We address these ch

Minghe Wang, Trever Schirmer, Mohammadreza Malekabbasi, David Bermbach

Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, deploying MoE models requires all experts to reside in memory, creating a gap between the resource used by activated experts and the provisioned resources. This underutilization is further pronounced in multi-ten

Md Biplob Hosen, Md Alomgeer Hussein, Md Akmol Masud, Omar Faruque

Patient portals now give individuals direct access to their electronic health records (EHRs), yet access alone does not ensure patients understand or act on the complex clinical information contained in these records. The ArchEHR-QA 2026 shared task addresses this challenge by focusing on grounded question answering over EHRs, and this paper presents the sys

Davide Fazzini

The Standard Model (SM) predicts the universality of lepton couplings with the electroweak gauge bosons. Semileptonic decays of $b$-hadrons provide a powerful framework for testing the SM and probing possible New Physics effects. In particular, the processes mediated by charged-current interactions benefit from a relatively large branching fractions and theo

Riccardo Travaglino, Pasquale Calabrese

The study of Entanglement Asymmetry has emerged in recent years as a powerful tool to characterise the symmetry properties of quantum states in relation to a given charge operator through the lens of entanglement. While extremely powerful and general, the standard definition of asymmetry introduces significant non-Gaussian features in free-fermionic systems,

Aekta Aggarwal, Ganesh Vaidya

We study the entropy solution for a class of systems of nonlocal conservation laws in which the convective flux is convoluted with a kernel in both spatial and temporal variables. This formulation models the flux dependence on the solution within its spatial neighbourhood (nonlocal in space) as well as on prior states in time (nonlocal in time), thereby inco

C. A. Bertulani

This is a brief overview of the connection between neutron skin thickness in finite nuclei and the equation of state of neutron-rich matter, with applications to neutron stars. Multiple experimental probes are discussed, including dipole polarizability, parity-violating electron scattering, heavy-ion fragmentation, quasi-free scattering, and ultraperipheral

Ben Maybee, Francesca Morris, Juliane Schwendike, Ashar Aslam

Africa is the primary source of cyclonic vortices over the tropical Atlantic. Over both land and sea, these vortices are entwined with deep convective activity, with the majority being African Easterly Wave troughs. Their convective interactions have downstream impacts, since the same vortices provide the seed population for Atlantic basin tropical cyclone (

Giuseppe Catalano, Marco Fanizza, Francesco Anna Mele, Giacomo De Palma

The pure-loss channel is a fundamental model for describing noise in bosonic quantum platforms. It is characterised by a single parameter, the transmissivity, which quantifies the fraction of the input energy that reaches the output of the channel. In realistic scenarios, however, such as free-space quantum communication, the transmissivity is not fixed but

Zhuofan Lou, Shihang Zhang, Fangle Zhu, Shengjie Ye

We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty-aware framework for pedestrian attribute recognition (PAR). Unlike conventional deterministic methods, which fail to assess prediction reliability on low-quality samples, UAPAR effectively identifies unrelia

Andrés Díaz Lantada, José A. Yáñez, William Solórzano-Requejo, Monsur Islam

This study presents a versatile ontology and a useful codification scheme for describing all kinds of engineered living materials (ELMs). The different components of the ontology, namely: families according to the taxonomy for ELMs, industrial applications and synthesis or processing methods, are systematically organized, enumerated, classified, codified and

Shibin Deng, Jonas M. Peterson, Jonas Reimann, Heonjoon Park

Moir\'e superlattices of transition-metal dichalcogenides (TMDs) host strongly interacting Bose-Fermi mixtures in which bosonic excitons coexist with correlated electron lattices. Using ultrafast, time- and energy-resolved photoluminescence (PL) and reflectance microscopy, we show that strong exciton-electron and exciton-exciton repulsion can enable collecti

Sergei Igonin

Differential-difference matrix Lax representations (Lax pairs), gauge transformations, and discrete Miura-type transformations (MTs) belong to the main tools in the theory of (nonlinear) integrable differential-difference equations. For a given equation, two matrix Lax representations (MLRs) are said to be gauge equivalent if one of them can be obtained from

Attila Pintér, Javier Rico, Attila Répai, Jalal Al-Afandi

We present KAYRA, an end-to-end karyotyping system that operates inside the operational constraints of a clinical cytogenetic laboratory. KAYRA is architected as a containerized microservice pipeline whose ML stack combines an EfficientNet-B5 + U-Net semantic segmenter, a Mask R-CNN (ResNet-50 + FPN) instance detector, and a ResNet-18 classifier, orchestrate

Jinye Gan, Bozhong Zheng, Xiaohao Xu, Junye Ren

Existing 3D anomaly detection methods are built on a rigid prior: normal geometry is pose-invariant and can be canonicalized through registration or alignment. This prior does not hold for articulated objects with hinge or sliding joints, where valid pose changes induce structured geometric variations that cannot be collapsed to a single canonical template,

Dimitris Dimakopoulos, Shay B. Cohen, Ioannis Konstas

Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduce new facts outwith the parametric knowledge, giving rise to hallucinations. While it has been demonstrated that supervised fine-tuning (SFT) on new knowledge may exacerbate the pr

Paolo Secchi, Yuri Trakhinin, Tao Wang

We consider the free boundary problem for relativistic plasma--vacuum interfaces in two and three spatial dimensions. The plasma flow is governed by the equations of ideal relativistic magnetohydrodynamics, while the vacuum magnetic and electric fields satisfy Maxwell's equations. The plasma and vacuum magnetic fields are tangential to the interface, which m

Mohamed Camil Belhadjoudja, Mohamed Maghenem, Emmanuel Witrant

We consider a system of two coupled first-order linear hyperbolic partial differential equations modeling heat transport in a counter-flow heat exchanger: one equation describes the transport of a hot fluid, and the other the transport of a cold fluid in the opposite direction. For this system, we design a boundary observer that uses only the temperature of

Kevin M. Ryan, Detlef Beckmann, Venkat Chandrasekhar

Non-local conductance experiments using tunnel junctions can provide valuable spectroscopic information on both the transport and relaxation of quasiparticles in superconductors, as these techniques directly probe the quasiparticle charge and energy imbalance even at mK temperatures. In this work, we employ mesoscopic three terminal Cu and Al NIS devices to

Alejandro Gomez Cadavid, Pavle Nikačević, Pranav Chandarana, Sebastián V. Romero

We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-

Alessia Musumeci, Jacopo Nava, Silvia Pascoli, Filippo Sala

The nanohertz gravitational waves (GW) observed by pulsar timing arrays may originate from a cosmological first-order phase transition (PT) at $\sim$ 100 MeV. Taking this possibility seriously motivates the question: why 100 MeV? We point out that a PT at exactly those scales is predicted by the generation of the baryon asymmetry from a dark asymmetry via re

Riccardo Vocaturo, Mattia Trama

Ten years after the experimental discovery of Weyl semimetals, theoretical and experimental work has pointed to the possibility of realizing surface-only superconductivity at relatively high temperatures in these materials. A consensus is developing that this unusual form of superconductivity is mediated by surface electronic states unique to Weyl semimetals

Ezel Üsten, Anna Sieben, Mohcine Chraibi, Armin Seyfried

In pedestrian dynamics, the internal drive that propels individuals toward their goals is typically captured by a single, fixed parameter, the desired walking speed. This simplification overlooks that motivation fluctuates in response to changing spatial and social conditions within a crowd. This paper proposes a dynamic motivation model grounded in expectan

Akshay Karjol, Darrin M. Hanna

Deploying accurate object detection for Vulnerable Road User (VRU) safety on edge hardware requires balancing model capacity against computational constraints. Large models achieve high accuracy but fail under INT8 quantization required for edge deployment, while small models sacrifice detection performance. This paper presents a knowledge distillation (KD)

Alessandra Colla, Andrea Smirne, Heinz-Peter Breuer, Bassano Vacchini

We propose a method to evaluate general thermodynamic fluctuations in open quantum systems, based on performing a two-point measurement scheme on the system using dynamics-dependent thermodynamic observables. Our approach allows one to obtain exact equalities for fluctuations of path-dependent thermodynamic quantities such as work and heat, and to isolate co

Frank Ginac

The integration of Large Language Models (LLMs) into the software development lifecycle (SDLC) masks a critical socio-technical failure: Cognitive-Systemic Collapse. This paper introduces "Epistemological Debt," the hidden carrying cost incurred when engineers substitute logical derivation with passive AI verification. This debt erodes the mental mod

Stefano Maurelli, Ruggero Noris, Marcelo Oyarzo, Mario Trigiante

We discuss some new results on the construction of supersymmetric solutions of Type IIB supergravity of the form ${\rm WAdS}_3\times{\rm WS}^3\times T^4$, ${\rm WAdS}_3$ and ${\rm WS}^3$ denoting \emph{warped} anti-de Sitter spacetime and sphere, respectively. The distinctive feature of these backgrounds is that, in spite of them being supersymmetric, the wa

Pasquale Casaburi, Pierpaolo Vivo

Euclidean random matrices arise in a wide range of physical systems where interactions are determined by spatial configurations, including disordered media and cooperative phenomena in atomic ensembles. Unlike classical random matrix ensembles, their entries are strongly correlated through the geometry of the underlying random points, making their analytical

Sajel Surati, Rosanna Bellini, Emily Black

When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making. However, there is little empirical exploration of how professionals making high-stakes decisions, such as those related to employment, perceive their agency and level of control when working with genAI system

Kamran Shakoor, Azhar Farooq, Hassan Oubba

The purpose of this paper is to determine all Rota-Baxter operators on dual quaternion algebra $\mathcal{H}_d$ over the reals.

Yuxuan Tian, Yurun Jin, Bin Yu, Yukun Shi

Robotic manipulation requires reasoning about future spatial-temporal interactions and geometric constraints, yet existing Vision-Language-Action (VLA) policies often leave predictive representation weakly coupled with action execution, causing failures in tasks requiring precise spatial-temporal coordination. We propose STARRY, a world-model-enhanced action

Muhammad Ahsan Khan

The classification of maximal algebras of square block Toeplitz matrices is a considerably more difficult problem and has received relatively little attention in the existing literature. In this work, we approach the problem under the assumption that the entries belong to the Schur algebra. Within these settings, we obtain a complete classification of all ma

Zagorka Matić, Srboljub Simić, Peter Ván

The paper studies constitutive modelling of Korteweg fluids. Thermodynamic consistency, i.e. compatibility with entropy balance law, is achieved using Liu's method of multipliers. Appropriate constitutive assumptions facilitated inclusion of the capillary effects in the specific entropy. Korteweg stresses are derived from the equilibrium conditions -- vanish

Daniel de Abreu Pereira Uhr, Guilherme Valle Moura

This paper develops a doubly robust extension of local-projections difference-in-differences (LP-DiD) for staggered absorbing treatments. The resulting estimator, DRLPDID, preserves the LP-DiD local-stack ATT target and is consistent when either the local untreated-outcome regression or the local treatment-probability model is correctly specified. It also de

Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe

Many of the thousands of attested languages share common configurations of features, creating a spectrum from typologically very rare (e.g., object-verb-subject word order) or impossible languages to very common combinations of features (e.g., subject-object-verb word order). One central question is under what conditions such typological tendencies can be pr

Lilly Horvath-Makkos, Fayyaz Minhas

Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitation through heterogeneous, seasonal and nonlinear land-atmosphere feedbacks. Existing models struggle to capture these dynamics: convection is parameterised at coarse scales, tipping behaviour is poorly constrai

Adriano Zanin Zambom, Qing Wang

We introduce the ARCH-m(X) model, a semiparametric extension of the ARCH-X framework in which the effect of a multivariate exogenous covariate vector X on the conditional variance is modeled through an unknown nonparametric function m(), accommodating complex nonlinear relationships between external predictors and financial volatility. Within this model, we

Hindi Zouhair

We develop a precision framework for doubly charmed baryon decays based on symmetry-protected observables and effective-field-theory diagnostics. In nonleptonic $\Xi_{cc}$ decays, we construct a null combination of widths that vanishes in the heavy-diquark factorization limit, providing a direct probe of nonfactorizable QCD dynamics. For semileptonic decays,

Bao Pham, Mohammed J. Zaki, Luca Ambrogioni, Dmitry Krotov

When do language diffusion models memorize their training data, and how to quantitatively assess their true generative regime? We address these questions by showing that Uniform-based Discrete Diffusion Models (UDDMs) fundamentally behave as Associative Memories (AMs) $\textit{with emergent creative capabilities}$. The core idea of an AM is to reliably recov

Michael Wilson

We establish the rate at which the renormalized stress--energy tensor of a massless minimally coupled scalar field in the in-vacuum state of a collapsing null-shell spacetime approaches the corresponding Unruh-state value. At finite exterior radius, we establish the upper bound \[ |\Delta\langle T_{\mu\nu}\rangle|\leq C(r)\,t_s^{-3} \] from the Cauchy-surfac

Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu, Yingbo Tang

Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation behavior. Existing map-based methods rely on costly pre-built HD maps, while learning-based policies are mostly limited to indoor and short-horizon settings. To bridge this gap, we

Zihan Zhao, Baotong Lu, Shengjie Lin, Yizou Chen

Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step and extending the KV storage to CPU memory. In practice, however, these algorithmic savings rarely translate into end-to-end system-level ga

Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writing process, including citation recommendation, they have also led to the emergence of hallucinated citations that do not correspond to any existing work. Such citations not only u

Carlos Flores-Garrigós, Anton Simen, Qi Zhang, Enrique Solano

We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies beyond standard quadratic encodings. In contrast to QUBO-based approaches, the proposed model includes one-, two-, and three-body interaction terms derived from mutual-information

Mahya Ramezani, Holger Voos

This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios under limited simulation training. The framework combines a fixed rule-based high-level advisor with an online goal-conditioned low-level reinforcement learning (RL) controller. To stress-test early adaptation

Minh Toan Ho, Thanh Hieu Le, Cong Trinh Le, Hiroyuki Osaka

This paper investigates the properties of Choi polynomials and their fundamental role in the theory of positive linear maps between matrix algebras. By focusing on Hermitian symmetric biquadratic forms, we establish a connection between the positivity of these forms and the structure of positive maps. We specifically explore the construction of indecomposabl

Taylor J. Smith

The notion of a two-dimensional word arises naturally in the study of combinatorics on words, while the iterative construction of pedal triangles results in a rich dynamical system in the study of geometry. At first, these two classes of objects seem to be unrelated. However, it is known that for all $n \geq 1$, the number of primitive two-dimensional words

Liam Roditty, Ariel Sapir

We introduce a generalized family of $\left( 2\cdot \left\lfloor \frac{k}{2} \right\rfloor-1, 2\cdot \left\lceil \frac{k}{2} \right\rceil \cdot W_{1} +\max\left\{0,2\cdot\left(\left\lceil\frac{k}{2}\right\rceil-2\right)\right\}\cdot W_{2} \right)$-emulators with $\tilde O \left(n^{1+\frac{1}{k}}\right)$ edges, for any $k\in\mathbb{N}$, where $W_{i}$ is the $

Javier Gil Blázquez

I propose the \emph{Random Cloud} method, a training-free approach to neural architecture search that discovers minimal feedforward network topologies through stochastic exploration and progressive structural reduction. Unlike post-training pruning methods that require a full train-prune-retrain cycle, this method evaluates randomly initialized networks with

Takeshi Tsukada, Kazuyuki Asada, Kengo Hirata

A semantic model enjoys full definability if every semantic element in the model is a denotation of some proof or program. Full definability indicates that the model captures programs and proofs in a highly detailed manner. This paper studies full definability in a model based on the (bi)category of profunctors on groupoids, which is a proof-relevant variant

Shibing Chen, Yuanyuan Li, Xianduo Wang

Lutwak's affine quermassintegral theory is a foundational component of modern affine Brunn--Minkowski theory. Developed in the 1980s, it provides affine analogues of the classical quermassintegrals and has led to a rich family of sharp affine isoperimetric inequalities. A central question in this program, going back to Lutwak's 1988 work, is an Alexandrov--F

Riccardo Borghi

The resummation of superfactorially divergent series represents a significant computational challenge in mathematical physics. In the present paper the resummation of a specific class of Stieltjes series characterized by a moment sequence growing as $(2n)!$ will be addressed. Despite the fact that Carleman's condition is satisfied for these series, the conve

Francisco Correia, Kyle Corbett, Ehsan Khatami

The Kagome lattice Fermi-Hubbard model is one of the most physically rich, and at the same time most challenging, models to study in strongly-correlated physics. Among its special features are geometric frustration and a flat energy band that create conditions favorable to ferromagnetism near the band insulating limit. Here, we utilize two exact finite-tempe

Haoyuan Xu, Wei Miao, Geert Dhaene, Jad Beyhum

The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parame

Tarun Sunkesula Raghavendra, Yogesh Shelke, Stijn van der Ham, Anpuj Nair S

Self-propelled particles serve as minimal models for emulating the dynamic self-organization of microorganisms, yet most synthetic systems remain limited to a single mode of motion, namely active Brownian particles (ABPs). Here, we present an experimental strategy to encode various persistent random walks in ABPs by combining light-modulated propulsion stren

Petter Sandås, Íñigo Aréjula-Aísa, Sergio Iserte, Antonio J. Peña

High-performance computing (HPC) systems are increasingly exploring dynamic resource management and malleable MPI applications to better adapt to heterogeneous architectures, fluctuating workloads, and energy constraints. However, the correctness of the libraries that support these techniques is often evaluated through ad hoc experiments that can be difficul

Chenze Dong, Benjamin Horowitz, Adrian E. Bayer, Khee-Gan Lee

In this paper, we introduce Mujic{\Lambda} (Mapping the Universe with Jax-based Initial Condition Reconstr{\Lambda}ction), an optimization-based framework for reconstructing initial conditions from realistic galaxy spectroscopic redshift surveys. Unlike standard optimization-based approaches, Mujic{\Lambda} augments the L-BFGS algorithm with a projection ope

Victoria Peterson, Akshat Srivastava, Raghav Prabhakar

We present a Spatially Embedded Evolutionary Algorithm where robot individuals exist in a physically simulated 2D environment, must navigate to encounter potential mates, and compete for survival under various spatially-aware selection pressures. Using HyperNEAT evolved neural controllers for ARIEL gecko-inspired quadrupeds in MuJoCo, we investigate how spat

Yiqi Liu, Noelle Crawford, Michael Wang, Jilong Xue

To overcome the well-known memory bottleneck of AI chips, 3D stacked architectures that employ advanced packaging technology with high-density through-silicon vias (TSVs) pins have proven to be a promising solution. The 3D-stacked AI chip enables ultra-high memory bandwidth between compute and memory by stacking numerous DRAM banks atop many AI cores in a di

Taida Li, Yujun Yan, Fei Dou, Wenzhan Song

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we for

Mingbo Hong, Feng Liu, Caroline Gevaert, George Vosselman

Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, and style) from the source domain, leading to spurious correlations that hinder

Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang

This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic proble

Lukas Born, Admir Greljo, Anders Eller Thomsen

We identify a novel next-to-leading order renormalization effect in the dimension-six SMEFT with direct phenomenological impact. The Higgs-Yukawa operator that modifies the top-Higgs coupling $\kappa_t$ induces a shift in the $ W $ mass at two-loop order through a large anomalous dimension, rendering electroweak precision observables a powerful indirect prob

A. L. S. Santos Junior, M. Damaceno, A. C. Barbosa, N. A. Ribeiro

In this work, we demonstrate the effect of self-splitting spatial quantum correlations induced by Gouy phase engineering. In the process of spontaneous parametric down conversion the pump beam is structured with a mode superposition that produces a dynamical splitting and recombination of the light beam. This structure is transferred to the quantum correlati

Jeremy Diamond, Vincenzo Stoico

The Running Average Power Limit (RAPL) interface is widely used to estimate software energy consumption via CPU and DRAM counters, but tool design differences and high-frequency polling can introduce measurement overhead, namely, extra time and energy consumed by the tool itself.This paper quantifies the impact of RAPL-based tools on high-frequency (1 kHz) e

V. I. Kuznetsov, O. V. Trofimov

To study a nonlocal electron transport in an aluminum superconducting quasi-one-dimensional structure, we measured negative nonlocal (local) direct current voltages in the structure in a magnetic field near the critical temperature. The structure is a normal-superconducting at $T_{cn}<T<T_{cw}$ ($T_{cn}$ and $T_{cw}$ are the critical temperatures for narrow

Changhun Oh, Michał Oszmaniec, Oliver Reardon-Smith, Zoltán Zimborás

Establishing the precise computational boundary between classically tractable fermionic systems and those capable of genuine quantum advantage is a central challenge in quantum simulation. While injecting non-Gaussian ``magic" inputs into free-fermion circuits is widely expected to generate intractable complexity, we identify a physically motivated intermedi

Apurva Mudgal

We prove the Jordan curve theorem by generalizing the sweepline algorithm for trapezoidal decomposition of a polygon. Our proof uses Zorn's lemma (or, equivalently the axiom of choice). Though several proofs have been given for the Jordan curve theorem by various authors, ours is the {\bf first algorithmic proof} of Jordan curve theorem using computational g

Fan Wu, Anqi Liu, Jing Chen, Yuhua Li

Investor sentiment reflects the collective attitude of investors towards the asset, whether positive, negative or neutral. Market information, such as news and relevant social media posts, plays a significant role in shaping investor sentiment, which influences investment decisions accordingly. The sentiment for one single company may spill over to other rel

Ismail Belgacem

Hill functions, dominant in gene regulatory network modeling, carry fundamental limitations: at non-integer cooperativity exponents, routine when fitting dose-response data, derivatives diverge at the origin, complex arithmetic corrupts ODE trajectories, and zero output at zero activation traps models in off-states. This paper employs logistic-based models t

Zhaoyuan Cai, Xinglin Zhang

Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to be forgotten. However, existing FU methods mostly rely on synchronous coordination. This requirement forces the entire