November 2025 arXiv papers — page 157

Md Motaleb Hossen Manik, Md Zabirul Islam, Ge Wang

Activation functions are fundamental for enabling nonlinear representations in deep neural networks. However, the standard rectified linear unit (ReLU) often suffers from inactive or "dead" neurons caused by its hard zero cutoff. To address this issue, we introduce N-ReLU (Noise-ReLU), a zero-mean stochastic extension of ReLU that replaces negative activatio

Tomotaka Kuwahara, Marius Lemm, Carla Rubiliani

In this note, we study transport properties of the dynamics generated by translation-invariant and possibly long-ranged Hamiltonians of Bose-Hubbard type. For translation-invariant initial states with controlled boson density, we improve the known bound on the local repulsive energy at time $t$ from $\langle n^2_x\rangle_t\lesssim t^{2d}$ to $\langle n^2_x\r

Victor Kleptsyn, Alexandro Luna

We study the Hausdorff and box-counting dimensions of cookie-cutter-like sets formed by sequential dynamics of a finite number of expanding maps. Under some natural conditions, these dimensions turn out to be the minimum and maximum of the corresponding dimensions of the cookie-cutter sets generated by the individual expanding maps. In the case of one-parame

Donald Yau

A cornerstone of algebraic K-theory is the equivalence between the K-theory machines of May, Segal, and Elmendorf and Mandell. Equivariant algebraic K-theory enriches the theory with group actions, making it more powerful and complex. There are a number of equivariant K-theory machines that turn equivariant categorical data into equivariant spectra, the main

Punit Kumar, Asif Imran, Tevfik Kosar

This paper presents a detailed comparative analysis of the performance of three major Python data manipulation libraries - Pandas, Polars, and Dask - specifically when embedded within complete deep learning (DL) training and inference pipelines. The research bridges a gap in existing literature by studying how these libraries interact with substantial GPU wo

Jingyu Wu, Aditya Shrivastava, Jing Zhu, Alfy Samuel

Efficiently reranking documents retrieved from information retrieval (IR) pipelines to enhance overall quality of Retrieval-Augmented Generation (RAG) system remains an important yet challenging problem. Recent studies have highlighted the importance of Large Language Models (LLMs) in reranking tasks. In particular, Pairwise Reranking Prompting (PRP) has eme

Steven R. Majewski, James W. Davidson, Robert F. Wilson, Elliott P. Horch

NASA's TESS mission has unveiled a plethora of eclipsing binaries (EBs), among them hundreds of triples and higher order, hierarchical systems. These complex targets require follow-up observations to enable full characterization of system architectures and identify the most compact multiples expected to undergo the most dramatic dynamical evolution. We repor

Swadesh Vhakta, Denis Osipov, Reetam Sen Biswas, Amritanshu Pandey

This paper aims to proactively diagnose and manage frequency instability risks from a steady-state perspective, without the need for derivative-dependent transient modeling. Specifically, we jointly address two questions (Q1) Survivability: following a disturbance and the subsequent primary frequency response, can the system settle into a healthy steady stat

Tung Vu, Hai Nguyen, Cong Tran

Face replacement technology enables significant advancements in entertainment, education, and communication applications, including dubbing, virtual avatars, and cross-cultural content adaptation. Our LiveNeRF framework addresses critical limitations of existing methods by achieving real-time performance (33 FPS) with superior visual quality, enabling practi

Djordje Milićević, Xinhua Qin, Xiaosheng Wu

We establish power-saving estimates for general bilinear forms with Kloosterman sums modulo arbitrary q, including when both variables are shorter than the Polya-Vinogradov range. As an application, we obtain power-saving asymptotics for the second moment of (holomorphic or Maass) modular L-functions twisted with Dirichlet characters to an arbitrary large ad

Soumavo Ghosh, Paola Di Matteo, Chanda J. Jog, Neige Frankel

The Milky Way harbours a prominent m=1 lopsided distortion in both stellar and neutral gas distributions. On the other hand, chemo-dynamical studies have been proven to be effective in grasping the overall evolution of galaxies. Here, we investigate systematically the excitation and evolution of a merger-driven $m=1$ lopsidedness in a Milky Way (MW)-like hos

Amna Zafar, Muhammad Asfand Hafeez, Arslan Munir

The increasing deployment of the Internet of Things (IoT) edge devices in modern smart grid environments requires secure and efficient communication protocols specifically designed for resource-constrained environments. However, most existing authentication schemes either impose excessive computational overhead or lack robustness against advanced cyber threa

C. -Z. Jiang, J. -X. Wang, H. Sou, W. -K. Ren

The single-epoch virial method is a fundamental tool for estimating supermassive black hole (SMBH) masses in large samples of AGNs and has been extensively employed in studies of SMBH-galaxy co-evolution across cosmic time. However, since this method is calibrated using reverberation-mapped AGNs, its validity across the entire AGN population remains uncertai

Edouard Barrier, Nikku Madhusudhan

Sub-Neptunes represent the current frontier of exoplanet atmospheric characterisation. A proposed subset, Hycean planets, would have liquid water oceans and be potentially habitable, but there are many unanswered questions about their atmospheric dynamics and 3D climate states. To explore such climates in detail, we report a General Circulation Model (GCM) f

Raymond Cheng

This article describes a unirationality construction for general low degree complete intersections in projective space which is based on a variety of highly tangent lines. Applied to hypersurfaces, this implies that a general hypersurface of degree $d \geq 6$ in projective $n$-space is unirational as soon as $n \geq 2^{(d-1)2^{d-5}}$, significantly improving

L. Ghodsi, L. Kuhn, A. W. S. Man, P. Andreani

We perform one of the first spatially resolved studies of warm ($>$100 K) and cold (10-100 K) molecular gas in the circumgalactic medium (CGM), focusing on the brightest cluster galaxy (BCG) of a cool-core galaxy cluster, MACS1931-26 at z=0.35. This galaxy has a massive H$_2$ reservoir and a radio-loud active galactic nucleus (AGN) and is undergoing a starbu

Zewei Wu, Andrey Kravtsov, Harley Katz

We present model calculations of the reionization history of hydrogen using star formation histories, computed with a galaxy formation model which reproduces properties of local dwarf galaxies and UV luminosity functions of galaxies at $z=5-16$. We use the ionizing photon density functions predicted by the model along with different models for the escape fra

Hakim Atek, John Chisholm, Vasily Kokorev, Ryan Endsley

We present an overview of the JWST GLIMPSE program, highlighting its survey design, primary science goals, gravitational lensing models, and first results. GLIMPSE provides ultra-deep JWST/NIRCam imaging across seven broadband filters (F090W, F115W, F200W, F277W, F356W, F444W) and two medium-band filters (F410M, F480M), with exposure times ranging from 20 to

Samiur R. Mir, Carlos A. Argüelles, K. S. Babu, Vedran Brdar

Neutrino oscillation parameters are subject to renormalization group (RG) evolution, just like all couplings and masses of Standard Model (SM) particles. Within the SM extended with three massive neutrinos, it is well known that RG running effects in the neutrino sector are small. However, the RG running of the elements of the leptonic mixing (PMNS) matrix b

Bart F. A. van Baal, Anders Jerkstrand

In the nebular phase, supernovae are powered by radioactive decay and continuously fade, while their densities have decreased enough such that the expanding nebula becomes (largely) optically thin and the entire structure contributes to the emission. Models for the nebular phase need to take Non-Local Thermodynamic Equilibrium (NLTE) effects into account, wh

Lilli Nadol, Thomas Neukirch

We present a Python code for calculating and displaying magnetic field extrapolations from given two-dimensional boundary conditions, specifically from solar surface magnetograms. The code implements analytical magnetohydrostatic models that incorporate the transition from non-force-free to force-free magnetic fields in the solar atmosphere. It allows for di

Xiaoyu Liu, Jordi Tura, Albert Rico

Recently, a technique known as quantum symmetry test has gained increasing attention for detecting bipartite entanglement in pure quantum states. In this work we show that, beyond qualitative detection, a family of well-defined measures of bipartite and multipartite entanglement can be obtained with symmetry tests. We propose and benchmark several efficient

Sam Morrell, Tim Naylor, John Southworth, David K. Sing

The ability to make accurate determinations of planetary parameters is inextricably linked to measuring physical parameters of the host star, in particular the stellar radius. In this paper we fit the stellar spectral energy distributions of exoplanet hosts to measure their radii, making use of only archival photometry, the $Gaia$ parallaxes and $Gaia$ extin

Y. Y. Kovalev, M. F. Aller, A. K. Erkenov, J. L. Gómez

The physical mechanisms driving neutrino and electromagnetic flares in blazars remain poorly understood. We investigate a prominent multi-messenger flare in the quasar PKS 1424+240 to identify the processes responsible for its high-energy emission. We analyze the IceCube-240105A high-energy neutrino event together with contemporaneous observations in the gam

Sarbani Basu, Sylvain G. Korzennik

Early helioseismic results have shown that the tachocline has a prolate shape. However, the models used in those studies constrained the tachocline to be either prolate or oblate. We use helioseismic data obtained from long time series (2304 and 4608 days) to determine the shape of the solar tachocline. Like previous work, we use forward modeling methods for

Andrew W. Boyle, Luke G. Bouma, Andrew W. Mann

Most star clusters dissolve into the Galaxy over tens to hundreds of millions of years after they form. While recent Gaia studies have honed our view of cluster dispersal, the exact chronology of which star formation events begat which star cluster remnants remains unclear. This problem is acute after 100 Myr, when cluster remnants have spread over hundreds

Kyle L. Seyler, Hantao Zhang, Daniel Van Beveren, Costel R. Rotundu

The ability to rapidly manipulate domain walls (DWs) in magnetic materials is key to developing novel high-speed spintronic memory and computing devices. Antiferromagnetic (AFM) materials present a particularly promising platform due to their robustness against stray fields and their potential for exceptional DW velocities. Among various proposed driving mec

Özgür Esentepe, Eleonore Faber

We study Penrose tilings of the plane $\mathbb{R}^2$ and nonperiodic infinite frieze patterns from the point of view of Cohen--Macaulay representation theory: Triangulations of the completed infinity-gon correspond to subcategories of the Frobenius category $\mathcal{C}_2=\mathrm{CM}_{\mathbb{Z}}(\mathbb{C}[x,y]/(x^2))$, the singularity category of the curve

Subhash Bose, M. D. Stritzinger, A. Malmgaard, C. J. Miller

We report observations of Type Ia SN 2021hem, located in an apparently hostless environment. With a peak absolute B-band magnitude of -19.96 mag, and a lack of secondary maximum in near-infrared and i-band light curves make it resemble 2003fg-like events. The slowly evolving light curves, and the earliest spectrum showing CII absorption lines, further suppor

Tobias Weber, Niclas Heinsdorf, Michal Stekiel, Paul Steffens

Inelastic neutron scattering in the chiral magnet Cu$_2$OSeO$_3$ reveals strong non-reciprocal effects on magnon propagation at the boundary of the nuclear Brillouin zone. The non-reciprocal response is strongest at a central position between the zone corner and edge mid-point. We explain these results using an effective linear spin-wave model. While directi

Gal Shavit, Gil Refael

Collective bosonic excitations are a fascinating aspect of broken-symmetry correlated phases. A wealth of such phases emerged in tailored moir\'e heterostructures, where, in addition, new direct knobs of control exist. Our work explores how the associated collective bosonic modes can be directly manipulated and amplified via parametric driving. As we show, p

Robert R. Caldwell, Eric V. Linder

We clarify the role of the oft-misunderstood Null Energy Condition (NEC) in the context of the current cosmological data. In particular, the NEC implies the sum of the total energy density and pressure satisfies $\rho_{tot}+P_{tot} \ge 0$; the energy conditions do not apply separately to individual components of the cosmological fluid. Consequently, we show

Romain Ruzziconi, Céline Zwikel

Pursuing our analysis of [1], we study the gravitational solution space around a null hypersurface in the bulk of spacetime, such as a black hole or a cosmological horizon. We discuss the corresponding characteristic initial value problem both in the metric and Newman-Penrose formalisms, and establish an explicit dictionary between the two. This allows us to

Bruno Bucciotti, Felipe Figueroa, Guilherme L. Pimentel

We construct a large portion of the massive spectrum of the open bosonic string using light-cone quantization, providing explicit oscillator realizations for individual single-particle states as well as for full Regge trajectories. We show how combinations of transverse oscillators organize into irreducible SO(25) representations, and provide an algorithm fo

Jin-Fu Chen, Mengyao Hu, Jordi Tura

Nonlocality shapes quantum correlations, revealed through the violation of Bell inequalities. The intersection of all valid Bell inequalities is the so-called local polytope. In multipartite systems, characterizing the local polytope quickly becomes an intractable task as the system size increases. Optimizing Bell inequalities to maximize the ratio between t

Gökhan Yücel, Selçuk Bilir, Volkan Bakış, Zeki Eker

A method of obtaining bolometric corrections ($BC_{\rm V}$) from observed high-resolution, high-$S/N$ spectra is described. The method is applied to spectra of 128 stars collected from the literature with well-determined effective temperatures ($T_{\rm eff}$) with $S_\lambda(V)$ transparency profiles of Bessell and Landolt. Computed $BC_{\rm V}$ are found ac

Andrea Begnoni, Walter Del Pozzo, Matteo Pegorin, Joachim Pomper

Gravitational wave signals from compact binary coalescences offer a powerful and reliable probe of General Relativity. To date, the LIGO-Virgo-KAGRA collaboration has provided stringent consistency tests of General Relativity predictions. In this work, we present forecasts for the accuracy with which General Relativity can be tested using third-generation gr

Aurore Courtoy, Arturo Ibsen

In data-driven determination of Parton Distribution Functions (PDFs) in global QCD analyses, uncovering the true underlying distributions is complicated by a highly convoluted inverse problem. The determination of PDFs can be understood as the inference of a function supported on $[0,1]$, a problem that admits multiple acceptable solutions. An ensemble of so

B. Popovic, P. Shah, W. D. Kenworthy, R. Kessler

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low redshift surveys, retraining the SALT3 light curve model and fixing a numer

Christine Hao, Stephanie O'Neil, Mark Vogelsberger, Vinh Tran

The spatial extent of the environment's impact on galaxies marks a transitional region between cluster and field galaxies. We present a data-driven method to identify this region in galaxy clusters with masses $M_{200\rm ,mean}>10^{13} M_{\odot}$ at $z = 0$. Using resolved galaxy samples from the largest simulation volume of IllustrisTNG (TNG300-1), we exami

Vasily Kokorev, John Chisholm, Rohan P. Naidu, Seiji Fujimoto

The detection of strong Balmer breaks and absorption features in Little Red Dots (LRDs) suggests they host AGN embedded within dense gas envelopes, potentially powered by super-Eddington accretion. We present GLIMPSE-17775, a luminous ($L_{\rm bol}\sim10^{45}$ erg s$^{-1}$) LRD at $z=3.501$ behind Abell S1063 ($\mu\sim2$), observed with deep JWST/NIRCam and

Hannah Day, Roni Harnik, Yonatan Kahn, Shashin Pavaskar

The measurement of the anomalous electron magnetic moment $g-2$ through quantum transitions of a single trapped electron is the most stringent test of quantum field theory. These experiments are now so precise that they must account for the effects of the cavity containing the electron. Classical calculations of this "cavity shift" must subtract the electron

Amir Siraj, Christopher F. Chyba, Scott Tremaine

The `kernel' of the classical Kuiper belt was discovered by Petit et al. (2011) as a visual overdensity of objects with low ecliptic inclinations and eccentricities at semimajor axes near 44 AU. This raises the question - are there other structures present in the classical Kuiper belt? If there are, clustering algorithms applied to orbits transformed into fr

Francesco D'Eramo, Alessandro Lenoci, Tommaso Sassi

Quantifying the imprints of freeze-in dark matter (DM) on cosmological structures requires knowledge of its phase-space distribution. We investigate how variations in the cosmological history before nucleosynthesis, the "weather" of that epoch, give rise to distinct "seasons" in the DM momentum distribution that govern its warmness. Studying decay-driven pro

V. R. Shajiee, M. M. Sheikh-Jabbari, V. Taghiloo

It is well established that black holes possess entropy and behave as thermodynamic systems. Associating entropy with gravitational fields has not remained limited to black holes, necessitating the notion of the second law of thermodynamics in gravitating systems. There have been many ideas and attempts to prove the second law within gravitating systems star

Joshua Berger, Amit Bhoonah, Joseph Bramante, J. Leo Kim

In this work, we show that ultralight dark photons, which couple to the Standard Model photon through kinetic mixing, can potentially source galactic scale magnetic fields. Although these magnetic fields would be too weak to detect at present in galaxies due to plasma screening effects, we show that dark photons can provide the seed magnetic field strength (

Gilberto Colangelo, Martina Cottini, Martin Hoferichter, Simon Holz

Hadronic $\tau$ decays present an opportunity to determine the isovector part of the hadronic-vacuum-polarization contribution to the anomalous magnetic moment of the muon in a way complementary to $e^+e^-\to\text{hadrons}$ cross sections. However, the required isospin rotation is only exact in the isospin limit, and corrections need to be under control to d

Zhongyang Li, Ziyue Li, Tianyi Zhou

Sparse Mixture-of-Experts (MoE) have been widely adopted in recent large language models since it can efficiently scale up the model capability without increasing the inference cost. However, evaluations on broad downstream tasks reveal a consistent suboptimality of the routers in existing MoE LLMs, which results in a severe performance gap (e.g., 10-20% in

Zhao-Heng Yin, Pieter Abbeel

Despite years of research, real-time diverse grasp synthesis for dexterous hands remains an unsolved core challenge in robotics and computer graphics. We present Lightning Grasp, a novel high-performance procedural grasp synthesis algorithm that achieves orders-of-magnitude speedups over state-of-the-art approaches, while enabling unsupervised grasp generati

Anay Mehrotra, Grigoris Velegkas, Xifan Yu, Felix Zhou

We study language generation in the limit, where an algorithm observes an adversarial enumeration of strings from an unknown target language $K$ and must eventually generate new, unseen strings from $K$. Kleinberg and Mullainathan [KM24] proved that generation is achievable in surprisingly general settings. But their generator suffers from ``mode collapse,''

Jiageng Mao, Sicheng He, Hao-Ning Wu, Yang You

We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic visual demonstrations from language commands and images, offering a powerful yet underexplored source of training signals for robotics. However, directly retargeting pixel motions

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

We investigate the quantum phases of higher-spin Kitaev models using tensor network methods. Our results reveal distinct bond-ordered phases for spin-1, spin-$\tfrac{3}{2}$, and spin-2 models. In all cases, we find translational symmetry breaking with unit cells being tripled by forming valence-bond orders. However, these three phases are distinct, forming p

Nicolás García Trillos, Adam Quinn Jaffe, Bodhisattva Sen

The quantity of interest in the classical Cram\'er-Rao theory of unbiased estimation (e.g., the Cram\'er-Rao lower bound, its exact attainment for exponential families, and asymptotic efficiency of maximum likelihood estimation) is the variance, which represents the instability of an estimator when its value is compared to the value for an independently-samp

Yuxuan Sun, Manchen Wang, Shengyi Qian, William R. Wong

AI agents capable of controlling user interfaces have the potential to transform human interaction with digital devices. To accelerate this transformation, two fundamental building blocks are essential: high-quality datasets that enable agents to achieve complex and human-relevant goals, and robust evaluation methods that allow researchers and practitioners

Han Zhang, Yiqing Shen, Roger D. Soberanis-Mukul, Ankita Ghosh

Developing embodied AI for intelligent surgical systems requires safe, controllable environments for continual learning and evaluation. However, safety regulations and operational constraints in operating rooms (ORs) limit agents from freely perceiving and interacting in realistic settings. Digital twins provide high-fidelity, risk-free environments for expl

Tom Benhamou, Corey Bacal Switzer

We continue the study from \cite{BrendleFreidmanMontoya, vandervlugtlocalizationcardinals} of localization cardinals $\mfb_\kappa(\in^*)$ and $\mfd_\kappa(\in^*)$ and their variants at regular uncountable $\kappa$. We prove that if $\kappa$ is measurable then these cardinals trivialize. We also provide other fundamental restrictions in the most general setti

Hao Wang, Sathwik Karnik, Bea Lim, Somil Bansal

Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often leads to unpredictable or costly errors. To harness their capabilities more reliably in robotic systems, we empirically investigate practical strategies for integrating language model

Linzhan Mou, Jiahui Lei, Chen Wang, Lingjie Liu

We present DIMO, a generative approach capable of generating diverse 3D motions for arbitrary objects from a single image. The core idea of our work is to leverage the rich priors in well-trained video models to extract the common motion patterns and then embed them into a shared low-dimensional latent space. Specifically, we first generate multiple videos o

Carlo Iazeolla, Per Sundell

We review a new perspective on higher-spin holography, whereby Vasiliev's 4D higher-spin gravity emerges together with a 3D counterpart, consisting of coloured conformal matter fields coupled to topological conformal higher-spin and colour gauge fields, as two distinct reductions, characterised by dual structure groups, of a common parent model. The latter i

Zhengjie Xu, Ye Li, Kwan-yee Lin, Stella X. Yu

Falling is an inherent risk of humanoid mobility. Maintaining stability is thus a primary safety focus in robot control and learning, yet no existing approach fully averts loss of balance. When instability does occur, prior work addresses only isolated aspects of falling: avoiding falls, choreographing a controlled descent, or standing up afterward. Conseque

Sophia Tang, Yinuo Zhang, Pranam Chatterjee

Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due to computationally expensive and long simulations. Previous approaches leverage techniques such as flow or Schr\"odinger bridge matching to implicitly learn joint trajectories thr

Manon Berriche, Célia Nouri, Chloée Clavel, Jean-Philippe Cointet

We introduce SPOT (Stopping Points in Online Threads), the first annotated corpus translating the sociological concept of stopping point into a reproducible NLP task. Stopping points are ordinary critical interventions that pause or redirect online discussions through a range of forms (irony, subtle doubt or fragmentary arguments) that frameworks like counte

Bradley Cline, Catherine Bai, Sehui Jeong, Ling Xu

For their resilience and toughness, filamentous entanglements are ubiquitous in both natural and engineered systems across length scales, from polymer-chain- to collagen-networks and from cable-net structures to forest canopies. Textiles are an everyday manifestation of filamentous entanglement: the remarkable resilience and toughness in knitted fabrics aris

Tixuan Tan, Patrick J. Ledwith, Trithep Devakul

We introduce an ideal limit of rhombohedral graphene multilayers. In this limit, we show analytically how short-range repulsion stabilizes a layer-pseudospin skyrmion lattice, which generates an effective magnetic field and gives rise to a Chern band. This establishes the real-space origin of interaction-driven topology in moir\'e rhombohedral graphene. The

Benjamin Lebrun, Christoph Bartneck, David Kaber, Andrew Vonasch

People often reject offers that are too generous due to the perception of hidden drawbacks referred to as "phantom costs." We hypothesized that this perception and the decision-making vary based on the type of agent making the offer (human vs. robot) and the degree to which the agent is perceived to be autonomous or have the capacity for self-interest. To te

Jake Navas, Jaden Brewer, Jaime Diaz, Matheus Guedes de Andrade

With steady progress in the development of quantum networks, the question on how to best provide end-to-end characterization of such networks (Quantum Network Tomography) is quickly becoming more pressing. Initial results demonstrated how we can utilize multipartite entanglement distribution to determine error probabilities of single-Pauli channels and depol

Tianrui Feng, Zhi Li, Shuo Yang, Haocheng Xi

Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered. Previous image-based streaming diffusion models have powered efficient and creative live streaming products but have hit limits on temporal consistency due to the foundation of image-based designs. Recent advances in video diffusion have m

Zhaosong Lu, Sanyou Mei

In this paper we propose a sequential minimax optimization (SMO) method for solving a class of constrained bilevel optimization problems in which the lower-level part is a possibly nonsmooth convex optimization problem, while the upper-level part is a possibly nonconvex optimization problem. Specifically, SMO applies a first-order method to solve a sequence

Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev, Yingxue Zhang

Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promising solution is to use cascaded inference, where small, cheap models handle easy queries, and only the hardest examples are escalated to more powerful models. However, existing casca

Hadi Hosseini, Vishwa Prakash HV, Aditi Sethia, Jatin Yadav

Equitability is a fundamental notion in fair division which requires that all agents derive equal value from their allocated bundles. We study, for general (possibly non-monotone) valuations, a popular relaxation of equitability known as equitability up to one item (EQ1). An EQ1 allocation may fail to exist even with additive non-monotone valuations; for ins

C. Tegkelidis, J. Larsson, D. Alp

The accurate positional measurement of Supernova (SN) 1987A is important for determining the kick velocity of its compact object and the velocities of the ejecta and various shock components. In this work, we perform absolute astrometry to determine the position of SN 1987A. We used multi-epoch Hubble Space Telescope imaging to model the early ejecta and the

Aryanna Schiebelbein-Zwack, L. A. C van Son, Maya Fishbach, Will M. Farr

The mass distribution of neutron stars encodes information about their formation and binary evolution. We compare the masses of two distinct populations: I) the recently identified Gaia neutron stars in wide orbits with solar-like companions and, II) the assumed first-born recycled pulsar in Galactic double neutron star systems. Naively, one would expect the

Hyeryun Park, Byung Mo Gu, Jun Hee Lee, Byeong Hyeon Choi

In robotic surgery, surgeons fully engage their hands and visual attention in procedures, making it difficult to access and manipulate multimodal patient data without interrupting the workflow. To overcome this problem, we propose a Voice-Interactive Surgical Agent (VISA) built on a hierarchical multi-agent framework consisting of an orchestration agent and

Leanto Sunny, Abhinav Rijal, George Siopsis

The recently proposed QAOA-GPT framework demonstrated that generative pre-trained transformers can learn mappings between problem graphs and optimized quantum circuits for the Quantum Approximate Optimization Algorithm (QAOA). In this work, we extend QAOA-GPT to Higher-Order Unconstrained Binary Optimization (HUBO) problems, focusing on spin-glass Hamiltonia

Ethan Baron, Alan N. Amin, Ruben Weitzman, Debora Marks

Many proteins useful in modern medicine or bioengineering are challenging to make in the lab, fuse with other proteins in cells, or deliver to tissues in the body, because their sequences are too long. Shortening these sequences typically involves costly, time-consuming experimental campaigns. Ideally, we could instead use modern models of massive databases

Xiaoyou Chen, Mark L. Lewis

We generalize the definition of pseudo monomial characters and $M$-groups to the Brauer character and Isaacs' $\pi$-partial character settings. We prove an analogs of Isaacs's generalization of Taketa's theorem in those settings. We consider other analogs of results regarding $M$-groups in those settings.

Haoran Zhang, Wenhao Zhang, Xianping Wu

We study finite horizon linear quadratic control with additive noise in a perturbancewise framework that unifies the classical model, a constraint embedded affine policy class, and a distributionally robust formulation with a Wasserstein ambiguity set. Based on an augmented affine representation, we model feasibility as an affine perturbation and unknown noi

Engin Başakoğlu, Faruk Temur, Oğuz Yılmaz

We study the defocusing stochastic generalized Korteweg-de Vries equations (sgKdV) driven by additive noise, with a focus on mass-critical and supercritical nonlinearities. For integers $k \geq 4$, we establish local well-posedness almost surely up to scaling critical regularity. We also prove global well-posedness and scattering in $L^{2}_{x}(\mathbb{R})$ f

Gabriele Astorino, Lorenzo Valbusa Dall'Armi, Riccardo Buscicchio, Joachim Pomper

Bayesian inference requires determining the posterior distribution, a task that becomes particularly challenging when the dimension of the parameter space is large and unknown. This limitation arises in many physics problems, such as Mixture Models (MM) with an unknown number of components or the inference of overlapping signals in noisy data, as in the Lase

Sean McLeish, Ang Li, John Kirchenbauer, Dayal Singh Kalra

Recent advances in depth-recurrent language models show that recurrence can decouple train-time compute and parameter count from test-time compute. In this work, we study how to convert existing pretrained non-recurrent language models into depth-recurrent models. We find that using a curriculum of recurrences to increase the effective depth of the model ove

Xiaoyou Chen, Mark L. Lewis

Let $G$ be a finite group. Suppose $N$ is a normal subgroup of $G$. Recall that Gallagher's theorem states that if $\chi \in {\rm Irr} (G)$ satisfies $\chi_N$ is irreducible, then $\chi \beta$ is irreducible and distinct for all $\beta \in {\rm Irr} (G/N)$. Furthermore, if $\theta = \chi_N$, then these are all of the irreducible constituents of $\theta^G$. W

K M Nafi Asib, Sourav Saha, Mohammed Moshiul Hoque

Large Language Models (LLMs) have advanced the automated generation of code from natural language prompts. However, low-resource languages (LRLs) like Bangla remain underrepresented due to the limited availability of instruction-to-code datasets and evaluation benchmarks. To address this, the BLP Workshop at IJCNLP-AACL 2025 introduced a shared task on "Code

Yizhe Zhu, Zhang Ye, Boce Hu, Haibo Zhao

Visuotactile policy learning augments vision-only policies with tactile input, facilitating contact-rich manipulation. However, the high cost of tactile data collection makes sample efficiency the key requirement for developing visuotactile policies. We present EquiTac, a framework that exploits the inherent SO(2) symmetry of in-hand object rotation to impro

Himanshu Pal, Venkata Sai Pranav Bachina, Ankit Gangwal, Charu Sharma

Temporal Graph Neural Networks (TGNNs) are increasingly used in high-stakes domains, such as financial forecasting, recommendation systems, and fraud detection. However, their susceptibility to poisoning attacks poses a critical security risk. We introduce LoReTTA (Low Resource Two-phase Temporal Attack), a novel adversarial framework on Continuous-Time Dyna

Yu Huang, Zixin Wen, Aarti Singh, Yuejie Chi

The ability to reason lies at the core of artificial intelligence (AI), and challenging problems usually call for deeper and longer reasoning to tackle. A crucial question about AI reasoning is whether models can extrapolate learned reasoning patterns to solve harder tasks with longer chain-of-thought (CoT). In this work, we present a theoretical analysis of

Izaque Esteves, Regina Braga, José Maria David, Victor Stroele

One of the challenges of predictive maintenance is making decisions based on data in an agile and assertive way. Connected sensors and operational data favor intelligent processing techniques to enrich information and enable decision-making. Digital Twins (DTs) can be used to process information and support decision-making. DTs are a real-time representation

June Moh Goo, Zichao Zeng, Jan Boehm

LiDAR super-resolution addresses the challenge of achieving high-quality 3D perception from cost-effective, low-resolution sensors. While recent transformer-based approaches like TULIP show promise, they remain limited to spatial-domain processing with restricted receptive fields. We introduce FLASH (Frequency-aware LiDAR Adaptive Super-resolution with Hiera

Shaohang Han, Joris Verhagen, Jana Tumova

We study motion planning under Signal Temporal Logic (STL), a useful formalism for specifying spatial-temporal requirements. We pose STL synthesis as a trajectory optimization problem leveraging the STL robustness semantics. To obtain a differentiable problem without approximation error, we introduce an exact reformulation of the max and min operators. The r

Marthe Bonamy, Théotime Leclere, Timothé Picavet

We solve a recent question of Caro, Patk\'os and Tuza by determining the exact maximum number of edges in a bipartite connected graph as a function of the longest path it contains as a subgraph and of the number of vertices in each side of the bipartition. This was previously known only in the case where both sides of the bipartition have equal size and the

Bingyang Hu, Victor Lie

Let $n\in\mathbb{N}$, $\vec{\alpha}=(\alpha_1,\ldots,\alpha_n)\in (0,\infty)^n$, $\vec{\beta}=(\beta_1,\ldots,\beta_n)\in (\mathbb{R}\setminus\{0\})^n$, $\vec{f}:=(f_1,\ldots, f_n)\in \mathcal{S}^n(\mathbb{R})$ and set $$H_{n,\vec{\alpha},\vec{\beta}}(\vec{f})(x):=p.v. \int_{\mathbb{R}} f_1(x+\beta_1 t^{\alpha_1})\ldots f_n(x+\beta_n t^{\alpha_n}) \frac{dt}{

Pukang Ye, Junwei Luo, Xiaolei Dong, Yunbo Yang

Data duplication within large-scale corpora often impedes large language models' (LLMs) performance and privacy. In privacy-concerned federated learning scenarios, conventional deduplication methods typically rely on trusted third parties to perform uniform deletion, risking loss of informative samples while introducing privacy vulnerabilities. To address th

Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda

Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving reasoning performance, yet a principled understanding of their effectiveness and limitations remains incomplete. To bridge this gap, we develop an abstract theoretical framework and identify sufficient conditions

Anna N. Morozovska, Victor N. Pavlikov, Yuriy O. Zagorodniy, Iryna V. Kondakova

Experimental and theoretical studies of unusual polar, dielectric and magnetic properties of room temperature multiferroics, such as perovskites Pb(Fe$_{1/2}$Nb$_{1/2}$)O$_3$ (PFN) and Pb(Fe$_{1/2}$Ta$_{1/2}$)O$_3$ (PFT), are very important. We study the phase composition, dielectric, ferroic properties of the solid solutions PFN and PFT substituted with 5,

Benjamin Proudfoot, Richard Nolthenius, Bryan J. Holler, Ana Carolina de Souza-Feliciano

Recent observations of a stellar occultation have revealed the presence of a previously undiscovered small satellite around Quaoar. Orbiting near Quaoar's unusual ring system, this new satellite has the potential to provide significant insights into the formation and evolution of Quaoar and its ring system. In this letter, we characterize the orbit of this n

Dorje C. Brody, Rishindra Melanathuru

The decoherence phenomenon arising from an environmental monitoring of the state of a quantum system, as opposed to monitoring of a preferred observable, is worked out in detail using two equivalent formulations, namely, repeated applications of universal tomographic measurements using positive operator-valued measures, and its continuous time unravelling fr

Awwab A. Azam, Lexu Zhao, Jiabin Yu

$n$-particle reduced density matrices ($n$-RDMs) play a central role in understanding correlated phases of matter, but their calculation is often computationally inefficient for strongly-correlated states at large system sizes. In this work, we use neural network (NN) architectures to accelerate and even predict $n$-RDMs for large systems. Our underlying int

Dao Lan Vy Dinh, Anh Nguyen Thi Mai, Hung Tran, Giang Quynh Le Vu

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells may become inactive due to unexpected events such as power

Shrutimoy Das, Debanuj Nayak, Anirban Dasgupta

Linear regression is frequently applied in a variety of domains, some of which might contain sensitive information. This necessitates that the application of these methods does not reveal private information. Differentially private (DP) linear regression methods, developed for this purpose, compute private estimates of the solution. These techniques typicall

Vaibhav Mavi, Shubh Jaroria, Weiqi Sun

Reliability and failure detection of large language models (LLMs) is critical for their deployment in high-stakes, multi-step reasoning tasks. Prior work explores confidence estimation for self-evaluating LLM-scorer systems, with confidence scorers estimating the likelihood of errors in LLM responses. However, most methods focus on single-step outputs and ov

Cayetana Salinas-Rodriguez, Jonathan Rogers, Sarah H. Q. Li

We study a two-player dynamic Stackelberg game where the follower's intention is unknown to the leader. Classical formulations of the Stackelberg equilibrium (SE) assume that the follower's best response (BR) function is known to the leader. However, this is not always true in practice. We study a setting in which the leader receives updated beliefs about th