October 2025 arXiv papers — page 30

Navya Gupta, Christopher David White, Zohreh Davoudi

Quantum simulators hold great promise for studying real-time (Minkowski) dynamics of quantum field theories. Nonetheless, preparing non-trivial initial states remains a major obstacle. Euclidean-time Monte-Carlo methods yield ground-state spectra and static correlation functions that can, in principle, guide state preparation. In this work, we exploit this c

Atefe Ebrahimi, Franco Pellegrini, Stefano De Gironcoli

Cesium based halide perovskites, such as CsPbI3 and CsSnI3, have emerged as exceptional candidates for next generation photovoltaic and optoelectronic technologies, but their practical application is limited by temperature dependent phase transitions and structural instabilities. Here, we develop machine learning interatomic potentials within the LATTE frame

Ayelet Amster, Lioz Akirav, Rica Gonen, Erel Segal-Halevi

Budget aggregation is a process in which citizens vote by declaring their individual ideal budget allocation, and a pre-determined rule aggregates all votes into a single outcome. Recent theoretical work has proposed various aggregation rules, along with impossibility results for satisfying desirable axioms simultaneously. These analyses rely on assumptions

Shreshta Rajakumar Deshpande, Mrdjan Jankovic

This paper presents a decentralized Control Barrier Function (CBF) based approach for highway merging of Connected and Automated Vehicles (CAVs). In this control algorithm, each "host" vehicle negotiates with other agents in a control zone of the highway network, and enacts its own action, to perform safe and energy-efficient merge maneuvers. It uses predict

Mehrshad Eskandarpour, Hossein Soleimani

Efficient mobility management and load balancing are critical to sustaining Quality of Service (QoS) in dense, highly dynamic 5G radio access networks. We present a deep reinforcement learning framework based on Proximal Policy Optimization (PPO) for autonomous, QoS-aware load balancing implemented end-to-end in a lightweight, pure-Python simulation environm

Marcela Ribeiro, Arturo Fernández-Pérez

In this paper, we introduce the notions of the $k$-th Milnor number and the $k$-th Tjurina number for a germ of holomorphic foliation on the complex plane with an isolated singularity at the origin. We develop a detailed study of these invariants, establishing explicit formulas and relating them to other indices associated with holomorphic foliations. In par

Viviana Gómez, Gabriel Téllez, Fernando J. Gómez-Ruiz

Understanding how frustration and disorder shape relaxation in complex systems is a central problem in statistical physics and quantum annealing. Spin-glass models provide a natural framework to explore this connection, as their energy landscapes are governed by competing interactions and constrained topologies. We investigate the non-exponential relaxation

Mohammad Alminawi

Invariance of on-shell scattering amplitudes under field redefinitions is a well known property in field theory that corresponds to covariance of on-shell amputated connected functions. In recent years there have been great efforts to define a formalism in which the covariance is manifest at all stages of calculation, mainly resorting to geometrical interpre

Cesar Perez, Steven Cundiff

A robust and streamlined method is presented for efficiently extracting spectral diffusion from two-dimensional coherent spectra by employing the projection-slice theorem. The method is based on the optical Bloch equations for a single resonance that include a Frequency-Frequency Correlation Function (FFCF) in the time domain. Through the projection slice th

Varun, Bin-Bin Zhang, Xiao-Hong Zhao, Jun Yang

We present a time-resolved, joint Swift-Fermi spectral study of GRB 241030A (z=1.411) that cleanly isolates the synchrotron origin of its prompt emission and favors a matter-dominated, internal-shock scenario. The light curve shows two episodes separated by a quiescent gap. Episode I (0-45 s) is well described by a single power law with photon index $\simeq

Pooja Yadav, Tanuja Srivastava

In this paper, we study the maximum likelihood estimation of the parameters of the multivariate and matrix variate symmetric Laplace distributions through group actions. The multivariate and matrix variate symmetric Laplace distributions are not in the exponential family of distributions. We relate the maximum likelihood estimation problems of these distribu

Cesar Hilario, Karl Otto Stöhr

We investigate fibrations by non-hyperelliptic curves of arithmetic genus three and geometric genus one in characteristic two. Assuming that there is only one moving singularity and that its image in the Frobenius pullback of the fibration has degree one over the base, we provide a complete classification up to birational equivalence. This relies on an in-de

Nanyi Zheng, William A. Sands, Daniel Hayes, Andrew J. Christlieb

We extend our previous work on a semi-Lagrangian adaptive rank (SLAR) integrator, in the finite difference framework for nonlinear Vlasov-Poisson systems, to the general high-order tensor setting. The proposed scheme retains the high-order accuracy of semi-Lagrangian methods, ensuring stability for large time steps and avoiding dimensional splitting errors.

Antonio Grimaldi, Michele Stofella, Billy Hobbs, Theodoros K. Karamanos

Hydrogen-deuterium exchange (HDX) of protein backbone amides provides a powerful probe of conformational dynamics. However, when experiments are performed in H2O/D2O mixtures, quantitative interpretation is hindered by back exchange and isotope effects not captured by the classical Linderstrom-Lang (LL) model. We introduce a generalized Linderstrom-Lang (GLL

Murat Onem, Gizem Sengor

We revisit the literature on locality on de Sitter with the goal to organize the main results with respect to the representation theory of the isometry group of four dimensional de Sitter. We make use of the late-time behavior of two-point functions of principal and discrete series representation, both in physical and in field space and compare the role of t

Ross Silver, Francesca Civano, Xiurui Zhao, Samantha Creech

The James Webb Space Telescope (JWST) North Ecliptic Pole (NEP) Time-Domain Field (TDF) has been monitored by NuSTAR and XMM-Newton with a regular cadence for five years starting in 2019. The survey has accumulated 3.5Ms of NuSTAR exposure and 228 ks quasi-simultaneous XMM-Newton observations covering 0.31 deg^2. This paper presents the results from the most

Lujun Li, Yewei Song, Lama Sleem, Yiqun Wang

Grammar refers to the system of rules that governs the structural organization and the semantic relations among linguistic units such as sentences, phrases, and words within a given language. In natural language processing, there remains a notable scarcity of grammar focused evaluation protocols, a gap that is even more pronounced for low-resource languages.

Saumili Jana, John Kolinski, Detlef Lohse, Vatsal Sanjay

A liquid drop impacting a non-wetting rigid substrate laterally spreads, then retracts, and finally jumps off again. An elastic solid, by contrast, undergoes a slight deformation, contacts briefly, and bounces. The impact force on the substrate - crucial for engineering and natural processes - is classically described by Wagner's (liquids) and Hertz's (solid

Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala

The amount of surface water is thought to be critical for a planet's climate stability and thus habitability. However, the probability that a rocky planet may exhibit surface water at any point its evolution is dependent on multiple factors, such as the initial water mass, geochemical evolution, and interior composition. To date, studies have examined the in

Stepan L. Kuznetsov

The linguistic applications of the Lambek calculus suggest its semantics over algebras of formal languages. A straightforward approach to construct such semantics indeed yields a brilliant completeness theorem (Pentus 1995). However, extending the calculus with extra operations ruins completeness. In order to mitigate this issue, Wurm (2017) introduced a mod

Yassine El Kheir, Fabian Ritter-Guttierez, Arnab Das, Tim Polzehl

Recent synthetic speech detection models typically adapt a pre-trained SSL model via finetuning, which is computationally demanding. Parameter-Efficient Fine-Tuning (PEFT) offers an alternative. However, existing methods lack the specific inductive biases required to model the multi-scale temporal artifacts characteristic of spoofed audio. This paper introdu

Pietro Brighi, Andreas Nunnenkamp

Investigating the robustness of non-reciprocity in the presence of competing interactions is central to understanding non-reciprocal quantum matter. In this work, we use reservoir engineering to induce non-reciprocal hopping and pairing in the fermionic Kitaev chain, and reveal the emergence of a pairing-induced phase transition. The two phases appear in the

Jack T. Warfield, Kevin A. McKinnon, Sangmo Tony Sohn, Nitya Kallivayalil

We present proper motion (PM) measurements for Draco II, an ultra-faint dwarf satellite of the Milky Way. These PMs are measured using two epochs of Hubble Space Telescope Advanced Camera for Surveys (HST/ACS) imaging separated by a 7 year time baseline. Measuring PMs of low-luminosity systems is difficult due to the low number of member stars, requiring a p

Upalaparna Banerjee, Romy Grünhofer, Matthias König, Yibei Li

To date, the appearance and resummation of "super-leading" logarithms in hadron-hadron collisions has been studied only for massless parton states. We extend the formalism to include an arbitrary number of massive final states. We derive the corresponding anomalous dimension and identify an additional Coulomb phase that gives rise to a new source of super-le

Andrea Beraudo, Jean F. Du Plessis, Daniel Pablos, Krishna Rajagopal

Heavy quarks offer an invaluable hard probe of the droplets of quark gluon plasma (QGP) formed in heavy ion collisions at the LHC and RHIC. Given their large mass, they are predominantly produced in hard scattering processes at the earliest moment of a collision and given their rarity they almost never annihilate with a heavy antiquark subsequently. This mea

Jose M. Mena-Valle, Vicent Mateu, Pablo G. Ortega

The strong coupling $\alpha_s$ is determined with high precision from fits to lattice QCD simulations on the static energy. Our theoretical setup relies on R-improving the three-loop fixed-order prediction for the static energy by removing its $u=1/2$ renormalon and summing up the associated large (infrared) logarithms which, in combination with radius-depen

T. Dörstel, T. Iadecola, J. H. Wilson, M. Buchhold

We study frustration-free control, a measurement-feedback protocol for quantum state preparation that extends the concept of frustration-free Hamiltonians to stochastic dynamics. The protocol drives many-body systems into highly entangled target states, common dark states of all measurement projectors, through minimal local unitary corrections that realize a

Atsushi Tanimoto, Keigo Fukumura, Shoji Ogawa, Hirokazu Odaka

We investigate accretion disk winds commonly observed in galactic black hole (BH) X-ray binaries (XRB), which manifest as blueshifted absorption features in X-ray spectra. We model these winds as ideal magnetohydrodynamic outflows of hot plasma driven by global magnetic fields threading the accretion disk around the BH. Using Monte Carlo simulations with MON

Hamza Ahmed, Florent Baume, Paul-Konstantin Oehlmann

We study the worldvolume theories of stacks of $Spin(32)/\mathbb{Z}_2$ heterotic NS5-branes probing a transverse singularity $\mathfrak{g}$. We revisit and extend the original classification by Blum and Intriligator, and show that the resulting 6d Little String Theories (LSTs) are naturally labeled by affine dominant coweights of the singularity $\mathfrak{g

Jenny Wagner, David Benisty, Igor D. Karachentsev

This study of the M81 complex and its Hubble flow delivers new and improved Tip of the Red Giant Branch (TRGB)-based distances for nine member galaxies, yielding a total of 58 galaxies with high-precision TRGB distances. With those, we perform a systematic analysis of the group's dynamics in the core and its embedding in the local cosmic environment. Our ana

Joshua J. Brown, Gordon I. Ogilvie

We derive a simple, accurate, non-linear, global equation governing spiral density waves in thin, non-self-gravitating, inviscid accretion discs. These discs may have any slowly varying surface density or temperature profile. For specific 'self-similar' disc profiles, solutions to our equation match (novel) smooth non-linear exact spiral solutions derived vi

Ilin Lazar, Sugata Kaviraj, Garreth Martin, Aaron Watkins

We combine deep photometric data in the COSMOS and XMM-LSS fields with high-resolution cosmological hydrodynamical simulations to explore two key questions: (1) how does the galaxy stellar mass function, particularly in the dwarf (Mstar < 10^9.5 MSun ) regime, vary with environment, defined as distance from the large-scale structure (LSS) traced by nodes and

Seyed Morteza Hosseini

We present the first successful large-$N$ computation of the $S^3$ free energy in chiral $\mathcal{N}=2$ Chern-Simons-matter theories, long believed to evade the universal M2-brane scaling $F_{S^3}\!\sim\!N^{3/2}$. Using a stable numerical continuation method that directly solves the saddle-point equations, we obtain convergent large-$N$ solutions for benchm

Aaron J. Romanowsky, David Martínez-Delgado, Giuseppe Donatiello, Juan Miró-Carretero

We present the first stellar stream discovered with the Vera C. Rubin Observatory, around spiral galaxy M61 (NGC 4303) in Virgo First Look imaging. The stream is narrow, radially-oriented in projection, and ~50 kpc long. It has g-band surface brightness (SB) mu_g ~ 28 AB mag arcsec^-2, color g-z ~ 1.0, and stellar mass M_* ~ 2x10^8 M_Sun. This dwarf galaxy i

Andrea Donini, Marcela González, Martin Hirsch, Nicolás A. Neill

Lepton number violating meson decays, such as $M_1^- \to M_2^+\ell_1^-\ell_2^-$, provide constraints on $d=9$ $\Delta L = 2$ operators. RGE-improved bounds on the Wilson coefficients of these operators have been presented in the literature, taking into account perturbative QCD one-loop corrections and the corresponding operator mixing. Here, we present for t

Arun Debray, Weicheng Ye, Matthew Yu

We develop a systematic framework for constructing (3+1)-dimensional topological orders or topological quantum field theories (TQFTs) that realize specified anomalies of finite symmetries, as encountered in gauge theories with fermions or in fermionic lattice systems. Our approach generalizes the symmetry-extension construction to the fermionic setting, and

Steven B. Giddings

The crossed product, and consequent transition from von Neumann algebras of type III to II, is recovered from a truncation of more general gravitational dressing constructions, about certain spacetimes. This is done by extending "standard dressing" constructions previously used to give a perturbative definition of "gravitational splittings," defining approxi

Chonghyuk Song, Michal Stary, Boyuan Chen, George Kopanas

Autoregressive video diffusion models are capable of long rollouts that are stable and consistent with history, but they are unable to guide the current generation with conditioning from the future. In camera-guided video generation with a predefined camera trajectory, this limitation leads to collisions with the generated scene, after which autoregression q

Haoge Deng, Ting Pan, Fan Zhang, Yang Liu

Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), a simple yet powerful framework that bridges the gap with continuous approaches for the sca

Simon Schubotz, Eva Bittrich, Holger Merlitz, Quinn A. Besford

We resolve the Schr\"{o}der paradox for PNiPAAm brushes, showing experimentally that swelling at 100\% relative humidity (RH) matches the liquid state. This occurs via a sharp increase in swelling above 98\%~RH, a behavior standard models fail to explain. Our extended mean-field theory explains this via a positive feedback between swelling and solvent qualit

Tom Rudelius

In a recent work, Herderschee and Wall (HW) proved a bound on scalar field excursions in spatially flat FRW cosmologies. In this note, we give an alternate proof of their bound using the Friedmann equations, and we prove that it can be saturated only in universes with vanishing acceleration, $\ddot a =0$. We argue that in a realistic (eternal) inflation scen

Jian Yan, Zhuoxi Li, Yang Ning, Yong Chen

We revisit the classical problem of comparing regression functions, a fundamental question in statistical inference with broad relevance to modern applications such as data integration, transfer learning, and causal inference. Existing approaches typically rely on smoothing techniques and are thus hindered by the curse of dimensionality. We propose a general

Lei Gioia, Sanjay Moudgalya, Olexei I. Motrunich

The construction of parent Hamiltonians that possess a given state as their ground state is a well-studied problem. In this work, we generalize this notion by considering simple quantum states and examining the local Hamiltonians that have these states as exact eigenstates. These states often correspond to Quantum Many-Body Scars (QMBS) of their respective p

Yuan-Hang Zhang, Chesson Sipling, Massimiliano Di Ventra

Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing power-law distributions of avalanches. In fact, such memory-induced LRO (MILRO) arises in a wide range of physical systems. Here, we show that MILRO can be transferred to coupled sys

Yujie Wei, Shiwei Zhang, Hangjie Yuan, Yujin Han

Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model capacity while preserving computational efficiency. Despite its notable success in large language models (LLMs), existing attempts to apply MoE to Diffusion Transformers (DiTs) have yielded limited gains. We attribute this gap to fundamental differences between language and visual

Magdalena Lederbauer, Siddharth Betala, Valerie Gentzke, Anamaria Leonescu

Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered across decades of scientific literature and recorded in unstructured formats that are challenging to analyze systematically. In this work, we present LeMat-Synth Parser, a modular, open-source, and multi-modal extractio

Wei Shen, Jiawei Zhang, Minhui Huang, Cong Shen

We study bilevel optimization problems where the lower-level problems are strongly convex and have coupled linear constraints. To overcome the potential non-smoothness of the hyper-objective and the computational challenges associated with the Hessian matrix, we utilize penalty and augmented Lagrangian methods to reformulate the original problem as a single-

Yihao Li, Saeed Salehi, Lyle Ungar, Konrad P. Kording

Object binding, the brain's ability to bind the many features that collectively represent an object into a coherent whole, is central to human cognition. It groups low-level perceptual features into high-level object representations, stores those objects efficiently and compositionally in memory, and supports human reasoning about individual object instances

Ryan C. Challener, Megan Weiner Mansfield, Patricio E. Cubillos, Anjali A. A. Piette

Highly-irradiated giant exoplanets known as "ultra-hot Jupiters" are anticipated to exhibit large variations of atmospheric temperature and chemistry as a function of longitude, latitude, and altitude. Previous observations have hinted at these variations, but the existing data have been fundamentally restricted to probing hemisphere-integrated spectra, ther

Juraj Juraska, Tobias Domhan, Mara Finkelstein, Tetsuji Nakagawa

In this paper, we present our submissions to the unified WMT25 Translation Evaluation Shared Task. For the Quality Score Prediction subtask, we create a new generation of MetricX with improvements in the input format and the training protocol, while for the Error Span Detection subtask we develop a new model, GemSpanEval, trained to predict error spans along

Shuqing Li, Jiayi Yan, Chenyu Niu, Jen-tse Huang

Virtual Reality (VR) games require players to translate high-level semantic actions into precise device manipulations using controllers and head-mounted displays (HMDs). While humans intuitively perform this translation based on common sense and embodied understanding, whether Large Language Models (LLMs) can effectively replicate this ability remains undere

Ignacio Contreras-Zúñiga, Mathias Lambert, Benjamín Palacios, Cristian Tejos

Identifying and suppressing streaking artifacts is one of the most challenging problems in quantitative susceptibility mapping. The measured phase from tissue magnetization is assumed to be the convolution by the magnetic dipole kernel; direct inversion or standard regularization methods tend to create streaking artifacts in the estimated susceptibility. Thi

Miriam Schwarze, Hui Khee Looe, Björn Poppe, Leo Thomas

The cluster dose concept offers an alternative to the radiobiological effectiveness (RBE)-based model for describing radiation-induced biological effects. This study examines the application of a neural network to predict cluster dose distributions, with the goal of replacing the computationally intensive simulations currently required. Cluster dose distribu

Yueqi Song, Ketan Ramaneti, Zaid Sheikh, Ziru Chen

Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To th

Di Wu, Chengshuai Shi, Jing Yang, Cong Shen

Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique for post-training large language models. Despite its empirical success, the theoretical understanding of RLHF is still limited, as learning the KL-regularized target with only preference feedback poses additional challenges compared with canonical RL. Existing works mostly study

Hong Wang, Zhezheng Hao, Jian Luo, Chenxing Wei

Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This process involves exploring nodes (tokens) and dynamically modifying the model's policy at each node. When combined with data scheduling, this process yields further gains in data

Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin

LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irrever

Baixuan Li, Dingchu Zhang, Jialong Wu, Wenbiao Yin

Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, conventional parallel thinking faces two key challenges in this setting: inefficiency from repeatedly rolling out from scratch, and difficulty in integrating long-horizon reasoning trajec

Zhengwei Tao, Haiyang Shen, Baixuan Li, Wenbiao Yin

Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making. While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from low search eff

Yael Alush, Nicholas C. Stone, Sjoert van Velzen

Late-time light curve plateaus in tidal disruption events (TDEs) are often approximated as flat and time-independent. This simplification is motivated by theoretical modeling of spreading late time TDE disks, which predicts slow light curve evolution. However, if time evolution can be detected, late-time light curves yield more information than previously ac

Xuanzhong Chen, Zile Qiao, Guoxin Chen, Liangcai Su

Training large language model agents on tasks at the frontier of their capabilities is key to unlocking advanced reasoning. We introduce a data synthesis approach inspired by the educational theory of the Zone of Proximal Development (ZPD), which defines this frontier as tasks an LLM cannot solve alone but can master with guidance. To operationalize this, we

Yida Zhao, Kuan Li, Xixi Wu, Liwen Zhang

LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relative Policy Optimization (GRPO) discard this rich entity information, relying instead on sparse, outcome-based rewards. This critical limitation renders them unable to distinguish i

Zihan Liu, Zhikang Niu, Qiuyang Xiao, Zhisheng Zheng

Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reasoning. We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce S

Jun Wang, Ziyang Zhou, Ardalan Kahak, Suyi Li

Softening and onboarding computers and controllers is one of the final frontiers in soft robotics towards their robustness and intelligence for everyday use. In this regard, embodying soft and physical computing presents exciting potential. Physical computing seeks to encode inputs into a mechanical computing kernel and leverage the internal interactions amo

Alexandr Grebennikov, Matthew Kwan

For integers $k$ and $\ell$, let $\operatorname{ind}(k, \ell)$ be the maximum proportion of $k$-vertex subsets of a large graph that induce exactly $\ell$ edges. The edge-statistics theorem (conjectured by Alon-Hefetz-Krivelevich-Tyomkyn, and proved by Kwan-Sudakov-Tran, Fox-Sauermann, and Martinsson-Mousset-Noever-Truji\'c) asserts that, for $k \to \infty$

Shengjie Liu, Li Dong, Zhenyu Zhang

We present a framework for uncovering and exploiting dependencies among tools and documents to enhance exemplar artifact generation. Our method begins by constructing a tool knowledge graph from tool schemas,including descriptions, arguments, and output payloads, using a DeepResearch-inspired analysis. In parallel, we derive a complementary knowledge graph f

Fatimah Habis, Yuanxi Wang

The electron phonon coupling of a defect characterized by its Huang Rhys (HR) factor is a crucial metric determining its excited-state dynamics, relevant to defect applications as qubits and quantum emitters. However, HR factors remain challenging to calculate from first principles, complicated by convergence issues in excited-state relaxation and time consu

Yun Zhang, Zhaoliang Zheng, Johnson Liu, Zhiyu Huang

Infrastructure-based perception plays a crucial role in intelligent transportation systems, offering global situational awareness and enabling cooperative autonomy. However, existing camera-based detection models often underperform in such scenarios due to challenges such as multi-view infrastructure setup, diverse camera configurations, degraded visual inpu

Andreas Hauptmann, Leonid Kunyansky, Jenni Poimala

The inverse source problem arising in photoacoustic tomography and in several other coupled-physics modalities is frequently solved by iterative algorithms. Such algorithms are based on the minimization of a certain cost functional. In addition, novel deep learning techniques are currently being investigated to further improve such optimization approaches. A

Hari Padma, Prakash Sharma, Sophia F. R. TenHuisen, Filippo Glerean

We report the observation of an emergent charge order mode in the optically-excited cuprate ladder Sr$_{14}$Cu$_{24}$O$_{41}$. Near-infrared light in the ladder plane drives a symmetry-protected electronic metastable state together with a partial melting of the equilibrium charge order. Our time-resolved resonant inelastic x-ray scattering measurements at th

Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares, Laura Bégon-Lours

The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-memory computing with nanoscale resistive memories may play a crucial role in enabling the executi

Ruiying Chen

LLM-based autonomous research agents report false claims: tasks marked "complete" despite missing artifacts, contradictory metrics, or failed executions. EviBound is an evidence-bound execution framework that eliminates false claims through dual governance gates requiring machine-checkable evidence. Two complementary gates enforce evidence requirements. The

Rafael Franco Ribeiro Reis, Gabriel Elyas Gama Araujo, Danilo Kuritza, Alexandre Cavalheiro Dias

We employ first principles density-functional theory (DFT) and the Bethe-Salpeter equation (BSE) in the framework of tight-binding based maximally localized Wannier functions (MLWF-TB) model to investigate the electronic and optical properties of free-standing two-dimensional (2D) germanium dioxide phases. All investigated 2D GeO2 polymorphs exhibit ultra-wi

Bo Liu, Chuanyang Jin, Seungone Kim, Weizhe Yuan

Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where a single model acts in two roles: a Challenger that mines documents from a large corpus to generate diverse reasoning tasks, and a Reasoner that solves them. Through adversarial dy

Caleb Escobedo, Nataliya Nechyporenko, Shreyas Kadekodi, Alessandro Roncone

Real-time control is an essential aspect of safe robot operation in the real world with dynamic objects. We present a framework for the analysis of object-aware controllers, methods for altering a robot's motion to anticipate and avoid possible collisions. This framework is focused on three design considerations: kinematics, motion profiles, and virtual cons

Chengjie Fu, Di Lu, Shao-Jiang Wang

In the era of Planck cosmology, the inflationary paradigm is best fitted toward the cosmological attractor scenarios, including the induced inflation, universal attractors, conformal attractors, and special attractors that are cataloged as $\xi$-models and $\alpha$-models. The recent hint from the ACT results pushes the scalar spectral index closer to the sc

Moritz Scheer, Alberto Baiardi, Elisa Bäumer Marty, Zhi-Yuan Wei

A key challenge for quantum computers is the efficient preparation of many-body entangled states across many qubits. In this work, we demonstrate the preparation of matrix product states (MPS) using a renormalization-group(RG)-based quantum algorithm on superconducting quantum hardware. Compared to sequential generation, it has been shown that the RG-based p

Jef Laga

Let $\lambda\colon A\rightarrow A^{\vee}$ be a polarization on an abelian variety over a field $k$. If $k$ is not algebraically closed, there might not exist an ample line bundle on $A$ defined over $k$ that represents $\lambda$. To remedy this, Poonen and Stoll have asked the following question: does there exist a line bundle on an $A$-torsor that represent

Xun Liang, Huayi Lai, Hanyu Wang, Wentao Zhang

Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate

Jiaxuan Zhang, Yuquan Leng, Yixuan Guo, Chenglong Fu

For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A g

Bram De Cooman, Johan Suykens

Out of the many deep reinforcement learning approaches for autonomous driving, only few make use of the options (or skills) framework. That is surprising, as this framework is naturally suited for hierarchical control applications in general, and autonomous driving tasks in specific. Therefore, in this work the options framework is applied and tailored to au

Alp M. Sunol, James V. Roggeveen, Mohammed G. Alhashim, Henry S. Bae

Constitutive laws relate fluid stress to deformation and underpin predictions of non-Newtonian behavior in industrial and biological fluids. Standard characterization relies on measurements in idealized flows that often miss physics relevant to complex geometries. Existing data-driven methods overfit sparse data, lack geometry portability, or presuppose cons

Burak Varıcı, Che-Ping Tsai, Ritabrata Ray, Nicholas M. Boffi

Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual kernel, induced by the relationship between inputs and their contexts. Yet, these methods recover only the linear span of top eigenfunctions of the kernel, whereas exact spectral dec

Li Li, Tobias Brinkmann, Till Temmen, Markus Eisenbarth

With the increasing integration of intelligent driving functions into serial-produced vehicles, ensuring their functionality and robustness poses greater challenges. Compared to traditional road testing, scenario-based virtual testing offers significant advantages in terms of time and cost efficiency, reproducibility, and exploration of edge cases. We propos

Genesis Research Team, Alejandro Dobles, Nina Jovic, Kenneth Leidal

Accurately predicting the three-dimensional structures of protein-ligand complexes remains a fundamental challenge in computational drug discovery that limits the pace and success of therapeutic design. Deep learning methods have recently shown strong potential as structural prediction tools, achieving promising accuracy across diverse biomolecular systems.

Mia Kan, Yilin Liu, Niloy Mitra

Video transitions aim to synthesize intermediate frames between two clips, but naive approaches such as linear blending introduce artifacts that limit professional use or break temporal coherence. Traditional techniques (cross-fades, morphing, frame interpolation) and recent generative inbetweening methods can produce high-quality plausible intermediates, bu

Ryuichi Fukuoka, Anderson Macedo Setti

Let $M$ be a smooth manifold and $TM$ its tangent bundle. A $C^0$-Finsler structure of $M$ is a continuous function $F:TM \rightarrow \mathbb{R}$ such that $F$ restricted to each tangent space $T_xM$ of $M$ is an asymmetric norm. $F$ is strongly convex if $F\vert_{T_xM}$ is a strongly convex asymmetric norm for every $x \in M$. Let $G$ be a Lie group endowed

Adam Logan, Anthony Várilly-Alvarado, David Zureick-Brown

For an irreducible variety $X$ over a field $k$, the degree of irrationality $\operatorname{irr}_k X$ is the minimal degree of a dominant rational map $X \dashrightarrow \mathbb{P}_k^{\operatorname{\dim} X}$. When $X$ is a curve, this is simply the gonality of $X$. We determine the possible degrees of irrationality of del Pezzo surfaces over an assortment of

Parker Riley, Daniel Deutsch, Mara Finkelstein, Colten DiIanni

Human evaluation of machine translation is in an arms race with translation model quality: as our models get better, our evaluation methods need to be improved to ensure that quality gains are not lost in evaluation noise. To this end, we experiment with a two-stage version of the current state-of-the-art translation evaluation paradigm (MQM), which we call

Yifu Lu, Shengjie Liu, Li Dong

Agentic tool use has gained traction with the rise of agentic tool calling, yet most existing work overlooks the complexity of multi-turn tool interactions. We introduce OrchDAG, a synthetic data generation pipeline that models tool execution as directed acyclic graphs (DAGs) with controllable complexity. Using this dataset, we benchmark model performance an

Yabo Dong, Manqi Ruan, Kun Wang, Haijun Yang

Motivated by several mild excesses around 95~GeV, we investigate the prospects for a light scalar $S$ produced via Higgsstrahlung, $e^+e^- \to Z(\mu^+\mu^-)S$, at future $e^+e^-$ Higgs factories. We take the CEPC as a benchmark, with a center-of-mass energy of $\sqrt{s}=240$ GeV and an integrated luminosity of $L=20~\mathrm{ab}^{-1}$. We focus on the decay m

Ghislain Fourier, Yuhuai Zhou

We study the algebraic and geometric structure related to tensor nuclear norms. We show that the unit ball of the nuclear norm is the convex hull of an irreducible real variety and give an explicit description of its real vanishing ideal. As a consequence, we obtain a simple criterion to decide when a primary ideal is prime, and we use it to prove that the i

Arpita Das, Albert Li Tao, Luke M. Fernley, Fritz von Gierke

Using an ultracold gas of $^{87}$Rb$^{133}$Cs molecules, we perform hyperfine-resolved spectroscopy of transitions from the vibronic ground state to the lowest rovibrational states of the electronic state $\mathrm{b}\,^3\Pi_0$, as a function of magnetic field. These transitions are spin forbidden, resulting in narrow linewidths, and feature near-diagonal Fra

Krishnadas Mohandas, Piotr J. Górski, Krzysztof Suchecki, Georges Andres

We investigate a process of growth of a signed network that strictly adheres to Heider structural balance rules, resulting in two opposing, growing factions. New agents make contact with a random existing agent and join one of the factions with the bias $p$ towards the group they made contact with. The evolution of the group sizes can be mapped to a randomiz

Emanuele Bagnaschi, Lisa Biermann, Michael Spira

In this contribution, new developments for the Standard Model Higgs-boson decays will be summarized. This addresses extensions of the grids used by {\tt Hdecay} to implement finite NLO mass effects for $H\to gg$ as well as explicit numbers for the branching fraction for the strange-Yukawa induced part of $H\to s\bar s$ together with the related uncertainties

Xuanpu Zhang, Xuesong Niu, Ruidong Chen, Dan Song

Recently, image editing based on Diffusion-in-Transformer models has undergone rapid development. However, existing editing methods often lack effective control over the degree of editing, limiting their ability to achieve more customized results. To address this limitation, we investigate the MM-Attention mechanism within the DiT model and observe that the

Alexander Lidiak, Jacob Swain, David L. Craig, Joseph Hickie

Advances in quantum technologies are often limited by slow device characterization, complex tuning requirements, and scalability challenges. Spin qubits in electrostatically defined quantum dots provide a promising platform but are not exempt from these limitations. Simulations enhance our understanding of such devices, and in many cases, rapid feedback betw

A. D. Lavrukhina, B. Demkov, K. Malanchev, M. V. Pruzhinskaya

We present the largest ground-based catalogue of M-dwarf flares to date, comprising 1,229 time-resolved events identified in Zwicky Transient Facility Data Release 17. Using high-cadence ZTF observations collected between April 2018 and September 2020, we analyzed over 93 million variable light curves containing 4.1 billion photometric measurements. Flare ca

Pengcheng Qiu, Chaoyi Wu, Junwei Liu, Qiaoyu Zheng

We present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning, enabling them to manage multi-turn interactive diagnostic processes, adaptively select examinations, and commit to final diagnoses. Unlike instruction-tuned models trained on static data, our method acquires diagnostic strategies through dynamic

Veronica Thai, Rui Li, Meng Ling, Shuning Jiang

Interpretation of giga-pixel whole-slide images (WSIs) is an important but difficult task for pathologists. Their diagnostic accuracy is estimated to average around 70%. Adding a second pathologist does not substantially improve decision consistency. The field lacks adequate behavioral data to explain diagnostic errors and inconsistencies. To fill in this ga