December 2025 arXiv papers — page 116

Tyler Becker, Zachary Sunberg

Many strategic planning problems require agents to act simultaneously, making turn-based search unsuitable and requiring a normal form game to be solved at each tree state. We introduce Simultaneous AlphaZero, a learning and search method for continuous-state, two-player zero-sum deterministic Markov games. A learned value function bootstraps finite-depth re

Qimin Feng, Tianjun Han, Qiang Zhong

Classical descriptions of flapping propulsion near a free surface emphasize the energetic penalties of wave generation, treating the interface primarily as an energy sink. Here, we show that the same deformable boundary can also act as a phase-dependent kinematic constraint on vertical force generation. Using force measurements, particle image velocimetry an

Arin Manohar, Mario Cromaz, Christopher Campbell, Heather Crawford

The Gamma-Ray Energy Tracking Array (GRETA) is a next-generation gamma-ray spectrometer designed to push the frontiers of nuclear structure and astrophysics experiment. Its high sensitivity is enabled by high-precision localization of gamma-ray interactions within its active detector volume, and the subsequent tracking of gamma-ray scattering sequences. In o

Lily Erickson

With the advent of machine learning and quantum computing, the 21st century has gone from a place of relative algorithmic security, to one of speculative unease and possibly, cyber catastrophe. Modern algorithms like Elliptic Curve Cryptography (ECC) are the bastion of current cryptographic security protocols that form the backbone of consumer protection ran

Haipeng Li, Nanzhe Wang, Louis J. Durlofsky, Biondo L. Biondi

Quantitative monitoring of subsurface changes is essential for ensuring the safety of geological CO2 sequestration. Full-waveform monitoring (FWM) can resolve these changes at high spatial resolution, but conventional deterministic inversion lacks uncertainty quantification and incorporates only limited prior information. Deterministic approaches can also yi

Jacob Schnell, Aditya Makkar, Gunadi Gani, Aniket Srinivasan Ashok

Various weather modelling problems (e.g., weather forecasting, optimizing turbine placements, etc.) require ample access to high-resolution, highly accurate wind data. Acquiring such high-resolution wind data, however, remains a challenging and expensive endeavour. Traditional reconstruction approaches are typically either cost-effective or accurate, but not

Nickolay S. Martynenko

The indirect ground-based observations of cosmic rays through extensive air showers in modern experiments typically involve the use of Monte Carlo simulations to determine the characteristics of the primary particles. These simulations necessitate assumptions about particle interactions at energies that have not yet been experimentally probed, which introduc

Bhavesh Kumar, Roger Jin, Jeffrey Quesnelle

As language models scale to trillions of parameters, distributed training across many GPUs becomes essential, yet gradient synchronization over high-bandwidth, low-latency networks remains a critical bottleneck. While recent methods like Dion reduce per-step communication through low-rank updates, they synchronize at every step regardless of the optimization

Zirui Zhang, Yuhan Yao, Marat Gilfanov, Sergey Sazonov

We select seventy tidal disruption event (TDE) candidates among X-ray transients discovered during the eROSITA all-sky surveys in the Eastern Galactic hemisphere between December 2020 and February 2022 (eRASS1--5). We cross-match each X-ray source to a host galaxy in archival optical surveys using Bayesian likelihood-ratio techniques and obtain Keck/LRIS spe

Zien Zhu, Chih-En Hsu, Benran Zhang, Zhenfa Zheng

In compound semiconductors and insulators, the polar electron-phonon coupling diverges at long range, known as the Fr\"ohlich interaction. Modern first-principles electron-phonon calculations treat the Fr\"ohlich interaction in a semiclassical electrostatic formalism based on density-functional perturbation theory. Here, using many-body $GW$ perturbation the

Jonathan J. Wang, Dvira Segal

We demonstrate tuning of the phononic thermal conductance in single molecules with carbon-chain backbones through modifications of terminal groups and halogen substitution of hydrogen atoms. Our simulations focus on intrinsic molecular properties, and we employ a workflow based on {\it ab initio} molecular dynamics, enabling the training and development of m

Yongjun He, Shuai Zhang, Jiading Gai, Xiyuan Zhang

As large language models (LLMs) continue to scale and new GPUs are released even more frequently, there is an increasing demand for LLM post-training in heterogeneous environments to fully leverage underutilized mid-range or previous-generation GPUs and alleviate the shortage of homogeneous high-end GPUs within a single availability zone. However, achieving

Jamsheed Mistri

Olympic Taekwondo has faced challenges in spectator engagement due to static, defensive gameplay and contentious scoring. Current Protector and Scoring Systems (PSS) rely on impact sensors and simplistic logic, encouraging safe strategies that diminish the sport's dynamism. This paper proposes an AI-powered scoring system that integrates existing PSS sensors

Nikita Belyaev, Rostislav Konoplich, Kirill Prokofiev

Precise modelling of a signal in processes with multiple observables, exhibiting a complex dependency on the underlying parameters, is often a difficult and challenging task. Predicting the results of experimental measurements in high-energy physics reactions serves a good example. The reaction rates and distributions of momenta of the final state particles,

Thomas A. Trainor

Identified-hadron spectra from 2.76 TeV Pb-Pb and $p$-$p$ collisions are analyzed via a two-component (soft + hard) model (TCM) of hadron production in high-energy nuclear collisions. The object of study is evidence for jet suppression in small and large collision systems. Conventional methods include Pb-Pb centrality determination via classical Glauber mode

Bhawana Chhaglani

Indoor airborne transmission poses a significant health risk, yet current monitoring solutions are invasive, costly, or fail to address it directly. My research explores the untapped potential of ambient audio sensing to estimate key transmission risk factors such as ventilation, aerosol emissions, and occupant distribution non-invasively and in real time. I

Vitalii Sliusar, Domenico Della Volpe, Benjamin Garcia, Gilles Koziol

The Hanbury Brown-Twiss (HBT) effect, discovered in the 1950s and further developed in the 1960s, was originally used to estimate stellar angular diameters through intensity correlations measured by spatially separated detectors. Further developments started from HBT experiments to exploit quantum bunching of photons in incoherent light sources played founda

Sandy Fraser, Patryk Wielopolski

We introduce Sparse Concept Anchoring, a method that biases latent space to position a targeted subset of concepts while allowing others to self-organize, using only minimal supervision (labels for <0.1% of examples per anchored concept). Training combines activation normalization, a separation regularizer, and anchor or subspace regularizers that attract ra

Tina Tian, Xinyu Wang, Andrew L. Orekhov, Fujun Ruan

Many cable management tasks involve separating out the different cables and removing tangles. Automating this task is challenging because cables are deformable and can have combinations of knots and multiple interwoven segments. Prior works have focused on untying knots in one cable, which is one subtask of cable management. However, in this paper, we focus

Bichan Wu, Jonathan Pillow

Reduced rank regression (RRR) is a statistical method for finding a low-dimensional linear mapping between a set of high-dimensional inputs and outputs. In recent years, RRR has found numerous applications in neuroscience, in particular for identifying "communication subspaces" governing the interactions between brain regions. This tutorial article seeks to

Nathan X. Marshak, Kathlynn Simotas, Zarija Lukić, Hyunbae Park

Numerical methods for radiative transfer play a key role in modern-day astrophysics and cosmology, including study of the inhomogeneous reionization process. In this context, ray tracing methods are well-regarded for accuracy but notorious for high computational cost. In this work, we extend the capabilities of the Nyx N-body / hydrodynamics code, coupling r

Uriel Singer, Yaron Lipman

Transition Matching (TM) is an emerging paradigm for generative modeling that generalizes diffusion and flow-matching models as well as continuous-state autoregressive models. TM, similar to previous paradigms, gradually transforms noise samples to data samples, however it uses a second ``internal'' generative model to implement the transition steps, making

Jason Pillay, Cristina Tortora, Antonio Punzo, Andriette Bekker

Medical data often exhibit characteristics that make cluster analysis particularly challenging, such as missing values, outliers, and cluster features like skewness. Typically, such data would need to be preprocessed -- by cleaning outliers and missing values -- before clustering could be performed. However, these preliminary steps rely on objective function

Yin Liu, Jianwen Cai, Didong Li

Classical statistical learning theory predicts a U-shaped relationship between test loss and model capacity, driven by the bias-variance trade-off. Recent advances in modern machine learning have revealed a more complex pattern, double-descent, in which test loss, after peaking near the interpolation threshold, decreases again as model capacity continues to

Eray Erturk, Maryam M. Shanechi

Real-time decoding of target variables from multiple simultaneously recorded neural time-series modalities, such as discrete spiking activity and continuous field potentials, is important across various neuroscience applications. However, a major challenge for doing so is that different neural modalities can have different timescales (i.e., sampling rates) a

Eray Erturk, Saba Hashemi, Maryam M. Shanechi

Local field potentials (LFPs) can be routinely recorded alongside spiking activity in intracortical neural experiments, measure a larger complementary spatiotemporal scale of brain activity for scientific inquiry, and can offer practical advantages over spikes, including greater long-term stability, robustness to electrode degradation, and lower power requir

Yeqin Liu, Yu Shen

In 2004, Han proposed the following conjecture: let $B$ be a finite-dimensional $k$-algebra. If $\mathrm{HH}_{n}(B)\neq 0$ for only finitely many $n\in \mathbb{Z}$, then $B$ is smooth. This conjecture can be generalized to the DG setting: let $B$ be a finite-dimensional DG $k$-algebra. If $\mathrm{HH}_{n}(B)\neq 0$ for only finitely many $n\in \mathbb{Z}$, t

Jiachen Tao, Benjamin Planche, Van Nguyen Nguyen, Junyi Wu

Accurately modeling light transport is essential for realistic image synthesis. Photon mapping provides physically grounded estimates of complex global illumination effects such as caustics and specular-diffuse interactions, yet its per-view radiance estimation remains computationally inefficient when rendering multiple views of the same scene. The inefficie

Vihan Lakshman, Blaise Munyampirwa, Julian Shun, Benjamin Coleman

Modern vector databases enable efficient retrieval over high-dimensional neural embeddings, powering applications from web search to retrieval-augmented generation. However, classical theory predicts such tasks should suffer from the curse of dimensionality, where distances between points become nearly indistinguishable, thereby crippling efficient nearest-n

Leyang Xue, Meghana Madhyastha, Myungjin Lee, Amos Storkey

The foundation-model ecosystem remains highly centralized because training requires immense compute resources and is therefore largely limited to large cloud operators. Edge-assisted foundation model training that harnesses spare compute on edge devices offers a more democratized alternative. However, existing edge-training approaches fall short: they strugg

Prudhvi N. Bhattiprolu, Stephen P. Martin, James D. Wells

Higgsinos can be the lightest supersymmetric particles, allowing for either a full or partial dark matter interpretation, with the correct thermal freeze-out abundance obtained for masses near 1.1 TeV. Dark matter direct detection experimental results, now rapidly approaching the neutrino fog, imposes increasingly stringent requirements on higgsino purity. W

Fatemeh Zahra Majidi, Katia Biazzo, Maria Tsantaki, Amelia Bayo

Stellar magnetic activity, manifested through spots (faculae and flares), fundamentally shapes the exoplanets' environments. For low-mass stars in particular, where most habitable-zone planets reside, the variable magnetic phenomena can dominate atmospheric chemistry, surface radiation levels, long-term atmospheric escape, and ultimately habitability. Howeve

Yuhan Tang, Kangxin Cui, Jung Ho Park, Yibo Zhao

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching, which controls the holding intervals for batched sets of requests and vehicles, reveals an inherent trade-off between matching and pickup delays. The resulting environment wi

Terence Tao

Let $n \geq 1$, and let $p : {\bf C} \to {\bf C}$ be a monic polynomial of degree $n$. It was conjectured by Erd\H{o}s, Herzog, and Piranian that the maximal length of lemniscate $\{z \in {\bf C}: |p(z)| = 1\}$ is attained by the polynomial $p(z) = z^n-1$. In this paper, building upon a previous analysis of Fryntov and Nazarov, we establish this conjecture f

R. Abou Yassine, J. Adamczewski-Musch, C. Asal, M. Becker

We present the first observation of $\mathrm{^{3}_\Lambda H}$ and $\mathrm{^{4}_\Lambda H}$ in Ag+Ag collisions at $\mathrm{\sqrt{s_{NN}}}$ = 2.55 GeV, emitted around mid-rapidity. The hypernuclei are reconstructed via their two-body decay channels and identified through their weak-decay topology, employing an artificial neural network for enhanced discrimin

Sayak Chakrabarty, Souradip Pal

Unlike traditional recommendation tasks, finite user time budgets introduce a critical resource constraint, requiring the recommender system to balance item relevance and evaluation cost. For example, in a mobile shopping interface, users interact with recommendations by scrolling, where each scroll triggers a list of items called slate. Users incur an evalu

Fatemeh Zahra Majidi, Amelia Bayo, Marc Audard, Francisco José Galindo-Guil

Stars and planets can be seen as the second fundamental building blocks of baryons in the universe (only second to the dust and gas in molecular clouds). Their formation involves dust grain growth of many orders of magnitude and a myriad of processes operating at time scales from a few tens to millions of years. Thus, investigating the formation and evolutio

Fei Fang, Laura Forastiere

When individuals engage in social or physical interactions, a unit's outcome may depend on the treatments received by others. In such interference environments, we provide a unified framework characterizing a broad class of spillover estimands as weighted averages of unit-to-unit spillover effects, with estimand-specific weights. We then develop design-based

Vladimir Guletskii

We prove the conjecture stated by Spencer Bloch in 1975 and saying that the Albanese kernel of a smooth projective surface is 0, provided its second cohomology group is algebraic.

João Morais, Akshay Malhotra, Shahab Hamidi-Rad, Ahmed Alkhateeb

Machine learning for wireless systems is commonly studied using standardized stochastic channel models (e.g., TDL/CDL/UMa) because of their legacy in wireless communication standardization and their ability to generate data at scale. However, some of their structural assumptions may diverge from real-world propagation. This paper asks when these models are s

James Bagrow, Josh Bongard

Efforts to improve Kolmogorov--Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring, undermining the interpretability that makes KANs attractive in the first place. Here we study overprovisioned architectures combined with sparsification, deep supervision, and depth selection, to learn compact, i

Gary Lupyan

Acknowledging that large language models have learned to use language can open doors to breakthrough language science. Achieving these breakthroughs may require abandoning some long-held ideas about how language knowledge is evaluated and reckoning with the difficult fact that we have entered a post-Turing test era.

Hajnal Andréka, Zalán Gyenis, István Németi

Algebras of relations form an algebraic framework for the study of logical systems, extending the correspondence between Boolean algebras and propositional logic. Tarski&#39;s representable cylindric algebras $RCA_α$, and Halmos&#39; representable polyadic algebras $RPA_α$ both provide algebraic counterparts to first-order logic. In this paper, we show that

Abdul Matin, Rupasree Dey, Tanjim Bin Faruk, Shrideep Pallickara

Integrating domain knowledge into deep learning has emerged as a promising direction for improving model interpretability, generalization, and data efficiency. In this work, we present a novel knowledge-guided ViT-based Masked Autoencoder that embeds scientific domain knowledge within the self-supervised reconstruction process. Instead of relying solely on d

Veronica Mangiaterra, Hamad Al-Azary, Chiara Barattieri di San Pietro, Paolo Canal

As Large Language Models (LLMs) are increasingly being used in scientific research, the issue of their trustworthiness becomes crucial. In psycholinguistics, LLMs have been recently employed in automatically augmenting human-rated datasets, with promising results obtained by generating ratings for single words. Yet, performance for ratings of complex items,

Akhmadillo Mamirov, Faiaz Azmain, Hanyu Wang

AI model documentation is fragmented across platforms and inconsistent in structure, preventing policymakers, auditors, and users from reliably assessing safety claims, data provenance, and version-level changes. We analyzed documentation from five frontier models (Gemini 3, Grok 4.1, Llama 4, GPT-5, and Claude 4.5) and 100 Hugging Face model cards, identify

Haoyu Li, Isaac J Michaud, Ayan Biswas, Han-Wei Shen

Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed uncertainties. GPR also has the benefit of reducing I/O bandwidth and storage requirements for large scientific simulations. However, the reconstruction from the GPR models suffer

Minyoung Hwang

Energy-energy correlators (EECs), which are energy-weighted cross-sections of particle pairs, offer incisive probes into QCD dynamics, across the full scale of jet evolution, by separating energy scales in the jet fragmentation through the angular distance of the resulting particle pairs. Charged EECs probe the energy flux carried by pairs of the same or opp

Yuan-Shan Zhang, Zichen Yang, Chuanlian Xiao, Masahiko Isobe

Ta$_2$NiSe$_5$ continues to draw interest for its 326 K phase transition, whose dual electronic and structural nature reflects a complex interplay of electron-hole (excitonic) and electron-lattice interactions. Most studies that have attempted to decipher the relative importance of these interactions, particularly through charge transfer, have been limited t

Alexei V Karnaukhov, Sergei F Lyuksyutov, Artem V Aliakin, Mikhail E Prokhorov

System identification method (SIM) was used to evaluate the Earth equilibrium climate sensitivity. According to our simulations, the equilibrium climate sensitivity was found to be between 2 deg C and 7 deg C. Analysis of the changes in heat inventory of oceans, atmosphere, land, and cryosphere was based on the experimental data of IPCC6. The equation derive

Jonathan Spraggett

This thesis work presents a more efficient and effective approach to training control-related tasks for humanoid robots using Reinforcement Learning (RL). The traditional RL methods are limited in adapting to real-world environments, complexity, and natural motions, but the proposed approach overcomes these limitations by using curriculum training and Advers

Bartłomiej Starosta, Sławomir T. Wierzchoń, Piotr Borkowski, Dariusz Czerski

Graph Spectral Clustering methods (GSC) allow representing clusters of diverse shapes, densities, etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Furthermore, the presence of documents witho

Bisakh Banerjee, Mohammad Alwardat, Tapabrata Maiti, Selin Aviyente

Identifying the graphical structure underlying the observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a singular graph under the presumption that the observed data are uniform. However, many contexts involve heterogeneous datasets that feature multiple closely related graphs, typical

Grant P. Donnelly, Cory M. Whitcomb, Lindsey Hands, Sara E. Duval

We present the Spitzer/IRS Mapping Legacy Archive (SIMLA); a complete set of mid-infrared spectral cubes built from low-resolution mapping-mode fixed-target observations from Spitzer/IRS (5.2-38 micron, R~60-130). Contained in this dataset are spectral maps for several hundred spatially-resolved and unresolved objects, including galaxies, molecular clouds, s

Josiah Roberts, Biswas Rijal, Simon Divilov, Jon-Paul Maria

We present the Plan for Robust and Accurate Potentials (PRAPs), a software package for training and using moment tensor potentials (MTPs) in concert with the Machine Learned Interatomic Potentials (MLIP) software package. PRAPs provides an automated workflow to train MTPs using active learning procedures, and a variety of utilities to ease and improve workfl

Robert Szalai

The paper demonstrates that invariant foliations are accurate, data-efficient and practical tools for data-driven modelling of physical systems. Invariant foliations can be fitted to data that either fill the phase space or cluster about an invariant manifold. Invariant foliations can be fitted to a single trajectory or multiple trajectories. Over and underf

Ke Zhang, Yiqun Mei, Jiacong Xu, Vishal M. Patel

Producing long, coherent video sequences with stable 3D structure remains a major challenge, particularly in streaming scenarios. Motivated by this, we introduce Endless World, a real-time framework for infinite, 3D-consistent video generation.To support infinite video generation, we introduce a conditional autoregressive training strategy that aligns newly

Mersad Fathizadeh, Hosna Kianfar

Geotechnical and seismic applications, ranging from site response analysis and HVSR simulations to dispersion curve modeling, increasingly depend on large, well-labeled datasets for robust model development. However, the scarcity of publicly available borehole datasets, coupled with the proprietary nature of high-quality field records, creates a significant

Michael Döll, Andreas Müller, Bernd Ulmann

Memristor-based in-memory computing has emerged as a promising paradigm to overcome the constraints of the von Neumann bottleneck and the memory wall by enabling fully parallelisable and energy-efficient vector-matrix multiplications. We investigate the effect of nonlinear, memristor-driven weight updates on the convergence behaviour of neural networks train

James K Ruffle, Samia Mohinta, Guilherme Pombo, Asthik Biswas

The benefits of artificial intelligence (AI) human partnerships-evaluating how AI agents enhance expert human performance-are increasingly studied. Though rarely evaluated in healthcare, an inverse approach is possible: AI benefiting from the support of an expert human agent. Here, we investigate both human-AI clinical partnership paradigms in the magnetic r

Derek C. Gomes, Tapan C. Adhyapak

We show that curvatures in general ambient flow profiles can align shape-asymmetric active particles, revealing a previously overlooked competition with externally applied aligning fields. Focusing on the ubiquitous case of channel flows, we then investigate the fundamental consequences of this competition for the dynamics of shape-asymmetric active particle

Tue-Thu Van-Dinh, Hoang-Duy Tran, Truong-Binh Duong, Mai-Hanh Pham

Infographic Visual Question Answering (InfographicVQA) evaluates a model's ability to read and reason over data-rich, layout-heavy visuals that combine text, charts, icons, and design elements. Compared with scene-text or natural-image VQA, infographics require stronger integration of OCR, layout understanding, and numerical and semantic reasoning. We introd

Christian Howard, Roohollah Ghobadi, Nazanin Dehghan, Alessio D'Errico

Spontaneous parametric down-conversion is the primary source of position-correlated and momentum-anticorrelated photon pairs that form the canonical Einstein-Podolsky-Rosen (EPR) state. Their transverse spatial correlations are usually analyzed within the thin-crystal approximation, where the two-photon wavefunction is assumed to factorize into independent f

Ivan Saetchnikov, Elina Tcherniavskaia, Andreas Ostendorf, Anton Saetchnikov

Accurate, label-free quantification of multiple analytes in complex biological media remains a major challenge due to limited multiplexing, signal cross-correlations, and inconsistency across sensor samples and measurement runs. We introduce a multiplexed whispering-gallery-mode (WGM) biosensing framework that overcomes these barriers by jointly advancing ph

Travon Lucius, Christian Koch, Jacob Starling, Julia Zhu

We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset via SPY) by trading the underlying index ETF, using the option surface and macro variables only as state infor

Nicolas Crampe, Simon Lafrance, Charles Robillard, Luc Vinet

New exactly solvable one-dimensional XX spin chain models that exhibit perfect state transfer are defined. These models have inhomogeneous couplings and magnetic fields determined from the three-term recurrence relations satisfied by the q-Racah and para q-Racah polynomials. Due to this connection with orthogonal polynomials, the one-excitation sector can be

Xabier García-Martínez, Andrés Pérez-Rodríguez

This short note provides positive answers to two conjectures of Camacho, Khudoyberdiyev, and Omirov on the classification of complete evolution algebras. Our approach is based on analysing the solution set of a generic non-linear polynomial system of equations using elementary tools from algebraic geometry. We also obtain new results on subalgebras and idemp

David H. Brooks, Harry P. Warren

The theoretical expectation that coronal loops should expand with height contrasts with observations that typically show constant cross-sections. We investigate the idea that this discrepancy results from loops being composed of fine threads whose expansion occurs below the resolution limits of instruments like SDO/AIA. In this paper, we present two signific

Paolo Amore, Victor Figueroa, Raymundo Ramos

We have performed a detailed exploration of the energy landscape for configurations of points on the sphere, interacting via the logarithmic potential, and corresponding to local minima of the total energy, up to $N = 160$. The growth of $N_{\rm conf}$ (number of distinct configurations) is exponential, as for the Thomson problem, although weaker. Using the

Giovanni Gentili, Luigi Vezzoni

We revisit the second order estimate for solutions to the quaternionic Calabi-Yau problem on hyperk\"ahler manifolds, originally established by Dinew and Sroka. In this note, we present a simplified argument to derive the estimate.

Wangtao Lu, Kuanrong Shen, Ruming Zhang

This paper is concerned with the problem of an acoustic wave scattering in a locally perturbed periodic structure. As the total wavefield is non-quasi-periodic, effective truncation techniques are pursued for high-accuracy numerical solvers. We adopt the Green's function for the background periodic structure to construct a boundary integral equation (BIE) on

Gal Bouskila, Avner Fleischer, Ofer Neufeld

Photoelectron circular dichroism (PECD) is a method where randomly oriented chiral molecules are photoionized due to irradiation by circularly-polarized lasers, yielding large chiral signals in the photoelectron momentum distribution. Recently, PECD was explored with polarization-tailored light such as bi-chromatic and non-collinear drivers, which still prod

Steve Nwaiwu, Nipat Jongsawat, Anucha Tungkasthan

Causal reasoning in Large Language Models spanning association, intervention, and counterfactual inference is essential for reliable decision making in high stakes settings. As deployment shifts toward edge and resource constrained environments, quantized models such as INT8 and NF4 are becoming standard. Yet the impact of precision reduction on formal causa

Ely Hahami, Ishaan Sinha, Lavik Jain, Josh Kaplan

Can large language models introspect, that is, accurately detect perturbations to their own internal states? We systematically investigate this question using activation steering in Meta-Llama-3.1-8B-Instruct. First, we show that the binary detection paradigm used in prior work conflates introspection with a methodological artifact: apparent detection accura

Khalfalla Awedat, Mohamed Abidalrekab, Mohammad El-Yabroudi

Vertical beam dropout in spinning LiDAR sensors triggered by hardware aging, dust, snow, fog, or bright reflections removes entire vertical slices from the point cloud and severely degrades 3D perception in autonomous vehicles. This paper proposes a Graph Attention Network (GAT)-based framework that reconstructs these missing vertical channels using only the

Xuyang Liu, Zijian Zhang, Zhen Li, Jiahang Sun

Leader election serves a well-defined role in leader-based Byzantine Fault Tolerant (BFT) protocols. Existing reputation-based leader election frameworks for partially synchronous BFTs suffer from either protocol-specific proofs, narrow applicability, or unbounded recovery after network stabilization, leaving an open problem. This paper presents a novel prot

Antar Bandyopadhyay, Kunal Joshi

We consider some further generalizations of the novel random graph models as introduced by Bandyopadhyay and Sen \cite{BaSe2025} and find asymptotic for the degree of a fixed vertex and along with the asymptotic degree distribution. We show that in the \emph{case of the inverse power law} the order of these statistics is much slower than the case of the simp

Fernando De Terán, Froilán M. Dopico

First, we prove that the set of $n\times n$ complex matrices is the closure of a certain open subset whose elements have a very specific canonical form under congruence, which is uniquely determined up to the values of some parameters, but which has a slightly different expression depending on whether $n$ is even or odd. As a consequence, the canonical form

Sebastian Gonzalez La Corte, Thomas G. J. Chandler, Saverio E. Spagnolie, Ned S. Wingreen

Natural bacterial habitats are often complex fluids with viscoelastic and anisotropic responses to stress; for example, they can take the form of liquid crystals (LCs), with elongated microscopic constituents that collectively align while still retaining the ability to flow. However, laboratory studies typically focus on cells in simple liquids or complex fl

Franck Le, Keith Grueneberg, Erich Nahum, Vadim Sheinin

Tabular data are central to many real-world systems. While recent tabular transformers and in-context learners such as SAINT, TP-BERTa, TabPFN, TabICL, and MITRA incorporate limited inter-row reasoning, most approaches still lack an explicit mechanism to model relationships among instances, even though similar samples often share related outcomes. We investi

Marcela Vallejo, Ema Huerta, Joselen M. Pena, Jose Leiva

The rising demand for higher education has led universities to offer courses in multiple formats, including Intensive Instruction Courses (IICs), to meet the needs of a diverse student body. While active teaching methods improve physics understanding in standard courses, little research has examined their effectiveness in IICs. This research explored the mos

Thomas B. Bahder

Recent investigations into High-Energy QCD have identified entanglement entropy as a crucial observable, linking parton distributions to the structure of the quantum vacuum. While momentum-space entanglement has been extensively studied in Deep Inelastic Scattering (DIS), the spatial realization of this entanglement in confined systems remains an open questi

Vladimer Khasia

We present DeepVekua, a hybrid architecture that unifies geometric deep learning with spectral analysis to solve partial differential equations (PDEs) in sparse data regimes. By learning a diffeomorphic coordinate transformation that maps complex geometries to a latent harmonic space, our method outperforms state-of-the-art implicit representations on advect

Seungman Choi, Peter Menart, Andrew Schramka, Leif Bauer

Low-photon phase imaging is essential in applications where the signal is limited by short exposure times, faint targets, or the need to protect delicate samples. We address this challenge with Poisson Wavefront Imaging (PWI), an optimization-based method that incorporates Poisson photon statistics and a smoothness prior to improve wavefront reconstruction.

Shaun Baek, Sam Liu, Joseph Ukpong

Large Language Models (LLMs) act as powerful reasoning engines but struggle with "symbol grounding" in embodied environments, particularly when information is asymmetrically distributed. We investigate the Privileged Information Bias (or "Curse of Knowledge"), where a knowledgeable "Leader" agent fails to guide a sensor-limited "Follower" due to a lack of Th

Sotiris Chatzimiltis, Mahdi Boloursaz Mashhadi, Mohammad Shojafar, Merouane Debbah

Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks (RANs) opens up numerous opportunities for applying these systems. Securing the RAN is a key area, particularly through automating

Takahiro S. Yamamoto, Kipp Cannon, Hayato Motohashi, Hiroaki W. H. Tahara

Efficient searches for gravitational waves from compact binary coalescence are crucial for gravitational wave observations. We present a proof-of-concept for a method that utilizes a neural network taking an SNR map, a stack of SNR time series calculated by the matched filter, as input and predicting the presence or absence of gravitational waves in observat

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We present the first amplitude analysis of the hadronic decay $D^+\to\pi^+\pi^0\pi^0$, using $e^{+}e^{-}$ collision data collected with the BESIII detector at a center-of-mass energy of 3.773~GeV, corresponding to an integrated luminosity of 20.3~fb$^{-1}$. The fit fractions of the intermediate processes are measured, in which the $D^+ \to \rho(770)^+\pi^0$

Yuxin Lin, Silvia Spezzano, Olli Sipilä, Jaime E. Pineda

We present the first spatially resolved map of methanimine CH2NH in the prestellar core L1544 using the IRAM 30m telescope. The 2$_{0,2}$-1$_{0,1}$ line at 127 GHz was mapped with 20" resolution ($\sim$2800 au), revealing extended CH2NH emission across the core. The peak line intensity coincides with the well-known c-C3H2 peak, while the integrated intensity

Haowen Wang, Xiaoping Yuan, Fugang Zhang, Rui Jian

Generating articulated assets is crucial for robotics, digital twins, and embodied intelligence. Existing generative models often rely on single-view inputs representing closed states, resulting in ambiguous or unrealistic kinematic structures due to the entanglement between geometric shape and joint dynamics. To address these challenges, we introduce ArtGen

Vladimir Berman

We present a simple structure based model of how words are formed from morphemes. The model explains two major empirical facts: the typical distribution of word lengths and the appearance of Zipf like rank frequency curves. In contrast to classical explanations based on random text or communication efficiency, our approach uses only the combinatorial organiz

Fei Wang, Yuka Fujii, Ben Burningham, Jinping He

Emission spectroscopy is an invaluable tool for probing the atmospheres of brown dwarfs and exoplanets, but interpretations based on flux spectra alone often suffer from degeneracies among temperature structure, chemical composition, and cloud properties. Thermal emission spectropolarimetry offers complementary sensitivity to these atmospheric characteristic

Douglas Buchanan

We extend existing connections between random walks, branching processes, and spatial branching processes, and their respective scaling limits, to include processes in dependent random environments. More specifically, we prove new scaling limits of a random walk in a dependent random environment, an associated branching process in a dependent random environm

Gill Barequet, Sariel Har-Peled

$\renewcommand{\Re}{\mathbb{R}}$We present an efficient $O (n + 1/\varepsilon^{4.5})$-time algorithm for computing a $(1+\varepsilon$)-approximation of the minimum-volume bounding box of $n$ points in $\Re^3$. We also present a simpler algorithm (for the same purpose) whose running time is $O (n \log{n} + n / \varepsilon^3)$. We give some experimental result

Vishnu Iyer, Ross Parker, Atanas G. Stefanov

We investigate the existence and spectral stability of traveling wave solutions for a class of fourth-order semilinear wave equations, commonly referred to as beam equations. Using variational methods based on a constrained maximization problem, we establish the existence of smooth, exponentially decaying traveling wave profiles for wavespeeds in the interva

Evgeny A. Stepanov

Accurately describing many-body effects in multi-orbital systems remains a major challenge in theoretical condensed matter physics. At present, there is a significant methodological gap between the numerical tools used in ab initio computational materials science and those developed to study strong electronic correlations. The former can treat realistic, lar

Pulak Kundu, Uzzwal Kumar Mallick

The introduction of mongooses from Indian subcontinent to Amami Oshima Island, Japan, aimed at controlling the population of venomous Habu snakes, has led to significant ecological disruptions, raising concerns about the long-term sustainability of the islands biodiversity. To highlight the unintended consequences of such interventions and the necessity of u

Yawen Shao, Jie Xiao, Kai Zhu, Yu Liu

Group Relative Policy Optimization (GRPO) has proven highly effective in enhancing the alignment capabilities of Large Language Models (LLMs). However, current adaptations of GRPO for the flow matching-based image generation neglect a foundational conflict between its core principles and the distinct dynamics of the visual synthesis process. This mismatch le

Swayam Bhanded

Recent advances have significantly improved the training efficiency of diffusion transformers. However, these techniques have largely been studied in isolation, leaving unexplored the potential synergies from combining multiple approaches. We present SR-DiT (Speedrun Diffusion Transformer), a framework that systematically integrates token routing, architectu

Helena Cristina Vasconcelos, Maria Meirelles, Reşit Özmenteş

We develop a unified theoretical framework for thin-film hydrodynamics on inclined solid substrates, integrating capillarity, intermolecular forces, gravitational symmetry breaking, confined transport and stochastic wetting into a single formulation. Starting from lubrication theory with capillary curvature and disjoining-pressure interactions, we derive a g