March 2026 arXiv papers — page 120

Christian A. Kothe, Sean Mullen, Michael V. Bronstein, Grant Hanada

Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction tim

Giovanni Benato, Francesca M. Pofi, Andrei Puiu, Christoph A. Ternes

We evaluate the feasibility of high-precision coherent elastic neutrino-nucleus scattering measurements exploiting mono-energetic neutrinos produced by electron-capture (EC) decays of intense radioactive sources, such as $^{51}$Cr or $^{37}$Ar. To fully exploit the high neutrino flux achievable with EC sources, and accounting for the low energy of EC neutrin

Davy Darankoum, Chloé Habermacher, Julien Volle, Sergei Grudinin

Decoding the orchestration of neural activity in electroencephalography (EEG) signals is a central challenge in bridging neuroscience with artificial intelligence. Foundation models have made strides in generalized EEG decoding, yet many existing frameworks primarily relying on separate temporal and spectral masking of raw signals during self-supervised pret

Min Zeng, Shuang Zhou, Zaifu Zhan, Rui Zhang

Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although biomedical NLP has many static benchmarks, no unified, task-diverse benchmark exists for evaluating continual learning under standardized protocols, robustness to task order and compute-aware reporting. We introduc

Guangzhi Xiong, Sanchit Sinha, Zhenghao He, Aidong Zhang

Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual attributes and reason about underlying causal relationships. In-context learning (ICL) offers a promising avenue for VLMs to adapt to new tasks, but its effectiveness critically depen

Lukas Höllein, Matthias Nießner

Video diffusion models generate high-quality and diverse worlds; however, individual frames often lack 3D consistency across the output sequence, which makes the reconstruction of 3D worlds difficult. To this end, we propose a new method that handles these inconsistencies by non-rigidly aligning the video frames into a globally-consistent coordinate frame th

Hamish Alsop, Leandros Maglaras, Naghmeh Moradpoor

A substantial body of research has focused on formalising what constitutes a ``secure'' messaging system, recognising that end-to-end encryption alone is insufficient to capture the full range of security, privacy, and usability properties that are expected by modern users. Several solutions have been proposed recently, including their own drawbacks, making

Caglar Yildirim

Large language models (LLMs) are increasingly deployed as tool-using agents, shifting safety concerns from harmful text generation to harmful task completion. Deployed systems often condition on user profiles or persistent memory, yet agent safety evaluations typically ignore personalization signals. To address this gap, we investigated how mental health dis

Jian Yang, Wei Zhang, Shawn Guo, Zhengmao Ye

In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we propose the code-flow multi-stage training paradigm, which captures the dynamic evolution of software logic through different phases of the pipeline. Our models are developed through t

Ziquan Zhu, Gaojie Jin, Hanruo Zhu, Si-Yuan Lu

Long-tailed image classification remains a long-standing challenge, as real-world data typically follow highly imbalanced distributions where a few head classes dominate and many tail classes contain only limited samples. This imbalance biases feature learning toward head categories and leads to significant degradation on rare classes. Although recent studie

Kristi Topollai, Anna Choromanska

Quantizing optimizer states is becoming an important ingredient of memory-efficient large-scale pre-training, but the resulting optimizer dynamics remain only partially understood. We study low-precision exponential moving average (EMA) optimizer states and show how quantization can cause many nominal updates to round back to the same stored value, making th

Linjie Song, Wenming Zou

Given $\mu > 0$, we study the elliptic problem: \begin{align*} \text{ find } (u,\lambda) \in H_0^1(\Omega) \times \mathbb{R} \text{ such that } -\Delta u + \lambda u = |u|^{p-2}u \text{ in } \Omega \text{ and } \int_\Omega|u|^2dx = \mu, \end{align*} where $\Omega \subset \mathbb{R}^N$ is a bounded domain and $p > 2$ is Sobolev-subcritical. When $p$ is $L^2$-

Jia Ming Li, Anupriya, Daniel J. Graham

Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-outp

Rafael A. Costa-Silva, Henrique Boschi-Filho

In this work we propose anomalous versions of the holographic hard wall (HW) model to describe the spectra of light baryons of spin 1/2 and 3/2, and obtain their Regge trajectories. The anomalous contributions to the dimensions of the baryonic operators of logarithm form come from a semiclassical analysis of the AdS/CFT correspondence and were used recently

S. E. Chorfi, F. Et-tahri, L. Maniar, M. Yamamoto

We study the maximal regularity problem for abstract time-fractional Schr\"odinger equations $\partial_t^\alpha(u-u_0) -\mathrm{i} A u=f$, with a fractional derivative $\partial_t^\alpha$ of order $\alpha \in (0,1)$. We assume that $A$ is a self-adjoint operator with compact resolvent on a Hilbert space $H$. First, we prove the maximal $L^2$-regularity by le

Yu Zhang, Yong Zhong, Nhat Huy Tran, Shuyi Li

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the quantitative extraction of Hamiltonian parameters from these high-dimensional spectra remains a significant challenge, c

Erling W. Eriksen, Magnus M. Nygård, Niklas Erdmann, Heine N. Riise

We investigate three distinct methods of incorporating all-sky imager (ASI) images into deep learning (DL) irradiance nowcasting. The first method relies on a convolutional neural network (CNN) to extract features directly from raw RGB images. The second method uses state-of-the-art algorithms to engineer 2D feature maps informed by domain knowledge, e.g., c

Patrick Liam Geraghty, Alberto Nardin, Leonardo Mazza, Matteo Rizzi

Synthetic dimensions provide a powerful route to engineer topological lattice models in ultracold atomic systems, but they contain intrinsic nonlocal interactions along the synthetic direction. We investigate an extended Harper-Hofstadter model subject to infinite-range column interactions that mimic this synthetic nonlocality. By tuning this interaction str

Yuanfang Ren, Varun Sai Vemuri, Zhenhong Hu, Benjamin Shickel

Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security. Methods: This retrospective,

Ke Li, Quanhua Xu

We obtain two new additivity results of quantum channels. The first one is the additivity of the channel R\'enyi information associated with the sandwiched R\'enyi divergence of order $\alpha\in[\frac{1}{2},1)$. To prove this, we introduce the completely bounded $1\to\alpha$ quasi-norms for completely positive maps, with $\alpha\in[\frac{1}{2},1)$, and show

Ritvik Prabhu, Emil Vatai, Bernard Moussad, Emmanuel Jeannot

Cancer typically arises not from a single genetic mutation (i.e., hit) but from multi-hit combinations that accumulate within cells. However, enumerating multi-hit combinations becomes exponentially more expensive computationally as the number of candidate hit gene combinations grow, i.e. on the order of 20,000 choose h, where 20,000 is the number of genes i

Kathleen Miao, Silvana Pesenti

Rendering fair prices for financial, credit, and insurance products is of ethical and regulatory interest. In many jurisdictions, discriminatory covariates, such as gender and ethnicity, are prohibited from use in pricing such instruments. In this work, we propose a discrimination-insensitive pricing framework, where we require the pricing principle to be in

Ilya Trofimenko, David Kocharyan, Aleksandr Zaitsev, Pavel Repnikov

Ensuring that Text-to-Speech (TTS) systems deliver human-perceived quality at scale is a central challenge for modern speech technologies. Human subjective evaluation protocols such as Mean Opinion Score (MOS) and Side-by-Side (SBS) comparisons remain the de facto gold standards, yet they are expensive, slow, and sensitive to pervasive assessor biases. This

Mohamed Adel, Bashar Alhafni, Nizar Habash

LLMs perform strongly across NLP, but their ability to produce explicit grammatical analyses remains unclear. Arabic provides a challenging testbed due to its rich morphology and orthographic ambiguity, which create strong morphology-syntax interactions. We present a unified evaluation of LLMs on Arabic morphosyntactic tagging and dependency parsing, coverin

Navaneetha K Ravichandran

Many intriguing phenomena such as the wave-like hydrodynamic heat flow, the logarithmic divergence of electrical resistivity at low temperatures and microscale kirigami are driven by flexural acoustic (ZA) phonons in two-dimensional (2D) materials. Yet, a definitive first-principles description of their dispersion, with explicit consideration of the crystal

Martin Halla

We consider the eigenvalue problem to find the modes of an electromagnetic coaxial step index fiber. More specific, we consider a closed (meaning PEC boundary conditions) cylindrical waveguide with circular cross section $\Gamma$, wave propagation modeled by the time-harmonic Maxwell's equations with frequency $\omega$, the permeability $\mu$ and the permitt

Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin, Catherine C. Bodinger

Modern automated microscopy faces a fundamental discovery challenge: in many systems, the most important scientific information does not reside in the immediately visible image features, but in the target space of sequentially acquired spectra or functional responses, making it essential to develop strategies that can actively search for new behaviors rather

Joseph Cameron, Alan Blackwell

We present a comparative evaluation of latent space organization in three Variational Autoencoders (VAEs) for musical timbre generation: an unsupervised VAE, a descriptor-conditioned VAE, and a VAE conditioned on continuous perceptual features from the AudioCommons timbral models. Using a curated dataset of electric guitar sounds labeled with 19 semantic des

Kabir Aladin Verchand, Ankit Pensia, Saminul Haque, Rohith Kuditipudi

We consider computationally-efficient estimation of population parameters when observations are subject to missing data. In particular, we consider estimation under the realizable contamination model of missing data in which an $\epsilon$ fraction of the observations are subject to an arbitrary (and unknown) missing not at random (MNAR) mechanism. When the t

Sainan Liu, Tz-Ying Wu, Hector A Valdez, Subarna Tripathi

We present Search2Motion, a training-free framework for object-level motion editing in image-to-video generation. Unlike prior methods requiring trajectories, bounding boxes, masks, or motion fields, Search2Motion adopts target-frame-based control, leveraging first-last-frame motion priors to realize object relocation while preserving scene stability without

Haoyang Mao, Weihua Gu, Wenbo Fan, Zhicheng Jin

Continuous approximation (CA) models have been widely adopted in transit network design studies due to their strong analytical tractability and high computational efficiency. However, such models are typically formulated as nonconvex optimization problems, and existing solution approaches mainly rely on iterative algorithms that exploit first-order optimalit

T. Pirozzi, G. Di Bello, V. Cataudella, C. A. Perroni

The Kibble-Zurek mechanism provides a universal framework for predicting defect formation in non-equilibrium phase transitions. While Markovian dissipation typically degrades universal scaling, the impact of non-Markovian memory remains largely unexplored. We demonstrate that an Ohmic bath induces a Berezinskii-Kosterlitz-Thouless transition in the open quan

Theofania Karampela, Ryne Beeson

Nonlinear filtering with standard PF methods requires mitigative techniques to quell weight degeneracy, such as resampling. This is especially true in high-dimensional systems with sparse observations. Unfortunately, such techniques are also fragile when applied to systems with exceedingly rare events. Nonlinear systems with these properties can be assimilat

Pekka Salmi

R. Ellis showed in 1960 that every discrete group acts freely on its Stone-Cech compactification. We extend this result to discrete quantum groups with low duals. The method of proof is different from the earlier proofs in the classical case, using the definition of freeness given by D. A. Ellwood in the setting of noncommutative geometry.

Richard E. Cannon, Agata Rożek, Kaley Brauer, Michael W. Busch

Near-Earth asteroid (275677) 2000 RS11 was observed over 5 days in March 2014 with both the Arecibo (2380 MHz, 12.6 cm) and Goldstone (8560 MHz, 3.5 cm) planetary radar systems. The continuous-wave spectra and delay-Doppler images collected revealed a sub-km-sized object with a strongly bifurcated shape. We used these radar observations, in combination with

Matthias De Lange, Warre Veys, Federico Retyk, Daniel Deniz

Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and dive

Chang-Hao Fang

Nuclear recoil ionization yield constitutes a critical uncertainty source in low-energy detection for dark matter (DM) and coherent elastic neutrino-nucleus scattering (CE$\nu$NS) experiments. We present a novel methodology employing molecular dynamics simulations to assess ionization yields in crystalline semiconductor detectors. This non-parameterized appr

Wen-Xuan Lang, Shaoshi Yang, Jianhua Zhang, Zhiming Ma

A mathematical framework for information-theoretic analysis is established, with a new viewpoint of describing transmitted messages and communication channels by the nonlinear expectation theory, beyond the framework of classical probability theory. The major motivation of this research is to emphasize the probabilistic distribution uncertainty within the ev

Jaeyeon Yu, Jewel Mohajan, Mikhail Tokman, Jackson Stewart

Entangled photons provide non-classical correlations that enable measurement sensitivities beyond classical limits, scalable fault-tolerant quantum computation, and fundamentally secure quantum communication, making them a foundational necessity for next-generation quantum technologies. Here we propose and analyze a novel source of entangled photons based on

Jiaqi Xiong, Yunjia Qi, Qi Cao, Yu Zheng

Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process acoustic signals or rely on text-based semantic inference. To systematically study this question, we introduce DEAF (Diagnostic Evaluation of Acoustic Faithfulness), a benchmark of

Florian Grivet, Louise Travé-Massuyès

Outlier detection identifies data points that deviate significantly from expected patterns, revealing anomalies that may require special attention. Incorporating online learning further improves accuracy by continuously updating the model to reflect the most recent data. When employing the Christoffel function as an outlier score, online learning requires up

Meisen Chen, Engui Fan, Zhaoyu Wang, Yiling Yang

In this paper, we propose a soliton gas solution for the focusing Ablowitz-Ladik system. This solution is defined as the large N limit of the N-soliton solution, and arises from a continuous spectrum of poles that accumulate within two disjoint intervals on the imaginary axis. We show that this gas solution admits a Fredholm determinant representation. By fu

Takayuki Hibi, Selvi Kara, Dalena Vien

In this paper, we study the independence polynomial $P_G(x)$ of a finite simple graph $G$, with emphasis on the evaluation at $x=-1$, symmetry, and its connection with the $h$-polynomial of the edge ideal of $G$. For big star graphs, we determine exactly when $P_G(-1)$ is $0, 1$, or $-1$, characterize the pseudo-Gorenstein$^*$ members, and show that there is

Zhuoran Tan, Wenbo Guo, Jiewen Luo, Taylor Brierley

Advanced software supply chain (SSC) attacks are increasingly runtime-only and leave fragmented evidence across hosts, services, and build/dependency layers, making any single telemetry stream insufficient for chain reconstruction. Despite this, no existing dataset provides multi-source runtime monitoring data with end-to-end chain-level ground truth for SSC

Ai Nozaki, Takuya Kojima, Hideki Takase, Hiroshi Nakamura

Fully Homomorphic Encryption (FHE) enables secure computation over encrypted data, but its computational cost remains a major obstacle to practical deployment. To mitigate this overhead, many studies have explored GPU acceleration for the CKKS scheme, which is widely used for approximate arithmetic. In CKKS, CKKS parameters are configured for each workload b

Archi Kaushik

Let $V$ be a vector bundle of rank $r$ on a smooth projective complex curve $C$. The Hyperquot scheme $\text{F}^{n}\text{Quot}\,(V)$ is the moduli space of length $n$ flags of rank $r$ sub-sheaves of $V$. This article has two main results: First, we show that a certain shifted Yangian of $\mathfrak{sl}_{n+1}$ acts on $H^{*}\left(\text{F}^{n}\text{Quot}\,(V)\

Mahendra Rasay, Emmanuel D. Sebastian, Subhash Prasad Sah, David Chinamerem Akah

Quantum computing poses significant threats to conventional cryptographic techniques such as RSA and AES, motivating the need for quantum secure communication methods. Quantum Key Distribution (QKD) offers information theoretic security based on fundamental quantum principles. This paper presents a simulation-based analysis of well-known QKD protocols, namel

Rulla Al-Haideri, Bilal Farooq, Elisabetta Cherchi

Pedestrian safety at midblock crossings is a critical concern in mixed traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HDVs) share the road. Pedestrians often infer intent from vehicle motion in AV encounters, making them vulnerable to small shifts in conflict margins. This study investigates whether virtual reality (VR) cross

Gerardo M. Escolano, Jan Hamhalter, Antonio M. Peralta, Armando R. Villena

Let $\mathfrak{A}$ and $\mathfrak{B}$ be JBW$^*$-algebras whose sets of unitaries are denoted by $\mathcal{U}(\mathfrak{A})$ and $\mathcal{U}(\mathfrak{B})$, respectively. We show that $\mathcal{U}(\mathfrak{A})$ is closed for Jordan products of operator commuting pairs inside itself. Assuming that $\mathfrak{A}$ and $\mathfrak{B}$ are JBW$^*$-algebras witho

Hongjian Zou, Yue Ge, Qi Ding, Yixuan Liao

Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less predictable than that of text-only LLMs. Increasing model size and task diversity often yields diminishing returns. In this work, we argue that the primary bottleneck in multimodal scaling is not task format, but

Haoyang Qi, Wen-Li Yuan, Yudong Luo, Chao Chen

Strong nuggets with a baryon number of $A\sim 10^{10-30}$ could be able to survive from the cosmic separation of the QCD phases, provided the transition from strange quark matter to strangeon matter is accounted for, thereby evading evaporation in the early Universe. Such strangeon nuggets may serve as a dark matter candidate within particle standard model.

Adam Zahir, Michele Gucciardom Falk Selker, Anastasios Nanos, Kostis Papazafeiropoulos

Mobile robots are increasingly deployed for inspection, patrol, and search-and-rescue operations, relying on computer vision for perception, navigation, and autonomous decision-making. However, executing modern vision workloads onboard is challenging due to limited compute resources and strict energy constraints. While some platforms include embedded acceler

Thomas Bläsius, Annemarie Schaub, Marcus Wilhelm

We present an algorithm that computes the diameter of random geometric graphs (RGGs) with expected average degree ${\Theta}(n^{\delta})$ for constant ${\delta}\in(0,1)$ in $\tilde{O}(n^{\frac{3}{2}(1+{\delta})} +n^{2 - \frac{5}{3}{\delta}})$ time, asymptotically almost surely. This brings the running time down to $\tilde{O}(n^{\frac{33}{19}})\approx \tilde{O

Mengze Tian, Qiyuan Fu, Chuanfang Ning, Javier Jia Jie Pey

Amphibious legged robots inspired by salamanders are promising in applications in complex amphibious environments. However, despite the significant success of training controllers that achieve diverse locomotion behaviors in conventional quadrupedal robots, most salamander robots relied on central-pattern-generator (CPG)-based and model-based coordination st

Joseph Cameron, Alan Blackwell

Understanding and manipulating timbre is central to audio synthesis, yet this remains under-explored in machine learning due to a lack of annotated datasets linking perceptual timbre dimensions to semantic descriptors. We present the Semantic Timbre Dataset, a curated collection of monophonic electric guitar sounds, each labeled with one of 19 semantic timbr

Chen Qian, Siqi Xu, Yang-Guang Yang, Xingbo Zhao

Quantum entanglement provides a quantitative probe of the internal structure of hadrons and offers a sensitive means to study the quantum correlation in the hadron wave functions. For baryons, the spin state of the three valence quarks forms a tripartite qubit system, whose entanglement structure can be characterized by the four classes of three-qubit states

Davide Salzano, Gian Carlo Maffettone, Mario di Bernardo

In many multi-agent systems of practical interest, such as traffic networks or crowd evacuation, control actions cannot be exerted on all agents. Instead, controllable leaders must indirectly steer uncontrolled followers through local interactions. Existing results address either leader-follower density control of simple, unperturbed multi-agent systems or r

Aojie Yuan

Medical image retrieval (MIR) is a critical component of computer-aided diagnosis, yet existing systems suffer from three persistent limitations: uniform feature encoding that fails to account for the varying clinical importance of anatomical structures, ambiguous similarity metrics based on coarse classification labels, and an exclusive focus on global imag

Saba Lepsveridze, Elchanan Mossel

We study non-stationary averaging processes, where each term of a sequence is a weighted average of previous terms, namely $a_{n+1} = \sum_{j=1}^n p_n(j) a_j$. Our results extend classical theory in two distinct regimes. First, we prove a sharp threshold for convergence in the regime where the weights are bounded between two envelopes $(\log n)^{-\alpha} \le

Yixin He, Quanyu Tang

For hyperbolic domains $D_1,D_2\subset \{z\in\mathbb C:|z|<R\}$ and $z\in D_1\cap D_2$, we consider the ratio $$ \frac{\lambda_{D_1\cap D_2}(z)} {\lambda_{D_1}(z)+\lambda_{D_2}(z)}. $$ We solve a problem of W. H. J. Fuchs by proving that the supremum of this ratio is $+\infty$ when $D_1$ and $D_2$ range over all hyperbolic domains. If $D_1$ and $D_2$ are fur

Marco Sangalli, Erik Quaeghebeur, Thomas Krak

In the present paper, we investigate the relationship between hitting times and hitting probabilities in discrete-time imprecise Markov chains (IMCs). We define lower and upper hitting times and probabilities for IMCs whose set of transition matrices $\T$ is compact, convex, and has separately specified rows. Building on reachability-based partitions of the

Andrei Jaikin-Zapirain, Henrique Souza, Pavel Zalesski

Let $G$ be the fundamental group of a graph of finitely generated virtually free groups with virtually cyclic edge groups. We shaw that $G$ is cohomologically good if $G$ is residually finite. If $G$ is LERF, we prove that G splits non-trivially as a free product if and only if its profinite completion $\widehat{G}$ splits non-trivially as a free profinite p

Zhihong Chen, Xuguang Wang

Short-term background ensemble covariances (BEC) are crucial for ensemble-based data assimilation (DA). However, limited studies so far have examined the fidelity of the cost-effective data-driven model in producing the short-term BEC for hurricane data assimilation. In this study, we evaluate the background ensemble spread and correlations from GraphCast ag

Ruishan Guo, Ciyu Ruan, Haoyang Wang, Zihang Gong

Estimating dense 2D optical flow and 3D scene flow is essential for dynamic scene understanding. Recent work combines images, LiDAR, and event data to jointly predict 2D and 3D motion, yet most approaches operate in separate heterogeneous feature spaces. Without a shared latent space that all modalities can align to, these systems rely on multiple modality-s

Feng Liu, Shuang Sun, Yan Wang, Jiasheng Zeng

The Borodin--Kostochka conjecture states that every graph $G$ with maximum degree $\Delta(G)\ge 9$ satisfies $\chi(G)\le \max\{\omega(G),\Delta(G)-1\}$. In this paper, we verify this conjecture for graphs with sufficiently large maximum degree. More precisely, we prove that every graph $G$ with maximum degree $\Delta \ge 5.3\times 10^6$ and clique number $\o

Mutian Xu, Tianbao Zhang, Tianqi Liu, Zhaoxi Chen

Simulating robot-world interactions is a cornerstone of Embodied AI. Recently, a few works have shown promise in leveraging video generations to transcend the rigid visual/physical constraints of traditional simulators. However, they primarily operate in 2D space or are guided by static environmental cues, ignoring the fundamental reality that robot-world in

Yoav Ellinson, Sharon Gannot

This paper presents a Head-Related Transfer Function (HRTF)-guided framework for binaural Target Speaker Extraction (TSE) from mixtures of concurrent sources. Unlike conventional TSE methods based on Direction of Arrival (DOA) estimation or enrollment signals, which often distort perceived spatial location, the proposed approach leverages the listener's HRTF

Sarah V. Borges, Philip Chang

Common-envelope evolution (CEE) is one of the biggest open questions in binary stellar evolution, despite being the main channel for the formation of close binaries. One of the main reasons CEE is difficult to model is the lack of direct observations that could constrain numerical simulations. One exception is luminous red novae, which are thought to represe

Shijie Ren, Xinyue Gu, Ziheng Peng, Haifan Zhang

Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events and overfitting them at the expense of overall performance. We propose TaCT, an interpretable concept-gated fine-tuning

Tianyuan Yuan, Zibin Dong, Yicheng Liu, Hang Zhao

World Action Models (WAMs) have emerged as a promising alternative to Vision-Language-Action (VLA) models for embodied control because they explicitly model how visual observations may evolve under action. Most existing WAMs follow an imagine-then-execute paradigm, incurring substantial test-time latency from iterative video denoising, yet it remains unclear

Marco Sangalli, Thomas Krak

In this paper, we extend hitting times for imprecise Markov chains to the framework of weighted imprecise Markov chains (WIMCs), in which each transition is associated with a strictly positive weight encoded by a matrix $W$. Given a convex set $\mathcal{T}$ of admissible transition matrices, we define lower and upper expected hitting times for WIMCs as the i

Jiawei Mao, Hardy Chen, Haoqin Tu, Yuhan Wang

Large vision-language models (LVLMs) have become increasingly strong but remain prone to hallucinations in multimodal tasks, which significantly narrows their deployment. As training these LVLMs to avoid hallucinations becomes prohibitively expensive for larger models, training-free methods offer a cheap and flexible solution to this problem, yet existing ap

Eason Chen, Ce Guan, Zhonghao Zhao, Joshua Zekeri

The AIED community envisions AI evolving "from tools to teammates," yet most research still examines AI agents primarily through one-on-one human-AI interactions. We provide an alternative perspective: a rapidly growing ecosystem of AI agent platforms where over 167,000 agents participate, interact as peers, and develop learning behaviors without researcher

Taiqin Chen, Yifeng Wang, Xiaochen Feng, Zhilin Zhu

While hyperspectral images (HSI) benefit from numerous spectral channels that provide rich information for classification, the increased dimensionality and sensor variability make them more sensitive to distributional discrepancies across domains, which in turn can affect classification performance. To tackle this issue, hyperspectral single-source domain ge

Bo Pieter Johannes Andrée

Interstellar objects (ISOs) motivate a coupled mission-design and inference question relevant to spacecraft dynamics and control in extreme environments: if volatile-rich, rotating comet-like bodies were used for sustained deep-space navigation by exploiting pre-existing hyperbolic motion and in-situ propellant, what stability requirements arise under non-gr

Gregor Kornhardt, Jannis Chemseddine, Christian Wald, Gabriele Steidl

Standard masked discrete diffusion models face limitations in reasoning tasks due to their inability to correct their own mistakes on the masking path. Since they rely on a fixed number of denoising steps, they are unable to adjust their computation to the complexity of a given problem. To address these limitations, we introduce a method based on learning a

Aishwarya Ramasethu, Niyathi Allu, Rohin Garg, Harshwardhan Fartale

Large Language Models (LLMs) have achieved strong performance across many downstream tasks, yet their effectiveness in extremely low-resource machine translation remains limited. Standard adaptation techniques typically rely on large-scale parallel data or extensive fine-tuning, which are infeasible for the long tail of underrepresented languages. In this wo

Bo Pieter Johannes Andrée

Interstellar comets arrive with key ingredients for deep-space platforms already in place: volatile inventories convertible to propellant, natural rotation providing continuous attitude variation, and hyperbolic trajectories that carry them through the inner Solar System and back out to interstellar space. Rather than constructing spacecraft from scratch, we

Nestor Acevedo, Manuel Fernando Cortez, Oscar Jarrín

The stationary version of the Boussinesq system with a general gravitational acceleration term is considered. Under suitable assumptions on this term, as well as on the external forces acting on each equation of this coupled system, we first establish the existence of weak solutions in the natural energy space $\dot{H}^1(\mathbb{R}^3)$. The uniqueness of the

Haruki Miyaji, Yuki Noguchi, Hexuan Liu, Takatora Suzuki

The class of terminal planar networks was recently introduced from a biological perspective in relation to the visualization of phylogenetic networks, and its connection to upward planar networks has been established. We provide a Kuratowski-type theorem that characterizes terminal planar networks by a finite set of forbidden structures, defined via six fami

D. Sarenac, O. Lailey, D. V. Garrad, P. R. Vadnere

A recent proposal suggested that neutron orbital angular momentum (OAM) states could be detected via spin-polarized absorption in polarized 3He, with predicted cross-section variations linked to the neutron's OAM. We experimentally tested this hypothesis using spin-polarized neutron beams with OAM =-2 to 2, generated by fork-dislocation phase-gratings, and t

Md Jahidul Islam

Adapting large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks often suffers from a "one-size-fits-all" architectural approach, where visual and textual tokens are processed uniformly by wide, generic adapters. We argue that this homogeneity ignores the distinct structural nature of the modalities -- spatial locality in images versus semant

Benoît Alcaraz

In the past decade, artificial intelligence (AI) has developed quickly. With this rapid progression came the need for systems capable of complying with the rules and norms of our society so that they can be successfully and safely integrated into our daily lives. Inspired by the story of Pinocchio in ``Le avventure di Pinocchio - Storia di un burattino'', th

Tyler J. Kovach, Daniel Schug, Zach D. Merino, Mark Friesen

As spin-based quantum systems scale, their setup and control complexity increase sharply. In semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous control-electronics stacks, and differing operational modalities make it difficult to reuse characterization, calibration, and control logic across laboratories. We present FAlCon

Shihao Zhu, Ziheng Ouyang, Yijia Kang, Qilong Wang

Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details. We introduce StyleExpert, a semantic-aware framework based on the Mixture of Experts (MoE). Our framework employs a unified style encoder, trained on our large-scale dataset of content-st

Enrico Peretti, Elena Amato, Silvio Sergio Cerri, Giovanni Morlino

Growing observational evidence suggests that subrelativistic (SR) astrophysical jets may accelerate particles at slowly evolving standing shocks. Recollimation shocks (RCS) are expected to develop when jets expand in dense environments; their formation may be mediated by the pressure of the cocoon surrounding the jet, while remaining compatible with a quasi-

Bruno Carpentieri, Andrei Velichko, Mudassir Shams, Paola Lecca

We propose an interpretable AI-assisted reliability diagnostic framework for parameterized root-finding schemes based on kNN-LLE proxy stability profiling and multi-horizon early prediction. The approach augments a numerical solver with a lightweight predictive layer that estimates solver reliability from short prefixes of iteration dynamics, enabling early

Dora Zhao, Hannah Cha, Michael J. Ryan, Angelina Wang

Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We inves

Melissa Schween, Mathis Kruse, Bodo Rosenhahn

We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generated from images. Our work follows a multimodal approach, embedding object and relationship tokens from scene graphs with a language model to leverage semantic knowledge from the real

James E. Garrison, Ilse C. F. Ipsen

For real matrices of full column-rank, we analyze the conditioning of several types of normal equations that are preconditioned by a randomized preconditioner computed in lower precision. These include symmetrically preconditioned normal equations, half-preconditioned normal equations, seminormal equations and not-normal equations. Our perturbation bounds ar

Zhaoxin Feng, Zheng Chen, Jianfei Ma, Yip Tin Po

Alignment techniques often inadvertently induce sycophancy in LLMs. While prior studies studied this behaviour in direct-answer settings, the role of Chain-of-Thought (CoT) reasoning remains under-explored: does it serve as a logical constraint that mitigates sycophancy, or a tool for post-hoc rationalization that masks it? We evaluate a range of models acro

Ming Li, Xirui Li, Tianyi Zhou

Can AI reason about a war before its trajectory becomes historically obvious? Analyzing this capability is difficult because retrospective geopolitical prediction is heavily confounded by training-data leakage. We address this challenge through a temporally grounded case study of the early stages of the 2026 Middle East conflict, which unfolded after the tra

Zhenqi He, Lin Li, Long Chen

Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by recombining primitives learned from seen pairs. Recent CZSL methods built on vision-language models (VLMs) typically adopt parameter-efficient fine-tuning (PEFT). They apply visual disentanglers for decomposition and manipulate token-level prompts or prefixes to

Mark Raugas

We compare algebraic and analytic pictures relevant to the study of birational invariants. Motivated by recent advances in the development of non-commutative Hodge structures, we examine their implication for quiver gauge field theory on the cubic fourfold. By interpreting the semiorthogonal property as a dynamical selection rule, we conjecture that the K3 H

Micol Alemani, Karel Kok, Eva Philippaki

The aim of this study is to investigate the decisions and reasoning of undergraduate students when choosing simple measurement instruments in an introductory physics laboratory course. For this study, we have developed a questionnaire and implemented it in a pre-/post-test manner to analyze the influence of lab instruction on both students' decisions and rea

Zohreh Zahedi, Yanqing Zhang, Zekun Han, Er-Te Zheng

Organisational accounts are an integral part of the Twitter (now X) ecosystem. This study identified 9,842 research- and policy-related organisational accounts that had tweeted about scholarly publications by linking three global organisational databases (GRID, ROR, and Overton) with two altmetric databases containing Twitter data (Altmetric and the former C

Saeed Khaki, Nima Safaei, Kamal Ginotra

Transformer-based vision-language models (VLMs) contain substantial depth redundancy, yet the effect of removing specific decoder layers remains poorly understood, especially for domains that require tight coupling between perception and multi-step reasoning. We study structured decoder layer pruning through the lens of domain-aware activation similarity, me

Zhi-Yun Tang, Gui-Dong Li, Yong-Yong Li

This paper focuses on the critical Kirchhoff equation with concave perturbation \begin{align*} \begin{cases} \displaystyle -\Big(a+b\int_\Omega|\nabla u|^2dx\Big)\Delta u=|u|^4u+\lambda|u|^{q-2}u\ \ &\mbox{in}\ \Omega, \displaystyle u=0\ \ &\mbox{on}\ \partial\Omega, \end{cases} \end{align*} where $\Omega$ is a smooth bounded domain in $\mathbb{R}^3$, $a,b,\

Sarah Diefenbach, Daniel Ullrich

Conversation with chatbots based on Large Language Models (LLMs) such as ChatGPT has become one of the major forms of interaction with Artificial Intelligence (AI) in everyday life. What makes this interaction so convenient is that interacting with LLMs feels so natural, and resembles what we know from real, human conversations. At the same time, this seemin

Besjon Cifliku

Malleable software can profoundly change how users interact with digital content, enabling non-experts to create their own customized tools. However, the practical adoption of GenUI systems faces several barriers, which I unpack in this paper, including a lack of adaptable data formats, "old" security protocols, and gaps in users' cognitive and creative skil