Tootfinder

Reproducibility: The New Frontier in AI Governance
Israel Mason-Williams, Gabryel Mason-Williams
https://arxiv.org/abs/2510.11595 https://arxiv.org/pdf/251…

Reproducibility: The New Frontier in AI Governance
AI policymakers are responsible for delivering effective governance mechanisms that can provide safe, aligned and trustworthy AI development. However, the information environment offered to policymakers is characterised by an unnecessarily low Signal-To-Noise Ratio, favouring regulatory capture and creating deep uncertainty and divides on which risks should be prioritised from a governance perspective. We posit that the current publication speeds in AI combined with the lack of strong scientifi…

Offline Reinforcement Learning with Generative Trajectory Policies
Xinsong Feng, Leshu Tang, Chenan Wang, Haipeng Chen
https://arxiv.org/abs/2510.11499 https://

Offline Reinforcement Learning with Generative Trajectory Policies
Generative models have emerged as a powerful class of policies for offline reinforcement learning (RL) due to their ability to capture complex, multi-modal behaviors. However, existing methods face a stark trade-off: slow, iterative models like diffusion policies are computationally expensive, while fast, single-step models like consistency policies often suffer from degraded performance. In this paper, we demonstrate that it is possible to bridge this gap. The key to moving beyond the limitati…

Spatial-Temporal Multi-Scale Quantization for Flexible Motion Generation
Zan Wang, Jingze Zhang, Yixin Chen, Baoxiong Jia, Wei Liang, Siyuan Huang
https://arxiv.org/abs/2508.08991

Spatial-Temporal Multi-Scale Quantization for Flexible Motion Generation
Despite significant advancements in human motion generation, current motion representations, typically formulated as discrete frame sequences, still face two critical limitations: (i) they fail to capture motion from a multi-scale perspective, limiting the capability in complex patterns modeling; (ii) they lack compositional flexibility, which is crucial for model's generalization in diverse generation tasks. To address these challenges, we introduce MSQ, a novel quantization method that compre…

emg2tendon: From sEMG Signals to Tendon Control in Musculoskeletal Hands
Sagar Verma
https://arxiv.org/abs/2508.08269 https://arxiv.org/pdf/2508.08269

emg2tendon: From sEMG Signals to Tendon Control in Musculoskeletal Hands
Tendon-driven robotic hands offer unparalleled dexterity for manipulation tasks, but learning control policies for such systems presents unique challenges. Unlike joint-actuated robotic hands, tendon-driven systems lack a direct one-to-one mapping between motion capture (mocap) data and tendon controls, making the learning process complex and expensive. Additionally, visual tracking methods for real-world applications are prone to occlusions and inaccuracies, further complicating joint tracking…

Using LLMs to Capture Users' Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08512 https://

Using LLMs to Capture Users' Temporal Context for Recommendation
Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, temporal contextual factors of user preferences, such as transient short-term interests and enduring long-term tastes. This paper presents an assessment of Large Language Models (LLMs) for generating semantically rich, time-aware user profiles. We do not propose a novel end-to-end recommendation architecture; instead, the c…

U-Turn: Enhancing Incorrectness Analysis by Reversing Direction
Flavio Ascari, Roberto Bruni, Roberta Gori, Azalea Raad
https://arxiv.org/abs/2510.09292 https://

U-Turn: Enhancing Incorrectness Analysis by Reversing Direction
O'Hearn's Incorrectness Logic (IL) has sparked renewed interest in static analyses that aim to detect program errors rather than prove their absence, thereby avoiding false alarms -- a critical factor for practical adoption in industrial settings. As new incorrectness logics emerge to capture diverse error-related properties, a key question arises: can the combination of (in)correctness techniques enhance precision, expressiveness, automation, or scalability? Notable frameworks, such as outcome…

One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations
Kohei Oda, Po-Min Chuang, Kiyoaki Shirai, Natthawut Kertkeidkachorn
https://arxiv.org/abs/2510.09293

One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations
Sentence embedding methods have made remarkable progress, yet they still struggle to capture the implicit semantics within sentences. This can be attributed to the inherent limitations of conventional sentence embedding methods that assign only a single vector per sentence. To overcome this limitation, we propose DualCSE, a sentence embedding method that assigns two embeddings to each sentence: one representing the explicit semantics and the other representing the implicit semantics. These embe…

Chemical composition of planetary hosts: II. Abundances of neutron-capture elements
A. Sharma, E. Stonkut\.e, A. Drazdauskas, R. Minkevi\v{c}i\=ut\.e, \v{S}. Mikolaitis, G. Tautvai\v{s}ien\.e, U. Jonauskait\.e
https://arxiv.org/abs/2508.09979

Chemical composition of planetary hosts: II. Abundances of neutron-capture elements
We present a study of neutron-capture element abundances (Sr, Y, Zr, Ba, La, Ce, Nd, Pr, and Eu) in a large and homogeneous sample of 160 F-, G-, and K-type planet-host stars located in the northern hemisphere, including 32 stars in multi-planetary systems. The sample hosts a total of 175 high-mass planets and 47 Neptunian and super-Earth planets. High-resolution spectra were obtained with the 1.65-metre telescope at the Molėtai Astronomical Observatory using a fibre-fed spectrograph covering …

Trump praises FBI Director Kash Patel for speedy capture of Charlie Kirk assassin (Brooke Singman/Fox News)
https://www.foxnews.com/politics/trump-praises-fbi-director-kash-patel-record-breaking-speed-capture-charlie-kirk-assassin
http://www.memeorandum.com/250913/p40#a250913p40

Deep Finite Temperature Bootstrap
V. Niarchos, C. Papageorgakis, A. Stratoudakis, M. Woolley
https://arxiv.org/abs/2508.08560 https://arxiv.org/pdf/2508.08…

Deep Finite Temperature Bootstrap
We introduce a novel method to bootstrap crossing equations in Conformal Field Theory and apply it to finite temperature theories on $S^1\times \mathbb{R}^{d-1}$. The proposed approach does not rely on positivity constraints and does not employ uncontrolled truncation schemes. Instead, we capture the contribution of an infinite number of operators in conformal block expansions using suitable functions, which are bootstrapped (numerically) together with a finite number of exposed CFT data. Our a…

PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures
Hind Atbir, Farah Cherfaoui, Guillaume Metzler, Emilie Morvant, Paul Viallard
https://arxiv.org/abs/2510.11169 https:…

PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures
PAC generalization bounds on the risk, when expressed in terms of the expected loss, are often insufficient to capture imbalances between subgroups in the data. To overcome this limitation, we introduce a new family of risk measures, called constrained f-entropic risk measures, which enable finer control over distributional shifts and subgroup imbalances via f-divergences, and include the Conditional Value at Risk (CVaR), a well-known risk measure. We derive both classical and disintegrated PAC…

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2508.08545

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Developers insert logging statements in source code to capture relevant runtime information essential for maintenance and debugging activities. Log level choice is an integral, yet tricky part of the logging activity as it controls log verbosity and therefore influences systems' observability and performance. Recent advances in ML-based log level prediction have leveraged large language models (LLMs) to propose log level predictors (LLPs) that demonstrated promising performance improvements (AU…

LibraryLens: An Interactive Tool for Exploring and Arranging Digital Bookshelves
Trevor DePodesta, Johanna Beyer
https://arxiv.org/abs/2510.09502 https://a…

LibraryLens: An Interactive Tool for Exploring and Arranging Digital Bookshelves
Existing digital book management platforms often fail to capture the rich spatial and visual cues inherent to physical bookshelves, hindering users' ability to fully engage with their collections. We present LibraryLens, a novel visualization tool that addresses these shortcomings by enabling users to create, explore, and interact with immersive, two-dimensional representations of their personal libraries. The tool also caters to the growing trend of social sharing within online book communitie…

Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach
Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sanmi Koyejo, Peter Spirtes, Kun Zhang
https://arxiv.org/abs/2508.08337

Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach
Social determinants are variables that, while not directly pertaining to any specific individual, capture key aspects of contexts and environments that have direct causal influences on certain attributes of an individual. Previous algorithmic fairness literature has primarily focused on sensitive attributes, often overlooking the role of social determinants. Our paper addresses this gap by introducing formal and quantitative rigor into a space that has been shaped largely by qualitative proposa…

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha
https://arxiv.org/abs/2510.10126

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network ev…

SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
Zonghao Ying, Yangguang Shao, Jianle Gan, Gan Xu, Junjie Shen, Wenxin Zhang, Quanchen Zou, Junzheng Shi, Zhenfei Yin, Mingchuan Zhang, Aishan Liu, Xianglong Liu
https://arxiv.org/abs/2510.10073

SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in real-world environments, they face serious security risks, motivating the design of security evaluation benchmarks. Existing benchmarks provide only partial coverage, typically restricted to narrow scenarios such as user-level prompt manipulation, and thus fail to capture the broad range of agent vulnerabilities. To address this gap, we present \tool{…

Hybrid Ridgelet Deep Neural Networks for Data-Driven Arbitrage Strategies
Bahadur Yadav, Sanjay Kumar Mohanty
https://arxiv.org/abs/2510.10599 https://arxi…

Hybrid Ridgelet Deep Neural Networks for Data-Driven Arbitrage Strategies
In this study, we propose a novel model framework that integrates deep neural networks with the Ridgelet Transform. The Ridgelet Transform on Borel measurable functions is used for arbitrage detection on high-dimensional sparse structures. This transform also enhances the expressive power of neural networks, enabling them to capture complex and high-dimensional market structures. Theoretically, we determine profitable trading strategies by optimizing hybrid ridgelet deep neural networks. Furthe…

USA Tariffs Effect: Machine Learning Insights into the Stock Market
Mridul Patel
https://arxiv.org/abs/2510.10877 https://arxiv.org/pdf/2510.10877

USA Tariffs Effect: Machine Learning Insights into the Stock Market
The imposition of tariffs by President Trump during his second term had far-reaching consequences for global markets, including Australia. This study investigates how both the announcement and subsequent implementation of these tariffs, specifically on 02-Apr-2025, affected the Australian stock market, focusing on the S\&P/ASX 200 index over the period from 21-Jan-2025 to 25-Jul-2025. To accurately capture the significance and behavior of market fluctuations, the exploratory data analysis (EDA)…

Semi-parametric Markov models for multi-type point patterns
Ib Thorsgaard Jensen, Jean-Fran\c{c}ois Coeurjolly, Rasmus Waagepetersen
https://arxiv.org/abs/2510.11226 https://

Semi-parametric Markov models for multi-type point patterns
Multi-type Markov point processes offer a flexible framework for modelling complex multi-type point patterns where it is pertinent to capture both interactions between points as well as large scale trends depending on observed covariates. However, estimation of interaction and covariate effects may be seriously biased in the presence of unobserved spatial confounders. In this paper we introduce a new class of semi-parametric Markov point processes that adjusts for spatial confounding through a …

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
Amir Bahador Javadi, Philip Pong
https://arxiv.org/abs/2510.09411 https://

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power, and voltage references. Deep symbolic regression demonstrated more accuracy in capturing complex system dynamics, though it required subst…

A study of 80 known pulsars at 185 MHz using MWA incoherent drift-scan observations
Ting Yu, Hongyu Gong, Zhifu Gao, Zhongli Zhang, Zhigang Wen, Yujie Wang, Tao An
https://arxiv.org/abs/2510.09046

A study of 80 known pulsars at 185 MHz using MWA incoherent drift-scan observations
A systematic study of 80 known pulsars observed at 185 MHz has been conducted using archival incoherent-sum data from the Murchison Widefield Array (MWA). The dataset comprises 48 drift-scan observations from the MWA Voltage Capture System, covering approximately 30,000 square degrees of sky with sensitivities reaching about 8 mJy in the deepest regions. An optimized PRESTO-based search pipeline was deployed on the China SKA Regional Centre infrastructure. This enabled the detection of 80 known…

Anchoring the Universe with Characteristic Redshifts using Raychaudhuri Equation Informed Reconstruction Algorithm (REIRA)
Shibendu Gupta Choudhury, Purba Mukherjee, Anjan Ananda Sen
https://arxiv.org/abs/2510.09602

Anchoring the Universe with Characteristic Redshifts using Raychaudhuri Equation Informed Reconstruction Algorithm (REIRA)
We study the robustness and physical implications of a set of characteristic redshifts that capture key features of the late-time Universe. Using both model-independent reconstructions as well as different dark energy (DE) parameterizations, we show that these redshifts remain stable across cosmological models and reconstruction algorithm, making them reliable geometric anchors of the expansion history. Moreover, the Alcock-Paczyński corrections at these redshift anchors are found to be unity …

Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
Beige Jerry Jin, Leila Wehbe
https://arxiv.org/abs/2510.09415 ht…

Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially localize brain activity, a unified picture that preserves both high resolutions remains an unsolved challenge with existing source localization or MEG-fMRI fusion methods, especially for single-trial naturalistic data. We collected whole-head MEG when subjects…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e
https://arxiv.org/abs/2510.11269 https://…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Generative AI (GenAI) chatbots are now pervasive in digital ecosystems, yet their network traffic remains largely underexplored. This study presents an in-depth investigation of traffic generated by three leading chatbots (ChatGPT, Copilot, and Gemini) when accessed via Android mobile apps for both text and image generation. Using a dedicated capture architecture, we collect and label two complementary workloads: a 60-hour generic dataset with unconstrained prompts, and a controlled dataset bui…

Sketch Animation: State-of-the-art Report
Gaurav Rai, Ojaswa Sharma
https://arxiv.org/abs/2510.10218 https://arxiv.org/pdf/2510.10218

Sketch Animation: State-of-the-art Report
Sketch animation has emerged as a transformative technology, bridging art and science to create dynamic visual narratives across various fields such as entertainment, education, healthcare, and virtual reality. This survey explores recent trends and innovations in sketch animation, with a focus on methods that have advanced the state of the art. The paper categorizes and evaluates key methodologies, including keyframe interpolation, physics-based animation, data-driven, motion capture, and deep…

NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg
Nashira Baena, Mariana da Silva, Irina Grigorescu, Aakash Saboo, Saga Masui, Jaques-Donald Tournier, Emma C. Robinson
https://arxiv.org/abs/2508.09757

NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg
Understanding individual cortical development is essential for identifying deviations linked to neurodevelopmental disorders. However, current normative modelling frameworks struggle to capture fine-scale anatomical details due to their reliance on modelling data within a population-average reference space. Here, we present a novel framework for learning individual growth trajectories from biomechanically constrained, longitudinal, diffeomorphic image registration, implemented via a hierarchica…

Optimal Autonomous MLIP Dataset Building
Vincent G. Fletcher, Albert P. Bart\'ok, Livia B. P\'artay
https://arxiv.org/abs/2508.08864 https://arxiv.…

Optimal Autonomous MLIP Dataset Building
We propose a novel approach for constructing training databases for Machine Learning Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of automation, its suitability for iterative learning, and its independence from prior knowledge of stable phases, avoiding bias towards pre-existing structural data. The approach uses Nested Sampling (NS) to explore the configuration space…

Should it really be that hard to model the chirality induced spin selectivity effect?
J. Fransson
https://arxiv.org/abs/2510.11240 https://arxiv.org/pdf/25…

Should it really be that hard to model the chirality induced spin selectivity effect?
The chirality induced spin selectivity effect remains a challenge to capture with theoretical modeling. While at least a decade was spent on independent electron models, which completely fail to reproduce the experimental results, the lesson to be drawn out of these efforts is that a correct modeling of the effect has to include interactions among the electrons. In the discussion of the phenomenon ones inevitably encounters the Onsager reciprocity and time-reversal symmetry, and questions wheth…

HUGR: A Quantum-Classical Intermediate Representation
Mark Koch, Agust\'in Borgna, Seyon Sivarajah, Alan Lawrence, Alec Edgington, Douglas Wilson, Craig Roy, Luca Mondada, Lukas Heidemann, Ross Duncan
https://arxiv.org/abs/2510.11420

HUGR: A Quantum-Classical Intermediate Representation
We introduce the Hierarchical Unified Graph Representation (HUGR): a novel graph based intermediate representation for mixed quantum-classical programs. HUGR's design features high expressivity and extensibility to capture the capabilities of near-term and forthcoming quantum computing devices, as well as new and evolving abstractions from novel quantum programming paradigms. The graph based structure is machine-friendly and supports powerful pattern matching based compilation techniques. Inspi…

Three Birds with One Stone: Core-Collapsed SIDM Halos as the Common Origin of Dense Perturbers in Lenses, Streams, and Satellites
Hai-Bo Yu
https://arxiv.org/abs/2510.11006 http…

Three Birds with One Stone: Core-Collapsed SIDM Halos as the Common Origin of Dense Perturbers in Lenses, Streams, and Satellites
We show that core-collapsed self-interacting dark matter halos of mass $\sim 10^6\,{M_\odot}$, originally simulated to explain the dense perturber of the GD-1 stellar stream, also reproduce the structural properties inferred for the dense perturber detected in the strong lensing system JVAS B1938+666 from radio observations. Furthermore, these halos are sufficiently compact and dense to gravitationally capture field stars in satellite galaxies of the Milky Way, providing a natural explanation f…

(Anti)Gravitron: A Statistical Physics Perspective on Multidimensional Metrics of Polarizing Inequality
Rolando Gonzales Martinez
https://arxiv.org/abs/2510.09436 https://

(Anti)Gravitron: A Statistical Physics Perspective on Multidimensional Metrics of Polarizing Inequality
This paper introduces a novel framework for measuring multidimensional inequality based on a statistical physics reinterpretation of centrifugal and centripetal forces in rotating systems. Inspired by the mechanics of the Gravitron and extended via the conceptual AntiGravitron, this study proposes a new class of inequality metrics grounded in multivariate mixtures of Beta distributions. These composite metrics capture three key structural dimensions of polarizing inequality: the number and bala…

Double-functorial representation of regular structures
Jos\'e Siqueira
https://arxiv.org/abs/2508.06637 https://arxiv.org/pdf/2508.06637

Double-functorial representation of regular structures
It is well-known that bicategories of spans capture the Beck-Chevalley relationship underlying the adjunctions suitable for the semantics of existential quantification. By viewing hyperdoctrines from a double-categorical lens, this paper shows that we can also characterise the Frobenius property: (generalised) regular hyperdoctrines correspond to those lax symmetric monoidal double pseudofunctors from spans to quintets whose monoidal laxators provide companion commuter cells (in the sense of Pa…

Emergence of Homophily under Contextual Mechanisms
Jiaxing Weng, Haijun Yang, Tongyu Wang
https://arxiv.org/abs/2510.09821 https://arxiv.org/pdf/2510.09821…

Emergence of Homophily under Contextual Mechanisms
This paper introduces a tractable model to study incentive-compatible homophily under both external environments--such as exogenous shocks or policy constraints--and internal micromotives based on interactive attributes. We propose a set of invariants that capture main features of homophily and the well-defined partition dynamics leading to perfect global homophily. The criteria for homophily formation are characterized via isomorphism. Within this framework, we demonstrate the emergence of mac…

HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication
Heng Zhang, Yuling Shi, Xiaodong Gu, Zijian Zhang, Haochen You, Lubin Gan, Yilei Yuan, Jin Huang
https://arxiv.org/abs/2510.10611

HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication
Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approaches face two primary challenges: (i) \textit{Ineffective group collaboration modeling}, as they rely on pairwise edge representations in graph structures, limiting their ability to capture relationships among multiple agents; and (ii) \textit{Limited task-adaptiveness in communication topology design}, leading to exces…

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition
Yushuo Zheng, Zicheng Zhang, Xiongkuo Min, Huiyu Duan, Guangtao Zhai
https://arxiv.org/abs/2510.08928 h…

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition
Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce LM Fight Arena (Large Model Fight Arena), a novel framework that evaluates LMMs by pitting them against each other in the classic fighting game Mortal Kombat II, a task requiring rapid visual understanding and tactical, sequential decision-making. In a controlled tournament, we test six leading open- and closed-source models, where each agent operat…

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Zihao Zhao, Christopher Yeh, Lingkai Kong, Kai Wang
https://arxiv.org/abs/2510.11590 https://…

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error. To date, existing DFL methods typically rely on deterministic point predictions, which are often insufficient to capture the intrinsic stochasticity of real-world environments. To address this challenge, we propose the first diffusion-based DFL approach, which trains a diffusion model to represent the di…

Diagonal Artifacts in Samsung Images: PRNU Challenges and Solutions
David V\'azquez-Pad\'in, Fernando P\'erez-Gonz\'alez, Alejandro Mart\'in-Del-R\'io
https://arxiv.org/abs/2510.09509

Diagonal Artifacts in Samsung Images: PRNU Challenges and Solutions
We investigate diagonal artifacts present in images captured by several Samsung smartphones and their impact on PRNU-based camera source verification. We first show that certain Galaxy S series models share a common pattern causing fingerprint collisions, with a similar issue also found in some Galaxy A models. Next, we demonstrate that reliable PRNU verification remains feasible for devices supporting PRO mode with raw capture, since raw images bypass the processing pipeline that introduces ar…

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Weilong Chen, Franz G\"orlich, Paul Fuchs, Julija Zavadlav
https://arxiv.org/abs/2510.11148

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body interactions, can provide accurate approximations of the potential of mean force (PMF) in CG models. Current CG MLPs are typically trained in a bottom-up manner via force matching, which in practice relies on configurations sampled from the unbiased equilibrium Bo…

An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification
Ba-Quang Nguyen
https://arxiv.org/abs/2510.11537 https://arxiv…

An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification
We propose a novel neural architecture named TextGraphFuseGAT, which integrates a pretrained transformer encoder (PhoBERT) with Graph Attention Networks for token-level classification tasks. The proposed model constructs a fully connected graph over the token embeddings produced by PhoBERT, enabling the GAT layer to capture rich inter-token dependencies beyond those modeled by sequential context alone. To further enhance contextualization, a Transformer-style self-attention layer is applied on …

Reducing quantum resources for observable estimation with window-assisted coherent QPE
Harriet Apel, Cristian L. Cortes, Jessica Lemieux, Mark Steudtner
https://arxiv.org/abs/2508.06677

Reducing quantum resources for observable estimation with window-assisted coherent QPE
Quantum Phase Estimation (QPE) routines are known to fail probabilistically even with perfect gates and input states. This effect stems from an incompatibility of finite-sized quantum registers to capture a phase within QPE with phase angles of infinite precision, and the effect extend even beyond what would be reasonably expected from rounding. This effect can be partially mitigated by biasing the phase register with a window, or taper state, from classical signal processing. This paper focuse…

Guiding Energy-Efficient Locomotion through Impact Mitigation Rewards
Chenghao Wang, Arjun Viswanathan, Eric Sihite, Alireza Ramezani
https://arxiv.org/abs/2510.09543 https://…

Guiding Energy-Efficient Locomotion through Impact Mitigation Rewards
Animals achieve energy-efficient locomotion by their implicit passive dynamics, a marvel that has captivated roboticists for decades.Recently, methods incorporated Adversarial Motion Prior (AMP) and Reinforcement learning (RL) shows promising progress to replicate Animals' naturalistic motion. However, such imitation learning approaches predominantly capture explicit kinematic patterns, so-called gaits, while overlooking the implicit passive dynamics. This work bridges this gap by incorporating…

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy
https://arxiv.org/abs/2510.10321

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework combines these graph representations with light-weight (<4B) local LLMs, uniting topological features with semantic reasoning while avoiding the cost and privacy concerns of large cloud models. Evaluated on…

Parametrization of Symmetry in Data
Jian Liu, Dong Chen, Guo-Wei Wei
https://arxiv.org/abs/2508.07531 https://arxiv.org/pdf/2508.07531

Parametrization of Symmetry in Data
Symmetry plays a fundamental role in understanding natural phenomena and mathematical structures. This work develops a comprehensive theory for studying the persistent symmetries and degree of asymmetry of finite point configurations over parameterization in metric spaces. Leveraging category theory and span categories, we define persistent symmetry groups and introduce novel invariants called symmetry barcodes and polybarcodes that capture the birth, death, persistence, and reappearance of sym…

Efficient Autoregressive Inference for Transformer Probabilistic Models
Conor Hassan, Nasrulloh Loka, Cen-You Li, Daolang Huang, Paul E. Chang, Yang Yang, Francesco Silvestrin, Samuel Kaski, Luigi Acerbi
https://arxiv.org/abs/2510.09477

Efficient Autoregressive Inference for Transformer Probabilistic Models
Transformer-based models for amortized probabilistic inference, such as neural processes, prior-fitted networks, and tabular foundation models, excel at single-pass marginal prediction. However, many real-world applications, from signal interpolation to multi-column tabular predictions, require coherent joint distributions that capture dependencies between predictions. While purely autoregressive architectures efficiently generate such distributions, they sacrifice the flexible set-conditioning…

Effect of Finite-Temperature $\beta$-Decay Rates on the Rapid Neutron Capture Process
Yukiya Saito, Ante Ravli\'c, Pranav Nalamwar, Rebecca Surman
https://arxiv.org/abs/2510.08772

Effect of Finite-Temperature $β$-Decay Rates on the Rapid Neutron Capture Process
$β$-decay is known to play an essential role in the rapid neutron capture process ($r$-process) during $(n, γ) \leftrightarrow (γ, n)$ equilibrium and freeze-out when the neutron-rich nuclei decay back to stability. Recent systematic theoretical studies on $β$-decay at finite temperature indicated that under hot conditions ($T\sim10$~GK), a significant acceleration of $β$-decay rates is expected, especially for nuclei near stability. This corresponds to the early stage of the $r$-process. …

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
Jiaobiao Zhao, Kefeng Huang, Xiaoxiang Li, Mingxia Zhang, Peian Li, Jie Yang, Wenbo Liu, Yiming Zhao, Weidong Hu, Jianjun Ma
https://arxiv.org/abs/2510.09906

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Ex…

Experience how cosmic events become streaming data in this session by Frank Munz from this year's Berlin Buzzwords. Using basic OSS technologies and running on the Databricks Lakehouse, learn how to capture and analyse supernova data from NASA satellites, use natural language to analyse data streams, and create your own map of cosmic events.
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On the Origin of Neutron-capture Elements in r-I and r-II Stars: A Differential-abundance Analysis
Pallavi Saraf, Thirupathi Sivarani, Timothy C. Beers, Yutaka Hirai, Masaomi Tanaka, Carlos Allende Prieto, Drisya Karinkuzhi
https://arxiv.org/abs/2508.08847

On the Origin of Neutron-capture Elements in r-I and r-II Stars: A Differential-abundance Analysis
We present a strictly line-by-line differential analysis of a moderately $r$-process-enhanced star ($r$-I: HD~107752) with respect to a strongly $r$-process-enhanced star ($r$-II: CS~31082-0001) to investigate the possible common origin of their heavy-element nucleosynthesis with high-precision abundances. This study employs ESO data archive high-resolution and high signal-to-noise spectra taken with the UVES (VLT) spectrograph. Considering only the lines in common in both spectra, we estimate …

Intelligent Prediction and Optimization of Open-Hole Wellbore Multiphysics Stability: A Synergistic PINN-DRL Approach
Yu Song, Zehua Song, Jin Yang, Kejin Chen, Kun Jiang, Jizhou Tang
https://arxiv.org/abs/2510.09638

Intelligent Prediction and Optimization of Open-Hole Wellbore Multiphysics Stability: A Synergistic PINN-DRL Approach
To address the dual challenge of predicting multiphysics-induced instability and optimizing drilling fluid parameters for open-hole wellbores under long-term exposure, a high-fidelity system of coupled governing equations was developed. This system integrates seepage, hydration-induced softening, thermal diffusion, and elasto-plastic response to capture the nonlinear dynamics of wellbore stability evolution. A two-dimensional numerical model in a polar coordinate system was established using CO…

Metadata Management for AI-Augmented Data Workflows
Jinjin Zhao, Sanjay Krishnan
https://arxiv.org/abs/2508.06814 https://arxiv.org/pdf/2508.06814

Metadata Management for AI-Augmented Data Workflows
AI-augmented data workflows introduce complex governance challenges, as both human and model-driven processes generate, transform, and consume data artifacts. These workflows blend heterogeneous tools, dynamic execution patterns, and opaque model decisions, making comprehensive metadata capture difficult. In this work, we present TableVault, a metadata governance framework designed for human-AI collaborative data creation. TableVault records ingestion events, traces operation status, links exec…

EGGCodec: A Robust Neural Encodec Framework for EGG Reconstruction and F0 Extraction
Rui Feng, Yuang Chen, Yu Hu, Jun Du, Jiahong Yuan
https://arxiv.org/abs/2508.08924 https://

EGGCodec: A Robust Neural Encodec Framework for EGG Reconstruction and F0 Extraction
This letter introduces EGGCodec, a robust neural Encodec framework engineered for electroglottography (EGG) signal reconstruction and F0 extraction. We propose a multi-scale frequency-domain loss function to capture the nuanced relationship between original and reconstructed EGG signals, complemented by a time-domain correlation loss to improve generalization and accuracy. Unlike conventional Encodec models that extract F0 directly from features, EGGCodec leverages reconstructed EGG signals, wh…

Self-Supervised Representation Learning with ID-Content Modality Alignment for Sequential Recommendation
Donglin Zhou, Weike Pan, Zhong Ming
https://arxiv.org/abs/2510.10556 htt…

Self-Supervised Representation Learning with ID-Content Modality Alignment for Sequential Recommendation
Sequential recommendation (SR) models often capture user preferences based on the historically interacted item IDs, which usually obtain sub-optimal performance when the interaction history is limited. Content-based sequential recommendation has recently emerged as a promising direction that exploits items' textual and visual features to enhance preference learning. However, there are still three key challenges: (i) how to reduce the semantic gap between different content modality representatio…

Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization
Shuzhi Xu, Yifan Guo, Hiroki Kawabe, Kentaro Yaji
https://arxiv.org/abs/2510.08830

Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization
Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-h…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
https://arxiv.org/abs/2510.11172 https:/…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity, have continued to be developed in various directions. Moreover, the Bayesian generalized fused lasso is often used as a method that efficiently provides estimation under the natural assumption that regression coefficients of adjacent regions tend to take the same value. In most Bayesian methods, the selection of pri…

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image
Geonhee Sim, Gyeongsik Moon
https://arxiv.org/abs/2508.09973 https://

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image
Two major approaches exist for creating animatable human avatars. The first, a 3D-based approach, optimizes a NeRF- or 3DGS-based avatar from videos of a single person, achieving personalization through a disentangled identity representation. However, modeling pose-driven deformations, such as non-rigid cloth deformations, requires numerous pose-rich videos, which are costly and impractical to capture in daily life. The second, a diffusion-based approach, learns pose-driven deformations from la…

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Bicheng Wang, Jinping Wang, Yibo Sue
https://arxiv.org/abs/2510.09082

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only capture pairwise relationships, while there may be rich non-pairwise structured relationships in the network. First-order GNNs have diffic…

SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping
Marc Brinner, Sina Zarrie{\ss}
https://arxiv.org/abs/2510.11599

SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping
We propose SemCSE-Multi, a novel unsupervised framework for generating multifaceted embeddings of scientific abstracts, evaluated in the domains of invasion biology and medicine. These embeddings capture distinct, individually specifiable aspects in isolation, thus enabling fine-grained and controllable similarity assessments as well as adaptive, user-driven visualizations of scientific domains. Our approach relies on an unsupervised procedure that produces aspect-specific summarizing sentences…

Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
Javier Albert-Smet, Zoraida Frias, Luis Mendo, Sergio Melones, Eduardo Yraola
https://arxiv.org/abs/2510.09239

Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (E2E) Quality of Service (QoS) and its variability. In this work, we leverage large-scale crowdsourced measurements-including E2E, radio, co…

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Lars Ohnemus, Nils Hantke, Max Wei{\ss}er, Kai Furmans
https://arxiv.org/abs/2510.09483

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scal…

CodeWatcher: IDE Telemetry Data Extraction Tool for Understanding Coding Interactions with LLMs
Manaal Basha, Aime\^e M. Ribeiro, Jeena Javahar, Cleidson R. B. de Souza, Gema Rodr\'iguez-P\'erez
https://arxiv.org/abs/2510.11536

CodeWatcher: IDE Telemetry Data Extraction Tool for Understanding Coding Interactions with LLMs
Understanding how developers interact with code generation tools (CGTs) requires detailed, real-time data on programming behavior which is often difficult to collect without disrupting workflow. We present \textit{CodeWatcher}, a lightweight, unobtrusive client-server system designed to capture fine-grained interaction events from within the Visual Studio Code (VS Code) editor. \textit{CodeWatcher} logs semantically meaningful events such as insertions made by CGTs, deletions, copy-paste action…

Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Janaina Mourao-Miranda, Vanessa G\'omez-Verdejo
https://arxiv.org/abs/2510.09513

Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limitations for machine learning algorithms. In this work, we propose a Bayesian approach designed to efficiently handle these challenges while providing interpretable solutions. Our approach integrates (1) a generative formulation to capture cross-view relationships with a semi-supervised strategy, and (2) a discriminati…

Binary clusters in the Galactic disk I: Systematic identification and classification using Gaia DR3
Guimei Liu, Yu Zhang, Jing Zhong, Songmei Qin, Yueyue Jiang, Li Chen
https://arxiv.org/abs/2508.09127

Binary clusters in the Galactic disk I: Systematic identification and classification using Gaia DR3
Aims. We aim to identify and classify BCs using high-precision astrometric and kinematic data, and to investigate their physical properties, mutual gravitational interactions, and formation rates. Methods. We used a comprehensive star cluster catalog that contains 4,084 high-quality clusters. Based on spatial and kinematic proximity, we identified 400 cluster pairs involving 686 unique clusters. These pairs were classified into three types: primordial BCs, systems formed through tidal capture o…

Hybrid Long and Short Range Flows for Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Ajmal Saeed Mian, Lei Wei, Gang Li, Scott Schaefer, Ying He
https://arxiv.org/abs/2508.08542

Hybrid Long and Short Range Flows for Point Cloud Filtering
Point cloud capture processes are error-prone and introduce noisy artifacts that necessitate filtering/denoising. Recent filtering methods often suffer from point clustering or noise retaining issues. In this paper, we propose Hybrid Point Cloud Filtering ($\textbf{HybridPF}$) that considers both short-range and long-range filtering trajectories when removing noise. It is well established that short range scores, given by $\nabla_{x}\log p(x_t)$, may provide the necessary displacements to move …

A Hybrid Agent-Based and System Dynamics Framework for Modelling Project Execution and Technology Maturity in Early-Stage R&D
R. W. S. Pessoa, M. H. N{\ae}ss, J. C. Bijos, C. M. Rebello, D. Colombo, L. Schnitman, I. B. R. Nogueira
https://arxiv.org/abs/2510.09688

A Hybrid Agent-Based and System Dynamics Framework for Modelling Project Execution and Technology Maturity in Early-Stage R&D
This paper presents a hybrid approach to predict the evolution of technological maturity in R and D projects, using the oil and gas sector as an example. Integrating System Dynamics (SD) and Agent Based Modelling (ABM) allows the proposed multi level framework to capture uncertainties in work effort, team size, and project duration, which influence technological progress. While AB SD hybrid models are established in other fields, their use in R and D remains limited. The model combines system l…

Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
Jeffri Murrugarra-LLerena, Haoran Niu, K. Suzanne Barber, Hal Daum\'e III, Yang Trista Cao, Paola Cascante-Bonilla
https://arxiv.org/abs/2508.09245

Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
As visual assistant systems powered by visual language models (VLMs) become more prevalent, concerns over user privacy have grown, particularly for blind and low vision users who may unknowingly capture personal private information in their images. Existing privacy protection methods rely on coarse-grained segmentation, which uniformly masks entire private objects, often at the cost of usability. In this work, we propose FiGPriv, a fine-grained privacy protection framework that selectively mask…

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang, Zhiyong Lu, Jiawei Han, Hwanjo Yu
https://arxiv.org/abs/2510.09897

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often struggle to capture fine-grained scientific concepts in texts due to their reliance on holistic embeddings and limited domain understanding. Recent approaches leverage large language models (LLMs) to extract fine-grained semantic entities and enhance semantic matching, but they typically treat entities as independent fr…

LLP: LLM-based Product Pricing in E-commerce
Hairu Wang, Sheng You, Qiheng Zhang, Xike Xie, Shuguang Han, Yuchen Wu, Fei Huang, Jufeng Chen
https://arxiv.org/abs/2510.09347 http…

LLP: LLM-based Product Pricing in E-commerce
Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges in setting prices for their second-hand products efficiently. Therefore, numerous studies have been proposed for automating price prediction. However, most of them are based on static regression models, which suffer from poor generalization performance and fail to capture market dynamics (e.g., the price of a used iPho…

Solution-corrected Constant Potential Model for CO2 Electrocatalysis in Ionic Liquids
Jikai Sun, Alejandro Gallegos, Runtong Pan, Jianzhong Wu
https://arxiv.org/abs/2508.09272 h…

Solution-corrected Constant Potential Model for CO2 Electrocatalysis in Ionic Liquids
The selection of suitable ionic liquids (ILs) is critical for CO2 capture and electrocatalytic conversion into valuable chemical products. The screening process can be enhanced with theoretical simulations that reveal the property-performance relationship of ILs, accelerating the identification of optimal candidates. However, anhydrous ionic liquids exhibit low dielectric constants and high ion concentrations, challenging traditional first-principles calculations. Additionally, the spatial dist…

The Effects of Sunspots on Spectral Line Shapes in the Visible
Claire Komori, John M. Brewer, Lily L. Zhao
https://arxiv.org/abs/2508.09030 https://arxiv.o…

The Effects of Sunspots on Spectral Line Shapes in the Visible
We present a comparative spectral analysis to explore the ability of a cooler Sun model to accurately capture the spectral line shape changes caused by Sunspots. In the search for small Earth-like planets, the effects of stellar surface activity can overwhelm the $\sim$10 cm/s planetary RV signal. This necessitates the development of new stellar modeling methods and a greater understanding of the impact of surface activity on stellar spectra. Some attempts to model out noise from Sunspot activi…

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Meghan Wilkinson, Robert H Thomson
https://arxiv.org/abs/2509.09564 https://

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Supervised machine learning techniques rely on labeled data to achieve high task performance, but this requires the labels to capture some meaningful differences in the underlying data structure. For training network intrusion detection algorithms, most datasets contain a series of attack classes and a single large benign class which captures all non-attack network traffic. A review of intrusion detection papers and guides that explicitly state their data preprocessing steps identified that the…

Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks
Jeena Javahar, Tanya Budhrani, Manaal Basha, Cleidson R. B. de Souza, Ivan Beschastnikh, Gema Rodriguez-Perez
https://arxiv.org/abs/2510.11516

Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks
The use of AI code-generation tools is becoming increasingly common, making it important to understand how software developers are adopting these tools. In this study, we investigate how developers engage with Amazon's CodeWhisperer, an LLM-based code-generation tool. We conducted two user studies with two groups of 10 participants each, interacting with CodeWhisperer - the first to understand which interactions were critical to capture and the second to collect low-level interaction data using…

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
Joseph H. R. Isaac, Harish Saradagam, Nallamothu Pardhasaradhi
https://arxiv.org/abs/2508.09152

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
With the advent of 5G networks and technologies, ensuring the integrity and performance of packet core traffic is paramount. During network analysis, test files such as Packet Capture (PCAP) files and log files will contain errors if present in the system that must be resolved for better overall network performance, such as connectivity strength and handover quality. Current methods require numerous person-hours to sort out testing results and find the faults. This paper presents a novel AI/ML-…

Accent-Invariant Automatic Speech Recognition via Saliency-Driven Spectrogram Masking
Mohammad Hossein Sameti, Sepehr Harfi Moridani, Ali Zarean, Hossein Sameti
https://arxiv.org/abs/2510.09528

Accent-Invariant Automatic Speech Recognition via Saliency-Driven Spectrogram Masking
Pre-trained transformer-based models have significantly advanced automatic speech recognition (ASR), yet they remain sensitive to accent and dialectal variations, resulting in elevated word error rates (WER) in linguistically diverse languages such as English and Persian. To address this challenge, we propose an accent-invariant ASR framework that integrates accent and dialect classification into the recognition pipeline. Our approach involves training a spectrogram-based classifier to capture …

Listen through the Sound: Generative Speech Restoration Leveraging Acoustic Context Representation
Soo-Whan Chung, Min-Seok Choi
https://arxiv.org/abs/2508.08953 https://…

Listen through the Sound: Generative Speech Restoration Leveraging Acoustic Context Representation
This paper introduces a novel approach to speech restoration by integrating a context-related conditioning strategy. Specifically, we employ the diffusion-based generative restoration model, UNIVERSE++, as a backbone to evaluate the effectiveness of contextual representations. We incorporate acoustic context embeddings extracted from the CLAP model, which capture the environmental attributes of input audio. Additionally, we propose an Acoustic Context (ACX) representation that refines CLAP embe…

Generating Query-Relevant Document Summaries via Reinforcement Learning
Nitin Yadav, Changsung Kang, Hongwei Shang, Ming Sun
https://arxiv.org/abs/2508.08404 https://

Generating Query-Relevant Document Summaries via Reinforcement Learning
E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predictions, as product titles often lack sufficient detail to capture query intent. While product descriptions provide richer information, their verbosity and length make them unsuitable for real-time ranking, particularly for computationally expensive architectures like cross-encoder ranking models. To address this challe…

Power Spectrum, Bispectrum, 2- and 3-Point Correlation Function, and Beyond
Zachary Slepian, Farshad Kamalinejad, Alessandro Greco
https://arxiv.org/abs/2508.06762 https://

Power Spectrum, Bispectrum, 2- and 3-Point Correlation Function, and Beyond
N-Point Correlation Functions, usually with N = 2, 3, and their Fourier-space analogs power spectrum and bispectrum, are major tools used in cosmology to capture the clustering of large-scale structure. We outline how the clustering these functions capture emerges, explain that inflation produces a 2PCF or power spectrum but that subsequent evolution eventually produces a 3PCF or bispectrum, and beyond (and that inflation may do so as well at some level). Furthermore, in principle the Universe …

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
Junda Ye, Zhongbao Zhang, Li Sun, Siqiang Luo
https://arxiv.org/abs/2509.09337 https://

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to capture complex, high-order subgraph patterns. leading to insufficient structural expressiveness. Recent efforts have attempted to enhance structural expressiveness by integrating random walk kernels into GNNs. However, these methods are inherently designed for graph-level tasks, which limits their applicability to ot…

OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Weixuan Wang, Dongge Han, Daniel Madrigal Diaz, Jin Xu, Victor R\"uhle, Saravan Rajmohan
https://arxiv.org/abs/2508.09124

OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Autonomous agents powered by large language models (LLMs) are increasingly deployed in real-world applications requiring complex, long-horizon workflows. However, existing benchmarks predominantly focus on atomic tasks that are self-contained and independent, failing to capture the long-term contextual dependencies and multi-interaction coordination required in realistic scenarios. To address this gap, we introduce OdysseyBench, a comprehensive benchmark for evaluating LLM agents on long-horizo…

Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring
Zihan Fang, Zheng Lin, Senkang Hu, Yihang Tao, Yiqin Deng, Xianhao Chen, Yuguang Fang
https://arxiv.org/abs/2508.09085

Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring
Outdoor health monitoring is essential to detect early abnormal health status for safeguarding human health and safety. Conventional outdoor monitoring relies on static multimodal deep learning frameworks, which requires extensive data training from scratch and fails to capture subtle health status changes. Multimodal large language models (MLLMs) emerge as a promising alternative, utilizing only small datasets to fine-tune pre-trained information-rich models for enabling powerful health status…

IoTFuzzSentry: A Protocol Guided Mutation Based Fuzzer for Automatic Vulnerability Testing in Commercial IoT Devices
Priyanka Rushikesh Chaudhary, Rajib Ranjan Maiti
https://arxiv.org/abs/2509.09158

IoTFuzzSentry: A Protocol Guided Mutation Based Fuzzer for Automatic Vulnerability Testing in Commercial IoT Devices
Protocol fuzzing is a scalable and cost-effective technique for identifying security vulnerabilities in deployed Internet of Things devices. During their operational phase, IoT devices often run lightweight servers to handle user interactions, such as video streaming or image capture in smart cameras. Implementation flaws in transport or application-layer security mechanisms can expose IoT devices to a range of threats, including unauthorized access and data leakage. This paper addresses the ch…

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Alin Fan, Hanqing Li, Sihan Lu, Jingsong Yuan, Jiandong Zhang
https://arxiv.org/abs/2510.11066

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR) prediction frameworks. However, existing approaches typically adopt modality-centric modeling strategies that process ID-based and multimodal embeddings independently, failing to capture fine-grained interactions between content semantics and behavioral signals. In this paper, we propose Decoupled Multimodal Fusion (…

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Xi Xuan, Zimo Zhu, Wenxin Zhang, Yi-Cheng Lin, Tomi Kinnunen
https://arxiv.org/abs/2508.09294

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Advances in speech synthesis intensify security threats, motivating real-time deepfake detection research. We investigate whether bidirectional Mamba can serve as a competitive alternative to Self-Attention in detecting synthetic speech. Our solution, Fake-Mamba, integrates an XLSR front-end with bidirectional Mamba to capture both local and global artifacts. Our core innovation introduces three efficient encoders: TransBiMamba, ConBiMamba, and PN-BiMamba. Leveraging XLSR's rich linguistic repr…

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong, Xinhui Wu, Lin Chen, Feng Wei
https://arxiv.org/abs/2509.09321

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training, and competition solving. However, existing benchmarks remain limited in task coverage, domain diversity, difficulty modeling, and evaluation rigor, failing to capture the full capabilities of such agents in realistic settings. We present TAM Bench, a diverse, realistic, and st…

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08454

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling methods, such as averaging or concatenating item embeddings, which fail to capture the nuanced nature of user preference dynamics, particularly the interactions between long-term and short-term preferences. In this work, we propose LLM-driven Temporal User Profiling (LLM-TUP), a novel method for user profiling that explic…

Improving Human Motion Plausibility with Body Momentum
Ha Linh Nguyen, Tze Ho Elden Tse, Angela Yao
https://arxiv.org/abs/2509.09496 https://arxiv.org/pdf/…

Improving Human Motion Plausibility with Body Momentum
Many studies decompose human motion into local motion in a frame attached to the root joint and global motion of the root joint in the world frame, treating them separately. However, these two components are not independent. Global movement arises from interactions with the environment, which are, in turn, driven by changes in the body configuration. Motion models often fail to precisely capture this physical coupling between local and global dynamics, while deriving global trajectories from jo…

Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Sirui Xu, Yu-Wei Chao, Liuyu Bian, Arsalan Mousavian, Yu-Xiong Wang, Liang-Yan Gui, Wei Yang
https://arxiv.org/abs/2509.09671

Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Hand-object motion-capture (MoCap) repositories offer large-scale, contact-rich demonstrations and hold promise for scaling dexterous robotic manipulation. Yet demonstration inaccuracies and embodiment gaps between human and robot hands limit the straightforward use of these data. Existing methods adopt a three-stage workflow, including retargeting, tracking, and residual correction, which often leaves demonstrations underused and compound errors across stages. We introduce Dexplore, a unified …

Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
Wuyuao Mai, Geng Hong, Qi Liu, Jinsong Chen, Jiarun Dai, Xudong Pan, Yuan Zhang, Min Yang
https://arxiv.org/abs/2509.09207

Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
Penetration testing is critical for identifying and mitigating security vulnerabilities, yet traditional approaches remain expensive, time-consuming, and dependent on expert human labor. Recent work has explored AI-driven pentesting agents, but their evaluation relies on oversimplified capture-the-flag (CTF) settings that embed prior knowledge and reduce complexity, leading to performance estimates far from real-world practice. We close this gap by introducing the first real-world, agent-orient…

The fate of Gaia's wide binaries: Interplay of white-dwarf recoil and tidal capture
Christopher E. O'Connor
https://arxiv.org/abs/2509.08880 https://

The fate of Gaia's wide binaries: Interplay of white-dwarf recoil and tidal capture
White dwarfs (WDs) receive natal velocity boosts of $\sim 1 \, \mathrm{km \, s^{-1}}$ due to recoil from asymmetric mass loss during the late asymptotic giant branch (AGB) stage. In a wide binary, the acceleration of a proto-WD exerts a torque, modifying the orbital eccentricity. Potential signatures of this effect have been detected in Gaia's astrometric binary sample. At the same time, an AGB star's puffy envelope facilitates strong tidal interactions in binaries with periapsis separations of…

Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems
Shuangquan Lyu, Ming Wang, Huajun Zhang, Jiasen Zheng, Junjiang Lin, Xiaoxuan Sun
https://arxiv.org/abs/2510.10109

Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems
This paper designs and implements an explainable recommendation model that integrates knowledge graphs with structure-aware attention mechanisms. The model is built on graph neural networks and incorporates a multi-hop neighbor aggregation strategy. By integrating the structural information of knowledge graphs and dynamically assigning importance to different neighbors through an attention mechanism, the model enhances its ability to capture implicit preference relationships. In the proposed me…

Visual Grounding from Event Cameras
Lingdong Kong, Dongyue Lu, Ao Liang, Rong Li, Yuhao Dong, Tianshuai Hu, Lai Xing Ng, Wei Tsang Ooi, Benoit R. Cottereau
https://arxiv.org/abs/2509.09584

Visual Grounding from Event Cameras
Event cameras capture changes in brightness with microsecond precision and remain reliable under motion blur and challenging illumination, offering clear advantages for modeling highly dynamic scenes. Yet, their integration with natural language understanding has received little attention, leaving a gap in multimodal perception. To address this, we introduce Talk2Event, the first large-scale benchmark for language-driven object grounding using event data. Built on real-world driving scenarios, …

Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings
Ngoc N. Tran, Anwar Said, Waseem Abbas, Tyler Derr, Xenofon D. Koutsoukos
https://arxiv.org/abs/2508.06734

Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings
Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets, yet we find they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family - a scenario where strong generalization would typically be expected. This highlights a key limitation in existing approaches: both the model architectures and their structure-only representations often fail to capture deeper semantic patterns. In this work, we propose a robust se…

Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
Lalitesh Morishetti, Abhay Kumar, Jonathan Scott, Kaushiki Nag, Gunjan Sharma, Shanu Vashishtha, Rahul Sridhar, Rohit Chatter, Kannan Achan
https://arxiv.org/abs/2508.09636

Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrates tabular and non-tabular data, leveraging a pre-trained TinyBERT model for semantic embeddings and a novel sampling technique to capture diverse customer behaviors. We evaluate our model against several baselines, including XGBoost, TabNet, FT-Transformer, DCN-V2, and MMoE, focusing on their ability to handle mixed d…

InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
Sirui Xu, Dongting Li, Yucheng Zhang, Xiyan Xu, Qi Long, Ziyin Wang, Yunzhi Lu, Shuchang Dong, Hezi Jiang, Akshat Gupta, Yu-Xiong Wang, Liang-Yan Gui
https://arxiv.org/abs/2509.09555

InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remain challenging due to dataset limitations. Existing datasets often lack extensive, high-quality motion and annotation and exhibit artifacts such as contact penetration, floating, and incorrect hand motions. To address these issues, we introduce InterAct, a large-scale 3D HOI benchmark featuring dataset and methodological advancements. Fir…

BRoverbs -- Measuring how much LLMs understand Portuguese proverbs
Thales Sales Almeida, Giovana Kerche Bon\'as, Jo\~ao Guilherme Alves Santos
https://arxiv.org/abs/2509.08960

BRoverbs -- Measuring how much LLMs understand Portuguese proverbs
Large Language Models (LLMs) exhibit significant performance variations depending on the linguistic and cultural context in which they are applied. This disparity signals the necessity of mature evaluation frameworks that can assess their capabilities in specific regional settings. In the case of Portuguese, existing evaluations remain limited, often relying on translated datasets that may not fully capture linguistic nuances or cultural references. Meanwhile, native Portuguese-language dataset…

Locality in Image Diffusion Models Emerges from Data Statistics
Artem Lukoianov, Chenyang Yuan, Justin Solomon, Vincent Sitzmann
https://arxiv.org/abs/2509.09672 https://…

Locality in Image Diffusion Models Emerges from Data Statistics
Among generative models, diffusion models are uniquely intriguing due to the existence of a closed-form optimal minimizer of their training objective, often referred to as the optimal denoiser. However, diffusion using this optimal denoiser merely reproduces images in the training set and hence fails to capture the behavior of deep diffusion models. Recent work has attempted to characterize this gap between the optimal denoiser and deep diffusion models, proposing analytical, training-free mode…

PP-Motion: Physical-Perceptual Fidelity Evaluation for Human Motion Generation
Sihan Zhao, Zixuan Wang, Tianyu Luan, Jia Jia, Wentao Zhu, Jiebo Luo, Junsong Yuan, Nan Xi
https://arxiv.org/abs/2508.08179

PP-Motion: Physical-Perceptual Fidelity Evaluation for Human Motion Generation
Human motion generation has found widespread applications in AR/VR, film, sports, and medical rehabilitation, offering a cost-effective alternative to traditional motion capture systems. However, evaluating the fidelity of such generated motions is a crucial, multifaceted task. Although previous approaches have attempted at motion fidelity evaluation using human perception or physical constraints, there remains an inherent gap between human-perceived fidelity and physical feasibility. Moreover,…

CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Yifan Wang, Shen Gao, Jiabao Fang, Rui Yan, Billy Chiu, Shuo Shang
https://arxiv.org/abs/2509.09342

CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail to capture real-time user preferences, while Conversational Recommendation Systems (CRS) excel at eliciting immediate interests through natural language interactions but neglect historical behavior. To bridge this gap, we propose CESRec, a novel framework that integrates the long-term preference modeling of SRS with t…

Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
Jihyun Moon, Charmgil Hong
https://arxiv.org/abs/2509.08338 https://arxiv.org/pdf/2509.08338

Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
Accurate and early diagnosis of malignant melanoma is critical for improving patient outcomes. While convolutional neural networks (CNNs) have shown promise in dermoscopic image analysis, they often neglect clinical metadata and require extensive preprocessing. Vision-language models (VLMs) offer a multimodal alternative but struggle to capture clinical specificity when trained on general-domain data. To address this, we propose a retrieval-augmented VLM framework that incorporates semantically…

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
Meixiu Long, Duolin Sun, Dan Yang, Junjie Wang, Yue Shen, Jian Wang, Peng Wei, Jinjie Gu, Jiahai Wang
https://arxiv.org/abs/2508.07995

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matches. However, many real-world queries involve abstract reasoning, analogical thinking, or multi-step inference, which existing retrievers often struggle to capture. To address this challenge, we present \textbf{DIVER}, a retrieval pipeline tailored for reasoning-intensive information retrieval. DIVER consists of four …

Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
Van-Khang Nguyen, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le
https://arxiv.org/abs/2508.08042

Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, text, audio, etc.) offer rich information to address this issue, existing approaches often employ simplistic integration methods such as concatenation, average pooling, or fixed weighting schemes, which fail to capture the complex relationships between modalities. Our study propo…