Tootfinder

RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
Arefin Niam, Tevfik Kosar, M S Q Zulkar Nine
https://arxiv.org/abs/2509.05207

RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on large-scale graphs poses significant challenges. Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge. This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-…

Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
Sedat Dogan, Nina Dethlefs, Debarati Chakraborty
https://arxiv.org/abs/2510.05761 ht…

Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
Predicting the virality of online content remains challenging, especially for culturally complex, fast-evolving memes. This study investigates the feasibility of early prediction of meme virality using a large-scale, cross-lingual dataset from 25 diverse Reddit communities. We propose a robust, data-driven method to define virality based on a hybrid engagement score, learning a percentile-based threshold from a chronologically held-out training set to prevent data leakage. We evaluated a suite …

📷 Two-stage framework reconstructs sharp 4D scenes from blurry handheld videos
#imaging

Two-stage framework reconstructs sharp 4D scenes from blurry handheld videos
Neural Radiance Fields (NeRF) is a fascinating technique that creates three-dimensional (3D) representations of a scene from a set of two-dimensional (2D) images, captured from different angles. It works by training a deep neural network to predict the color and density at any point in 3D space.

Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning
Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin, Yan Wu
https://arxiv.org/abs/2510.06068

Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning
Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology embedding and an eigengrasp set. Conditio…

Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
https://arxiv.org/abs/2510.02512

Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
Leveraging query variants (QVs), i.e., queries with potentially similar information needs to the target query, has been shown to improve the effectiveness of query performance prediction (QPP) approaches. Existing QV-based QPP methods generate QVs facilitated by either query expansion or non-contextual embeddings, which may introduce topical drifts and hallucinations. In this paper, we propose a method that retrieves QVs from a training set (e.g., MS MARCO) for a given target query of QPP. To a…

Active learning and explicit electrostatics enable accurate modeling of electrolytes
Olga Chalykh, Mikhail Polovinkin, Dmitry Korogod, Nikita Rybin, Alexander Shapeev
https://arxiv.org/abs/2510.03479

Active learning and explicit electrostatics enable accurate modeling of electrolytes
Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecula…

QB Kyler Murray on Cardinals' plans for new 2028 training facility: 'Hopefully I'm here to see it' https://www.nfl.com/news/qb-kyler-murray-on-cardinals-plans-for-new-2028-training-facility-hopefully-i-m-here-t…

oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
Daniel Dehtyriov, Jonathan F. MacArt, Justin Sirignano
https://arxiv.org/abs/2510.02982

oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
Deep learning (DL) has demonstrated promise for accelerating and enhancing the accuracy of flow physics simulations, but progress is constrained by the scarcity of high-fidelity training data, which is costly to generate and inherently limited to a small set of flow conditions. Consequently, closures trained in the conventional offline paradigm tend to overfit and fail to generalise to new regimes. We introduce an online optimisation framework for DL-based Reynolds-averaged Navier--Stokes (RANS…

Pretraining Large Language Models with NVFP4
NVIDIA, Felix Abecassis, Anjulie Agrusa, Dong Ahn, Jonah Alben, Stefania Alborghetti, Michael Andersch, Sivakumar Arayandi, Alexis Bjorlin, Aaron Blakeman, Evan Briones, Ian Buck, Bryan Catanzaro, Jinhang Choi, Mike Chrzanowski, Eric Chung, Victor Cui, Steve Dai, Bita Darvish Rouhani, Carlo del Mundo, Deena Donia, Burc Eryilmaz, Henry Estela, Abhinav Goel, Oleg Goncharov, Yugi Guvvala, Robert Hesse, Russell Hewett, Herbert Hum, Ujval Kapasi,…

Pretraining Large Language Models with NVFP4
Large Language Models (LLMs) today are powerful problem solvers across many domains, and they continue to get stronger as they scale in model size, training set size, and training set quality, as shown by extensive research and experimentation across the industry. Training a frontier model today requires on the order of tens to hundreds of yottaflops, which is a massive investment of time, compute, and energy. Improving pretraining efficiency is therefore essential to enable the next generation…

Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples
Aminata Sow, Issiaka Traore, Tidiane Diallo, Mohamed Traore, Abdramane Ba
https://arxiv.org/abs/2510.01064

Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples
In this article, we analyze the near-infrared (NIR) spectra of fifty-eight (58) commercial tablets of 500 mg of paracetamol from different origins (that is, with different batch numbers) in the local markets in Bamako. The NIR spectra were recorded in the spectral range 930 nm-1700 nm. The samples are divided into forty-eight (48) samples forming the set of calibration (training set) and ten (10) samples used as the validation or test set. To perform multivariate calibration, we apply-three non…

A major southern California highway was being shut down while the U.S. Marine Corps stages a demonstration set to involve live fire on Oct. 18,
pitting the state's governor against the federal government yet again.
Interstate 5 will be shut down from Harbor Drive to Basilone Road,
a stretch of the main artery over 15 miles,
from 11 a.m. to 3 p.m. local time, the California Highway Patrol announced the morning of Oct. 18.
The news comes after days of back-an…

Highway shuts down as Marines’ show sparks clash with Newsom
The Marines said in an earlier statement that the event would take place "on approved training ranges and comport with established safety protocols."

Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
Xinyue Liu, Jianyuan Wang, Biao Leng, Shuo Zhang
https://arxiv.org/abs/2508.18007

Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
Fully Unsupervised Anomaly Detection (FUAD) is a practical extension of Unsupervised Anomaly Detection (UAD), aiming to detect anomalies without any labels even when the training set may contain anomalous samples. To achieve FUAD, we pioneer the introduction of Knowledge Distillation (KD) paradigm based on teacher-student framework into the FUAD setting. However, due to the presence of anomalies in the training data, traditional KD methods risk enabling the student to learn the teacher's repres…

Current tech companies be like

Training Variational Quantum Circuits Using Particle Swarm Optimization
Marco Mordacci, Michele Amoretti
https://arxiv.org/abs/2509.15726 https://arxiv.org…

Training Variational Quantum Circuits Using Particle Swarm Optimization
In this work, the Particle Swarm Optimization (PSO) algorithm has been used to train various Variational Quantum Circuits (VQCs). This approach is motivated by the fact that commonly used gradient-based optimization methods can suffer from the barren plateaus problem. PSO is a stochastic optimization technique inspired by the collective behavior of a swarm of birds. The dimension of the swarm, the number of iterations of the algorithm, and the number of trainable parameters can be set. In this …

Source Separation for A Cappella Music
Luca A. Lanzend\"orfer, Constantin Pinkl, Florian Gr\"otschla
https://arxiv.org/abs/2509.26580 https://arx…

Source Separation for A Cappella Music
In this work, we study the task of multi-singer separation in a cappella music, where the number of active singers varies across mixtures. To address this, we use a power set-based data augmentation strategy that expands limited multi-singer datasets into exponentially more training samples. To separate singers, we introduce SepACap, an adaptation of SepReformer, a state-of-the-art speaker separation model architecture. We adapt the model with periodic activations and a composite loss function …

Training Camp Notebook 8/14: Jakobi Meyers, Maxx Crosby set the tone in joint practice with 49ers https://www.raiders.com/news/training-camp-notebook-8-14-jakobi-meyers-maxx-crosby-set-the-tone-in-joint-practice-with-49er…

Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao, Juno Nam, Daniel S. Levine
https://arxiv.org/abs/2510.00224

Learning from the electronic structure of molecules across the periodic table
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of accompanying data in the Hamiltonian matrix H behind these datasets has so far gone unused for this purpose. Here, we provide a recipe for integrating the orbital interaction data within H towards training pipelines for atomic-level properties. We first introduce HEL…

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture
Minghao Hu, Junzhe Wang, Weisen Zhao, Qiang Zeng, Lannan Luo
https://arxiv.org/abs/2508.20212

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture
Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures (ISAs). It is thus important to extend malware detection capacity to multiple ISAs. However, training a deep learning-based malware detection model usually requires a large number of labeled malware samples. The process of coll…

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Semiparametric Learning from Open-Set Label Shift Data
Siyan Liu, Yukun Liu, Qinglong Tian, Pengfei Li, Jing Qin
https://arxiv.org/abs/2509.14522 https://a…

Semiparametric Learning from Open-Set Label Shift Data
We study the open-set label shift problem, where the test data may include a novel class absent from training. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric density ratio model framework that en…

*Huge* fan of the first version, thrilled to see an update:
https://mastodon.social/@Meyerweb/115202971430972273
Post:

Local Observability of a Class of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. M\"uller
https://arxiv.org/abs/2508.20544 https://…

Local Observability of a Class of Feedforward Neural Networks
Beyond the traditional neural network training methods based on gradient descent and its variants, state estimation techniques have been proposed to determine a set of ideal weights from a control-theoretic perspective. Hence, the concept of observability becomes relevant in neural network training. In this paper, we investigate local observability of a class of two-layer feedforward neural networks~(FNNs) with rectified linear unit~(ReLU) activation functions. We analyze local observability of…

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Sepp\"al\"ainen, Katsiaryna Haitsiukevich, Hoang Phuc Hau Luu, Anton Bj\"orklund, Kai Puolam\"aki
https://arxiv.org/abs/2508.19028

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading to a decline in predictive performance on unseen data. A standard solution is early stopping using a hold-out validation set, which halts the minimisation when the validation loss stops decreasing. However, this hold-out set reduces the data available for training. This paper presents {\sc gradstop}, a novel stochast…

NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation
Ruozhen He, Moayed Haji-Ali, Ziyan Yang, Vicente Ordonez
https://arxiv.org/abs/2510.02307

NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation
Text-to-image diffusion models trained on a fixed set of resolutions often fail to generalize, even when asked to generate images at lower resolutions than those seen during training. High-resolution text-to-image generators are currently unable to easily offer an out-of-the-box budget-efficient alternative to their users who might not need high-resolution images. We identify a key technical insight in diffusion models that when addressed can help tackle this limitation: Noise schedulers have u…

Training Camp Notebook 8/14: Jakobi Meyers, Maxx Crosby set the tone in joint practice with 49ers https://www.raiders.com/news/training-camp-notebook-8-14-jakobi-meyers-maxx-crosby-set-the-tone-in-joint-practice-with-49er…

An update to ECMWF's machine-learned weather forecast model AIFS
Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio, Simon Lang, Zied Ben Bouall\`egue, Jesper Dramsch, Mihai Alexe, Mario Santa Cruz, Sara Hahner, Harrison Cook, Helen Theissen, Mariana Clare, Cathal O'Brien, Jan Polster, Linus Magnusson, Gert Mertes, Florian Pinault, Baudouin Raoult, Patricia de Rosnay, Richard Forbes, Matthew Chantry

Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
Manasi Chhibber, Jagabandhu Mishra, Tomi H. Kinnunen
https://arxiv.org/abs/2509.24674 https://a…

Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
We propose a novel zero-shot source tracing framework inspired by advances in speaker verification. Specifically, we adapt the SSL-AASIST system for attack classification, ensuring that the attacks used for training are disjoint from those used to form fingerprint-trial pairs. For backend scoring in attack verification, we explore both zero-shot approaches (cosine similarity and Siamese) and few-shot approaches (MLP and Siamese). Experiments on our recently introduced STOPA dataset suggest that…

Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
Kelvyn K. Bladen, D. Richard Cutler, Alan Wisler
https://arxiv.org/abs/2510.00557 ht…

Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
In many machine learning problems, understanding variable importance is a central concern. Two common approaches are Permute-and-Predict (PaP), which randomly permutes a feature in a validation set, and Leave-One-Covariate-Out (LOCO), which retrains models after permuting a training feature. Both methods deem a variable important if predictions with the original data substantially outperform those with permutations. In linear regression, empirical studies have linked PaP to regression coefficie…

Electron neural closure for turbulent magnetosheath simulations: energy channels
George Miloshevich, Luka Vranckx, Felipe Nathan de Oliveira Lopes, Pietro Dazzi, Giuseppe Arr\`o, Giovanni Lapenta
https://arxiv.org/abs/2510.00282

Electron neural closure for turbulent magnetosheath simulations: energy channels
In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit Particle-in-Cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with…

RAVEN: RAnking and Validation of ExoplaNets
Andreas Hadjigeorghiou, David J. Armstrong, Kaiming Cui, Marina Lafarga Magro, Luis Agust\'in Nieto, Rodrigo F. D\'iaz, Lauren Doyle, Vedad Kunovac
https://arxiv.org/abs/2509.17645

RAVEN: RAnking and Validation of ExoplaNets
We present RAVEN, a newly developed vetting and validation pipeline for TESS exoplanet candidates. The pipeline employs a Bayesian framework to derive the posterior probability of a candidate being a planet against a set of False Positive (FP) scenarios, through the use of a Gradient Boosted Decision Tree and a Gaussian Process classifier, trained on comprehensive synthetic training sets of simulated planets and 8 astrophysical FP scenarios injected into TESS lightcurves. These training sets al…

Overcoming Output Dimension Collapse: How Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Kenya Otsuka, Yoshihiro Nagano, Yukiyasu Kamitani
https://arxiv.org/abs/2509.15832

Overcoming Output Dimension Collapse: How Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Advances in brain-to-image reconstruction are enabling us to externalize the subjective visual experiences encoded in the brain as images. Achieving such reconstruction with limited training data requires generalization beyond the training set, a task known as zero-shot prediction. Despite its importance, we still lack theoretical guidelines for achieving efficient and accurate reconstruction. In this paper, we provide a theoretical analysis of two widely used models for translating brain activ…

In case you don't want any Microsoft AI to be trained ("improved") with your personal data and content from LinkedIn, you should probably toggle the according switch in your LinkedIn privacy settings to "Off". Default is set to "On".
#generativeAI #dataprivacy

Selective Retrieval-Augmentation for Long-Tail Legal Text Classification
Boheng Mao
https://arxiv.org/abs/2508.19997 https://arxiv.org/pdf/2508.19997

Selective Retrieval-Augmentation for Long-Tail Legal Text Classification
Legal text classification is a fundamental NLP task in the legal domain. Benchmark datasets in this area often exhibit a long-tail label distribution, where many labels are underrepresented, leading to poor model performance on rare classes. This paper proposes Selective Retrieval-Augmentation (SRA) as a solution to this problem. SRA focuses on augmenting samples belonging to low-frequency labels in the training set, preventing the introduction of noise for well-represented classes, and require…

Score-based Membership Inference on Diffusion Models
Mingxing Rao, Bowen Qu, Daniel Moyer
https://arxiv.org/abs/2509.25003 https://arxiv.org/pdf/2509.25003…

Score-based Membership Inference on Diffusion Models
Membership inference attacks (MIAs) against diffusion models have emerged as a pressing privacy concern, as these models may inadvertently reveal whether a given sample was part of their training set. We present a theoretical and empirical study of score-based MIAs, focusing on the predicted noise vectors that diffusion models learn to approximate. We show that the expected denoiser output points toward a kernel-weighted local mean of nearby training samples, such that its norm encodes proximit…

Content-Adaptive Inference for State-of-the-art Learned Video Compression
Ahmet Bilican, M. Ak{\i}n Y{\i}lmaz, A. Murat Tekalp
https://arxiv.org/abs/2510.07283 https://

Content-Adaptive Inference for State-of-the-art Learned Video Compression
While the BD-rate performance of recent learned video codec models in both low-delay and random-access modes exceed that of respective modes of traditional codecs on average over common benchmarks, the performance improvements for individual videos with complex/large motions is much smaller compared to scenes with simple motion. This is related to the inability of a learned encoder model to generalize to motion vector ranges that have not been seen in the training set, which causes loss of perf…

From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun, Mohammad Reza Nikoo, Fang Chen, Amir H. Gandomi
https://arxiv.org/abs/2509.21341

From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification
We study symbolic surrogate modeling of frozen Transformer embeddings to obtain compact, auditable classifiers with calibrated probabilities. For five benchmarks (SST2G, 20NG, MNIST, CIFAR10, MSC17), embeddings from ModernBERT, DINOv2, and SigLIP are partitioned on the training set into disjoint, information-preserving views via semantic-preserving feature partitioning (SPFP). A cooperative multi-population genetic program (MEGP) then learns additive, closed-form logit programs over these views…

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Zihao Zheng, Zeyu Xie, Xuenan Xu, Wen Wu, Chao Zhang, Mengyue Wu
https://arxiv.org/abs/2509.00683

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Controllable text-to-audio generation (TTA) has attracted much attention recently. Although existing works can achieve fine-grained controllability based on timestamp information, sound event categories are limited to a fixed set. Moreover, since only simulated data is used for training, the generated audio quality and generalization performance on real data are limited. To tackle this issue, we propose PicoAudio2, improving temporal-controllable TTA via a new data processing pipeline and model…

McMining: Automated Discovery of Misconceptions in Student Code
Erfan Al-Hossami, Razvan Bunescu
https://arxiv.org/abs/2510.08827 https://arxiv.org/pdf/251…

McMining: Automated Discovery of Misconceptions in Student Code
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples wher…

AuraSight: Generating Realistic Social Media Data
Lynnette Hui Xian Ng, Bianca N. Y. Kang, Kathleen M. Carley
https://arxiv.org/abs/2509.08927 https://arxi…

AuraSight: Generating Realistic Social Media Data
This document details the narrative and technical design behind the process of generating a quasi-realistic set X data for a fictional multi-day pop culture episode (AuraSight). Social media post simulation is essential towards creating realistic training scenarios for understanding emergent network behavior that formed from known sets of agents. Our social media post generation pipeline uses the AESOP-SynSM engine, which employs a hybrid approach of agent-based and generative artificial intell…

Efficient Solutions for Mitigating Initialization Bias in Unsupervised Self-Adaptive Auditory Attention Decoding
Yuanyuan Yao, Simon Geirnaert, Tinne Tuytelaars, Alexander Bertrand
https://arxiv.org/abs/2509.14764

Efficient Solutions for Mitigating Initialization Bias in Unsupervised Self-Adaptive Auditory Attention Decoding
Decoding the attended speaker in a multi-speaker environment from electroencephalography (EEG) has attracted growing interest in recent years, with neuro-steered hearing devices as a driver application. Current approaches typically rely on ground-truth labels of the attended speaker during training, necessitating calibration sessions for each user and each EEG set-up to achieve optimal performance. While unsupervised self-adaptive auditory attention decoding (AAD) for stimulus reconstruction ha…

Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversight
Ugur Dinc, Jibak Sarkar, Philipp Schubert, Sabine Semrau, Thomas Weissmann, Andre Karius, Johann Brand, Bernd-Niklas Axer, Ahmed Gomaa, Pluvio Stephan, Ishita Sheth, Sogand Beirami, Annette Schwarz, Udo Gaipl, Benjamin Frey, Christoph Bert, Stefanie Corradini, Rainer Fietkau, Florian Putz

Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversight
Introduction: Large language models (LLM) have shown great potential in clinical decision support. GPT-5 is a novel LLM system that has been specifically marketed towards oncology use. Methods: Performance was assessed using two complementary benchmarks: (i) the ACR Radiation Oncology In-Training Examination (TXIT, 2021), comprising 300 multiple-choice items, and (ii) a curated set of 60 authentic radiation oncologic vignettes representing diverse disease sites and treatment indications. For …

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
Yehonatan Refael, Guy Smorodinsky, Ofir Lindenbaum, Itay Safran
https://arxiv.org/abs/2509.21296

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
The memorization of training data by neural networks raises pressing concerns for privacy and security. Recent work has shown that, under certain conditions, portions of the training set can be reconstructed directly from model parameters. Some of these methods exploit implicit bias toward margin maximization, suggesting that properties often regarded as beneficial for generalization may actually compromise privacy. Yet despite striking empirical demonstrations, the reliability of these attacks…

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld
https://arxiv.org/abs/2510.08906 h…

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy-system (Styblinski-Tang function) as well as for molecular dynamics traject…

Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
Yanlong Peng, Zhigang Wang, Ziwen He, Pengxu Chang, Chuangchuang Zhou, Yu Yan, Ming Chen
https://arxiv.org/abs/2509.07646

Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple co…

Imitative Membership Inference Attack
Yuntao Du, Yuetian Chen, Hanshen Xiao, Bruno Ribeiro, Ninghui Li
https://arxiv.org/abs/2509.06796 https://arxiv.org/p…

Imitative Membership Inference Attack
A Membership Inference Attack (MIA) assesses how much a target machine learning model reveals about its training data by determining whether specific query instances were part of the training set. State-of-the-art MIAs rely on training hundreds of shadow models that are independent of the target model, leading to significant computational overhead. In this paper, we introduce Imitative Membership Inference Attack (IMIA), which employs a novel imitative training technique to strategically constr…

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas
Francesco Sovrano, Gabriele Dominici, Rita Sevastjanova, Alessandra Stramiglio, Alberto Bacchelli
https://arxiv.org/abs/2508.11278

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas
Human cognitive biases in software engineering can lead to costly errors. While general-purpose AI (GPAI) systems may help mitigate these biases due to their non-human nature, their training on human-generated data raises a critical question: Do GPAI systems themselves exhibit cognitive biases? To investigate this, we present the first dynamic benchmarking framework to evaluate data-induced cognitive biases in GPAI within software engineering workflows. Starting with a seed set of 16 hand-cra…

In-domain SSL pre-training and streaming ASR
Jarod Duret, Salima Mdhaffar, Ga\"elle Laperri\`ere, Ryan Whetten, Audrey Galametz, Catherine Kobus, Marion-C\'ecile Martin, Jo Oleiwan, Yannick Est\`eve
https://arxiv.org/abs/2509.12101

In-domain SSL pre-training and streaming ASR
In this study, we investigate the benefits of domain-specific self-supervised pre-training for both offline and streaming ASR in Air Traffic Control (ATC) environments. We train BEST-RQ models on 4.5k hours of unlabeled ATC data, then fine-tune on a smaller supervised ATC set. To enable real-time processing, we propose using chunked attention and dynamic convolutions, ensuring low-latency inference. We compare these in-domain SSL models against state-of-the-art, general-purpose speech encoders …

Symmetry breaking for inductive logic programming
Andrew Cropper, David M. Cerna, Matti J\"arvisalo
https://arxiv.org/abs/2508.06263 https://arxiv.org…

Symmetry breaking for inductive logic programming
The goal of inductive logic programming is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a method to break symmetries in the hypothesis space. We implement our idea in answer set programming. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach …

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…

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
Robin Trombetta, Carole Lartizien
https://arxiv.org/abs/2508.12927

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in fields, such as industrial inspection or medical imaging, where acquiring labels is costly or when we want to avoid introducing biases in the type of anomalies that can be spotted. In this work, we propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings …

Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation
Wei-Teng Chu, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi
https://arxiv.org/abs/2509.20681

Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation
Implicit representations have been widely applied in robotics for obstacle avoidance and path planning. In this paper, we explore the problem of constructing an implicit distance representation from a single image. Past methods for implicit surface reconstruction, such as \emph{NeuS} and its variants generally require a large set of multi-view images as input, and require long training times. In this work, we propose Fast Image-to-Neural Surface (FINS), a lightweight framework that can reconstr…

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
David Chanin, Adri\`a Garriga-Alonso
https://arxiv.org/abs/2508.16560 https://

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
Sparse Autoencoders (SAEs) extract features from LLM internal activations, meant to correspond to single concepts. A core SAE training hyperparameter is L0: how many features should fire per token on average. Existing work compares SAE algorithms using sparsity--reconstruction tradeoff plots, implying L0 is a free parameter with no single correct value. In this work we study the effect of L0 on BatchTopK SAEs, and show that if L0 is not set precisely, the SAE fails to learn the underlying featu…

Sparse Multiview Open-Vocabulary 3D Detection
Olivier Moliner, Viktor Larsson, Kalle {\AA}str\"om
https://arxiv.org/abs/2509.15924 https://arxiv.org/p…

Sparse Multiview Open-Vocabulary 3D Detection
The ability to interpret and comprehend a 3D scene is essential for many vision and robotics systems. In numerous applications, this involves 3D object detection, i.e.~identifying the location and dimensions of objects belonging to a specific category, typically represented as bounding boxes. This has traditionally been solved by training to detect a fixed set of categories, which limits its use. In this work, we investigate open-vocabulary 3D object detection in the challenging yet practical s…

Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation
Haozhe Xu, Xiaohua Wang, Changze Lv, Xiaoqing Zheng
https://arxiv.org/abs/2508.05657

Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation
Conversational recommender systems (CRSs) enhance recommendation quality by engaging users in multi-turn dialogues, capturing nuanced preferences through natural language interactions. However, these systems often face the false negative issue, where items that a user might like are incorrectly labeled as negative during training, leading to suboptimal recommendations.Expanding the label set through data augmentation presents an intuitive solution but faces the challenge of balancing two key as…

CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model
Zhuoyuan Yu, Yuxing Long, Zihan Yang, Chengyan Zeng, Hongwei Fan, Jiyao Zhang, Hao Dong
https://arxiv.org/abs/2508.10416

CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model
Existing vision-and-language navigation models often deviate from the correct trajectory when executing instructions. However, these models lack effective error correction capability, hindering their recovery from errors. To address this challenge, we propose Self-correction Flywheel, a novel post-training paradigm. Instead of considering the model's error trajectories on the training set as a drawback, our paradigm emphasizes their significance as a valuable data source. We have developed a me…

The Majority is not always right: RL training for solution aggregation
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha, Asli Celikyilmaz, Jason Weston, Ilia Kulikov
https://arxiv.org/abs/2509.06870

The Majority is not always right: RL training for solution aggregation
Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language models (LLMs) on challenging reasoning tasks. While most prior work relies on simple majority voting or reward model ranking to aggregate solutions, these approaches may only yield limited benefits. In this work, we propose to learn aggregation as an explicit reasoning skill: given a set of candidate solutions, we train an…

Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel
https://arxiv.org/abs/2508.13822 https://

Improving Deep Learning for Accelerated MRI With Data Filtering
Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data curation strategies for improving MRI reconstruction. We assemble a large dataset of raw k-space data from 18 public sources consisting of 1.1M images and construct a diverse evaluation set comprising 48…

Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison
Marianna Nezhurina, Taishi Nakamura, Timur Carstensen, Niccol\`o Ajroldi, Ville Komulainen, David Salinas, Jenia Jitsev
https://arxiv.org/abs/2509.09009

Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison
We introduce open-sci-ref, a family of dense transformer models trained as research baselines across multiple model (0.13B to 1.7B parameters) and token scales (up to 1T) on 8 recent open reference datasets. Evaluating the models on various standardized benchmarks, our training runs set establishes reference points that enable researchers to assess the sanity and quality of alternative training approaches across scales and datasets. Intermediate checkpoints allow comparison and studying of the …

Split Conformal Classification with Unsupervised Calibration
Santiago Mazuelas
https://arxiv.org/abs/2510.07185 https://arxiv.org/pdf/2510.07185

Split Conformal Classification with Unsupervised Calibration
Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong performance guarantees with minimal computational costs. However, they require to use calibration samples composed by labeled examples different to those used for training. This requirement can be highly inconvenient, as it prevents the use of all labeled examples for training …

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Fam\'a, Roberto Pereira, Charalampos Kalalas, Paolo Dini, Lorena Qendro, Fahim Kawsar, Mohammad Malekzadeh
https://arxiv.org/abs/2510.08374

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored. The substantial computational demands required for training conventional CL frameworks pose a set of challenges, particularly in terms of energy consumption, data availability, and memory usage. We conduct an evaluation of four widely used CL frameworks: SimCLR, MoCo, SimSiam, and Barlow Twins. We focus on the practi…

Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection
Janne Laakkonen, Ivan Kukanov, Ville Hautam\"aki
https://arxiv.org/abs/2509.13878 https://

Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection
Foundation models such as Wav2Vec2 excel at representation learning in speech tasks, including audio deepfake detection. However, after being fine-tuned on a fixed set of bonafide and spoofed audio clips, they often fail to generalize to novel deepfake methods not represented in training. To address this, we propose a mixture-of-LoRA-experts approach that integrates multiple low-rank adapters (LoRA) into the model's attention layers. A routing mechanism selectively activates specialized experts…

All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Caiqi Zhang, Chang Shu, Ehsan Shareghi, Nigel Collier
https://arxiv.org/abs/2509.12908

All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to generalize to reasoning tasks. To address this gap, we propose a set of training-free, graph-based confidence estimation methods tailored to reasoning tasks. Our approach models reasoning paths as directed graphs and estimates confidence by exploiting graph properties such as centrality, path convergence, and path weightin…

In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging
Valentina Corbetta, Floris Six Dijkstra, Regina Beets-Tan, Hoel Kervadec, Kristoffer Wickstr{\o}m, Wilson Silva
https://arxiv.org/abs/2508.13880

In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging
Deep learning models in medical imaging often achieve strong in-distribution performance but struggle to generalise under distribution shifts, frequently relying on spurious correlations instead of clinically meaningful features. We introduce LCRReg, a novel regularisation approach that leverages Latent Concept Representations (LCRs) (e.g., Concept Activation Vectors (CAVs)) to guide models toward semantically grounded representations. LCRReg requires no concept labels in the main training set …

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
Monica Sekoyan, Nithin Rao Koluguri, Nune Tadevosyan, Piotr Zelasko, Travis Bartley, Nick Karpov, Jagadeesh Balam, Boris Ginsburg
https://arxiv.org/abs/2509.14128

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
This report introduces Canary-1B-v2, a fast, robust multilingual model for Automatic Speech Recognition (ASR) and Speech-to-Text Translation (AST). Built with a FastConformer encoder and Transformer decoder, it supports 25 languages primarily European. The model was trained on 1.7M hours of total data samples, including Granary and NeMo ASR Set 3.0, with non-speech audio added to reduce hallucinations for ASR and AST. We describe its two-stage pre-training and fine-tuning process with dynamic d…

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Dong Xu, Zhangfan Yang, Jenna Xinyi Yao, Shuangbao Song, Zexuan Zhu, Junkai Ji
https://arxiv.org/abs/2508.10775

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Three-dimensional generative models increasingly drive structure-based drug discovery, yet it remains constrained by the scarce publicly available protein-ligand complexes. Under such data scarcity, almost all existing pipelines struggle to learn transferable geometric priors and consequently overfit to training-set biases. As such, we present IBEX, an Information-Bottleneck-EXplored coarse-to-fine pipeline to tackle the chronic shortage of protein-ligand complex data in structure-based drug de…

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, Shaodong Chen, Yingyi Zhang, Chao Feng, Jiao Ran
https://arxiv.org/abs/2510.09358

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings in existing benchmarks that overestimate model capability due to significant overlap in training tests. In this work, we propose leveraging…

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
Manuel Hinz, Maximilian Mauel, Patrick Seifner, David Berghaus, Kostadin Cvejoski, Ramses J. Sanchez
https://arxiv.org/abs/2510.12618

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
High-dimensional recordings of dynamical processes are often characterized by a much smaller set of effective variables, evolving on low-dimensional manifolds. Identifying these latent dynamics requires solving two intertwined problems: discovering appropriate coarse-grained variables and simultaneously fitting the governing equations. Most machine learning approaches tackle these tasks jointly by training autoencoders together with models that enforce dynamical consistency. We propose to decou…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Shalev Manor, Mohammad Kohandel
https://arxiv.org/abs/2509.07245 https://arxiv.org/pdf/2…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offl…