Tootfinder

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…

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…

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…

Chip giants' efforts to turn Phoenix into a US hub may hinge on training local workers; an estimated 115K local chip jobs are set to be created in four years (Peter S. Goodman/New York Times)
https://www.nytimes.com/2025/12/04/business/tsmc-arizona-workers-…

A Growing U.S. Tech Hub Needs Workers. Colleges Try to Keep Up.
The success of efforts to turn Phoenix into a dominant center of semiconductor manufacturing may hinge on efforts to train local workers.

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…

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…

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…

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Jaesun Kim, Jinmu You, Yutack Park, Yunsung Lim, Yujin Kang, Jisu Kim, Haekwan Jeon, Deokgi Hong, Seung Yul Lee, Saerom Choi, Yongdeok Kim, Jae W. Lee, Seungwu Han
https://arxiv.org/abs/2510.11241

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that jointly optimizes universal and task-specific parameters through selective regularization, coupled with a domain-bridging set (DBS) that aligns …

📷 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.

Crosslisted article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld

ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
Xueze Kang, Guangyu Xiang, Yuxin Wang, Hao Zhang, Yuchu Fang, Yuhang Zhou, Zhenheng Tang, Youhui Lv, Eliran Maman, Mark Wasserman, Alon Zameret, Zhipeng Bian, Shushu Chen, Zhiyou Yu, Jin Wang, Xiaoyu Wu, Yang Zheng, Chen Tian, Xiaowen Chu
https://arxiv.org/abs/2510.00…

ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
Large-scale LLM pretraining today spans $10^{5}$--$10^{6}$ accelerators, making failures commonplace and elasticity no longer optional. We posit that an elastic-native training system must simultaneously ensure (i) Parameter Consistency, (ii) low Mean Time to Recovery (MTTR), (iii) high post-change Throughput, and (iv) Computation Consistency. This objective set not has never been jointly attained by prior work. To achieve these goals, we present ElasWave, which provides per-step fault toleranc…

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 …

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…

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…

Current tech companies be like

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…

Training-Free Out-Of-Distribution Segmentation With Foundation Models
Laith Nayal, Hadi Salloum, Ahmad Taha, Yaroslav Kholodov, Alexander Gasnikov
https://arxiv.org/abs/2510.02909

Training-Free Out-Of-Distribution Segmentation With Foundation Models
Detecting unknown objects in semantic segmentation is crucial for safety-critical applications such as autonomous driving. Large vision foundation models, includ- ing DINOv2, InternImage, and CLIP, have advanced visual representation learn- ing by providing rich features that generalize well across diverse tasks. While their strength in closed-set semantic tasks is established, their capability to detect out- of-distribution (OoD) regions in semantic segmentation remains underexplored. In this …

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…

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…

#LinkedIn & affiliates will harvest "your personal data and content you create" for its generative models on Nov 3: https://www.techradar.com/pro/linkedin-set

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…

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…

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 …

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."

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…

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…

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 …

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

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 …

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…

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…

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…

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…

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…

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…

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…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/12]:
- Generation Properties of Stochastic Interpolation under Finite Training Set
Yunchen Li, Shaohui Lin, Zhou Yu

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…

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

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…

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…

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…

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…

A Scalable Distributed Framework for Multimodal GigaVoxel Image Registration
Rohit Jena, Vedant Zope, Pratik Chaudhari, James C. Gee
https://arxiv.org/abs/2509.25044 https://

A Scalable Distributed Framework for Multimodal GigaVoxel Image Registration
In this work, we propose FFDP, a set of IO-aware non-GEMM fused kernels supplemented with a distributed framework for image registration at unprecedented scales. Image registration is an inverse problem fundamental to biomedical and life sciences, but algorithms have not scaled in tandem with image acquisition capabilities. Our framework complements existing model parallelism techniques proposed for large-scale transformer training by optimizing non-GEMM bottlenecks and enabling convolution-awa…

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…

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…

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…