Search: “research”

On the Theoretical Limits of Learning Unconstrained Interatomic Potentials

arXiv:2610.15078v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic mot

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Research

One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns

DGX agent

arXiv:2610.14074v1 Announce Type: new Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interact

researcharxiv-cs-lg

14 Oct 2026

One-Shot Learning for Double-Shot Latte Art: Adapting Cobot Pouring Skills with Dynamic Movement Primitives

arXiv:2610.16179v1 Announce Type: new Abstract: Latte art is a complex task requiring dexterity and precision, making it a challenging and commercially relevant task. A one-shot learning system is emp

researcharxiv-cs-ro

OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

arXiv:2610.16219v1 Announce Type: cross Abstract: Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existi

Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality

arXiv:2610.16464v1 Announce Type: cross Abstract: We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially

Online learning of neural state-space models

arXiv:2607.17614v2 Announce Type: replace-cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) mod

Operator Surrogates

arXiv:2610.18210v1 Announce Type: new Abstract: This chapter provides a mathematical introduction to operator surrogates, which are finite-parametric maps approximating complex (typically nonlinear) o

Optimal Transport PAC-Bayes Bounds

arXiv:2610.16560v1 Announce Type: new Abstract: PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in term

Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming

arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl

PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image

arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo

Perfect Reconstruction Cannot Show Whether Two Codebooks on a VAE's Latent Space Agree

arXiv:2610.14012v1 Announce Type: new Abstract: Many systems read the latent space of a variational autoencoder (VAE) as discrete codes, and rely on each latent being read back as the code it was writ

Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams

arXiv:2610.17848v1 Announce Type: new Abstract: Persistence diagrams (PDs) represent multiscale topological information as multisets of birth-death pairs (b,d). How best to use them with Transformers,

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re

PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation

arXiv:2610.17066v1 Announce Type: new Abstract: Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to matc

Phone Segmentation and Recognition through Phonological Activation Mapping

arXiv:2607.09020v2 Announce Type: replace-cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic

Physics-Aware Deepfake Detection via Distance-Speech Consistency

arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio

Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting

arXiv:2610.14014v1 Announce Type: new Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely da

Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs

arXiv:2610.14028v1 Announce Type: new Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks.

Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking

arXiv:2604.04690v3 Announce Type: replace Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setup

researcharxiv-cs-ro

PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary

arXiv:2610.16066v1 Announce Type: new Abstract: Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target

Planner-Guided Recursive Exploration for Diffusion Language Model Decoding

arXiv:2607.15655v2 Announce Type: replace-cross Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternativ

PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes

arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets

PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting

arXiv:2610.17605v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of indiv

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing

arXiv:2605.24273v2 Announce Type: replace Abstract: Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantifica

PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection

arXiv:2509.19774v3 Announce Type: replace Abstract: Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the

Tutorials

PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models

arXiv:2610.16980v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models are increasingly developed, published and used across scientific fields. Researchers, students and industry profes

tutorialsarxiv-cs-lg

Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects

arXiv:2610.16809v1 Announce Type: cross Abstract: This study addresses the prediction of audio popularity on an online sharing platform by leveraging both metadata and acoustic feature representations

Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals

arXiv:2610.16983v1 Announce Type: cross Abstract: Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which c

Prediction with Expert Advice under Heavy-Tailed Losses

arXiv:2610.14904v1 Announce Type: new Abstract: We study prediction with expert advice over T rounds with K experts under heavy-tailed losses whose (1+epsilon)-moment is bounded by heta>0 for some eps

Preserving Updates in Low-Bit Recurrent State Quantization

arXiv:2610.15709v1 Announce Type: new Abstract: Linear attention and recurrent models compress past tokens into a fixed-size state that is updated at every step. However, storing these states in high

Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching

arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo

Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

arXiv:2610.14942v1 Announce Type: new Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem

Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics

arXiv:2610.16327v1 Announce Type: new Abstract: Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensi

Probe and Skip: Self-Predictive Token Skipping for Efficient Long-Context LLM Inference

arXiv:2601.13155v3 Announce Type: replace-cross Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token

ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts

arXiv:2610.16287v1 Announce Type: new Abstract: Automated visual inspection is essential for efficient repair and recycling of metallic components. AI-based defect detection is a strong fit. However,

ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models

arXiv:2610.16134v1 Announce Type: new Abstract: Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples. For skeleton-based action recognitio

Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion

arXiv:2610.14654v1 Announce Type: new Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.

Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images

arXiv:2610.15286v1 Announce Type: new Abstract: The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the t

Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations

arXiv:2610.17265v1 Announce Type: new Abstract: Federated learning allows hospitals to train a shared mortality model without pooling their records, but published comparisons of aggregation strategies

PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting

arXiv:2610.17936v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics wit

Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid

arXiv:2610.14840v1 Announce Type: new Abstract: We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggre

researcharxiv-cs-ro

PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems

arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe

QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing

arXiv:2610.16390v1 Announce Type: cross Abstract: A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining ex

QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing

arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne

Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design

arXiv:2604.07379v2 Announce Type: replace-cross Abstract: From butterfly wings to remnants of nuclear detonation, aperiodic order repeatedly emerges in nature, often exhibiting reduced sensitivity to

Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models

arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte

Random Feature Pruning for Multi-Class Classification

arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me

Random Fourier Features in Near-Linear Time

arXiv:2610.14751v1 Announce Type: new Abstract: Random Fourier features approximate a shift-invariant kernel in R^d, such as the Gaussian kernel exp(-frac12|x-y|_2^2), using independent samples from i