Search: “research”

AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures

arXiv:2605.04754v1 Announce Type: new Abstract: Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approxi

Research

Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning

DGX agent

arXiv:2605.04406v1 Announce Type: new Abstract: The integration of Symmetric Positive Definite (SPD) matrices into deep learning has historically relied on fixed algebraic Riemannian metrics. Analogou

researcharxiv-cs-lg

7 May 2026

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing

arXiv:2602.03452v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language models on deterministic outcome reasoning tasks. Prio

Bilinear Mamba-Koopman Neural MPC for Varying Dynamics

arXiv:2605.04793v1 Announce Type: new Abstract: Koopman-based neural MPC models generate time-varying dynamics from historical data, but preserve convexity by enforcing that the system operator is ind

BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference

arXiv:2605.04087v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold St(p,d), the set of p imes d column-orthonormal matrices, is fundamental in statistics, machine learning, and s

Bridging Input Feature Spaces Towards Graph Foundation Models

arXiv:2605.04834v1 Announce Type: new Abstract: Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but

Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images

arXiv:2601.11689v2 Announce Type: replace-cross Abstract: Multimodal image registration between diffusion MRI (dMRI) and T1-weighted (T1w) MRI images is a critical step for aligning diffusion-weighted

Calibrating Tabular Anomaly Detection via Optimal Transport

arXiv:2602.06810v2 Announce Type: replace Abstract: Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in di

Can AI Help You Get Over Your Breakup? One Session with a Belief-Reframing Chatbot Shows Sustained Distress Reduction

arXiv:2605.03261v1 Announce Type: cross Abstract: Romantic breakups are among the most common and intense sources of psychological distress. We evaluated *overit*, a single-session AI chatbot that use

CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

arXiv:2605.04495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly op

Cardiovascular disease classification using radiomics and geometric features from cardiac CT

arXiv:2506.22226v2 Announce Type: replace-cross Abstract: Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilita

CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

arXiv:2605.04641v1 Announce Type: new Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviat

Centrality-Based Pruning for Efficient Echo State Networks

arXiv:2603.20684v2 Announce Type: replace Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectivenes

Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams

arXiv:2505.05880v2 Announce Type: replace-cross Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between t

Computing Thiele Rules on Interval Elections and their Generalizations

arXiv:2605.03067v1 Announce Type: new Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially

Conditional outlier detection for clinical alerting

arXiv:2605.05124v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electr

Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR

arXiv:2504.11101v4 Announce Type: replace Abstract: Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite

Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling

arXiv:2605.04662v1 Announce Type: new Abstract: Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints a

Contextual Memory-Enhanced Source Coding for Low-SNR Communications

arXiv:2605.04400v1 Announce Type: cross Abstract: While Separate Source-Channel Coding (SSCC) retains the practical benefits of modular system design, its effectiveness in noisy text transmission is f

Continual Distillation of Teachers from Different Domains

arXiv:2605.04059v1 Announce Type: cross Abstract: Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit

Critical Windows of Complexity Control: When Transformers Decide to Reason or Memorize

arXiv:2605.04396v1 Announce Type: new Abstract: Recent work has shown that Transformers' compositional generalization is governed by complexity control, initialization scale and weight decay, which st

Cross-Tokenizer Likelihood Scoring Algorithms for Language Model Distillation

arXiv:2512.14954v2 Announce Type: replace Abstract: Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Si

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series

arXiv:2410.23222v3 Announce Type: replace Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale

Deco: Extending Personal Physical Objects into Pervasive AI Companion through a Dual-Embodiment Framework

arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c

Deepfake Audio Detection Using Self-supervised Fusion Representations

arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level

DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation

arXiv:2605.04324v1 Announce Type: new Abstract: Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target d

Demystifying Manifold Constraints in LLM Pre-training

arXiv:2605.04418v1 Announce Type: new Abstract: The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization la

Denoising Particle Filters: Learning State Estimation with Single-Step Objectives

arXiv:2602.19651v2 Announce Type: replace-cross Abstract: Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at max

Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models

arXiv:2605.04239v1 Announce Type: new Abstract: Optical satellite image time series are extensively used in many Earth observation applications, including agriculture, climate monitoring, and land sur

Detecting Deepfakes via Hamiltonian Dynamics

arXiv:2605.04405v1 Announce Type: new Abstract: Driven by the rapid development of generative AI models, deepfake detectors are compelled to undergo periodic recalibration to capture newly developed s

Detecting Hallucinations in Large Language Models via Internal Attention Divergence Signals

arXiv:2605.05025v1 Announce Type: new Abstract: We propose a lightweight and single-pass uncertainty quantification method for detecting hallucinations in Large Language Models. The method uses attent

DIAL: Direct Iterative Adversarial Learning for Realistic Multi-Turn Dialogue Simulation

arXiv:2512.20773v4 Announce Type: replace Abstract: Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate huma

Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification

arXiv:2603.29917v2 Announce Type: replace Abstract: This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybr

Diffusion for generating/editing ASTs? [D]

researchr-machinelearning

Recent advances in diffusion-based language models enable controllable sequence generation, but applying them to structured code remains challenging, prompting exploration of syntax-aware diffusion fr

Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction

arXiv:2605.04234v1 Announce Type: new Abstract: Implicit neural representations (INRs) have emerged as a powerful paradigm for medical imaging via physics-informed unsupervised learning. Classical INR

DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting

arXiv:2602.16233v2 Announce Type: replace-cross Abstract: Circuit cutting decomposes a large quantum circuit into smaller subcircuits whose outputs are classically reconstructed to recover original ex

Distributional Principal Autoencoders

arXiv:2404.13649v2 Announce Type: replace-cross Abstract: Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However,

DPD-Cancer: Explainable Graph-Based Deep Learning for Small Molecule Anti-Cancer Activity Prediction

arXiv:2603.26114v2 Announce Type: replace Abstract: DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for pr

DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers

arXiv:2509.14640v2 Announce Type: replace Abstract: Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices

EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation

arXiv:2605.04062v1 Announce Type: new Abstract: Recent years have witnessed an increasing interest in deploying LLMs on resource-constrained devices, among which quantization has emerged as a promisin

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework

arXiv:2605.04079v1 Announce Type: new Abstract: Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports t

Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators

arXiv:2605.04164v1 Announce Type: new Abstract: Wildfires are a major producer of fine particulate matter, impacting human health and the electrical grid. Accurately forecasting smoke impacts over lon

Ensuring Reliability in Programming Knowledge Tracing: A Re-evaluation of Attention-augmented Models and Experimental Protocols

arXiv:2605.04727v1 Announce Type: new Abstract: Programming Knowledge Tracing (PKT) has recently advanced through hybrid approaches that integrate attention-based feature modeling for code representat

Entropic Riemannian Neural Optimal Transport

arXiv:2605.04255v1 Announce Type: cross Abstract: Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian man

Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs

arXiv:2605.03227v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. However, their ability to perform ex

Evidence-based anomaly detection in clinical domains

arXiv:2605.04664v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic ano

External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images

arXiv:2605.05082v1 Announce Type: cross Abstract: We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultras