Search: “research”

Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding

arXiv:2606.24957v1 Announce Type: new Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a

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Dziri Voicebot: An End-to-End Low-Resource Speech-to-Speech Conversational System for Algerian Dialect

DGX agent

arXiv:2606.26003v1 Announce Type: new Abstract: Automatic speech and language technologies are still heavily biased toward high-resource languages, limiting their applicability to dialectal and low-re

researcharxiv-cs-cl

25 Jun 2026

EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis

arXiv:2606.25465v1 Announce Type: new Abstract: While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent co

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

arXiv:2606.25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate und

Efficient Cross-Scale Invertible Hiding Network with Spatial-Frequency Collaboration and Non-Invertible Mechanism

arXiv:2606.25547v1 Announce Type: new Abstract: Image hiding aims to conceal image-level messages within cover images at the same resolution. Invertible neural networks (INN)-based image hiding has em

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

arXiv:2606.25010v1 Announce Type: cross Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabil

End-to-End Voice Intent Recognition for Spontaneous Human-Drone Interaction with Naive Users

arXiv:2606.24910v1 Announce Type: cross Abstract: Voice control offers an intuitive alternative to manual drone piloting, yet most existing systems rely on rigid command vocabularies that fail to hand

researcharxiv-cs-ai

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA

arXiv:2606.25562v1 Announce Type: cross Abstract: This paper presents an energy-efficient hardware acceleration of the convolutional layers in the U-Net architecture for image segmentation, implemente

Entropy-Controlled Flow Matching

arXiv:2602.22265v2 Announce Type: replace-cross Abstract: Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or sto

Erased, but Not Gone: Output Forgetting Is Not True Forgetting

arXiv:2606.25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference. But if out

Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction

arXiv:2606.24915v1 Announce Type: new Abstract: End-to-end automatic speech recognition systems frequently hallucinate rare entities and domain-specific terms, especially in low-resource languages. Wh

Estimating condition number with Graph Neural Networks

arXiv:2603.10277v3 Announce Type: replace Abstract: In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks (GNNs). For efficient dep

Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models

arXiv:2606.25436v1 Announce Type: cross Abstract: Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major chall

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

arXiv:2606.25606v1 Announce Type: new Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ea

ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning

arXiv:2606.24994v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often prod

Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval

arXiv:2606.25237v1 Announce Type: cross Abstract: We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Con

Fault of Our Stars: Behavioral Drivers of Rating-Sentiment Incongruence

arXiv:2606.25518v1 Announce Type: new Abstract: When people share experiences online, they often express thoughts in two ways: a star rating and a written review. In sentiment analysis, ratings are wi

FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks

arXiv:2606.25201v1 Announce Type: new Abstract: Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals. Highly sophist

Frequency Domain Reservoir Computing

arXiv:2606.24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requi

From Rubble Simulation to Active Magnetic Mapping: Quantum Sensing for Disaster Response

arXiv:2606.25957v1 Announce Type: new Abstract: Locating survivors of building collapses within the first 72 hours is a critical challenge in disaster response, and existing sensing modalities provide

FUTO Swipe: Layout-Agnostic Neural Swipe Decoding

arXiv:2606.25247v1 Announce Type: cross Abstract: Neural swipe decoders are typically tied to the keyboard they were trained on, requiring a new corpus and training run for each layout. In this report

Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

arXiv:2606.25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying pro

GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization

arXiv:2505.13731v4 Announce Type: replace Abstract: Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the

Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator

arXiv:2606.25753v1 Announce Type: new Abstract: Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable wavegu

GRAFT: Graph-Based Affordance Transfer via Part Correspondence

arXiv:2606.25241v1 Announce Type: new Abstract: Generalizing robotic manipulation to unseen objects remains challenging, as learning-based approaches require many demonstrations and fail in few-shot s

Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs

arXiv:2606.25842v1 Announce Type: new Abstract: Existing multi-modal large language models (MLLMs) face significant challenges in processing long video sequences due to strict input token limitations.

GROVE: Grounded Pedestrian Simulation via Natural Language for Interactive Social Robot Navigation

arXiv:2606.25504v1 Announce Type: new Abstract: Pedestrian simulation is a critical component for training and deploying social robot navigation approaches, yet it remains a largely rigid system that

Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation

arXiv:2606.25278v1 Announce Type: new Abstract: Multi-modal fusion and multi-model ensembling are prevalent in enhancing the performance of 3D semantic segmentation. Despite the impressive performance

Holographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails

arXiv:2606.24948v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whos

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off

arXiv:2510.01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising e

How Large Language Models Source Brand Reputation Across Languages and Markets

arXiv:2606.25787v1 Announce Type: cross Abstract: When a large language model (LLM) answers a question about a company, it grounds the answer in retrieved web sources, and those sources decide what th

How Modular Is a Frontier Mixture-of-Experts? A Pre-registered Causal Test in Which Apparent Expert Modularity Mostly Dissolves

arXiv:2606.25092v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models route each token to a few of many experts, inviting the hypothesis that experts form functional modules tied to c

Hybrid deep learning-based phase diversity method for wavefront reconstruction

arXiv:2606.25855v1 Announce Type: cross Abstract: The efficiency of high-power laser systems is limited by wavefront distortions in the beam, particularly non-common path aberrations, which reduce the

Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering

arXiv:2606.25338v1 Announce Type: new Abstract: Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA),

ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction

arXiv:2603.14915v2 Announce Type: replace Abstract: A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure,

Improved Large Language Diffusion Models

arXiv:2606.25331v1 Announce Type: new Abstract: Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present iLLaDA, an 8B masked diffusion

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors

arXiv:2606.25971v1 Announce Type: new Abstract: Modern neural network training relies on optimizers such as Adam and Muon which act on each weight matrix as a single object. Yet every weight matrix ca

In-context Region-based Drag: Drag Any Region to Any Shape

arXiv:2606.25907v1 Announce Type: new Abstract: Diffusion models have shown promise in drag-style editing. Previous works mainly focus on point-based drag, which is inherently ambiguous. This paper fo

Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems

arXiv:2606.25083v1 Announce Type: new Abstract: Accurate extended pose estimation (orientation, velocity, and position) for IMU-instrumented articulated rigid-body systems is a key challenge in roboti

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization

arXiv:2606.26009v1 Announce Type: new Abstract: Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansa

Large-Scale Tunnel Air--Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planne

arXiv:2606.25393v1 Announce Type: new Abstract: Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

arXiv:2506.18414v3 Announce Type: replace Abstract: Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification,

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

arXiv:2606.25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require carefu

Learning Non-Vacuous Generalization Bounds from Optimization

arXiv:2206.04359v3 Announce Type: replace Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen

Learning Perceptive Platform Adaptive Locomotion Controllers for Quadrupedal Robots

arXiv:2606.25179v1 Announce Type: new Abstract: Universal quadrupedal locomotion remains limited by the difficulty of integrating perception across diverse robot morphologies. State-of-the-art control

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

Learning with Monotone Adversarial Corruptions

arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversa

Limitations of SGD for Multi-Index Models Beyond Statistical Queries

arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T