Search: “models”

A Computer Vision Approach to Visual Fraud Detection in Phishing Websites Using YOLOv8

arXiv:2609.34015v1 Announce Type: cross Abstract: Phishing remains one of the most common vectors for financial and identity fraud, and most detection systems still rely on inspecting a page's URL, HT

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning

DGX agent

arXiv:2509.11070v4 Announce Type: replace-cross Abstract: We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer

researcharxiv-cs-lg

29 Sept 2026

A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

arXiv:2404.14019v3 Announce Type: replace Abstract: Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentatio

ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis

arXiv:2601.00416v2 Announce Type: replace Abstract: Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation

safetyarxiv-cs-cv

ABO-Med: Accelerated Bilevel Optimization for Few-Shot Medical Image Classification

arXiv:2609.33176v1 Announce Type: new Abstract: In recent years, bilevel optimization has been widely used in a variety of machine learning tasks. However, prior bilevel optimization algorithms genera

Adapting neural operators for mechanics decisions under changing operating conditions

arXiv:2609.33978v1 Announce Type: cross Abstract: Neural operators can accelerate repeated nonlinear mechanics calculations, but their accuracy can deteriorate as operating conditions move beyond the

Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration

arXiv:2609.35415v1 Announce Type: cross Abstract: Frozen adaptive cruise control (ACC) policies can violate constraints when deployment dynamics differ from their training conditions. We propose resid

safetyarxiv-cs-cv

Applications

Adaptive Weighted h-Transform Sampling for Coarse-Guided Visual Generation

arXiv:2603.12057v3 Announce Type: replace Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various rea

applicationsarxiv-cs-cv

Admissible Diffusion for Multimodal Interventional Trajectories

arXiv:2609.34433v1 Announce Type: new Abstract: Generating a plausible clinical trajectory does not establish what would happen under a different treatment. We present ADMIT, a framework combining irr

Agents

AgentWare: Automating the Lifecycle of Agentic Applications across the Edge-to-Cloud Continuum

arXiv:2609.34586v1 Announce Type: cross Abstract: Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity

agentsarxiv-cs-lg

AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splatting

arXiv:2609.34176v1 Announce Type: new Abstract: Radiance fields need hundreds of views, and their placement matters as much as their number. Next-best-view (NBV) selection for 3D Gaussian Splatting (3

AI-driven Dispensing of Coral Reseeding Devices for Broad-scale Restoration of the Great Barrier Reef

arXiv:2509.01019v3 Announce Type: replace Abstract: Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral specie

Agents

AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning

arXiv:2609.34188v1 Announce Type: cross Abstract: Formulaic alpha discovery is a core challenge in quantitative trading, as identifying alphas that work well together remains difficult. Recent reinfor

agentsarxiv-cs-lg

An Active-Bottleneck Mechanism for Weak-to-Strong Generalization

arXiv:2609.33835v1 Announce Type: cross Abstract: Weak-to-strong generalization (W2SG) occurs when a student trained on a teacher's predictions outperforms that teacher. We study when this happens und

An End-to-End Latent-Rollout Approach for Pushing Few-Step ImageNet-256 Generation to FID 1.11 without Frechet Losses

arXiv:2609.32376v1 Announce Type: new Abstract: Iterative generation poses a joint optimization problem across steps, as intermediate predictions shape subsequent computations and ultimately determine

An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients

arXiv:2609.35437v1 Announce Type: cross Abstract: Quantitative characterization of lymph node morphology is important for assessing axillary lymph node metastasis in breast cancer. However, surfaces r

An RL View of OPD: Least Square Policy Distillation for Sample-Efficient LLM Reasoning

arXiv:2609.35505v1 Announce Type: new Abstract: We study on-policy distillation (OPD) through the lens of reinforcement learning, establishing a connection between the reverse-KL objective in OPD and

Local Ai

Analog-Friendly Predictive Coding without Activation Derivatives

arXiv:2609.32350v1 Announce Type: new Abstract: Predictive coding (PC) is a local, energy-based alternative to backpropagation (BP) whose iterative inference dynamics make it attractive for implementa

local-aiarxiv-cs-lg

Anatomy-Structured Hierarchical MIL for Weakly-Supervised Thoracic Disease Detection in Chest X-rays

arXiv:2609.33520v1 Announce Type: new Abstract: Weakly-supervised thoracic disease detection in chest X-rays (CXR) is challenging due to subtle appearances and complex anatomical overlap, motivating a

Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans

arXiv:2609.33611v1 Announce Type: new Abstract: Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents rai

safetyarxiv-cs-cv

Agents

Attention-based Hierarchical Variational Information Bottleneck for Robust Multi-Agent Communication under Variable Bandwidth

arXiv:2609.34860v1 Announce Type: new Abstract: Learning-based multi-agent communication under limited bandwidth does not only require deciding what to communicate, but also structuring messages so th

agentsarxiv-cs-lg

Attribution Without a Second Pass: Inline Per-Sample Gradient Provenance at ~1% Overhead

arXiv:2609.32380v1 Announce Type: new Abstract: Data attribution methods used in practice (TRAK, LoGRA, EK-FAC) are post-hoc: after training they make a second pass over the training set to recompute

Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification

arXiv:2609.33662v1 Announce Type: cross Abstract: Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, wh

Beyond Correctness: Evaluating Semantic Knowledge in Cross-Table Transfer

arXiv:2609.34098v1 Announce Type: new Abstract: Semantic knowledge is increasingly used to bridge heterogeneous schemas in tabular learning, but how much does that knowledge actually improve predictio

Beyond MSE: Rician Likelihood Denoising for Self-Supervised Cardiac T2 and T1rho MRI

arXiv:2609.31777v1 Announce Type: cross Abstract: Magnetic resonance imaging involves an inherent trade-off among spatial resolution, acquisition time, and noise. This trade-off contributes to long sc

Beyond Retrieval Relevance: Scene-Grounded Risk Entailment for Vision-Language Driving

arXiv:2609.34145v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) gives vision--language driving systems access to external safety knowledge, yet a retrieved risk rule may be releva

safetyarxiv-cs-ro

Beyond Sparsity: Weight Location and Network Context in Pruned MRI Reconstruction

arXiv:2609.31812v1 Announce Type: cross Abstract: Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with di

Tutorials

Beyond the Training Horizon: Mechanisms and Limits of Length Generalization in Looped Transformers

arXiv:2609.33144v1 Announce Type: new Abstract: Looped Transformers can generalize to reasoning chains longer than those encountered during training, but the computations enabling this behavior and li

tutorialsarxiv-cs-lg

Brain-Conditioned Action Policies for Neural Motor Decoding

arXiv:2609.34561v1 Announce Type: new Abstract: Motor brain-computer interfaces (BCIs) aim to decode motor intention, enabling people with paralysis to control external devices. Neural motor decoding

Building Transformation Layers for Riemannian Neural Networks

arXiv:2609.35436v1 Announce Type: cross Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. On

Applications

Calibration-Free Surface Normals Estimation in Vision-Based Tactile Sensing using Universal Photometric Stereo

arXiv:2609.31754v1 Announce Type: new Abstract: Vision-based tactile sensors are a popular solution for capturing rich contact surface geometry. However, to obtain high-detail contact surface normals

applicationsarxiv-cs-cv

CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

arXiv:2609.33106v1 Announce Type: new Abstract: Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the o

CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image

arXiv:2412.12906v3 Announce Type: replace Abstract: Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D

CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis

arXiv:2609.32559v1 Announce Type: new Abstract: Accurate classification of anterior segment diseases is crucial for ophthalmic screening and diagnosis. However, slit-lamp image analysis remains challe

CODEBLOCK: Learning to Supervise Code at the Right Granularity

arXiv:2606.18286v2 Announce Type: replace Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provid

Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos

arXiv:2609.33754v1 Announce Type: new Abstract: Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in fi

Conditioned Direct Feedback Alignment via Activity and Error Geometry

arXiv:2607.18574v2 Announce Type: replace Abstract: Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error. Even when this feedback provides useful cred

Contextual Distributionally Robust Optimization with Causal and Continuous Structure

arXiv:2601.11016v3 Announce Type: replace-cross Abstract: We propose a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the und

Applications

Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity

arXiv:2609.32026v1 Announce Type: cross Abstract: Physical systems evolve continuously in time, yet their states are typically observed only at discrete times. Generating trajectories consistent with

applicationsarxiv-cs-lg

ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering

arXiv:2609.32038v1 Announce Type: new Abstract: Extended Reality (XR) users do not directly perceive the output of a rendering engine. Instead, rendered images pass through a post-processing pipeline

Tutorials

Copy the Same, Distill the Difference: Initializing Linear Vision Transformers

arXiv:2609.35745v1 Announce Type: new Abstract: Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient

tutorialsarxiv-cs-cv

CORTEX: A Verified Experience Layer for Generalist Agents

arXiv:2609.33260v1 Announce Type: cross Abstract: An agent can solve a task today and face the same task under new facts, tools, or governing knowledge tomorrow. Most agent systems can retrieve releva

Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents

arXiv:2609.33875v1 Announce Type: cross Abstract: Outcome-only reinforcement learning gives software engineering (SWE) agents a terminal success signal but little direct guidance about intermediate de

Counterfactual Self-Evolving Agents for Evidence-Grounded Reasoning

arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task

Cross-Modal Knowledge Distillation for Acoustic Pedestrian Detection

arXiv:2609.31785v1 Announce Type: cross Abstract: Audio-only pedestrian detection is attractive for urban sensing but limited by weak acoustic cues. An appealing strategy is cross-modal knowledge dist

Local Ai

Cross-Rollout Bellman Closure for Long-Horizon Agentic Reinforcement Learning

arXiv:2609.35082v1 Announce Type: new Abstract: Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon

local-aiarxiv-cs-lg

cSVR: Convolutional Slice-to-Volume Reconstruction

arXiv:2601.07519v2 Announce Type: replace-cross Abstract: Unpredictable fetal motion during MRI scans can result in oblique 2D slices that are difficult to interpret and often leads to prolonged scan

D-JEPA: Design-Recoverable JEPA Representation with Swappable Physics Decoders

arXiv:2609.33110v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimension