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Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers

arXiv:2510.22555v2 Announce Type: replace-cross Abstract: Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions.

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Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping

DGX agent

arXiv:2605.02153v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mappi

researcharxiv-cs-cv

5 May 2026

Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation

arXiv:2601.12964v2 Announce Type: replace Abstract: Self-supervised pretraining in remote sensing is mostly done using mid-spatial resolution (MR) image datasets due to their high availability. Given

tutorialsarxiv-cs-cv

CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint

arXiv:2605.01479v1 Announce Type: new Abstract: Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free

CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning

arXiv:2605.01309v1 Announce Type: new Abstract: Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few.

safetyarxiv-cs-cv

Cut-In Gap Acceptance Toward Autonomous vs. Human-Driven Vehicles: Evidence from the Waymo Open Motion Dataset

arXiv:2605.01485v1 Announce Type: new Abstract: Autonomous vehicles (AVs) are widely known to follow conservative, rule-based motion policies that surrounding drivers can learn to anticipate. A direct

safetyarxiv-cs-ro

CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control

arXiv:2603.15013v2 Announce Type: replace Abstract: Autonomous bicycles offer a promising agile solution for urban mobility and last-mile logistics. However, conventional control strategies often stru

safetyarxiv-cs-ro

CyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation

model-releasesarxiv-cs-cl

arXiv:2603.01865v3 Announce Type: replace Abstract: LLM-as-judge evaluation has become standard practice for open-ended model assessment; however, judges exhibit systematic biases that cannot be avera

Data-Driven, Geometry-Aware Optimal-Transport Calibration of Flavor Tagger

arXiv:2605.01363v1 Announce Type: cross Abstract: Flavor-tagging calibrations are often provided either as scale factors measured at a finite set of working points or as binned corrections to a chosen

tutorialsarxiv-cs-lg

DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training

arXiv:2605.01989v1 Announce Type: new Abstract: Distributed machine learning (ML) training has become a necessity with the prevalence of billion to trillion-parameter-scale models. While prior work ha

Decision Boundary-aware Generation for Long-tailed Learning

arXiv:2605.01468v1 Announce Type: new Abstract: Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this prob

safetyarxiv-cs-cv

Decision-Focused Learning via Tangent-Space Projection of Prediction Error

arXiv:2605.01361v1 Announce Type: new Abstract: Decision-Focused Learning (DFL) trains predictors to improve downstream decision quality, but computing regret gradients typically requires differentiat

Decoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended Generation

model-releasesarxiv-cs-cl

arXiv:2605.02348v1 Announce Type: new Abstract: Large language models pick up social biases from the data they are trained on and carry those biases into downstream applications, often reinforcing ste

Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation

model-releasesarxiv-cs-cv

arXiv:2605.01448v1 Announce Type: cross Abstract: Cross-task generalization is a core challenge in open-world robotic manipulation, and the key lies in extracting transferable manipulation knowledge f

Decouple and Cache: KV Cache Construction for Streaming Video Understanding

arXiv:2605.01858v1 Announce Type: new Abstract: Streaming video understanding requires processing unbounded video streams with limited memory and computation, posing two key challenges. First, continu

tutorialsarxiv-cs-cv

Deep learning-based pavement performance modeling using multiple distress indicators and road work history

arXiv:2605.01914v1 Announce Type: new Abstract: The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other u

Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals

arXiv:2605.00849v1 Announce Type: cross Abstract: Multi-antenna receiving systems have become a prevalent technical solution in communication systems. Meanwhile, deep learning has achieved significant

Deep neural networks with Fisher vector encoding for medical image classification

model-releasesarxiv-cs-cv

arXiv:2605.01667v1 Announce Type: new Abstract: Orderless encoding methods have shown to improve Convolutional Neural Networks (CNNs) for image classification in the context of limited availability of

Deep Thinking by Markov Chain of Continuous Thoughts

arXiv:2509.25020v2 Announce Type: replace Abstract: Transformer-based models can perform complicated reasoning by generating reasoning paths token by token. While effective, this approach often requir

Deep Time Series Models: A Comprehensive Survey and Benchmark

arXiv:2407.13278v3 Announce Type: replace Abstract: Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other dat

Deep Variational Inference Symbolic Regression

arXiv:2605.01067v1 Announce Type: new Abstract: Symbolic regression discovers explicit, interpretable equations without assuming a functional form in advance. A Bayesian approach strengthens this thro

Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising

arXiv:2602.17205v2 Announce Type: replace-cross Abstract: The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbo

researcharxiv-cs-ai

DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns

arXiv:2603.16969v2 Announce Type: replace-cross Abstract: This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent

safetyarxiv-cs-lg

Degradation-Aware Adaptive Context Gating for Unified Image Restoration

arXiv:2605.01236v1 Announce Type: new Abstract: Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradati

tutorialsarxiv-cs-cv

Delayed homomorphic reinforcement learning for environments with delayed feedback

arXiv:2604.03641v2 Announce Type: replace Abstract: Reinforcement learning in real-world systems often involves delayed feedback, which breaks the Markov assumption and impedes both learning and contr

safetyarxiv-cs-lg

Hardware

DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning

arXiv:2510.09883v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve state-of-the-art performance on challenging benchmarks by generating long chains of intermediate steps, but th

hardwarearxiv-cs-cl

Democratizing the medieval English legal tradition

model-releasesarxiv-cs-cl

arXiv:2605.00977v1 Announce Type: cross Abstract: The record of the beginning of the most widespread legal system in the world is contained in millions of pages of handwritten text. Most of the record

Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography

arXiv:2605.02245v1 Announce Type: new Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep arch

Denoising data using convex relaxations

arXiv:2605.02327v1 Announce Type: cross Abstract: We study the problem of denoising observations (Y_i=X_i+Z_i), where the latent variables (X_i) are sampled from a low-dimensional manifold in (R^n) an

Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages

arXiv:2605.02608v1 Announce Type: new Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-

researcharxiv-cs-cl

Descending into the Modular Bootstrap

arXiv:2604.01275v2 Announce Type: replace-cross Abstract: In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerica

Detecting Adversarial Data via Provable Adversarial Noise Amplification

arXiv:2605.02109v1 Announce Type: new Abstract: The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathe

Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification

model-releasesarxiv-cs-cv

arXiv:2605.01283v1 Announce Type: new Abstract: Plants, crops and their yields are essential to our very existence, but diseases and pests cause large losses every year. As such it is vital to ensure

DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection

arXiv:2511.13108v3 Announce Type: replace Abstract: The rapid progress of generative models such as GANs and diffusion models has led to the widespread proliferation of AI-generated images, raising co

DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA

arXiv:2605.00905v1 Announce Type: new Abstract: Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive

researcharxiv-cs-cl

Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score

arXiv:2603.23985v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deploymen

Local Ai

Differentiable Kernel Ridge Regression for Deep Learning Pipelines

arXiv:2605.02313v1 Announce Type: new Abstract: Deep neural networks dominate modern machine learning, while alternative function approximators remain comparatively underexplored at scale. In this wor

local-aiarxiv-cs-lg

Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark

arXiv:2605.01040v1 Announce Type: cross Abstract: The co-optimization of geometry and physical parameters remains challenging in transient multiphysics systems involving moving boundaries, nonlinear m

Differential Parity: Relative Fairness Between Two Sets of Decisions

arXiv:2112.11279v4 Announce Type: replace Abstract: With AI systems widely applied to assist humans in decision-making processes such as talent hiring, school admission, and loan approval; there is an

safetyarxiv-cs-lg

Local Ai

Diffusion Operator Geometry of Feedforward Representations

arXiv:2605.01107v1 Announce Type: new Abstract: Neural networks transform data through learned representations whose geometry affects separation, contraction, and generalization. Recent work studies t

local-aiarxiv-cs-lg

Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models

arXiv:2605.02715v1 Announce Type: cross Abstract: Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural a

Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding

arXiv:2605.00894v1 Announce Type: new Abstract: Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many cur

DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration

arXiv:2503.15984v3 Announce Type: replace Abstract: Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. Howe

applicationsarxiv-cs-cv

DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing

arXiv:2605.02417v1 Announce Type: new Abstract: With recent advancements in large-scale pre-trained text-to-image (T2I) models, training-free image editing methods have demonstrated remarkable success

Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation

arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g

safetyarxiv-cs-cv

Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis

arXiv:2605.01848v1 Announce Type: new Abstract: Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pat

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection

arXiv:2512.20670v2 Announce Type: replace Abstract: Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal disc

Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models

arXiv:2602.00217v2 Announce Type: replace Abstract: Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational costs.