All entries

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

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

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

DGX agent

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

researcharxiv-cs-cv

5 May 2026

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

applicationsarxiv-cs-lg

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.

DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation

arXiv:2411.14295v3 Announce Type: replace Abstract: Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video

Distributed Algorithm with Emergent Area Partitioning and Base Station's Situation Awareness for Multi-Robot Patrolling

arXiv:2605.01501v1 Announce Type: new Abstract: Patrolling with multiple robots offers efficient surveillance to detect and manage undesired situations. This necessitates improved patrol efficiency an

agentsarxiv-cs-ro

Distributional Causal Mediation via Conditional Generative Modeling

arXiv:2605.01765v1 Announce Type: cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional ch

applicationsarxiv-cs-lg

Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching

arXiv:2605.00941v1 Announce Type: cross Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem. Existing

Divide and Conquer: Decoupled Representation Alignment for Multimodal World Models

arXiv:2605.01896v1 Announce Type: new Abstract: Emerging multi-modal world models attempt to jointly generate videos across diverse modalities (e.g., RGB, depth, and mask), yet they fail to fully expl

safetyarxiv-cs-cv

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr

safetyarxiv-cs-cl

Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation

arXiv:2605.02192v1 Announce Type: new Abstract: Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation,

agentsarxiv-cs-ro

Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting

arXiv:2605.02752v1 Announce Type: new Abstract: Open-world text-guided class-agnostic counting (CAC) has emerged as a flexible paradigm for counting arbitrary object classes by using natural language

applicationsarxiv-cs-cv

DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity

arXiv:2605.01852v1 Announce Type: new Abstract: Multi-view 3D reconstruction, namely, structure-from-motion followed by multi-view stereo, is a fundamental component of 3D computer vision. In general,

DR-SNE: Density-Regularized Stochastic Neighbor Embedding

arXiv:2605.02060v1 Announce Type: new Abstract: Dimensionality reduction methods such as t-SNE are designed to preserve local neighborhood structure but do not explicitly account for how probability m

safetyarxiv-cs-lg

Dual-branch Robust Unlearnable Examples

arXiv:2605.01718v1 Announce Type: new Abstract: Unlearnable examples (UEs) aim to compromise model training by injecting imperceptible perturbations to clean samples. However, existing UE schemes exhi

Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks

arXiv:2605.02416v1 Announce Type: cross Abstract: In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for LEO satellite networks. The pro

researcharxiv-cs-lg

DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction

arXiv:2601.15416v2 Announce Type: replace Abstract: Sparse-view Cone-Beam Computed Tomography reconstruction from limited X-ray projections remains a challenging problem in medical imaging due to the

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning

arXiv:2605.02196v1 Announce Type: new Abstract: Machine unlearning aims to remove specified training data to satisfy privacy regulations such as GDPR. However, existing evaluations assume identical pr

DynamicPO: Dynamic Preference Optimization for Recommendation

arXiv:2605.00327v1 Announce Type: cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferen

researcharxiv-cs-ai

Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head

arXiv:2605.01227v1 Announce Type: new Abstract: Quadrupedal locomotion plays a critical role in enabling agile, versatile movement across complex terrains. Understanding and estimating the underlying

safetyarxiv-cs-ro

Dynamics Distillation for Efficient and Transferable Control Learning

arXiv:2605.01516v1 Announce Type: new Abstract: Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, prope

safetyarxiv-cs-ro

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically

agentsarxiv-cs-cv

Local Ai

DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation

arXiv:2605.02759v1 Announce Type: cross Abstract: Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their app

local-aiarxiv-cs-cv

E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory

model-releasesarxiv-cs-ai

arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir

EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion

arXiv:2605.01916v1 Announce Type: new Abstract: Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and

agentsarxiv-cs-cv

Tutorials

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting

arXiv:2605.00850v1 Announce Type: cross Abstract: Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile do

tutorialsarxiv-cs-lg

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics

arXiv:2605.01548v1 Announce Type: cross Abstract: Electrocardiogram (ECG) biometrics have emerged as a promising modality for continuous, liveness-aware authentication in wearable systems. However, ma

applicationsarxiv-cs-cv

Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions

arXiv:2410.08491v2 Announce Type: replace Abstract: Automated vehicles promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains cha

safetyarxiv-cs-ro

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

arXiv:2605.02275v1 Announce Type: new Abstract: Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved pe

EditPropBench: Measuring Factual Edit Propagation in Scientific Manuscripts

model-releasesarxiv-cs-cl

arXiv:2605.02083v1 Announce Type: new Abstract: Local factual edits in scientific manuscripts often create non-local revision obligations. If a dataset changes from 215 to 80 documents, claims such as

EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

arXiv:2605.01238v1 Announce Type: cross Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning e

Hardware

Effective Capacitance Modeling Using Graph Neural Networks

arXiv:2507.03787v2 Announce Type: replace Abstract: Static timing analysis is a crucial stage in the VLSI design flow that verifies the timing correctness of circuits. Timing analysis depends on the p

hardwarearxiv-cs-lg

Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback

arXiv:2506.18315v2 Announce Type: replace-cross Abstract: LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies h

researcharxiv-cs-ai

Efficient Parameter Estimation of Truncated Boolean Product Distributions

arXiv:2007.02392v3 Announce Type: replace Abstract: We study the problem of estimating the parameters of a Boolean product distribution in d dimensions, when the samples are truncated by a set S subse

Efficient Preference Poisoning Attack on Offline RLHF

arXiv:2605.02495v1 Announce Type: new Abstract: Offline Reinforcement Learning from Human Feedback (RLHF) pipelines such as Direct Preference Optimization (DPO) train on a pre-collected preference dat

Efficient Reasoning with Hidden Thinking

model-releasesarxiv-cs-cl

arXiv:2501.19201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Mod

Tutorials

EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer

arXiv:2605.01732v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinde

tutorialsarxiv-cs-cl

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders

model-releasesarxiv-cs-cl

arXiv:2605.01372v1 Announce Type: new Abstract: Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively en

Embody4D: A Generalist 4D World Model for Embodied AI

arXiv:2605.01799v1 Announce Type: new Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representat

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

arXiv:2512.11415v2 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of la

applicationsarxiv-cs-lg

EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness

arXiv:2605.01024v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown prom

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment

model-releasesarxiv-cs-ai

arXiv:2605.00731v1 Announce Type: cross Abstract: While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHG

Local Ai

Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities

arXiv:2605.00960v1 Announce Type: cross Abstract: We introduce energy-based constraint networks -- a modality-agnostic architecture that learns structural coherence from contrastive pairs. The system

local-aiarxiv-cs-cl

ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection

arXiv:2502.06774v4 Announce Type: replace Abstract: Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference a

Enforcing tail calibration when training probabilistic forecast models

arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated

researcharxiv-cs-lg