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DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization

arXiv:2605.18508v1 Announce Type: cross Abstract: Programmatic reinforcement learning (PRL) offers an interpretable alternative to deep reinforcement learning by representing policies as human-readabl

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DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers

DGX agent

arXiv:2605.16732v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art image generation quality but incur substantial memory and computational costs at inference. While

safetyarxiv-cs-cv

19 May 2026

DISA: Offline Importance Sampling for Distribution-Matching LLM-RL

arXiv:2605.17295v1 Announce Type: cross Abstract: Modern reasoning agents are increasingly evaluated on their ability to generate multiple valid solution paths, plans, or tool-use traces for a given i

safetyarxiv-cs-cl

DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes

model-releasesarxiv-cs-cv

arXiv:2601.13839v2 Announce Type: replace Abstract: Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage asse

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603.12676v2 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harde

Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study

model-releasesarxiv-cs-cl

arXiv:2602.12015v2 Announce Type: replace Abstract: Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input a

Distilling Tabular Foundation Models for Structured Health Data

arXiv:2605.18702v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) achieve strong performance on health datasets, but their inference cost and infrastructure requirements limit practic

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning

arXiv:2605.16776v1 Announce Type: cross Abstract: Mitigating sensitive and harmful outputs is fundamental to ensuring safe deployment of LLMs. Existing approaches typically follow two paradigms: Knowl

Distributed 3D Leader-Follower Formation Control with Field-of-View Safety via Control Barrier Functions

arXiv:2605.17533v1 Announce Type: cross Abstract: This letter proposes a distributed 3D leader-follower formation (3D-LFF) control framework for multi-UAV systems that achieves formation tracking whil

safetyarxiv-cs-ro

Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication

arXiv:2601.10705v3 Announce Type: replace Abstract: We study a semi-asynchronous client-server perceptron trained via iterative parameter mixing (IPM-style averaging): clients run local perceptron upd

Tutorials

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

arXiv:2502.20981v2 Announce Type: replace Abstract: In Open-set Supervised Anomaly Detection (OSAD), the existing methods typically generate pseudo anomalies to compensate for the scarcity of observed

tutorialsarxiv-cs-cv

Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation

arXiv:2502.02463v3 Announce Type: replace-cross Abstract: While Bayesian inference provides a principled framework for reasoning under uncertainty, its widespread adoption is limited by the intractabi

Divergence-Suppressing Couplings for Rectified Flow

arXiv:2605.17733v1 Announce Type: new Abstract: The promise of Rectified Flow rests on producing self-generated couplings whose trajectories are straight, or nearly so. In practice, trajectories gener

Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?

arXiv:2605.17694v1 Announce Type: new Abstract: Power differences shape human communication through well documented socio cognitive effects, including language coordination, pronoun usage, authority b

safetyarxiv-cs-cl

Do Robots Really Need Anthropomorphic Hands? A Comparison of Human and Robotic Hands

arXiv:2508.05415v3 Announce Type: replace Abstract: Human manipulation skills represent a pinnacle of their voluntary motor functions, requiring the coordination of many degrees of freedom and process

researcharxiv-cs-ro

Do Vision-Language-Models show human-like logical problem-solving capability in point and click puzzle games?

arXiv:2605.11223v2 Announce Type: replace Abstract: Vision-Language(-Action) Models (VLMs) are increasingly applied to interactive environments, yet existing benchmarks often overlook the complex phys

Do You Need Text Rectification? Soft Attention Mask Embedding for Rectification-Free Scene Text Spotting

arXiv:2605.18173v1 Announce Type: new Abstract: End-to-end scene text spotting, which unifies text detection and recognition within a single framework, has witnessed remarkable progress driven by deep

researcharxiv-cs-cv

DocOS: Towards Proactive Document-Guided Actions in GUI Agents

arXiv:2605.18048v1 Announce Type: new Abstract: While Graphical User Interface (GUI) agents have shown promising performance in automated device interaction, they primarily depend on static parametric

DocReward: A Document Reward Model for Structuring and Stylizing

arXiv:2510.11391v3 Announce Type: replace-cross Abstract: Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic

Does Weight Decay Enhance Training Stability?

arXiv:2605.16622v1 Announce Type: new Abstract: In modern deep learning, weight decay is often credited with 'stabilizing' training dynamics, diverging from its classical role as a static regularizati

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

arXiv:2602.08354v5 Announce Type: replace Abstract: Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thou

Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis

arXiv:2605.17729v1 Announce Type: cross Abstract: Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains li

Domain Transfer Becomes Identifiable via a Single Alignment

arXiv:2605.17918v1 Announce Type: cross Abstract: Domain transfer (DT) maps source to target distributions and supports tasks such as unsupervised image-to-image translation, single-cell analysis, and

Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence

arXiv:2601.11956v2 Announce Type: replace-cross Abstract: Reliable reasoning in Large Language Models (LLMs) is challenged by their propensity for hallucination. While augmenting LLMs with Knowledge G

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models

arXiv:2605.17432v1 Announce Type: new Abstract: Large language models (LLMs) are commonly adapted to downstream tasks through fine-tuning, but fine-tuning data often contains sensitive information tha

DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing

arXiv:2605.16990v1 Announce Type: new Abstract: While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a sign

safetyarxiv-cs-cv

Drift Flow Matching

arXiv:2605.17244v1 Announce Type: cross Abstract: Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference

Agents

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

arXiv:2505.16278v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driv

agentsarxiv-cs-ai

DriveSafe: A Framework for Risk Detection and Safety Suggestions in Driving Scenarios

arXiv:2605.16892v1 Announce Type: cross Abstract: Comprehensive situational awareness is essential for autonomous vehicles operating in safety-critical environments, as it enables the identification a

DriveSafer: End-to-End Autonomous Driving with Safety Guidance

model-releasesarxiv-cs-cv

arXiv:2605.16737v1 Announce Type: cross Abstract: End-to-End (E2E) autonomous driving models have shown growing capability in recent years, with performance improving on increasingly challenging bench

DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection

model-releasesarxiv-cs-cv

arXiv:2605.18023v1 Announce Type: new Abstract: Open-Vocabulary Object Detection (OVD) models break the limitations of closed-set detection, enabling the iden- tification of unseen categories through

Agents

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting

arXiv:2604.07393v2 Announce Type: replace-cross Abstract: Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operatin

agentsarxiv-cs-ai

Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network

arXiv:2605.18190v1 Announce Type: cross Abstract: Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluat

researcharxiv-cs-cv

Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch

arXiv:2603.22056v2 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art (SOTA) performance across language tasks, but are costly to deploy due to their size and resou

safetyarxiv-cs-cl

DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition

arXiv:2605.17900v1 Announce Type: new Abstract: Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR

DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning

arXiv:2405.19189v3 Announce Type: replace Abstract: With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential i

safetyarxiv-cs-lg

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization

arXiv:2605.17486v1 Announce Type: cross Abstract: Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift

safetyarxiv-cs-lg

Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization

arXiv:2605.18316v1 Announce Type: new Abstract: Inferring time-varying graph structures from high-dimensional nodal observations is a fundamental problem arising in neuroscience, finance, climatology,

Agents

Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models

arXiv:2510.07799v2 Announce Type: replace-cross Abstract: The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designin

agentsarxiv-cs-ai

Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control

arXiv:2605.18373v1 Announce Type: cross Abstract: Robotic cloth folding is a challenging task, particularly when considering dynamic folding tasks, which aim at folding cloth by fast motions that leve

researcharxiv-cs-lg

Applications

Dynamics of collective creativity in AI art competitions

arXiv:2605.17141v1 Announce Type: new Abstract: Creativity is a fundamental aspect of how culture evolves, yet the mechanisms by which groups produce novelty are notoriously difficult to infer from th

applicationsarxiv-cs-ai

DynMuon: A Dynamic Spectral Shaping View of Muon

arXiv:2605.17109v1 Announce Type: cross Abstract: In recent years, Muon has emerged as the dominant method for training large language models, and transformers more broadly. The essential difference,

E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring

arXiv:2605.16882v1 Announce Type: new Abstract: Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model

researcharxiv-cs-cl

EAGT: Echocardiography Augmentation for Generalisability and Transferability

arXiv:2605.16427v1 Announce Type: cross Abstract: Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where coll

Early Stopping Chain-of-thoughts in Large Language Models

arXiv:2509.14004v2 Announce Type: replace Abstract: Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (C

researcharxiv-cs-cl

Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection

model-releasesarxiv-cs-cl

arXiv:2605.18512v1 Announce Type: new Abstract: In-context learning (ICL) is highly sensitive to which demonstrations appear in the prompt, but selecting them is expensive because the space of possibl

ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation

arXiv:2605.17580v1 Announce Type: new Abstract: Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evol

Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders

arXiv:2605.16825v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the rec