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Differentially Private Verification of Distribution Properties

model-releasesarxiv-cs-lg

arXiv:2604.10819v1 Announce Type: cross Abstract: A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of

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Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks

DGX agent

arXiv:2604.09653v1 Announce Type: cross Abstract: Beam alignment is a key challenge in directional mmWave and THz systems, where narrow beams require accurate yet low-overhead training. Existing learn

safetyarxiv-cs-ai

14 Apr 2026

Diffusion-CAM: Faithful Visual Explanations for dMLLMs

arXiv:2604.11005v1 Announce Type: new Abstract: While diffusion Multimodal Large Language Models (dMLLMs) have recently achieved remarkable strides in multimodal generation, the development of interpr

Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access

arXiv:2604.09904v1 Announce Type: cross Abstract: Polyanskiy proposed a framework for the unsourced multiple access channel (MAC) problem where users employ a common codebook in the finite blocklength

Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization

arXiv:2604.10953v1 Announce Type: new Abstract: The online 3D bin packing problem is important in logistics, warehousing and intelligent manufacturing, with solutions shifting to deep reinforcement le

applicationsarxiv-cs-ro

Digital hybridity and relics in cultural heritage: using corpus linguistics to inform design in emerging technologies from AI to VR

arXiv:2604.09669v1 Announce Type: cross Abstract: Hybrid technologies enable the blending of physical and digital elements, creating new ways to experience and interact with the world. Such technologi

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain

arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem

DINO_4D: Semantic-Aware 4D Reconstruction

arXiv:2604.09877v1 Announce Type: cross Abstract: In the intersection of computer vision and robotic perception, 4D reconstruction of dynamic scenes serve as the critical bridge connecting low-level g

Directional Mollification for Knot-Preserving C^{infty} Smoothing of Polygonal Chains with Explicit Curvature Bounds

arXiv:2603.21831v2 Announce Type: replace Abstract: Starting from a polygonal chain (a first-order polynomial spline) through prescribed knots (vertices), we introduce the extit{directional mollificat

researcharxiv-cs-ro

Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky

arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the

DisCo-FLoc: Using Dual-Level Visual-Geometric Contrasts to Disambiguate Depth-Aware Visual Floorplan Localization

arXiv:2601.01822v2 Announce Type: replace-cross Abstract: Since floorplan data is readily available, long-term persistent, and robust to changes in visual appearance, visual Floorplan Localization (FL

researcharxiv-cs-cv

Local Ai

Disco-RAG: Discourse-Aware Retrieval-Augmented Generation

arXiv:2601.04377v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowled

local-aiarxiv-cs-ai

Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation

arXiv:2604.06784v2 Announce Type: replace Abstract: Previous research on multi-party dialogue generation has predominantly leveraged structural information inherent in dialogues to directly inform the

researcharxiv-cs-cl

Discourse Diversity in Multi-Turn Empathic Dialogue

arXiv:2604.11742v1 Announce Type: cross Abstract: Large language models (LLMs) produce responses rated as highly empathic in single-turn settings (Ayers et al., 2023; Lee et al., 2024), yet they are a

Discrete Bayesian Sample Inference for Graph Generation

arXiv:2511.03015v2 Announce Type: replace Abstract: Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their dis

researcharxiv-cs-lg

Discrete Flow Maps

arXiv:2604.09784v1 Announce Type: cross Abstract: The sequential nature of autoregressive next-token prediction imposes a fundamental speed limit on large language models. While continuous flow models

safetyarxiv-cs-lg

Disentangled Point Diffusion for Precise Object Placement

arXiv:2604.11793v1 Announce Type: new Abstract: Recent advances in robotic manipulation have highlighted the effectiveness of learning from demonstration. However, while end-to-end policies excel in e

applicationsarxiv-cs-ro

DiSPA: Differential Substructure-Pathway Attention for Drug Response Prediction

arXiv:2601.14346v2 Announce Type: replace-cross Abstract: Accurate prediction of drug response in precision medicine requires models that capture how specific chemical substructures interact with cell

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment

arXiv:2510.24574v2 Announce Type: replace-cross Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The

Distributionally Robust K-Means Clustering

arXiv:2604.11118v1 Announce Type: new Abstract: K-means clustering is a workhorse of unsupervised learning, but it is notoriously brittle to outliers, distribution shifts, and limited sample sizes. Vi

researcharxiv-cs-lg

Distributionally Robust PAC-Bayesian Control

arXiv:2604.10588v1 Announce Type: new Abstract: We present a distributionally robust PAC-Bayesian framework for certifying the performance of learning-based finite-horizon controllers. While existing

safetyarxiv-cs-lg

Do Agent Rules Shape or Distort? Guardrails Beat Guidance in Coding Agents

arXiv:2604.11088v1 Announce Type: new Abstract: Developers increasingly guide AI coding agents through natural language instruction files (e.g., CLAUDE.md, .cursorrules), yet no controlled study has m

Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction

arXiv:2604.10786v1 Announce Type: cross Abstract: Narrative understanding requires multidimensional semantic structures. This study investigates whether BERT embeddings encode dimensions of fictional

Do Instance Priors Help Weakly Supervised Semantic Segmentation?

arXiv:2604.11170v1 Announce Type: new Abstract: Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a frame

researcharxiv-cs-cv

Do LLMs Build Spatial World Models? Evidence from Grid-World Maze Tasks

arXiv:2604.10690v1 Announce Type: new Abstract: Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

arXiv:2604.11322v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to to

Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment

arXiv:2603.07462v2 Announce Type: replace Abstract: Determining whether AI systems process information similarly to humans is central to cognitive science and trustworthy AI. While modern AI models ca

Do Neurons Dream of Primitive Operators? Wake-Sleep Compression Rediscovers Schank's Event Semantics

arXiv:2603.25975v2 Announce Type: replace-cross Abstract: We show that they do. Roger Schank's conceptual dependency theory proposed that all human events decompose into primitive operations -- ATRANS

Do Thought Streams Matter? Evaluating Reasoning in Gemini Vision-Language Models for Video Scene Understanding

arXiv:2604.11177v1 Announce Type: new Abstract: We benchmark how internal reasoning traces, which we call thought streams, affect video scene understanding in vision-language models. Using four config

Local Ai

Do vision models perceive illusory motion in static images like humans?

arXiv:2604.09853v1 Announce Type: new Abstract: Understanding human motion processing is essential for building reliable, human-centered computer vision systems. Although deep neural networks (DNNs) a

local-aiarxiv-cs-cv

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

arXiv:2604.09666v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) and its graph-based extensions (GraphRAG) are effective paradigms for improving large language model (LLM) reason

Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2601.03926v2 Announce Type: replace Abstract: The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined poli

DocRevive: A Unified Pipeline for Document Text Restoration

arXiv:2604.10077v1 Announce Type: new Abstract: In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent d

Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection

arXiv:2604.09920v1 Announce Type: new Abstract: Vision foundation models (VFMs) offer the promise of zero-shot object detection without task-specific training data, yet their performance in complex ag

applicationsarxiv-cs-cv

Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics

model-releasesarxiv-cs-lg

arXiv:2604.07389v2 Announce Type: replace Abstract: Crime pattern analysis is critical for law enforcement and predictive policing, yet the surge in criminal activities from rapid urbanization creates

Domain-Specific Data Generation Framework for RAG Adaptation

arXiv:2510.11217v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external ret

applicationsarxiv-cs-ai

DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts

arXiv:2511.11232v2 Announce Type: replace Abstract: Constructing a unified 3D scene understanding model has long been hindered by the significant topological discrepancies across different sensor moda

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation

arXiv:2508.15452v3 Announce Type: replace-cross Abstract: Numerous deep learning-based solutions have been developed for the automatic recognition of breast cancer using mammography images. However, t

applicationsarxiv-cs-cv

DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments

model-releasesarxiv-cs-ro

arXiv:2512.00375v2 Announce Type: replace Abstract: Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To addres

DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness

arXiv:2604.11703v1 Announce Type: new Abstract: People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG addresses t

DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping

arXiv:2604.03402v2 Announce Type: replace-cross Abstract: Smartphone cameras have gained immense popularity with the adoption of high-resolution and high-dynamic range imaging. As a result, high-perfo

safetyarxiv-cs-cv

dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models

arXiv:2603.18806v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them wi

Local Ai

Dual-Branch Remote Sensing Infrared Image Super-Resolution

arXiv:2604.10112v1 Announce Type: new Abstract: Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours,

local-aiarxiv-cs-cv

Agents

Dual-Control Frequency-Aware Diffusion Model for Depth-Dependent Optical Microrobot Microscopy Image Generation

arXiv:2604.11680v1 Announce Type: new Abstract: Optical microrobots actuated by optical tweezers (OT) are important for cell manipulation and microscale assembly, but their autonomous operation depend

agentsarxiv-cs-ro

Dual-Exposure Imaging with Events

arXiv:2604.10273v1 Announce Type: new Abstract: By combining complementary benefits of short- and long-exposure images, Dual-Exposure Imaging (DEI) enhances image quality in low-light scenarios. Howev

safetyarxiv-cs-cv

Dual-Margin Embedding for Fine-Grained Long-Tailed Plant Taxonomy

arXiv:2512.18994v2 Announce Type: replace Abstract: Taxonomic classification of ecological families, genera, and species underpins biodiversity monitoring and conservation. Existing computer vision me

applicationsarxiv-cs-cv

Dual-R-DETR: Resolving Query Competition with Pairwise Routing in Transformer Decoders

arXiv:2512.13876v2 Announce Type: replace Abstract: Detection Transformers (DETR) formulate object detection as a set prediction problem and enable end-to-end training without post-processing. However

researcharxiv-cs-cv

DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode

arXiv:2604.11514v1 Announce Type: cross Abstract: This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior ap

researcharxiv-cs-cl