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Design and Validation of a Low-Cost Smartphone Based Fluorescence Detection Platform Compared with Conventional Microplate Readers

arXiv:2604.14527v1 Announce Type: new Abstract: A low cost fluorescence-based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A

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Tutorials

DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts

DGX agent

arXiv:2604.14684v1 Announce Type: new Abstract: Visual prompted object detection enables interactive and flexible definition of target categories, thereby facilitating open-vocabulary detection. Since

tutorialsarxiv-cs-cv

17 Apr 2026

DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos

model-releasesarxiv-cs-cv

arXiv:2601.09240v2 Announce Type: replace Abstract: Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially und

DEX-Mouse: A Low-cost Portable and Universal Interface with Force Feedback for Data Collection of Dexterous Robotic Hands

arXiv:2604.15013v1 Announce Type: new Abstract: Data-driven dexterous hand manipulation requires large-scale, physically consistent demonstration data. Simulation and video-based methods suffer from s

researcharxiv-cs-ro

DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines

arXiv:2604.14314v1 Announce Type: cross Abstract: This manuscript introduces DharmaOCR Full and Lite, a pair of specialized small language models (SSLMs) for structured OCR that jointly optimize trans

Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value

arXiv:2506.13763v2 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not in

safetyarxiv-cs-cv

Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations

arXiv:2604.15302v1 Announce Type: cross Abstract: LLM-as-judge frameworks are increasingly used for automatic NLG evaluation, yet their per-instance reliability remains poorly understood. We present a

researcharxiv-cs-cl

Tutorials

Differentiable Object Pose Connectivity Metrics for Regrasp Sequence Optimization

arXiv:2604.14733v1 Announce Type: new Abstract: Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibi

tutorialsarxiv-cs-ro

Tutorials

Differentially Private Conformal Prediction

arXiv:2604.14621v1 Announce Type: cross Abstract: Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In

tutorialsarxiv-cs-lg

DigiForest: Digital Analytics and Robotics for Sustainable Forestry

arXiv:2604.14652v1 Announce Type: new Abstract: Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and w

agentsarxiv-cs-ro

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

arXiv:2411.00361v4 Announce Type: replace Abstract: Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However

safetyarxiv-cs-lg

Tutorials

DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering

arXiv:2604.15140v1 Announce Type: new Abstract: We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. DiscoTrace

tutorialsarxiv-cs-cl

Local Ai

Dissecting Failure Dynamics in Large Language Model Reasoning

arXiv:2604.14528v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains p

local-aiarxiv-cs-cl

Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems

arXiv:2604.14228v1 Announce Type: cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes

DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models

arXiv:2604.15016v1 Announce Type: new Abstract: EEG foundation models (FMs) achieve strong cross-subject and cross-task generalization but impose substantial computational and memory costs that hinder

Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error

arXiv:2510.26109v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs). However, existing

safetyarxiv-cs-lg

do you think founders would still pitch me, if like… i sold our call data to ai labs 👉👈

Yohei Nakajima humorously raises a question about the potential conflict of interest and reputational consequences of selling call data from founder pitches to AI labs while continuing to operate as a

agentsyohei-nakajima--x

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

arXiv:2604.15023v1 Announce Type: new Abstract: Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing app

safetyarxiv-cs-ro

Local Ai

DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA

arXiv:2511.22521v2 Announce Type: replace Abstract: Document visual question answering requires models not only to answer questions correctly, but also to precisely localize answers within complex doc

local-aiarxiv-cs-cv

Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis

arXiv:2604.14877v1 Announce Type: new Abstract: Does reinforcement learning genuinely expand what LLM agents can do, or merely make them more reliable? For static reasoning, recent work answers the se

agentsarxiv-cs-lg

Domain-Adaptive Model Merging Across Disconnected Modes

arXiv:2603.05957v2 Announce Type: replace-cross Abstract: Learning across domains is challenging when data cannot be centralized due to privacy or heterogeneity, which limits the ability to train a si

researcharxiv-cs-ai

Applications

Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations

arXiv:2604.14815v1 Announce Type: new Abstract: In NLP classification tasks where little labeled data exists, domain fine-tuning of transformer models on unlabeled data is an established approach. In

applicationsarxiv-cs-cl

Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

arXiv:2604.14572v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never

Doubly Outlier-Robust Online Infinite Hidden Markov Model

arXiv:2604.14322v1 Announce Type: cross Abstract: We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is missp

Tools

Download Cursor to use the agents window: http://cursor.com/download

Cursor is an AI-powered code editor that offers a download option for users to access the agents window feature. The agents window allows developers to leverage AI capabilities within the Cursor envir

toolscursor--x

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

arXiv:2509.03472v2 Announce Type: replace Abstract: Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to tr

DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

model-releasesarxiv-cs-lg

arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m

Dual Pose-Graph Semantic Localization for Vision-Based Autonomous Drone Racing

arXiv:2604.15168v1 Announce Type: new Abstract: Autonomous drone racing demands robust real-time localization under extreme conditions: high-speed flight, aggressive maneuvers, and payload-constrained

agentsarxiv-cs-ro

Applications

DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration

arXiv:2604.14560v1 Announce Type: new Abstract: Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coher

applicationsarxiv-cs-cv

DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models

arXiv:2602.22175v2 Announce Type: replace Abstract: Understanding and reasoning over long contexts is a crucial capability for language models (LMs). Although recent models support increasingly long c

researcharxiv-cs-cl

EchoAgent: Towards Reliable Echocardiography Interpretation with 'Eyes','Hands' and 'Minds'

arXiv:2604.05541v2 Announce Type: replace Abstract: Reliable interpretation of echocardiography (Echo) is crucial for assessing cardiac function, which demands clinicians to synchronously orchestrate

agentsarxiv-cs-cv

ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents

model-releasesarxiv-cs-ai

arXiv:2604.13097v1 Announce Type: cross Abstract: Embodied agents increasingly rely on modular capabilities that can be installed, upgraded, composed, and governed at runtime. Prior work has introduce

Edge-preserving noise for diffusion models

arXiv:2410.01540v4 Announce Type: replace Abstract: Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information importa

Local Ai

EEGDM: Learning EEG Representation with Latent Diffusion Model

arXiv:2508.20705v3 Announce Type: replace Abstract: Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover

local-aiarxiv-cs-lg

Efficient closed-form approaches for pose estimation using Sylvester forms

arXiv:2604.14747v1 Announce Type: new Abstract: Solving non-linear least-squares problem for pose estimation (rotation and translation) is often a time consuming yet fundamental problem in several rea

Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation

arXiv:2604.14849v1 Announce Type: new Abstract: Purpose: Adaptive skip modules can improve medical image segmentation, but searching for them is computationally costly. Implantable Adaptive Cells (IAC

ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving

arXiv:2604.14626v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have become the dominant architecture for large-scale language models, yet on-premises serving remains fundamentally mem

Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories

arXiv:2603.25903v2 Announce Type: replace Abstract: Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for e

safetyarxiv-cs-ro

[Emerging Ideas] Artificial Tripartite Intelligence: A Bio-Inspired, Sensor-First Architecture for Physical AI

arXiv:2604.13959v1 Announce Type: new Abstract: As AI moves from data centers to robots and wearables, scaling ever-larger models becomes insufficient. Physical AI operates under tight latency, energy

safetyarxiv-cs-ai

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions

arXiv:2512.08230v2 Announce Type: replace Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have pro

agentsarxiv-cs-ai

Enabling Agents to Communicate Entirely in Latent Space

arXiv:2511.09149v4 Announce Type: replace Abstract: While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling

agentsarxiv-cs-lg

Applications

Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

arXiv:2604.14678v1 Announce Type: cross Abstract: Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal con

applicationsarxiv-cs-ro

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

arXiv:2601.03448v2 Announce Type: replace Abstract: Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning o

researcharxiv-cs-cl

Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design

arXiv:2512.24120v2 Announce Type: replace Abstract: Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints requir

Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization

arXiv:2604.14267v1 Announce Type: new Abstract: Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavaila

safetyarxiv-cs-lg

Ernie Image Turbo is not bad at all (Using INT8 quant and Gemini for prompt enhancement, RTX 30 series GPU with low vram)

model-releasesr-stablediffusion

Ernie Image Turbo is a text-to-image generation model that can run efficiently on consumer-grade hardware like RTX 30 series GPUs with limited VRAM by using INT8 quantization. The post discusses techn

Estimating the Diameter at Breast Height of Trees in a Forest from RGB

arXiv:2505.03093v3 Announce Type: replace Abstract: Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon a

EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation

arXiv:2604.14306v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated high proficiency on English-centric medical examinations, their performance often declines when fac