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arXiv:2605.19337v1 Announce Type: new Abstract: A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve

agentsarxiv-cs-ai

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An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

DGX agent

arXiv:2504.08381v2 Announce Type: replace-cross Abstract: Epileptic seizures are transient neurological events characterized by abnormal and excessive neuron activity in the brain, which are often ass

applicationsarxiv-cs-lg

20 May 2026

An Objective Performance Evaluation of the LSTM Networks in Time Series Classification

arXiv:2605.19311v1 Announce Type: new Abstract: The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based algorithms, even in domains governed by w

Agents

AQuaUI: Visual Token Reduction for GUI Agents with Adaptive Quadtrees

arXiv:2605.19260v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently emerged as promising backbones for GUI-agent models, where high-resolution GUI screenshots are introduced t

agentsarxiv-cs-ai

AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning

arXiv:2605.19767v1 Announce Type: cross Abstract: Zeroth-order (ZO) optimization enables large-language-model fine-tuning without storing backpropagation activations, while LoRA supplies compact train

Artificial Intelligence, conceptual metaphors and conceptual engineering: Are AI-based framings of human behaviour and cognition successful?

arXiv:2504.07756v2 Announce Type: replace Abstract: Understanding human behaviour, neuroscience and psychology using concepts from the domain of AI is increasing in popularity. Given the massive integ

Atoms of Thought: Universal EEG Representation Learning with Microstates

arXiv:2605.20182v1 Announce Type: cross Abstract: Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-compu

Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models

arXiv:2605.19485v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning con

Automated Big Data Quality Assessment using Knowledge Graph Embeddings

arXiv:2605.18833v1 Announce Type: cross Abstract: Automated data quality assessment is crucial for managing big data, but existing solutions face challenges in achieving accurate context-aware assessm

applicationsarxiv-cs-ai

Local Ai

BabyMamba-HAR: Lightweight Selective State Space Models for Efficient Human Activity Recognition on Resource Constrained Devices

arXiv:2602.09872v2 Announce Type: replace Abstract: Human activity recognition (HAR) on resource constrained devices requires high accuracy across diverse sensor setups. Selective state space models (

local-aiarxiv-cs-cv

Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction

arXiv:2506.17036v2 Announce Type: replace-cross Abstract: Modern industrial systems are often subject to multiple failure modes, and their conditions are monitored by multiple sensors, generating mult

BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation

arXiv:2605.19518v1 Announce Type: new Abstract: Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify sem

Block-Sphere Vector Quantization

arXiv:2605.19972v1 Announce Type: cross Abstract: Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compresse

CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations

arXiv:2605.19649v1 Announce Type: new Abstract: Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits appli

researcharxiv-cs-cv

CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving

arXiv:2605.19837v1 Announce Type: cross Abstract: Adverse weather (rain, fog, sand, and snow) degrades camera-based object detection in autonomous vehicles. Existing enhancement-then-detect approaches

CLIC: Contextual Language-Informed Cardiac Pathology Classification

arXiv:2605.19132v1 Announce Type: new Abstract: The electrocardiogram (ECG) is the gold standard for non-invasive diagnosis of cardiac pathologies and is a fundamental pillar of cardiovascular medicin

ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.20176v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has a

Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation

arXiv:2605.19490v1 Announce Type: cross Abstract: Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based t

applicationsarxiv-cs-cv

CogScale: Scalable Benchmark for Sequence Processing

arXiv:2605.19758v1 Announce Type: new Abstract: The ability to maintain and manipulate information over time is a fundamental aspect of living beings and Artificial Intelligence. While modern models h

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy

model-releasesarxiv-cs-ro

arXiv:2509.14787v2 Announce Type: replace Abstract: Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. E

Component-Aware Structure-Preserving Style Transfer for Satellite Sim2Real 6D Pose Estimation

arXiv:2605.19624v1 Announce Type: cross Abstract: Monocular 6D pose estimation for non-cooperative satellites depends heavily on annotated training data, yet real satellite images with reliable pose l

Compositional Literary Primitives in Instruction-Tuned LLMs: Cross-Architectural SAE Features for Self, Style, and Affect

arXiv:2605.18808v1 Announce Type: cross Abstract: We characterize a compositional architecture of literary primitives in two instruction-tuned large language models (Llama 3.1 8B-Instruct and Gemma 2

Context-dependent manifold learning: A neuromodulated constrained autoencoder approach

arXiv:2603.11673v2 Announce Type: replace Abstract: Many physical systems exhibit a low-dimensional structure that varies with external parameters: link lengths in a robot, forcing constants in a flui

safetyarxiv-cs-lg

CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning

arXiv:2605.20075v1 Announce Type: cross Abstract: Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm

Counterfactual Likelihood Tests for Indirect Influence in Private Reasoning Channels

arXiv:2605.19092v1 Announce Type: cross Abstract: Reasoning systems increasingly separate intermediate computation into private and public channels, creating evaluation cases that look similar in tran

Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications

arXiv:2605.19299v1 Announce Type: new Abstract: The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data charact

Agents

Decentralized autonomous organization and blockchain-based incentivization framework for community-based facilities management

arXiv:2605.18773v1 Announce Type: cross Abstract: Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment w

agentsarxiv-cs-ai

Hardware

Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings

arXiv:2605.19737v1 Announce Type: cross Abstract: Digital Twin (DT) technology holds immense potential for surgical planning and personalized medicine. However, generating interactive, patient-specifi

hardwarearxiv-cs-cv

Deep Neural Network for Musical Instrument Recognition using MFCCs

arXiv:2105.00933v3 Announce Type: replace-cross Abstract: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the

Tutorials

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

arXiv:2605.19228v1 Announce Type: cross Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing

tutorialsarxiv-cs-ai

Differential-Integral Neural Operator for Long-Term Turbulence Forecasting

model-releasesarxiv-cs-cv

arXiv:2509.21196v3 Announce Type: replace-cross Abstract: Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applicati

Tutorials

Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography

arXiv:2605.19621v1 Announce Type: cross Abstract: Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical

tutorialsarxiv-cs-lg

DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model

arXiv:2602.23622v2 Announce Type: replace-cross Abstract: Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate pla

DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs

arXiv:2605.18915v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-

Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation

arXiv:2605.19293v1 Announce Type: cross Abstract: Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scal

safetyarxiv-cs-lg

Local Ai

Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding

arXiv:2605.20104v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, rece

local-aiarxiv-cs-ai

Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention

arXiv:2605.19726v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoreg

researcharxiv-cs-cv

Efficient Transferable Optimal Transport via Min-Sliced Transport Plans

arXiv:2511.19741v3 Announce Type: replace Abstract: Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems

safetyarxiv-cs-cv

EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors

arXiv:2604.02784v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or unground

researcharxiv-cs-cl

Entry-level guide to the use of large language models for medical research

arXiv:2410.18856v4 Announce Type: replace Abstract: Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools c

ESLD (External Surrogate Latent Defense): A Latent-Space Architecture for Faster, Stronger Prompt-Injection Defense

arXiv:2605.18918v1 Announce Type: cross Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web s

Fast and Lightweight Backdoor Detection via Head Random Probing

arXiv:2605.18908v1 Announce Type: cross Abstract: Deep neural networks (DNNs) remain critically vulnerable to backdoor attacks. Existing post-training detectors often require clean or surrogate data,

Hardware

FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing

arXiv:2605.20132v1 Announce Type: cross Abstract: Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of convent

hardwarearxiv-cs-lg

Fine-tuning Large Language Model for Automated Algorithm Design

arXiv:2507.10614v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLM

FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models

arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the

safetyarxiv-cs-cv

From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting

arXiv:2503.11615v3 Announce Type: replace Abstract: Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current

From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation

arXiv:2605.18865v1 Announce Type: cross Abstract: Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensiv

Geo-Data-Driven HD Map Generation Workflow with Integrated Reference-Free Constraint-Based Verification

arXiv:2605.18921v1 Announce Type: new Abstract: High-definition (HD) maps are core artifacts for automated driving systems, but their generation commonly relies on sensor-intensive mobile mapping camp

applicationsarxiv-cs-ro