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arXiv:2605.15997v1 Announce Type: new Abstract: Recent progress in deep learning has significantly advanced CT image analysis, particularly for segmentation tasks. However, these advances are largely

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Research

Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning

DGX agent

arXiv:2605.15523v1 Announce Type: new Abstract: Scene text editing aims to modify text in a target region of an image while preserving surrounding background style and texture. Existing methods rely s

researcharxiv-cs-cv

18 May 2026

Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

arXiv:2603.15269v2 Announce Type: replace Abstract: The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity he

Self-Supervised Learning by Curvature Alignment

arXiv:2511.17426v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has recently advanced through non-contrastive methods that couple an invariance term with variance, covariance,

safetyarxiv-cs-cv

SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization

arXiv:2510.25404v3 Announce Type: replace-cross Abstract: Optimizing an experimental system can be extremely challenging when each experiment is expensive, time-consuming, or difficult to perform. Exi

Agents

Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach

arXiv:2501.19128v5 Announce Type: replace-cross Abstract: In many real-world scenarios, reward signal for agents are exceedingly sparse, making it challenging to learn an effective reward function for

agentsarxiv-cs-ai

Tutorials

Simultaneous State Estimation and Online Model Learning in a Soft Robotic System

arXiv:2602.14092v2 Announce Type: replace-cross Abstract: Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and

tutorialsarxiv-cs-ro

SkyLink: A Large Vision-Language Model Driven Re-ranking Framework for Cross-View UAV geolocalization

arXiv:2603.08063v3 Announce Type: replace Abstract: Cross-view UAV geolocalization is fundamentally a challenging large-scale image retrieval task, aiming to determine the geographic coordinates of Un

Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

arXiv:2512.15693v2 Announce Type: replace Abstract: The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated vid

Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation

arXiv:2505.18853v2 Announce Type: replace Abstract: Diffusion models have achieved state-of-the-art performance in generating images, audio, and video, but their adaptation to text remains challenging

researcharxiv-cs-cl

Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning

arXiv:2601.12894v2 Announce Type: replace-cross Abstract: Diffusion Policy has dominated action generation due to its strong capabilities for modeling multi-modal action distributions, but its multi-s

Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis

arXiv:2605.15433v1 Announce Type: new Abstract: Electroencephalograph (EEG) timeseries signals are characterized by significant noise and coarse spatial resolution, which complicates the classificatio

STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System

arXiv:2605.16137v1 Announce Type: new Abstract: Generating simulation-ready tabletop scenes from task instructions is an intriguing and promising research direction in the field of Embodied AI. Howeve

safetyarxiv-cs-cv

STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices

arXiv:2605.15581v1 Announce Type: new Abstract: LLM-based root cause analysis (RCA) agents have recently emerged as a promising paradigm for incident diagnosis in microservice AIOps. However, their re

Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis

arXiv:2603.05917v3 Announce Type: replace-cross Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market e

researcharxiv-cs-ai

Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking

arXiv:2605.15528v1 Announce Type: new Abstract: Autonomous underwater vehicle (AUV) swarms are emerging as intelligent underwater networks, where each node must sense, communicate, process local data,

safetyarxiv-cs-ro

Testing properties of trees in graphical models with covariance queries

arXiv:2605.15996v1 Announce Type: cross Abstract: We consider the problem of testing properties of graphs underlying high-dimensional graphical models. We adopt the model of covariance queries introdu

Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction

arXiv:2605.15558v1 Announce Type: cross Abstract: High-resolution remote sensing imagery is critical for environmental monitoring, urban mapping, and land cover analysis, but its transmission is often

There are a lot of coding and reasoning benchmarks for AI agents, but not a lot for document understanding - which is a prerequisite for all…

model-releasesjerry-liu--x

There are a lot of coding and reasoning benchmarks for AI agents, but not a lot for document understanding - which is a prerequisite for all downstream knowledge work. We released ParseBench ~a month

Local Ai

Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems

arXiv:2605.15874v1 Announce Type: new Abstract: Industrial Water Treatment Systems (IWTS) are safety critical cyber-physical infrastructures and due to increased connectivity, these systems are expose

local-aiarxiv-cs-lg

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

arXiv:2509.22267v4 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery. While recent advances

safetyarxiv-cs-lg

Transformer Scalability Crisis: The First Comprehensive Empirical Analysis of Performance Walls in Modern Language Models

model-releasesarxiv-cs-lg

arXiv:2605.15413v1 Announce Type: new Abstract: Despite the remarkable success of transformer architectures in natural language processing, their scalability limitations remain poorly understood throu

Two-Dimensional Quantization for Geometry-Aware Audio Coding

arXiv:2512.01537v2 Announce Type: replace-cross Abstract: Recent neural audio codecs have achieved impressive reconstruction quality, typically relying on quantization methods such as Residual Vector

researcharxiv-cs-ai

Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation

arXiv:2605.15388v1 Announce Type: new Abstract: Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although

safetyarxiv-cs-lg

Unlocking Dense Metric Depth Estimation in VLMs

arXiv:2605.15876v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text

Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents

arXiv:2505.11708v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) agents are increasingly used to simulate sophisticated cyberattacks, but their decision-making processes remain op

safetyarxiv-cs-lg

Variational Autoregressive Networks with probability priors

arXiv:2605.16020v1 Announce Type: new Abstract: Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increas

Viability of perturbative expansion for quantum field theories on neurons

arXiv:2508.03810v4 Announce Type: replace-cross Abstract: Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local

Zero-Shot Goal Recognition with Large Language Models

arXiv:2605.15333v1 Announce Type: new Abstract: Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-know

3D Skew-Normal Splatting

arXiv:2605.15010v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading representation for real-time novel view synthesis and been widely adopted in various downstream ap

A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints

arXiv:2304.03641v4 Announce Type: replace-cross Abstract: Nonsmooth composite optimization with orthogonality constraints has a wide range of applications in statistical learning and data science. How

A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models

arXiv:2605.14929v1 Announce Type: new Abstract: Scaled Outer Product (SOP) is a post-training quantization methodology for large language model weights, designed to deliver near-lossless fidelity at 4

A Hormone-inspired Emotion Layer for Transformer language models (HELT)

arXiv:2605.13858v1 Announce Type: cross Abstract: Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fund

researcharxiv-cs-cl

Applications

A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures

arXiv:2605.14489v1 Announce Type: new Abstract: Building black-box models for dynamical systems from data is a challenging problem in machine learning, especially when asymptotic stability guarantees

applicationsarxiv-cs-lg

A Survey on Data-Dependent Worst-Case Generalization Bounds

model-releasesarxiv-cs-lg

arXiv:2605.13913v1 Announce Type: cross Abstract: Deep neural networks generalize well despite being heavily overparameterized, in apparent contradiction with classical learning theory based on unifor

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

arXiv:2510.19973v4 Announce Type: replace-cross Abstract: The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perce

Tutorials

A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems

arXiv:2605.14416v1 Announce Type: new Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRP

tutorialsarxiv-cs-ai

Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry

arXiv:2512.11855v2 Announce Type: replace-cross Abstract: Enforcing exact symmetry in machine learning models often yields significant gains in scientific applications, serving as a powerful inductive

safetyarxiv-cs-ai

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models

arXiv:2509.26100v2 Announce Type: replace Abstract: The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, exis

AMiD: Knowledge Distillation for LLMs with alpha-mixture Assistant Distribution

arXiv:2510.15982v3 Announce Type: replace-cross Abstract: Autoregressive large language models (LLMs) have achieved remarkable improvement across many tasks but incur high computational and memory cos

safetyarxiv-cs-ai

Hardware

An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization

arXiv:2605.14624v1 Announce Type: cross Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational e

hardwarearxiv-cs-ai

Local Ai

Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems

arXiv:2605.14331v1 Announce Type: cross Abstract: Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference re

local-aiarxiv-cs-ai

Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models

arXiv:2601.03969v2 Announce Type: replace Abstract: Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their cha

safetyarxiv-cs-ai

AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors

arXiv:2605.14341v1 Announce Type: new Abstract: Existing diffusion models have made significant progress in generating realistic images. However, their direct adaptation to remote sensing imagery ofte

Architecture-Aware Explanation Auditing for Industrial Visual Inspection

arXiv:2605.14255v1 Announce Type: cross Abstract: Industrial visual inspection systems increasingly rely on deep classifiers whose heatmap explanations may appear visually plausible while failing to i

Agents

Bad Seeing or Bad Thinking? Rewarding Perception for Vision-Language Reasoning

arXiv:2605.14054v1 Announce Type: new Abstract: Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal v

agentsarxiv-cs-ai

Beyond Cosine Similarity: Zero-Initialized Residual Complex Projection for Aspect-Based Sentiment Analysis

arXiv:2603.28205v2 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) faces critical challenges due to representation entanglement and false-negative collisions in real-valued emb

researcharxiv-cs-cl

BioHuman: Learning Biomechanical Human Representations from Video

arXiv:2605.14772v1 Announce Type: new Abstract: Understanding human motion beyond surface kinematics is crucial for motion analysis, rehabilitation, and injury risk assessment. However, progress in th