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The OncoReach Stylet for Brachytherapy: Design Evaluation and Pilot Study

arXiv:2601.13529v2 Announce Type: replace Abstract: Cervical cancer accounts for a significant portion of the global cancer burden among women. Interstitial brachytherapy (ISBT) is a standard procedur

researcharxiv-cs-ro

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Human DGX agent

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Research

The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization

DGX agent

arXiv:2605.16219v1 Announce Type: new Abstract: Differential privacy changes the effective sample size governing CVaR learning. For tail mass au, the privacy-relevant sample size is not n, but nau; eq

researcharxiv-cs-lg

18 May 2026

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

Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning

arXiv:2605.15692v1 Announce Type: new Abstract: We study episodic reinforcement learning with fixed reward and transition functions, but with episode-dependent admissible action sets that are observed

Time-Varying Deep State Space Models for Sequences with Switching Dynamics

arXiv:2605.15311v1 Announce Type: new Abstract: The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this chall

TokenButler: Token Importance is Predictable

arXiv:2503.07518v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on the Key-Value (KV) Cache to store token history, enabling efficient decoding of tokens. As the KV-Cache g

TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices

arXiv:2605.15611v1 Announce Type: new Abstract: Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) casc

safetyarxiv-cs-ai

Toward LLMs Beyond English-Centric Development

arXiv:2605.15613v1 Announce Type: new Abstract: Through an analysis of sequences generated by open-weight large language models (LLMs), we demonstrate that LLMs are heavily biased toward English. Whil

researcharxiv-cs-cl

Agents

Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling

arXiv:2605.15812v1 Announce Type: cross Abstract: Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet mos

agentsarxiv-cs-ai

Applications

Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics

arXiv:2605.15465v1 Announce Type: new Abstract: Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks su

applicationsarxiv-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

Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search

arXiv:2605.15649v1 Announce Type: new Abstract: Developing effective surrogates (performance predictors) for Neural Architecture Search (NAS) typically requires expensive fine-tuning or the engineerin

Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

model-releasesarxiv-cs-lg

arXiv:2602.08324v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computa

Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks

arXiv:2605.16085v1 Announce Type: cross Abstract: Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, de

Tutorials

Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation

arXiv:2605.15913v1 Announce Type: cross Abstract: Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reus

tutorialsarxiv-cs-ai

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment

arXiv:2605.16087v1 Announce Type: cross Abstract: Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI gui

safetyarxiv-cs-ai

Agents

Training on Documents About Monitoring Leads to CoT Obfuscation

arXiv:2605.15257v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is one of the most promising tools we have for detecting model misbehavior, but its effectiveness depends on models fa

agentsarxiv-cs-lg

Hardware

TrainMover: An Interruption-Resilient Runtime for ML Training

arXiv:2412.12636v3 Announce Type: replace-cross Abstract: Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solution

hardwarearxiv-cs-ai

Agents

Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction

arXiv:2510.10454v2 Announce Type: replace Abstract: Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electron

agentsarxiv-cs-ai

Transformer-like Inference from Optimal Control

arXiv:2605.15608v1 Announce Type: new Abstract: Decoder-only transformers compute the conditional probability of the next token from a sequence of past observations. This paper derives, from first pri

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

Transformers are Inherently Succinct

arXiv:2510.19315v3 Announce Type: replace-cross Abstract: We study succinctness as a measure of the expressive power of transformers. Succinctness -- how compactly a formalism can describe a language

TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions

model-releasesarxiv-cs-cv

arXiv:2602.05414v2 Announce Type: replace Abstract: Global warming has intensified the frequency and severity of extreme weather events, which degrade CCTV signal and video quality while disrupting tr

Tube Loss: A Novel Approach for Prediction Interval Estimation

arXiv:2412.06853v4 Announce Type: replace-cross Abstract: This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regr

Tutorials

Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance

arXiv:2605.15533v1 Announce Type: cross Abstract: Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model trainin

tutorialsarxiv-cs-ai

Applications

TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling

arXiv:2605.15579v1 Announce Type: cross Abstract: To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiri

applicationsarxiv-cs-cv

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

U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration

arXiv:2605.15421v1 Announce Type: new Abstract: In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that

researcharxiv-cs-cv

UAM: A Dual-Stream Perspective on Forgetting in VLA Training

arXiv:2605.15735v1 Announce Type: cross Abstract: Vision--language--action (VLA) models are typically built by fine-tuning a pretrained vision--language model (VLM) on action data. However, we show th

Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning

arXiv:2605.15894v1 Announce Type: cross Abstract: Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human healt

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

Applications

Unified Simulation of Lagrangian Particle Dynamics via Transformer

arXiv:2605.15305v1 Announce Type: cross Abstract: A unified simulator that can model diverse physical phenomena without solver-specific redesign is a long-standing goal across simulation science. We p

applicationsarxiv-cs-lg

Local Ai

UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization

arXiv:2510.03161v2 Announce Type: replace-cross Abstract: With the rapid advancements in image generation, synthetic images have become increasingly realistic, posing significant societal risks, such

local-aiarxiv-cs-ai

Universal Approximation of Nonlinear Operators and Their Derivatives

arXiv:2605.15285v1 Announce Type: cross Abstract: Derivative-Informed Operator Learning (DIOL), i.e. learning a (nonlinear) operator and its derivatives, is an open research frontier at the foundation

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

arXiv:2605.16230v1 Announce Type: cross Abstract: Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic struc

'Unlimited Realm of Exploration and Experimentation': Methods and Motivations of AI-Generated Sexual Content Creators

arXiv:2601.21028v2 Announce Type: replace-cross Abstract: AI-generated media is radically changing the way content is both consumed and produced on the internet, and in no place is this potentially mo

Unlocking Dense Metric Depth Estimation in VLMs

model-releasesarxiv-cs-cv

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

Unsupervised 3D Human Pose Estimation via Conditional Multi-view Ancestral Sampling

arXiv:2605.15583v1 Announce Type: new Abstract: We propose a method of estimating a 3D human pose from a single view without 3D supervision. The key to our method is to leverage the 2D diffusion prior

researcharxiv-cs-cv

Unsupervised Domain Shift Detection with Interpretable Subspace Attribution

arXiv:2605.15920v1 Announce Type: cross Abstract: We developed a tool for detecting domain shifts, namely subtle differences in the probability distributions of datasets. We identify these shifts usin

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

Local Ai

VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation

arXiv:2605.15661v1 Announce Type: cross Abstract: Classifier-free guidance (CFG) is the primary control over how strongly text semantics move a flow-based sampler, yet standard practice holds its scal

local-aiarxiv-cs-ai

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

VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing

model-releasesarxiv-cs-cl

arXiv:2605.15677v1 Announce Type: new Abstract: Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic

Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity

arXiv:2511.03606v2 Announce Type: replace-cross Abstract: The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics.

Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems

arXiv:2605.15228v1 Announce Type: new Abstract: Modern cloud and enterprise systems rely on identity-centric authorization, assuming that callers possessing valid credentials are safe to execute comma

safetyarxiv-cs-ai

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

Video Models Can Reason with Verifiable Rewards

arXiv:2605.15458v1 Announce Type: new Abstract: Video diffusion models have made rapid progress in perceptual realism and temporal coherence, but they remain primarily optimized for plausible generati

safetyarxiv-cs-cv

VideoGameBench: Can Vision-Language Models complete popular video games?

arXiv:2505.18134v3 Announce Type: replace Abstract: Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perfo