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arXiv:2605.01892v1 Announce Type: new Abstract: European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing but

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Safety

Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure

DGX agent

arXiv:2605.01091v1 Announce Type: cross Abstract: When a traffic signal controller adjusts green phases and a grid manager curtails power on the same corridor, each system may comply with its own obli

safetyarxiv-cs-ai

6 May 2026

On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization

arXiv:2511.11362v2 Announce Type: replace-cross Abstract: On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent m

local-aiarxiv-cs-cl

Toward a Science of Intent: Closure Gaps and Delegation Envelopes for Open-World AI Agents

arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems w

TriBench-Ko: Evaluating LLM Risks in Judicial Workflows

model-releasesarxiv-cs-cl

arXiv:2605.03792v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into legal workflows. However, existing benchmarks primarily address proxy tasks, such as bar e

When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models

arXiv:2605.02914v1 Announce Type: new Abstract: A guard model fine-tuned on entirely benign data can lose all safety alignment -- not through adversarial manipulation, but through standard domain spec

Research

AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs

arXiv:2605.02107v1 Announce Type: new Abstract: A team of mobile robots monitors spatially distributed processes and delivers measurements to a base, where AoI is measured from sensing start, capturin

researcharxiv-cs-ro

Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation

arXiv:2605.02405v1 Announce Type: new Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that

applicationsarxiv-cs-lg

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning

arXiv:2605.02196v1 Announce Type: new Abstract: Machine unlearning aims to remove specified training data to satisfy privacy regulations such as GDPR. However, existing evaluations assume identical pr

EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

model-releasesarxiv-cs-cv

arXiv:2605.02275v1 Announce Type: new Abstract: Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved pe

Feedback-Normalized Developer Memory for Reinforcement-Learning Coding Agents: A Safety-Gated MCP Architecture

model-releasesarxiv-cs-cl

arXiv:2605.01567v1 Announce Type: cross Abstract: Large language model (LLM) coding agents increasingly operate over repositories, terminals, tests, and execution traces across long software-engineeri

Hardware

LEAP: Layer-wise Exit-Aware Pretraining for Efficient Transformer Inference

arXiv:2605.01058v1 Announce Type: cross Abstract: Layer-aligned distillation and convergence-based early exit represent two predominant computational efficiency paradigms for transformer inference; ye

hardwarearxiv-cs-cl

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy

arXiv:2509.21173v5 Announce Type: replace Abstract: Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OO

safetyarxiv-cs-cv

LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging

model-releasesarxiv-cs-ro

arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm

Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids

arXiv:2603.02856v2 Announce Type: replace Abstract: Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environme

safetyarxiv-cs-ro

Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers

arXiv:2605.00869v1 Announce Type: cross Abstract: Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health m

local-aiarxiv-cs-cv

TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policies

arXiv:2605.01195v1 Announce Type: new Abstract: Recent imitation learning (IL) algorithms such as flow-matching and diffusion policies demonstrate remarkable performance in learning complex manipulati

safetyarxiv-cs-ro

Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices

model-releasesarxiv-cs-cv

arXiv:2507.04277v2 Announce Type: replace Abstract: Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Exist

Research

Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping

arXiv:2605.00315v1 Announce Type: cross Abstract: As climate extreme and disaster events become more frequent and intense, Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative ap

researcharxiv-cs-ai

Being-H0.7: A Latent World-Action Model from Egocentric Videos

arXiv:2605.00078v1 Announce Type: cross Abstract: Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to a

4 May 2026

Agent Name Service (ANS): A Proof-of-Concept Trust Layer for Secure AI Agent Discovery, Identity, and Governance in Kubernetes

arXiv:2604.26997v1 Announce Type: cross Abstract: Autonomous AI agent ecosystems require stronger mechanisms for secure discovery, identity verification, capability attestation, and policy governance.

Design Structure Matrix Modularization with Large Language Models

arXiv:2604.28018v1 Announce Type: cross Abstract: Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge

Energy-Efficient Plant Monitoring via Knowledge Distillation

arXiv:2604.27178v1 Announce Type: new Abstract: Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tas

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

arXiv:2604.27598v1 Announce Type: new Abstract: Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires

local-aiarxiv-cs-lg

Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty

arXiv:2604.27193v1 Announce Type: new Abstract: Automatic Emergency Braking (AEB) systems represent a safety-critical national interest, with the National Highway Traffic Safety Administration (NHTSA)

safetyarxiv-cs-ro

TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning

arXiv:2604.27861v1 Announce Type: cross Abstract: Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a seq

applicationsarxiv-cs-cl

WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning

arXiv:2604.27629v1 Announce Type: new Abstract: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models. To address data scarcity in semiconduct

A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio

arXiv:2409.06624v4 Announce Type: replace-cross Abstract: Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The hug

A Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Framework

arXiv:2604.25933v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which

Agents

Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations

arXiv:2604.26805v1 Announce Type: new Abstract: Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release mon

agentsarxiv-cs-ai

Research

FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving

arXiv:2604.26881v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, de

researcharxiv-cs-lg

Hierarchical adaptive control for real-time dynamic inference at the edge

arXiv:2604.26470v1 Announce Type: new Abstract: Industrial systems increasingly depend on Machine Learning (ML), and operate on heterogeneous nodes that must satisfy tight latency, energy, and memory

local-aiarxiv-cs-lg

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety

arXiv:2603.02259v2 Announce Type: replace-cross Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain esse

The hidden risks of temporal resampling in clinical reinforcement learning

arXiv:2602.06603v3 Announce Type: replace Abstract: Reinforcement learning (RL) is a type of artificial intelligence for making optimal choices. In healthcare, researchers generally use offline RL (OR

applicationsarxiv-cs-lg

A Systematic Post-Train Framework for Video Generation

arXiv:2604.25427v1 Announce Type: new Abstract: While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a signif

safetyarxiv-cs-cv

Carbon-Taxed Transformers: A Green Compression Pipeline for Overgrown Language Models

arXiv:2604.25903v1 Announce Type: cross Abstract: The accelerating adoption of Large Language Models (LLMs) in software engineering (SE) has brought with it a silent crisis: unsustainable computationa

Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers

arXiv:2604.25891v1 Announce Type: new Abstract: Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavio

Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing

arXiv:2602.11786v2 Announce Type: replace Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation acr

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices

arXiv:2604.25421v1 Announce Type: new Abstract: Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data, yet in mobile

GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution

arXiv:2604.25457v1 Announce Type: new Abstract: Despite recent advances, single-image super-resolution (SR) remains challenging, especially in real-world scenarios with complex degradations. Diffusion

Hardware

Laplace-Bridged Randomized Smoothing for Fast Certified Robustness

arXiv:2604.24993v1 Announce Type: new Abstract: Randomized Smoothing (RS) offers formal ell_2 guarantees for arbitrary base classifiers but faces two key practical bottlenecks: (i) it often relies on

hardwarearxiv-cs-lg

Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking

arXiv:2604.25405v1 Announce Type: new Abstract: Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by

local-aiarxiv-cs-cv

RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy

arXiv:2604.05594v2 Announce Type: replace Abstract: Pixel-level annotation is costly in low-resource dermoscopy. We present RABC-Net, a reliability-aware annotation-free segmentation system that combi

local-aiarxiv-cs-cv

Hardware

WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition

arXiv:2604.25611v1 Announce Type: new Abstract: Real-time automatic speech recognition (ASR) systems face a fundamental trade-off between transcription accuracy and computational efficiency, particula

hardwarearxiv-cs-cl

Algorithmic Administration and the EU AI Act: Legal Principles for Public Sector Use of AI

arXiv:2604.22765v1 Announce Type: cross Abstract: The increasing use of artificial intelligence (AI) by public authorities introduces both opportunities for innovation and significant challenges for t

AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation

arXiv:2604.24086v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have been demonstrated possessing strong zero-shot generalization for robot control, their massive parameter

Bridging Restoration and Generation Manifolds in One-Step Diffusion for Real-World Super-Resolution

arXiv:2604.24136v1 Announce Type: new Abstract: Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR) but suffer from computational bottlenecks due to iterative

Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes

arXiv:2604.23091v1 Announce Type: new Abstract: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for