Search: “deployment”

The Two Boundaries: Why Behavioral AI Governance Fails Structurally

arXiv:2604.27292v1 Announce Type: new Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed A

1 May 2026

Adversarial Robustness of NTK Neural Networks

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2604.25965v1 Announce Type: cross Abstract: Deep learning models are widely deployed in safety-critical domains, but remain vulnerable to adversarial attacks. In this paper, we study the adversa

safetyarxiv-cs-lg

30 Apr 2026

Camera-RFID Fusion for Robust Asset Tracking in Forested Environments

arXiv:2604.26241v1 Announce Type: new Abstract: Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuat

researcharxiv-cs-cv

Local Ai

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference

arXiv:2604.26557v1 Announce Type: cross Abstract: The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key cha

local-aiarxiv-cs-ai

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

arXiv:2604.26752v1 Announce Type: new Abstract: We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environmen

agentsarxiv-cs-cv

LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models

model-releasesarxiv-cs-ro

arXiv:2604.26569v1 Announce Type: new Abstract: Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementa

Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning

arXiv:2604.26516v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavi

Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital

arXiv:2604.26091v1 Announce Type: new Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX

OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction

arXiv:2604.25602v2 Announce Type: replace Abstract: Deploying production-ready multi-agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, obser

agentsarxiv-cs-ai

PATCH: Learnable Tile-level Hybrid Sparsity for LLMs

arXiv:2509.23410v4 Announce Type: replace-cross Abstract: Large language models (LLMs) deliver impressive performance but incur prohibitive memory and compute costs at deployment. Model pruning is an

PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty

arXiv:2604.26593v1 Announce Type: new Abstract: Digital twins provide a powerful paradigm for diagnostic and prognostic tasks in the monitoring and control of engineered systems; however, their deploy

researcharxiv-cs-lg

Local Ai

Select to Think: Unlocking SLM Potential with Local Sufficiency

arXiv:2604.26940v1 Announce Type: new Abstract: Small language models (SLMs) offer computational efficiency for scalable deployment, yet they often fall short of the reasoning power exhibited by their

local-aiarxiv-cs-cl

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations

arXiv:2604.26181v1 Announce Type: new Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input comp

agentsarxiv-cs-lg

Teaching LLM to be Persuasive: Reward-Enhanced Policy Optimization for Alignment from Heterogeneous Rewards

arXiv:2510.04214v3 Announce Type: replace Abstract: We deploy large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs). The ag

safetyarxiv-cs-cl

The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches

model-releasesarxiv-cs-cv

arXiv:2604.26317v1 Announce Type: new Abstract: The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One of

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall

arXiv:2505.13963v3 Announce Type: replace Abstract: Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's

researcharxiv-cs-cl

Applications

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

arXiv:2604.26174v1 Announce Type: new Abstract: Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environmen

applicationsarxiv-cs-cv

A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks

model-releasesarxiv-cs-lg

arXiv:2604.24810v1 Announce Type: new Abstract: Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant cha

asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics

model-releasesarxiv-cs-ro

arXiv:2604.24916v1 Announce Type: new Abstract: We introduce asRoBallet, to the best of our knowledge, the first successful deployment of reinforcement learning (RL) on a humanoid ballbot hardware. Hi

BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate

arXiv:2604.25203v1 Announce Type: new Abstract: Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs

safetyarxiv-cs-cl

Benchmarking and Adapting On-Device LLMs for Clinical Decision Support

arXiv:2601.03266v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly advanced in clinical decision-making, yet the deployment of proprietary systems is hindered by privacy con

Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion

arXiv:2604.25574v1 Announce Type: new Abstract: In autonomous driving, camera-radar fusion offers complementary sensing and low deployment cost. Existing methods perform fusion through input mixing, f

agentsarxiv-cs-cv

Cross-Lingual Jailbreak Detection via Semantic Codebooks

arXiv:2604.25716v1 Announce Type: new Abstract: Safety mechanisms for large language models (LLMs) remain predominantly English-centric, creating systematic vulnerabilities in multilingual deployment.

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environme

Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

arXiv:2512.20677v4 Announce Type: replace-cross Abstract: The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically eval

safetyarxiv-cs-cl

Local Ai

Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning

arXiv:2601.21293v2 Announce Type: replace Abstract: Reliability-centered prognostics for rotating machinery requires early-warning signals that remain accurate under nonstationary operating conditions

local-aiarxiv-cs-lg

The Structured Output Benchmark: A Multi-Source Benchmark for Evaluating Structured Output Quality in Large Language Models

arXiv:2604.25359v1 Announce Type: new Abstract: Large Language Models are increasingly being deployed to extract structured data from unstructured and semi-structured sources: parsing invoices, medica

A Decoupled Human-in-the-Loop System for Controlled Autonomy in Agentic Workflows

arXiv:2604.23049v1 Announce Type: new Abstract: AI agents are increasingly deployed to execute tasks and make decisions within agentic workflows, introducing new requirements for safe and controlled a

Applications

A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts

arXiv:2604.23271v1 Announce Type: new Abstract: Automated white blood cell (WBC) classification is essential for scalable leukaemia screening. However, real-world deployment is challenged by domain sh

applicationsarxiv-cs-cv

Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching

arXiv:2604.22901v1 Announce Type: new Abstract: Diffusion models achieve remarkable success in time series generation. However, slow inference limits their practical deployment. We propose E^2-CRF (Er

researcharxiv-cs-lg

Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines

arXiv:2604.23483v1 Announce Type: new Abstract: Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can

agentsarxiv-cs-ai

AmaraSpatial-10K: A Spatially and Semantically Aligned 3D Dataset for Spatial Computing and Embodied AI

arXiv:2604.23018v1 Announce Type: cross Abstract: Web-scale 3D asset collections are abundant, but rarely deployment-ready. Assets ship with arbitrary metric scale, incorrect pivots and forward axes,

An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress

arXiv:2604.24076v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accur

Architecture Matters for Multi-Agent Security

arXiv:2604.23459v1 Announce Type: cross Abstract: Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet i

agentsarxiv-cs-lg

Benchmarking and Mitigating Sycophancy in Medical Vision Language Models

arXiv:2509.21979v4 Announce Type: replace-cross Abstract: Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite thi

Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control

arXiv:2604.23863v1 Announce Type: new Abstract: Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state a

safetyarxiv-cs-ro

CRISP: Persistent Concept Unlearning via Sparse Autoencoders

arXiv:2508.13650v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preser

Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models

arXiv:2601.02455v2 Announce Type: replace-cross Abstract: Deploying Automatic Speech Recognition (ASR) models on memory-constrained edge devices requires aggressive low-bit weight quantization. Layer-

researcharxiv-cs-cl

Domain Fine-Tuning vs. Retrieval-Augmented Generation for Medical Multiple-Choice Question Answering: A Controlled Comparison at the 4B-Parameter Scale

arXiv:2604.23801v1 Announce Type: new Abstract: Practitioners deploying small open-weight large language models (LLMs) for medical question answering face a recurring design choice: invest in a domain

DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training

arXiv:2512.03847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown strong performance in LLM post-training, but real-world deployment often involves noisy or incomplete su

Evaluating Temporal Consistency in Multi-Turn Language Models

arXiv:2604.23051v1 Announce Type: new Abstract: Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios,

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Fixed-Reservoir vs Variational Quantum Architectures for Chaotic Dynamics: Benchmarking QRC and QPINN on the Lorenz System

arXiv:2604.23743v1 Announce Type: cross Abstract: Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systema

researcharxiv-cs-lg

GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility

arXiv:2604.24549v1 Announce Type: cross Abstract: Coordinating large populations of grid-edge devices requires learning methods that remain fully decentralised in deployment while still respecting thr

GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs

arXiv:2604.23366v1 Announce Type: new Abstract: Autonomous multi-agent LLM systems are increasingly deployed to investigate operational incidents and produce structured diagnostic reports. Their trust

GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation

model-releasesarxiv-cs-lg

arXiv:2604.24551v1 Announce Type: cross Abstract: Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mit

Hardware-Efficient FPGA Implementation of Sigmoid Function Using Mixed-Radix Hyperbolic Rotation CORDIC

arXiv:2604.23547v1 Announce Type: cross Abstract: Efficient hardware implementation of nonlinear activation functions is a crucial task in deploying artificial neural networks on resource-constrained

researcharxiv-cs-ai

Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis

model-releasesarxiv-cs-cv

arXiv:2604.23375v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and com