Search: “deployment”

Joseph Gordon-Levitt says “almost all” AI systems are “built on mass theft,” arguing that companies using large language models “shouldn’t b…

Joseph Gordon-Levitt argues that most AI systems are fundamentally built on 'mass theft,' contending that companies deploying large language models without proper authorization or compensation for tra

safetygary-marcus--x

3 May 2026

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Local Ai

Another example of greed. The PRO subscription!

DGX agent

Ollama Cloud launched in September 2025 with fixed-price subscription tiers (20/month Pro, 100/month Max) for cloud-hosted inference , while local deployment remains free with unlimited local usage .

local-air-ollama

2 May 2026

The Simpsons, on the trillion dollar buildout for GenAI.

Gary Marcus discusses concerns about the massive financial investment being directed toward generative AI development and deployment, likely examining whether the trillion-dollar spending is justified

safetygary-marcus--x

2 May 2026

A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion

arXiv:2501.07451v4 Announce Type: replace Abstract: Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g.

researcharxiv-cs-cv

Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

arXiv:2604.27720v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of

DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen

Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA

arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR

safetyarxiv-cs-ro

Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing

arXiv:2604.28142v1 Announce Type: cross Abstract: Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs subs

researcharxiv-cs-lg

Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard

arXiv:2604.28057v1 Announce Type: new Abstract: As autonomous vehicles slowly deploy into urban roads for limited use cases with significant edge case issues, closed facilities like marshaling yards p

agentsarxiv-cs-ro

GlowQ: Group-Shared LOw-Rank Approximation for Quantized LLMs

arXiv:2603.25385v2 Announce Type: replace-cross Abstract: Quantization techniques such as BitsAndBytes, AWQ, and GPTQ are widely used as a standard method in deploying large language models but often

Grounding Agent Memory in Contextual Intent

arXiv:2601.10702v2 Announce Type: replace-cross Abstract: Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under

KellyBench: A Benchmark for Long-Horizon Sequential Decision Making

arXiv:2604.27865v1 Announce Type: new Abstract: Language models are saturating benchmarks for procedural tasks with narrow objectives. But they are increasingly being deployed in long-horizon, non-sta

Hardware

MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems

arXiv:2604.26963v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic w

hardwarearxiv-cs-lg

Modeling Clinical Concern Trajectories in Language Model Agents

arXiv:2604.27872v1 Announce Type: new Abstract: Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumul

agentsarxiv-cs-ai

Local Ai

OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving

arXiv:2512.14044v3 Announce Type: replace-cross Abstract: The deployment of Vision-Language Models (VLMs) in safety-critical domains like autonomous driving (AD) is critically hindered by reliability

local-aiarxiv-cs-ai

Safe Bilevel Delegation (SBD): A Formal Framework for Runtime Delegation Safety in Multi-Agent Systems

arXiv:2604.27358v1 Announce Type: new Abstract: As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents

safetyarxiv-cs-ai

Hardware

Strait: Perceiving Priority and Interference in ML Inference Serving

arXiv:2604.28175v1 Announce Type: new Abstract: Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. How

hardwarearxiv-cs-lg

Test-Time Distillation for Continual Model Adaptation

arXiv:2506.02671v3 Announce Type: replace Abstract: Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to

safetyarxiv-cs-cv

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

safetyarxiv-cs-ai

Applications

AARP and Appian bring AI modernization to the back office

AI-led process modernization is redefining how enterprises deploy AI — moving beyond broad rollouts to embedding intelligence into specific, governed business processes that actually scale. But the li

applicationssiliconangle

Adversarial Robustness of NTK Neural Networks

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

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

Every year for AI Ascent, @gradypb, @Konstantine and I get to share some perspectives on AI and where things are headed. This year's talk wa…

Every year for AI Ascent, @gradypb, @Konstantine and I get to share some perspectives on AI and where things are headed. This year's talk was about the arrival of agents and the race to deploy them ac

agentssonya-huang--x

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

How T-Mobile’s private 5G network is changing the game for Major League Baseball

Major League Baseball opened the 2026 season with a technological first: the Automated Ball-Strike or ABS Challenge System, powered by T-Mobile’s private 5G network, is now deployed across all 29 U.S.

industrysiliconangle

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

Netomi banks $110M to embed agentic AI deeper into enterprise customer service

Enterprise customer experience platform provider Netomi Inc. today announced it has raised 110 million in new funding to expand its agentic artificial intelligence platform and accelerate deployment a

agentssiliconangle

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

safetyarxiv-cs-ai

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

Shipping Faster isn’t Learning Faster

This article argues that rapid software deployment and iteration do not necessarily translate to faster organizational learning or improved decision-making in data and AI projects. It likely explores

industrydatabricks

Sovereign AI puts government agencies at a crossroads between ambition and accountability

Governments worldwide are racing to show progress on AI, but the gap between declaring sovereign AI ambitions and actually deploying systems that mission operators will trust is widening fast. No plac

industrysiliconangle

Hardware

Speed Up Unreal Engine NNE Inference with NVIDIA TensorRT for RTX Runtime

The TensorRT for RTX plugin provides a runtime for Unreal Engine's Neural Network Engine (NNE), enabling efficient deployment of AI models directly within real-time applications. Developers can see 1.

hardwarenvidia-developer

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

What Google Cloud announced in AI this month

model-releasesgoogle-cloud-ai

Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more. We host

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

Agents are ready but your architecture probably isn't

This Databricks article discusses the gap between the readiness of AI agent technology and the organizational and technical architectures needed to deploy them effectively. It likely addresses infrast

industrydatabricks

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

model-releasesarxiv-cs-cl

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