Search: “deployment”

arXiv:2607.04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We pro

arXiv:2606.32009v1 Announce Type: new Abstract: Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributio

arXiv:2606.29581v1 Announce Type: cross Abstract: Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually

arXiv:2606.27353v1 Announce Type: new Abstract: Robots deployed in the real world rarely operate under a single fixed dynamics model: wind changes, payloads vary, batteries drain, contacts shift, and

arXiv:2602.18900v2 Announce Type: replace-cross Abstract: Privacy preserving machine learning deployments in sensitive deep learning applications; from medical imaging to autonomous systems; increasin

arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ

RunPod Hub is a centralized catalog of preconfigured AI repositories that you can browse, deploy, and share, optimized for RunPod's Serverless infrastructure to deploy in minutes. The platform include

arXiv:2606.08381v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to i

arXiv:2606.07902v1 Announce Type: new Abstract: Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we

arXiv:2606.07878v1 Announce Type: new Abstract: The KV cache is the memory bottleneck of long-horizon language model deployment. Practically, a deployable compactor must be lightweight enough to call

arXiv:2606.07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challengin

SpaceX's Starlink constellation has deployed a massive number of satellites in a relatively short timeframe, with approximately as many satellites launched in seven years as the cumulative total deplo

With hosted agents, we made it straightforward to build and deploy agents on Foundry. You write your logic, run azd deploy, and your agent is live. But “live” and “production-ready” aren’t the same th

arXiv:2606.02951v1 Announce Type: cross Abstract: Deploying language-driven agents in robotics requires evaluations that reflect real-world task demands: natural-language instructions with reproducibl

arXiv:2606.03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed

arXiv:2606.01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost. Classical models

Welcome to the second Cloud CISO Perspectives for May 2026. Today, Usman Chaudhary, Field CISO, Google Public Sector, offers a guide for CISOs protecting government agencies and critical infrastructur

arXiv:2605.27428v1 Announce Type: new Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time

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arXiv:2605.26409v1 Announce Type: cross Abstract: Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable

arXiv:2605.24294v1 Announce Type: cross Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propos

arXiv:2605.24279v1 Announce Type: new Abstract: A frontier language model's acknowledged 'helpful programming assistant' persona does not survive long agentic-coding sessions in the deployment regime

arXiv:2605.24212v1 Announce Type: cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outc

arXiv:2605.24077v1 Announce Type: cross Abstract: Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-spe

Written by: Takahiro Sugiyama, Peter Revelant, Mathew Potaczek Introduction In late 2025, Mandiant responded to a security incident involving a compromised web server running KnowledgeDeliver. Knowled

arXiv:2605.23238v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior i

Many development teams are familiar with the hesitation that comes right before pushing a new feature live. As AI helps developers write code faster, the gap between rapid code generation and safe pro

arXiv:2408.08812v2 Announce Type: replace Abstract: Deployed reinforcement learning agents often face safety requirements that are specified only after training, such as new hazard maps, revised risk

arXiv:2605.18899v1 Announce Type: cross Abstract: Generative LLM-based recommenders (LLM-Rec) require continual post-deployment updates, yet deployment logs provide only policy-shaped contextual bandi

As models and harnesses improve, agents are taking on increasingly complex tasks that can run for hours or even days. But as we push agents to do more, this has surfaced a new operational problem: lon

arXiv:2605.16035v1 Announce Type: cross Abstract: AI agents are increasingly deployed to act autonomously in the world, yet there is still no reliable way to trace a harmful agent back to the account

arXiv:2605.08195v1 Announce Type: new Abstract: Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragment

arXiv:2605.10763v1 Announce Type: new Abstract: LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors)

arXiv:2605.08137v1 Announce Type: cross Abstract: Weight pruning is widely advocated for deploying Large Language Models on resource-constrained IoT and edge devices, yet its impact on model fairness

This article examines how agentic AI systems are being deployed to edge computing environments, likely challenging common assumptions about what this deployment actually entails. It discusses practica

arXiv:2602.06932v3 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating spe

arXiv:2605.00298v1 Announce Type: new Abstract: Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Mark

A Deep Dive into Deep Agents 🚀DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections

arXiv:2604.24966v1 Announce Type: cross Abstract: Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a p

arXiv:2604.22098v1 Announce Type: new Abstract: Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data

last week we dropped a super comprehensive guide on runtime requirements for long running agents! this week i'll be diving into each requirement and runtime capability that meets it, here's the lineup

arXiv:2505.17639v3 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for depl

arXiv:2604.22591v1 Announce Type: new Abstract: The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, w

LangChain Community Spotlight: Distributed LangGraph Architecture with RemoteGraph 🔗 Production-ready example for distributed LangGraph apps with HTTP-invoked subgraphs. Key insight: RemoteGraph retur

arXiv:2604.21637v1 Announce Type: new Abstract: Where and how language models (LMs) are deployed determines who can benefit from them. However, there are several challenges that prevent effective depl

Replit enables dramatically accelerated software development cycles, reducing deployment timelines from 9 months to 3 days through its cloud-based development platform. The platform streamlines the bu