Search: “deployment”

arXiv:2608.27794v1 Announce Type: new Abstract: As neural networks are increasingly deployed on resource constrained edge devices, accurate prediction of latency and energy is critical for efficient n

arXiv:2608.26199v1 Announce Type: new Abstract: We ask whether AI agents powered by locally deployed large language models can reliably automate expert-defined hardware design workflows in an industry

arXiv:2608.26372v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous agents serving users on behalf of companies, placing them in settings where user and dep

arXiv:2608.26216v1 Announce Type: cross Abstract: The deployment of advanced, AI-enabled control algorithms in tokamak experiments requires robust integration with existing plasma control system (PCS)

arXiv:2606.22179v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as black-box classifiers in pipelines that automate confident decisions and route uncertain o

arXiv:2608.25642v1 Announce Type: new Abstract: Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment: the map requires only geotagged images, an

arXiv:2608.25325v1 Announce Type: cross Abstract: Deploying large language models for professional financial review requires more than measuring general financial competence: models must perform the s

arXiv:2608.24103v1 Announce Type: new Abstract: Commercial design platforms increasingly edit documents through large language model (LLM) agents, but two practical problems block reliable deployment:

arXiv:2608.23858v1 Announce Type: cross Abstract: The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments o

Uber has a lot in common with the cities it serves. Both are always changing and growing, both must carefully manage the resulting traffic to prevent congestion and sprawl. Uber has continuously evolv

arXiv:2608.23831v1 Announce Type: cross Abstract: While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist

arXiv:2608.23911v1 Announce Type: new Abstract: Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Po

arXiv:2608.23638v1 Announce Type: cross Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration

arXiv:2608.21693v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance,

Mariko Wakabayashi is a Principal Applied Scientist at Microsoft who leads the development of agentic AI workflows for cybersecurity operations. She specializes in evaluating LLM‑powered systems and a

arXiv:2608.21019v1 Announce Type: cross Abstract: Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is r

arXiv:2602.13710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment o

arXiv:2608.19889v1 Announce Type: new Abstract: The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down tomorrow? Imp

arXiv:2608.18296v1 Announce Type: cross Abstract: As CGM-based AI tools approach clinical deployment, whether their accuracy is equitable across patient demographics remains insufficiently tested. To

In modern enterprise data engineering, Apache Spark remains a cornerstone framework for processing massive datasets at scale. However, managing infrastructure such as provisioning clusters, tuning YAR

arXiv:2608.14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require inv

arXiv:2608.14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remai

arXiv:2608.13511v1 Announce Type: new Abstract: Repeated in-situ evaluation of ocean-glider planners requires scarce vehicles, operators, deployment and recovery resources, and ocean conditions that c

arXiv:2608.13474v1 Announce Type: new Abstract: Vision-language-action models (VLAs) are moving rapidly towards deployment as general-purpose manipulation policies, but we currently lack basic tools f

arXiv:2608.11770v1 Announce Type: new Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where

arXiv:2608.11083v1 Announce Type: cross Abstract: Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective ne

arXiv:2608.10933v1 Announce Type: new Abstract: Text-to-Video (T2V) generative models are vulnerable to jailbreak attacks in real-world deployment, leading them to produce harmful or inappropriate con

arXiv:2608.11191v1 Announce Type: cross Abstract: GUI Visual Grounding is a fundamental capability for GUI agents. Existing models typically freeze their parameters after deployment, limiting their ab

arXiv:2608.10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSec

arXiv:2608.08363v1 Announce Type: cross Abstract: Silicon carbide (SiC) power modules are increasingly deployed in automotive traction inverters, where condition monitoring is essential to prevent in-

arXiv:2608.07583v1 Announce Type: cross Abstract: Multi-agent LLM systems route among model-backed advisors, yet a deployer rarely knows before shipping whether routing will help at all. Prevailing ro

arXiv:2608.08471v1 Announce Type: new Abstract: Deployed LLM safety guardrails are predominantly static: trained once and frozen at release, while new jailbreak techniques and previously un-addressed

arXiv:2608.06571v1 Announce Type: cross Abstract: Deployed vision-language systems often gate their answers on confidence, making confidence robustness relevant to oversight. We study confidence reado

arXiv:2608.06512v1 Announce Type: new Abstract: Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups tha

arXiv:2608.06833v1 Announce Type: new Abstract: Image-goal navigation is a fundamental capability for embodied AI, yet its practical deployment is strained by strong prior assumptions. Existing method

Stripe just published how their company-wide AI agent works. The bar for building one just dropped to one engineer and one week It is called Kai. Their own words: a coding agent for non-engineers. You

arXiv:2608.06112v1 Announce Type: new Abstract: Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions locked

arXiv:2608.06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness. Yet m

arXiv:2608.04488v1 Announce Type: new Abstract: Despite rapid advances in large language models (LLMs), deploying and personalizing them on resource-constrained devices remains impractical due to high

arXiv:2608.04246v1 Announce Type: cross Abstract: Vision-language-action policies often fail under deployment-time distribution shifts such as clutter, distractor objects, lighting changes, novel obje

arXiv:2608.03838v1 Announce Type: new Abstract: Reasoning-based guard models improve LLM safeguards, but decoding explicit rationales for every interaction makes them costly to deploy. Although latent

arXiv:2608.02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records. This makes

arXiv:2608.01034v1 Announce Type: new Abstract: We introduce Opt.Gear, a foundation model designed for efficient on-device deployment, real-tim inference, and strong task capability. It includes a den

arXiv:2608.00559v1 Announce Type: new Abstract: AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to e

arXiv:2608.01388v1 Announce Type: cross Abstract: Runtime safety monitors based on Linear Temporal Logic (LTL) and finite automata (FSA) are increasingly deployed to intercept unsafe tool-call sequenc

arXiv:2602.10429v2 Announce Type: replace-cross Abstract: AIvilization v0 is a publicly deployed large-scale artificial society that couples a resource-constrained sandbox with a unified LLM-agent arc