Search: “deployment”

arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to as

arXiv:2609.02830v1 Announce Type: new Abstract: LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots. Despite the strong performance of recent state-

arXiv:2608.29284v1 Announce Type: new Abstract: Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-re

arXiv:2608.15693v1 Announce Type: new Abstract: Running large AI models on resource-constrained edge devices requires model compression to reduce model size and computation. What compresses well, howe

arXiv:2608.12035v1 Announce Type: cross Abstract: Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship.

arXiv:2603.16141v2 Announce Type: replace-cross Abstract: Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical

arXiv:2608.00419v1 Announce Type: cross Abstract: Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback lo

arXiv:2512.12816v2 Announce Type: replace Abstract: We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider

arXiv:2607.07695v1 Announce Type: new Abstract: We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task

arXiv:2607.02865v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies show promising zero-shot generalization, but often fail under deployment-time distribution shift, leadi

arXiv:2606.28027v1 Announce Type: cross Abstract: Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility a

arXiv:2606.06535v1 Announce Type: cross Abstract: Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack

arXiv:2605.27461v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have shown promising manipulation capabilities, yet their practical impact is often limited by the reliability dem

arXiv:2605.10601v1 Announce Type: new Abstract: AI deployment in sensitive domains such as health care, credit, employment, and criminal justice is often treated as unsafe to authorize until model int

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid sepa

arXiv:2603.06811v3 Announce Type: replace Abstract: With many organizations struggling to gain value from AI deployments, pressure to evaluate AI in an informed manner has intensified. Status quo AI e

arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The

arXiv:2604.24273v1 Announce Type: new Abstract: The deployment of intelligent reinforcement learning (RL) agents on resource-constrained edge devices remains a fundamental challenge due to the substan

arXiv:2604.17258v1 Announce Type: new Abstract: Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model

arXiv:2604.16408v1 Announce Type: new Abstract: We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support. Rather th

arXiv:2604.17007v1 Announce Type: new Abstract: Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present

arXiv:2604.12916v1 Announce Type: new Abstract: Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, phy

arXiv:2610.10617v1 Announce Type: cross Abstract: In post-deployment time, inputs to deep learning models may or may not be adversarially patched. Patch robustness certification on such inputs within

arXiv:2610.08450v1 Announce Type: new Abstract: Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespr

arXiv:2610.03817v1 Announce Type: cross Abstract: Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained f

IBM Corp. today announced a self-hosted deployment option for IBM Bob, its agentic software development platform, allowing enterprises to use artificial intelligence to build and modernize application

arXiv:2609.39223v1 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive po

Getting artificial intelligence into production has become the real test for enterprises. As the proof-of-concept phase ends, harder questions about cost, data and control are taking its place. That s

arXiv:2609.35075v1 Announce Type: new Abstract: Many sensing tasks obtain training labels only by deploying instruments in the field. With a limited number of sensor kits, a collection deadline, and m

arXiv:2609.29000v1 Announce Type: new Abstract: Robot policies deployed in real environments naturally accumulate mixed-quality experience, including successful executions, partial progress, and failu

arXiv:2609.30092v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to

arXiv:2609.25014v1 Announce Type: cross Abstract: Organizations fine-tune small language models on private data and then compress them to 4 bits for resource-efficient deployment. We show that the com

Financial Times: Google DeepMind cofounder Shane Legg warns that advancing AI must never run ahead of safety and launches the DeepMind Institute to explore the deployment of AGI — Shane Legg launches

SemiAnalysis: A deep dive into on-device and data center inference for robots, including a primer on robot models, deployments, supply chains, the “network wall”, and more — Robot Models, Silicon & DR

arXiv:2609.14146v1 Announce Type: cross Abstract: Vision-language-action (VLA) deployment can reduce inference latency while changing closed-loop task behavior. We evaluate HuggingFaceVLA/smolvla_libe

arXiv:2609.05512v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all c

AI factory deployment is becoming a workload-sizing exercise rather than a custom infrastructure project. Enterprises need infrastructure spanning edge inference and large-model training. Broadcom Inc

arXiv:2608.18733v1 Announce Type: cross Abstract: We present Flama, an open-source Python framework for developing and deploying production-ready web APIs, machine learning services, and large-languag

arXiv:2505.04608v5 Announce Type: replace-cross Abstract: Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of

arXiv:2608.11361v1 Announce Type: cross Abstract: Tokenizer vocabulary size is a foundational design choice in large language model (LLM) infrastructure, yet it is typically fixed at training time bas

arXiv:2608.09892v1 Announce Type: new Abstract: Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that

arXiv:2607.29397v1 Announce Type: new Abstract: Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latenc

arXiv:2607.26238v1 Announce Type: new Abstract: We investigate lightweight raptor-species classification for real-time edge deployment in wind-turbine collision mitigation. Using DINOv2-L (304M parame

arXiv:2508.02929v3 Announce Type: replace-cross Abstract: Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation sur

Enterprise AI is entering a new phase as organizations shift their focus from experimentation to production deployments that deliver measurable business outcomes. That transition is bringing AI token

arXiv:2607.13221v1 Announce Type: cross Abstract: Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow