Knowledge catalogue
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
arXiv:2607.13239v1 Announce Type: new Abstract: Foundation models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used for transportation management center
arXiv:2607.06766v1 Announce Type: cross Abstract: At Amazon Prime Video, we face the critical operational challenge of managing code deployments during live events and rapid feature releases without c
Physical AI and robotics are moving beyond impressive demonstrations into a new phase of practical deployment, with companies now targeting specific, high-value use cases in manufacturing and logistic
Madison Mills / Axios: The OpenAI Deployment Company agrees to acquire Northslope, an applied AI company founded by ex-Palantir employees, its second acquisition after buying Tomoro — The OpenAI Deplo
arXiv:2607.01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed. Freezing a pretrai
arXiv:2607.00368v1 Announce Type: new Abstract: Large language model test-time training (TTT) is often evaluated through local proxy metrics: models are updated on recent tokens, retrieved context, ta
Salesforce Inc. is launching a new prepackaged artificial intelligence agent for customer service, enabling organizations to quickly build and deploy AI agents. Today Salesforce announced Help Agent,
arXiv:2606.24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardwa
arXiv:2606.11806v1 Announce Type: new Abstract: Production LLM systems accumulate reusable operational experience, but the practical deployment issue is not merely whether such experience can help. It
arXiv:2606.10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time fun
arXiv:2602.02680v2 Announce Type: replace Abstract: The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch
arXiv:2605.31226v1 Announce Type: cross Abstract: Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static
arXiv:2605.24657v1 Announce Type: new Abstract: Major LLM platforms deploy models in an inference-only configuration: the model serves requests but never updates per-user weights. Users must repeatedl
arXiv:2605.24777v1 Announce Type: new Abstract: LiDAR model selection is a critical issue in roadside sensing systems, as it directly determines both perception capability and deployment cost. However
Nous Research conducted a large-scale deployment of Hermes today using isolated virtual machines and shared memory infrastructure via honchodotdev for team coordination. The post highlights enthusiasm
arXiv:2605.20956v1 Announce Type: new Abstract: Conformal triage converts predictive scores into deployment actions that either release a case, flag it for urgent attention, or defer it to human revie
arXiv:2605.20431v1 Announce Type: cross Abstract: Missing significant amounts of school during K-12 education is known to put students' cognitive and social development at risk. Alternatives such as h
arXiv:2605.16895v1 Announce Type: cross Abstract: End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgent
The enterprise artificial intelligence stack is getting smarter — but is it getting the context it needs to make agentic AI deployment actually work? The defining problem of the agentic era might neve
arXiv:2605.04454v1 Announce Type: cross Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under fixed inputs, such
Enterprise AI deployment has fundamentally expanded the cybersecurity attack surface, turning data pipelines, model training environments, identity management systems and supply chains into prime targ
SpaceX confirmed the successful deployment of the CAS500-2 satellite, according to an announcement by Elon Musk on X (formerly Twitter). The CAS500-2 is a commercial communications satellite that was
serving multiple users from a single agent deployment introduces three distinct problems. luckily, langsmith's agent server has a solution for each! 1. data isolation: your @auth.authenticate handler
As part of this deployment, you can interact with the agent in several ways We expose the agent via MCP and A2A, as well as with as a 'remote graph' API, which means you can interact with as you would
Enterprises are pushing past AI experiments and demanding production-grade deployments, but fragmented data and poorly scoped agents are stalling progress. The question is no longer whether AI agents
arXiv:2604.24038v1 Announce Type: new Abstract: Static benchmarks measure what AI agents can do at a fixed point in time but not how they are adopted, maintained, or experienced in deployment. We intr
arXiv:2604.24447v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are promising for generalist robot control, but on-robot deployment is bottlenecked by real-time inference under t
arXiv:2511.12340v2 Announce Type: replace Abstract: Efficient machine learning deployment requires models that account for hardware constraints. Because binary logic gates are the fundamental primitiv
arXiv:2604.20822v1 Announce Type: new Abstract: The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deploym
arXiv:2604.18655v1 Announce Type: cross Abstract: Deploying large language models (LLMs) on smartphones poses significant engineering challenges due to stringent constraints on memory, latency, and ru
arXiv:2604.19206v1 Announce Type: new Abstract: The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely ha
This DigitalOcean guide covers strategies and best practices for deploying and optimizing very large language models (600 billion+ parameters) on cloud infrastructure, focusing on inference performanc
arXiv:2604.12161v1 Announce Type: new Abstract: Tumor boards are multidisciplinary conferences dedicated to producing actionable patient care recommendations with live review of primary radiology and
arXiv:2604.12459v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential conten
arXiv:2604.12116v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks
Reuters: Sources: US banking regulators are questioning banks about their deployment of agentic AI, use of Chinese-developed AI models, and autonomous system safeguards — Banking regulators are steppi