Search: “deployment”

EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents

arXiv:2605.13841v1 Announce Type: cross Abstract: Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applica

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FeatCal: Feature Calibration for Post-Merging Models

DGX agent

arXiv:2605.13030v1 Announce Type: cross Abstract: Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often

local-aiarxiv-cs-ai

14 May 2026

Hardware

Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection

arXiv:2605.13108v1 Announce Type: new Abstract: Face presentation attack detection (FacePAD) remains challenging under diverse spoofing representation, including 2D print and replay, 3D mask-based spo

hardwarearxiv-cs-cv

GLASS: Global-Local Aggregation for Inference-time Sparsification of LLMs

arXiv:2508.14302v2 Announce Type: replace-cross Abstract: Inference-time sparsification is a promising path to deploy large language models (LLMs) on resource-constrained devices, yet existing trainin

local-aiarxiv-cs-ai

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?

arXiv:2605.12827v1 Announce Type: cross Abstract: Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses t

High-Rate Quantized Matrix Multiplication I

arXiv:2601.17187v2 Announce Type: replace-cross Abstract: This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of larg

researcharxiv-cs-ai

Tutorials

Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification

arXiv:2601.22853v3 Announce Type: replace Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete

tutorialsarxiv-cs-cv

Introducing Rime Mist v3 on Together AI, a production TTS family built for deterministic pronunciation and controllable voice output. AI nat…

applicationstogether-ai--x

Introducing Rime Mist v3 on Together AI, a production TTS family built for deterministic pronunciation and controllable voice output. AI natives can now deploy @rimelabs Mist v3 on Together AI dedicat

It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows

arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may

applicationsarxiv-cs-ai

LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information

arXiv:2605.13188v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generat

researcharxiv-cs-lg

Looks like @NVIDIAAI has released official getting started guide for hermes on DGX Spark! Great way for new folks to get started and believe…

model-releasesnous-research--x

NVIDIA AI has released an official getting started guide for Hermes on DGX Spark, providing new users with foundational resources and instructions for deploying or working with Hermes on NVIDIA's DGX

MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs

arXiv:2506.12876v2 Announce Type: replace Abstract: The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, s

safetyarxiv-cs-lg

PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records

arXiv:2601.09636v2 Announce Type: replace Abstract: While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more

Plan Before You Trade: Inference-Time Optimization for RL Trading Agents

arXiv:2605.12653v1 Announce Type: cross Abstract: Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for using price foreca

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

arXiv:2605.12869v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that ci

safetyarxiv-cs-ai

Query-Conditioned Test-Time Self-Training for Large Language Models

arXiv:2605.13369v1 Announce Type: cross Abstract: Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at i

Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs

arXiv:2605.13778v1 Announce Type: cross Abstract: Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by

applicationsarxiv-cs-cv

STORM: Segment, Track, and Object Re-Localization from a Single Image

arXiv:2511.09771v3 Announce Type: replace Abstract: Accurate 6D pose estimation and tracking are core capabilities for physical AI systems, yet real-world deployment remains brittle and labor-intensiv

applicationsarxiv-cs-cv

The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm

model-releasesarxiv-cs-lg

arXiv:2507.18553v4 Announce Type: replace Abstract: Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto mo

Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?

arXiv:2605.12684v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of thes

What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models

arXiv:2605.13105v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual sh

safetyarxiv-cs-ro

Agents

With every department under pressure to adopt AI, the infrastructure layer is being put to the test

As AI adoption accelerates, organizations are shifting their focus from experimentation to large-scale deployment. The challenge now is building secure and scalable systems that can support AI agents,

agentssiliconangle

ZKBoost: Zero-Knowledge Verifiable Training for XGBoost

arXiv:2602.04113v3 Announce Type: replace-cross Abstract: Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive setti

applicationsarxiv-cs-lg

A Survey of On-Policy Distillation for Large Language Models

arXiv:2604.00626v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to grow in both capability and cost, transferring frontier capabilities into smaller, deployable stud

safetyarxiv-cs-cl

A survey predicted that 43% of companies would be using AI “as scale” by now. In reality, the figure is just 19%.

A survey predicted that 43% of companies would be deploying AI at scale by the current date, but actual adoption has reached only 19%, indicating a significant gap between forecasted and realized AI i

safetygary-marcus--x

Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty

model-releasesarxiv-cs-cl

arXiv:2605.11436v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring a

AI ambition is crashing into a decade of deferred IT maintenance, says Red Hat CEO

Enterprise AI infrastructure modernization has reached a critical crossroads as organizations grapple with decades of technical debt while facing intense pressure to deploy AI — and returning to IT fu

applicationssiliconangle

Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training

model-releasesarxiv-cs-lg

arXiv:2605.12483v1 Announce Type: new Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice

Causal Bias Detection in Generative Artifical Intelligence

arXiv:2605.11365v1 Announce Type: cross Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness

safetyarxiv-cs-lg

Agents

Control Charts for Multi-agent Systems

arXiv:2605.11135v1 Announce Type: cross Abstract: Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal

agentsarxiv-cs-lg

Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach

model-releasesarxiv-cs-cl

arXiv:2605.12177v1 Announce Type: new Abstract: [Abridged] Production LLM deployments receive feedback from a non-random fraction of users: thumbs sit mostly in the tails of the satisfaction distribut

DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift

arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-

researcharxiv-cs-lg

Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

model-releasesarxiv-cs-cl

arXiv:2605.12178v1 Announce Type: cross Abstract: World models enable agents to anticipate the effects of their actions by internalizing environment dynamics. In enterprise systems, however, these dyn

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

model-releasesarxiv-cs-ro

arXiv:2603.28561v2 Announce Type: replace Abstract: The growing deployment of small Unmanned Aerial Systems (sUASs) in low-altitude airspaces has increased the need for reliable tactical deconfliction

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

arXiv:2605.11509v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control an

local-aiarxiv-cs-lg

http://blogs.nvidia.com/blog/rtx-ai-garage-hermes-agent-dgx-spark

NVIDIA's RTX AI Garage partnership with Nous Research demonstrates deploying Hermes agent models on DGX systems integrated with Apache Spark for accelerated AI workloads. The initiative showcases how

local-ainous-research--x

Agents

Introducing Managed Deep Agents: the fastest way to ship a production deep agent. ✅ Harness ✅ Context ✅ Code execution All managed for you. …

Introducing Managed Deep Agents: the fastest way to ship a production deep agent. ✅ Harness ✅ Context ✅ Code execution All managed for you. Deploy with a single line of code. https://www.langchain.com

agentsharrison-chase--x

Is that more or less accurate? Bullish for local AI as a solution to the energy & cloud shortages!

This post discusses how local AI deployment could address growing energy consumption and cloud computing capacity constraints as demand for AI infrastructure increases. The author, Hugging Face CEO Cl

local-aiclem-delangue--x

Martingale-Consistent Self-Supervised Learning

arXiv:2605.11846v1 Announce Type: new Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images.

researcharxiv-cs-lg

Metaphor Is Not All Attention Needs

arXiv:2605.12128v1 Announce Type: new Abstract: Large language models are increasingly deployed in safety-critical applications, where their ability to resist harmful instructions is essential. Althou

safetyarxiv-cs-cl

Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation

arXiv:2605.11479v1 Announce Type: new Abstract: Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this probl

safetyarxiv-cs-ro

One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue

arXiv:2605.05630v2 Announce Type: replace Abstract: Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objec

safetyarxiv-cs-cl

Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies

arXiv:2605.11453v1 Announce Type: cross Abstract: Practitioners deploying multi-agent large language model (LLM) systems must currently choose between communication topologies such as chain, star, mes

safetyarxiv-cs-lg

Principled Design of Diffusion-based Optimizers for Inverse Problems

arXiv:2605.11506v1 Announce Type: new Abstract: Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference tim

researcharxiv-cs-cv

Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection

arXiv:2603.23677v2 Announce Type: replace Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to e

safetyarxiv-cs-cv

Robust Promptable Video Object Segmentation

model-releasesarxiv-cs-cv

arXiv:2605.12006v1 Announce Type: new Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in

See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model

model-releasesarxiv-cs-cv

arXiv:2605.11817v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in robotics manipulation, yet their high computational cost hinders real-time deploy

SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection

arXiv:2605.11114v1 Announce Type: new Abstract: Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside

safetyarxiv-cs-ro