Search: “deployment”

When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch

arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appro

15 Oct 2026

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Your Transformer Is Secretly Elastic

DGX agent

arXiv:2610.20588v1 Announce Type: new Abstract: Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model famili

model-releasesarxiv-cs-lg

15 Oct 2026

ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models

arXiv:2610.18596v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more:

safetyarxiv-cs-lg

agent-risk: Risk Assessment for Changes to AI Agents

arXiv:2610.16280v1 Announce Type: cross Abstract: Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs. Extendi

Local Ai

An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification

arXiv:2610.17463v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient spatiotemporal pattern recognition at the edge, but efficient deployment and on-devi

local-aiarxiv-cs-lg

AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding

arXiv:2610.16985v1 Announce Type: new Abstract: Vision-language-action (VLA) models show strong potential for end-to-end autonomous driving by leveraging world knowledge and understanding to excel in

hardwarearxiv-cs-cv

Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.18167v1 Announce Type: new Abstract: End-to-end autonomous driving has achieved remarkable progress by planning trajectories directly from raw sensor data. Yet the step from planning to con

Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance

arXiv:2610.17799v1 Announce Type: new Abstract: Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout trainin

Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models

arXiv:2610.16835v1 Announce Type: cross Abstract: Model providers can support image protection, but open-weight deployment introduces practical constraints. We propose four requirements: protection ef

safetyarxiv-cs-cv

ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models

arXiv:2610.14427v1 Announce Type: cross Abstract: As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown. Existing detectors read the wa

Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection

model-releasesarxiv-cs-cv

arXiv:2610.14647v1 Announce Type: new Abstract: Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignme

Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2610.16813v1 Announce Type: new Abstract: Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning sp

Certified Robustness via Sparse Second-Order Cone Relaxations

arXiv:2610.16016v1 Announce Type: new Abstract: Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving

Agents

CoLA: Multi-VLA Coordination via Learned Adapters

arXiv:2610.15337v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are typically deployed as isolated single-agent policies. When several such agents share a workspace, they can coo

agentsarxiv-cs-lg

COMPASS: Membership Inference Attack Against Vision Large Language Models via Adaptive Noise Calibration

arXiv:2610.17332v1 Announce Type: new Abstract: Vision large language models (VLLMs) are deployed in privacy-sensitive domains, raising training-data exposure concerns. Existing gray-box membership in

DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting

arXiv:2610.14376v1 Announce Type: new Abstract: Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available. However, adaptation objectiv

Agents

Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems

arXiv:2610.16071v1 Announce Type: new Abstract: Reliable deployment of LLM-based multi-agent systems calls for runtime auditors that predict terminal outcomes from observed execution prefixes. These a

agentsarxiv-cs-lg

DVTR-KD: Distilling Vision-Language Models via Intermediate Visual Token Reconstruction

arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy

Applications

Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings

arXiv:2610.14214v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as

applicationsarxiv-cs-lg

Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios

arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla

FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving

arXiv:2610.18451v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production. Meanwhile, the

hardwarearxiv-cs-lg

Applications

Few-for-Many Best-Arm Identification in Multi-Objective Bandits

arXiv:2610.16840v1 Announce Type: new Abstract: Real-world systems often serve many objectives but can deploy only a few options, whose performance must be learned from noisy feedback. Modeling each o

applicationsarxiv-cs-lg

Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams

model-releasesgoogle-deepmind

Google DeepMind and the Singapore Government have launched a partnership to deploy an artificial‑intelligence tool designed to help citizens identify and avoid scams. Announced in May 2026, the initia

GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference

arXiv:2610.17321v1 Announce Type: cross Abstract: As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive. WebGPU is compelling in th

Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models

arXiv:2610.15781v1 Announce Type: new Abstract: Training-free pruning removes weights from pretrained large language models to reduce their deployment cost. However, existing approaches typically cons

LayerCanvas: Infographic Generation in AI Learning Assistants via Hybrid Layer-Set Diffusion

arXiv:2610.18763v1 Announce Type: new Abstract: Online learning platforms are increasingly deploying AI assistants to provide personalized, on-demand tutoring. These can leverage infographics to commu

LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2610.18843v1 Announce Type: new Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, ca

Applications

LLM Evaluation is a Forecasting Problem

arXiv:2610.17018v1 Announce Type: new Abstract: Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice,

applicationsarxiv-cs-lg

PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors

arXiv:2610.16103v1 Announce Type: new Abstract: Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert the

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

arXiv:2610.16129v1 Announce Type: new Abstract: Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every repor

PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems

arXiv:2610.13989v1 Announce Type: cross Abstract: Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-base

ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring

arXiv:2610.16309v1 Announce Type: new Abstract: Despite rapid progress, generalist robot policies remain brittle during execution. Reliable deployment therefore requires runtime monitoring that provid

Retraining-Free Depth Pruning of Vision Transformers: A Cross Domain Study of Layer Redundancy

arXiv:2610.15005v1 Announce Type: new Abstract: Transformer architectures have achieved strong results in natural language processing and computer vision, but their deployment is limited by high compu

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

arXiv:2606.15734v2 Announce Type: replace-cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate

RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories

arXiv:2610.18699v1 Announce Type: new Abstract: Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world mod

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor

SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy

model-releasesarxiv-cs-cv

arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec

Steertuning Policies from a Few Human Videos

model-releasesarxiv-cs-ro

arXiv:2610.18711v1 Announce Type: new Abstract: Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce St

Supermicro wants AI factories judged by how fast they go live, not by GPU count

AI factory deployment has long been a race to secure graphics processing units, but the builders of those systems are finding that compute is only half the problem. Data pipelines, storage costs and t

hardwaresiliconangle

Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints

arXiv:2610.18459v1 Announce Type: new Abstract: External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments. However, the

safetyarxiv-cs-lg

The Gradient That Top-k Distillation Never Computes

arXiv:2610.15589v1 Announce Type: new Abstract: Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a diverge

safetyarxiv-cs-lg

Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too

arXiv:2610.18516v1 Announce Type: new Abstract: Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and d

VLCQ: Post-training quantization for deep neural networks using variable length coding

arXiv:2610.15092v1 Announce Type: new Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not req

Local Ai

Flama: 8 years building a Python framework where ML models and LLMs are first-class, now written up on arXiv [P]

local-air-machinelearning

I started Flama in 2018 to take a trained model to a production API with as little ceremony as possible. With 2.0 LLMs became first-class alongside classic models, and together with Miguel Durán-Olive

13 Oct 2026

From Earth to Orbit, France’s Ecosystem Delivers Agentic Computing at Scale With NVIDIA

Companies in France are deploying agentic computing across industries spanning space, financial services, the public sector and scientific research, demonstrating how the country’s ecosystem has moved

hardwarenvidia-blog

13 Oct 2026

Local Ai

Want to use @ollama Cloud in a Sprite? We got you. You can now access Ollama Cloud through our Sprites Connector. https://ollama.com/blog/fl…

Ollama has released a Sprites Connector that lets users access Ollama Cloud directly from Fly‑io sprites, enabling deployment and management of AI models within those lightweight containers. The integ

local-aiollama--x

13 Oct 2026

10 Years of the SRE book: Announcing the second edition of 'Site Reliability Engineering”

model-releasesgoogle-cloud-ai

A decade ago, Google released Site Reliability Engineering: How Google Runs Production Systems with O'Reilly Media. What began as a collection of our practices for managing large-scale infrastructure

12 Oct 2026

Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans