Search: “deployment”

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

arXiv:2610.02074v1 Announce Type: new Abstract: Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomo

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Research

How Divergence Becomes Decision Flips in Compressed Language Models

DGX agent

arXiv:2610.00694v1 Announce Type: cross Abstract: Compression reports summarize how far a compressed language model moved from the dense one, usually by a KL divergence; a deployment that relies on th

researcharxiv-cs-ai

2 Oct 2026

How Many Categories Are Enough? Distribution-Free Certification Limits for Few-Shot Anomaly Thresholds

arXiv:2610.00236v1 Announce Type: new Abstract: Few-shot anomaly detectors are judged by ranking metrics, yet deployment requires an alarm threshold with a controlled false-alarm rate (FAR). We ask ho

Local Ai

Localisation-Aware Uncertainty for Pretrained Object Detection

arXiv:2610.01409v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existin

local-aiarxiv-cs-cv

Hardware

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

arXiv:2606.22826v2 Announce Type: replace Abstract: Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a process

hardwarearxiv-cs-ai

MOVE: Multimodal Open-world Verification and Expansion for Graph Learning

arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Exi

Applications

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger

applicationsarxiv-cs-ai

Online Planning for Sparse Ground Target Search from a High-Altitude UAV under Partial Observability

arXiv:2610.01067v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) searching for ground targets from a high altitude face a unique challenge, particularly when the target is already withi

agentsarxiv-cs-ro

PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization

arXiv:2610.01807v1 Announce Type: new Abstract: Reliable clinical deployment of deep medical image models is hindered by distribution shifts across scanners, sites, and acquisition protocols. Existing

researcharxiv-cs-cv

ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

arXiv:2602.23653v3 Announce Type: replace-cross Abstract: Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution

Reachability Is Not Generalization: Understanding Verb--Noun Decomposition in Assembly Action Recognition

arXiv:2610.00064v1 Announce Type: new Abstract: Assembly actions are compositional: they combine a manipulation with a part or tool. In deployment, systems routinely encounter novel combinations of fa

researcharxiv-cs-cv

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

arXiv:2610.00558v1 Announce Type: new Abstract: While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive

ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality

arXiv:2610.00710v1 Announce Type: new Abstract: As large language model (LLM) agents become widely adopted, they are increasingly deployed for tasks that require persistent monitoring or recurring act

agentsarxiv-cs-ai

Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference

arXiv:2610.00586v1 Announce Type: cross Abstract: Activation memory, not compute, limits CNN inference on constrained hardware such as microcontrollers. Direct in-place convolution removes the dual-bu

researcharxiv-cs-cv

Robust Evidential Learning Through Latent Consistency

arXiv:2610.01384v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial i

Safety in Self-Evolving Agents: A Survey

arXiv:2610.00093v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

arXiv:2610.00332v1 Announce Type: cross Abstract: Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current a

Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening

arXiv:2610.01005v1 Announce Type: cross Abstract: As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairne

safetyarxiv-cs-lg

WBAG: A Whole-Body and Attached-Geometry Safety Framework for Vision-Language-Action Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.01083v1 Announce Type: new Abstract: Vision-language-action (VLA) policies have demonstrated impressive capabilities in generalizable robotic manipulation, but their deployment in the real

A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks

arXiv:2609.39058v1 Announce Type: cross Abstract: Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for programmable wireless environments in the beyond the fifth generation (

A Width-Matched Comparison of Hybrid Quantum-Classical Self-Supervised Learning for Fingerprint Recognition

model-releasesarxiv-cs-lg

arXiv:2609.39172v1 Announce Type: cross Abstract: Fingerprint recognition is a widely deployed biometric, but supervised training requires large labeled enrollment sets. Self-supervised learning (SSL)

An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer

arXiv:2503.10118v3 Announce Type: replace-cross Abstract: The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world syste

safetyarxiv-cs-lg

AVERT-VLN: Abstention-aware Visual Error Recovery and Training for Vision-and-Language Navigation

arXiv:2609.39579v1 Announce Type: new Abstract: Deploying vision-and-language navigation (VLN) agents in unseen environments remains challenging because unfamiliar layouts and visual conditions can ca

Hardware

Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors

Learn how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS). This post shows how NAT's memo

hardwareaws-ml-blog

CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

arXiv:2609.38810v1 Announce Type: cross Abstract: As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual sign

Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality

arXiv:2603.01209v3 Announce Type: replace Abstract: In CodeAct, language-model agents write Python that calls tools and use execution feedback to choose actions. Persistent runtimes preserve Python va

agentsarxiv-cs-ai

EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making

arXiv:2609.38334v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model

safetyarxiv-cs-cl

In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

arXiv:2609.39215v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity.

JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation

arXiv:2609.40103v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as automated judges for AI-generated content, yet a single judge is unreliable and even a panel o

agentsarxiv-cs-cl

Learning Normal Diffusion Dynamics for Backdoor Defense in Text-to-Image Models

arXiv:2609.39548v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to the secure deployment of text-to-image (T2I) diffusion models. Existing defenses typically detect backdoors

researcharxiv-cs-ai

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

model-releasesarxiv-cs-lg

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm, b

Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response

model-releasesarxiv-cs-cl

arXiv:2601.19918v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in

Multi-Link Safety Filtering for VLA Policies Around Moving Hazards

arXiv:2609.40007v1 Announce Type: cross Abstract: A vision-language-action (VLA) policy can finish a manipulation task while knocking over objects unrelated to it, so task success alone does not show

safetyarxiv-cs-cv

Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control

model-releasesarxiv-cs-ro

arXiv:2609.39594v1 Announce Type: new Abstract: As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

model-releasesarxiv-cs-cl

arXiv:2609.39346v1 Announce Type: new Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SL

OP-CAD: On-Policy Clean-Audio Distillation for Robust Audio-Visual Reasoning

arXiv:2609.39150v1 Announce Type: new Abstract: Omni-modal large language models deployed in real-world environments encounter external noise that can interfere with their perception and understanding

safetyarxiv-cs-cv

PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration

arXiv:2609.38458v1 Announce Type: new Abstract: Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Pri

Tutorials

Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although re

tutorialsarxiv-cs-ai

SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision

arXiv:2609.39679v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

arXiv:2609.35596v2 Announce Type: replace-cross Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their cont

Security Properties of Neural Networks as Decision Problems

model-releasesarxiv-cs-lg

arXiv:2609.39768v1 Announce Type: cross Abstract: Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a backdoo

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for saf

safetyarxiv-cs-cv

Speculative Safety Honeypot: Toward Proactive Defense Against Multi-turn Agent Attacks

arXiv:2609.39549v1 Announce Type: cross Abstract: As Large Language Model (LLM) agents are increasingly deployed in complex environments, multi-turn interaction attacks have become a significant secur

StateTree: Enhancing Long-Term Dialogue Reasoning via Reinforcement Learning

arXiv:2609.38809v1 Announce Type: cross Abstract: Large language models deployed as personalized assistants must reason over long, evolving interaction histories. However, in long-term dialogue reason

researcharxiv-cs-ai

The Backdrop Exposes What the World Around an Agent Costs It

arXiv:2609.38469v1 Announce Type: cross Abstract: Agent benchmarks test agents in worlds that stay still. Deployed agents work in worlds that other people also change. Someone texts the agent to send

Local Ai

Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languages

arXiv:2609.38574v1 Announce Type: new Abstract: Large language models are frequently proposed as a route to AI-powered services for African communities, but they are least reliable exactly where the n

local-aiarxiv-cs-ai

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory

arXiv:2606.19998v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed across diverse tasks, yet they remain black boxes whose physical interactions ca

What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

arXiv:2609.39232v1 Announce Type: cross Abstract: EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streamin

researcharxiv-cs-ai