Search: “deployment”

EvalDetectBench: A Benchmark for Measuring Evaluation Awareness in Frontier Language Models

arXiv:2609.01611v1 Announce Type: new Abstract: Frontier large language models can often recognize when they are being evaluated, a capability known as evaluation awareness. If models behave different

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From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

DGX agent

arXiv:2604.02198v2 Announce Type: replace Abstract: While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as av

model-releasesarxiv-cs-ai

3 Sept 2026

Hardware

From Multi-Fisheye Sensing to Panoramic Perception: A Parallax-Aware Onboard Platform for Ultra-Low-Altitude UAVs

arXiv:2609.02319v1 Announce Type: new Abstract: Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and other obstacles. We present a parallax-aware

hardwarearxiv-cs-ro

Applications

Generating Medical Image Counterfactuals using Causal Explanations

arXiv:2609.02697v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by l

applicationsarxiv-cs-cv

Google named a Leader in 2026 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services

model-releasesgoogle-cloud-ai

For the ninth consecutive year, Gartner® has named Google a Leader in the Gartner Magic Quadrant™ for Strategic Cloud Platform Services, positioned furthest for Completeness of Vision. We believe this

H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

model-releasesarxiv-cs-lg

arXiv:2609.02684v1 Announce Type: new Abstract: Deploying 3D point cloud models on edge hardware such as the NVIDIA Jetson Orin Nano is severely constrained by compute and memory budgets. Existing com

Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis

arXiv:2609.02219v1 Announce Type: cross Abstract: Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light conditions, limited onboard compute, and r

safetyarxiv-cs-cv

Local Ai

Learning to Track from Privileged Target Appearances

arXiv:2609.02471v1 Announce Type: new Abstract: Target templates define what a visual tracker searches for, yet the templates available at inference trade off localization certainty with appearance fr

local-aiarxiv-cs-cv

Local Ai

MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs

arXiv:2609.02109v1 Announce Type: cross Abstract: Deploying heterogeneous AI models concurrently on a shared GPU introduces resource contention that complicates runtime scheduling. While surrogate mod

local-aiarxiv-cs-ai

On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers

arXiv:2609.01947v1 Announce Type: cross Abstract: Compact instruction-following rerankers are attractive for deployment, but conventional distillation pipelines typically train students by offline imi

Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture

arXiv:2609.01667v1 Announce Type: cross Abstract: Artificial Intelligence has shown to help improve agricultural practices, yet adoption remains limited: 69% of U.S. farmers have privacy concerns with

researcharxiv-cs-lg

Probing Cultural Signals in Large Language Models through Author Profiling

arXiv:2603.16749v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode.

Applications

RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection

arXiv:2609.02497v1 Announce Type: cross Abstract: Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-d

applicationsarxiv-cs-ai

Scaling agentic AI pilots across the enterprise

As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across

agentsmit-tech-review

Signal or Noise? Auditing Rotation-Induced Saliency Drift in Medical and Aerial Imaging

arXiv:2609.02224v1 Announce Type: cross Abstract: Post-hoc saliency maps such as Grad-CAM are increasingly used to audit why a deployed vision model made a decision, yet the heatmap drifts when the in

Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control

model-releasesarxiv-cs-lg

arXiv:2609.01676v1 Announce Type: new Abstract: Reinforcement learning achieves strong traffic signal control performance in simulation, yet policies trained in simulators often fail once deployed in

SocialMaze: A Benchmark for Evaluating and Enhancing Social Reasoning in Large Language Models in Complex Social Environments

arXiv:2505.23713v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in socially grounded applications, where success requires interpreting context, inferring oth

Video-Based Palm-Vein Authentication under Challenging Conditions

model-releasesarxiv-cs-cv

arXiv:2609.02776v1 Announce Type: new Abstract: Palm-vein biometrics are increasingly used for secure, contactless authentication. Yet real-world deployment exposes them to surface noise (sweat, dirt)

AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes

arXiv:2609.00052v1 Announce Type: cross Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to

Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation

arXiv:2609.01604v1 Announce Type: new Abstract: LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the intern

Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning

arXiv:2609.00351v1 Announce Type: cross Abstract: Large language models can hide hidden behaviors that activate only under narrow conditions, such as backdoor triggers, sleeper-agent deployment cues,

agentsarxiv-cs-ai

Local Ai

Enoki: Efficient Multi-Level Hallucination Detection

arXiv:2609.00581v1 Announce Type: new Abstract: Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a singl

local-aiarxiv-cs-cl

Exploring Collaboration between a language and a non-language agent

arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in man

FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data

arXiv:2508.04698v2 Announce Type: replace Abstract: LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Rece

HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?

arXiv:2609.01437v1 Announce Type: cross Abstract: As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly

HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

arXiv:2609.00450v1 Announce Type: cross Abstract: Block Quantization (BQ) is a promising approach for efficient deployment of large language models (LLMs), enabling low-precision computation with cont

Huskeys, which uses agentic AI to help companies block AI-driven attacks, raised a 27M Series A led by Blackstone Innovations Investments at a 100M+ valuation (Maria Armental/Wall Street Journal)

Maria Armental / Wall Street Journal: Huskeys, which uses agentic AI to help companies block AI-driven attacks, raised a 27M Series A led by Blackstone Innovations Investments at a 100M+ valuation — T

agentstechmeme

Incremental Risk Assessment of Progressive Elder Financial Scams via Instruction-Tuned Small Language Models

arXiv:2609.00005v1 Announce Type: new Abstract: Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple c

MeshSplatBench: A Unified Benchmark for Triangle-Based Neural Rendering

model-releasesarxiv-cs-cv

arXiv:2609.01306v1 Announce Type: cross Abstract: Triangle-based neural rendering bridges neural scene representations and conventional graphics pipelines by optimizing explicit geometric primitives c

Neurosymbolics for Data Engineering: Achieving Long Context Token Reduction Without Finetuning

arXiv:2609.00367v1 Announce Type: cross Abstract: Large Language Models are increasingly deployed for sophisticated data engineering tasks such as generating structured queries from natural language,

researcharxiv-cs-ai

Obstacle-Aware Autonomous Coverage and Navigation for Outdoor Robots

arXiv:2609.01384v1 Announce Type: new Abstract: Long-duration outdoor coverage with autonomous platforms remains challenging beyond classical planning: deployments face localization drift in open spac

agentsarxiv-cs-ro

PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance

arXiv:2609.01188v1 Announce Type: new Abstract: Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer servic

Probabilistic Model Checking of Autoregressive Neural Sequence Models

arXiv:2609.00838v1 Announce Type: cross Abstract: Test-set accuracy is silent on two issues that matter when deploying autoregressive neural sequence models: how much probability mass the system under

researcharxiv-cs-ai

REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent

arXiv:2609.00049v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ metho

Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers

arXiv:2609.01567v1 Announce Type: new Abstract: Vision-Language Models (VLMs) provide useful priors for interactive decision-making, but using them directly as policies is expensive and brittle: they

VoiceLongMemEval: Do Assistants Remember How You Sounded?

arXiv:2609.00570v1 Announce Type: new Abstract: With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long,

WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization

arXiv:2609.00284v1 Announce Type: cross Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traffi

Applications

Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

arXiv:2607.17883v2 Announce Type: replace-cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output tha

applicationsarxiv-cs-ai

A Degradation-Tolerance Benchmark for Camera-Only End-to-End Driving

model-releasesarxiv-cs-ro

arXiv:2608.29005v1 Announce Type: new Abstract: Camera-only end-to-end (E2E) driving models are nearing deployment, where the camera stream is degraded by blur, noise, low light, weather, frame loss,

AdaptAV: Continuous Adaption of Vision Models for Autonomous Vehicles Using Cloud-based Oracle

arXiv:2608.28673v1 Announce Type: new Abstract: Deploying vision perception models in autonomous vehicles requires that we prioritize inference speeds, resulting in a model with shallower architecture

agentsarxiv-cs-cv

ALTSTEER: Selective Safety Steering for Moving Beyond Hard Refusals to Constructive Alternatives

arXiv:2608.30197v1 Announce Type: new Abstract: Safety alignment is essential for deploying large language models, requiring systems to prevent harmful compliance while preserving helpfulness on benig

Beyond Fluency: A Rubric-Based Benchmark for Evaluating Saudi Dialect and Cultural Competence in Large Language Models

arXiv:2608.29990v1 Announce Type: cross Abstract: Large language models are increasingly deployed in Arabic-speaking markets, yet standard benchmarks overwhelmingly reward Modern Standard Arabic (MSA)

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models

arXiv:2510.03136v2 Announce Type: replace Abstract: Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large La

safetyarxiv-cs-cl

BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs

arXiv:2608.30646v1 Announce Type: cross Abstract: Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critic

Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects

arXiv:2608.30076v1 Announce Type: new Abstract: Single-GPU deployment of 70B-parameter language models on an NVIDIA GPU is constrained by device memory, long-context throughput, and engineering integr

Capability-Stratified Degradation in Ternary Language Models

arXiv:2608.28809v1 Announce Type: new Abstract: Extreme low-bit inference offers a route toward smaller models and constrained deployment. Ternary language models restrict weights to {-1,0,+1}, approa

researcharxiv-cs-ai

CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents

arXiv:2608.30147v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long-horizon, interactive, and stateful environments. In these settings, a single wrong a

safetyarxiv-cs-cl

Defining Operational Conditions for Safety-Critical AI-Based Systems from Data

arXiv:2601.22118v3 Announce Type: replace Abstract: Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in