Search: “deployment”

Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

arXiv:2609.03420v1 Announce Type: cross Abstract: Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its deploy

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Representational alignment yields generalizable safety in language models

DGX agent

arXiv:2609.04022v1 Announce Type: cross Abstract: Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet mode

model-releasesarxiv-cs-ai

4 Sept 2026

RL-ADA: A World-Feedback Framework for Adversarially Robust Enterprise Dialogue Agents

arXiv:2609.02902v1 Announce Type: new Abstract: Deploying task-oriented dialogue agents in enterprise customer support faces a persistent annotation bottleneck: robust training requires labelled inter

agentsarxiv-cs-cl

SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data

arXiv:2606.16276v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion o

The Natural Language Interaction Protocol and Standard for AI Agents

arXiv:2609.04135v1 Announce Type: new Abstract: AI agents are increasingly being developed and deployed across organizations using heterogeneous agent-development frameworks, AI models, tool interface

local-aiarxiv-cs-ai

Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment

arXiv:2601.04736v2 Announce Type: replace Abstract: Despite remarkable capability in multi-modal understanding, deploying Multi-modal Large Language Models (MLLMs) in open-ended conversational scenari

safetyarxiv-cs-cl

Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations

model-releasesarxiv-cs-lg

arXiv:2609.02928v1 Announce Type: cross Abstract: Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings such as ad-auctions, traffic routing, and r

When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents

arXiv:2609.03467v1 Announce Type: cross Abstract: Large language models (LLMs) are increas- ingly deployed as long-horizon conversational agents, motivating growing interest in mem- ory systems. Howev

You Can't Escape Your Own Activations : Evaluation Awareness and Multi-Agent Monitoring

arXiv:2609.03035v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in multi-agent systems, where they can collude while keeping their actions benign. Output monitors designed to de

agentsarxiv-cs-lg

A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models

arXiv:2609.02054v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in interactive systems where understanding user intent precisely is paramount. A key capability f

Agents That Model Agents: Five Principles Toward a Theory of Mind for 6G Networks

arXiv:2609.01779v1 Announce Type: cross Abstract: Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inte

Competitive Market Behavior of LLMs

arXiv:2609.02580v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participatin

Context Inference Attacks Without Jailbreaks

arXiv:2609.01663v1 Announce Type: cross Abstract: Agentic AI systems are increasingly deployed to process sensitive data at inference time, such as healthcare records or financial documents assembled

agentsarxiv-cs-lg

Debias-SparseGPT: Bias-Aware Pruning for Large Language Models

arXiv:2609.02496v1 Announce Type: new Abstract: Model compression techniques such as pruning and quantization facilitate the efficient deployment and acceleration of Large Language Models (LLMs). Howe

safetyarxiv-cs-cl

DESA-TTA: Dynamic EMA and Source Anchoring for Test-Time Adaptation

arXiv:2609.01795v1 Announce Type: new Abstract: Vision-language object detectors (VLODs) achieve strong zero-shot performance but remain vulnerable to distribution shifts during deployment. Mean-teach

Research

Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification

arXiv:2609.02028v1 Announce Type: new Abstract: Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing det

researcharxiv-cs-cv

DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation

arXiv:2609.02075v1 Announce Type: new Abstract: Industrial anomaly detection benefits from anomaly samples, yet newly deployed products typically provide only normal images, making anomaly samples dif

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

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

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

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

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

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

Research

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

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

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

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

Research

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