Search: “deployment”

Multi-AUV Marine Life Tracking with Single Hydrophone Payloads via a Hidden Markov Model Equipped Particle Filter

arXiv:2606.22335v1 Announce Type: new Abstract: Researchers tag and track marine animals to study migration patterns, human impacts on behavior, and behavioral shifts due to climate change. Accurate d

local-aiarxiv-cs-ro

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Safety

NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models

DGX agent

arXiv:2606.22537v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is essential for ensuring the robustness and reliability of object detection systems deployed in safety-critical app

safetyarxiv-cs-cv

23 Jun 2026

OFMU: Optimization-Driven Framework for Machine Unlearning

arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy

safetyarxiv-cs-lg

One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation

arXiv:2606.21415v1 Announce Type: new Abstract: Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain

safetyarxiv-cs-lg

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning

arXiv:2606.22878v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed at the network edge to provide pervasive generative AI services, decentralized federated learn

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents

arXiv:2606.20638v1 Announce Type: cross Abstract: Large language models are increasingly deployed as long-lived agents that must adapt across users, tasks, domains, modalities, and feedback regimes wi

local-aiarxiv-cs-lg

ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers

arXiv:2606.21947v1 Announce Type: new Abstract: Vision Transformers have achieved remarkable success in many fields, yet their deployment on edge devices remains challenging due to their substantial c

researcharxiv-cs-cv

Hardware

SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation

arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost

hardwarearxiv-cs-lg

SkillHarness: Harnessing Safe Skills for Computer-Use Agents

arXiv:2606.20636v1 Announce Type: cross Abstract: Computer-Use Agents (CUAs) are increasingly deployed in dynamic interactive environments, creating a growing need for continual skill learning during

safetyarxiv-cs-lg

SVD-Surgeon: Optimal Singular-Value Surgery for Large Language Model Compression

arXiv:2606.23568v1 Announce Type: new Abstract: Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their deployment is constrained by substantial memory and

Target-Aware Linear Regression Under Distribution Shift

arXiv:2606.22775v1 Announce Type: cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems. In many cases, the target marginals of covariates a

Topological summaries of fingerprint ridge patterns carry identity information

model-releasesarxiv-cs-cv

arXiv:2606.22029v1 Announce Type: new Abstract: Fingerprints are the most widely deployed biometric. Verifying whether two impressions come from the same finger typically relies on minutiae, small lan

Applications

Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach

arXiv:2606.20886v1 Announce Type: new Abstract: As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised

applicationsarxiv-cs-cv

Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Sampling

arXiv:2606.20878v1 Announce Type: new Abstract: Synthetic tabular data enables microdata sharing in regulated domains, yet deploying continuous-time generative models requires balancing analytical uti

researcharxiv-cs-lg

Agents

UniviewVLA: A Unified Multiview Vision-Language-Action Model with World Modeling

arXiv:2606.21501v1 Announce Type: new Abstract: Occluded tasks remain a bottleneck in robot manipulation. Existing solutions either deploy additional physical cameras requiring training-inference came

agentsarxiv-cs-ro

Wh0: Generative World Models as Scalable Sources of Egocentric Human Hand Manipulation Data

arXiv:2606.22136v1 Announce Type: new Abstract: Scaling dexterous manipulation requires generalization across objects, scenes, and tasks, yet existing data sources face a trade-off between scale and s

safetyarxiv-cs-ro

Agent Skill Evaluation and Evolution: Frameworks and Benchmarks

model-releasesarxiv-cs-cl

arXiv:2606.11435v1 Announce Type: new Abstract: The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evalua

AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable

arXiv:2606.11456v1 Announce Type: cross Abstract: The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may

Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

arXiv:2606.11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment. Chain-of-thought (CoT) reasoning is wi

Applications

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

arXiv:2606.12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attack

applicationsarxiv-cs-lg

Hardware

Characterizing Software Aging in GPU-Based LLM Serving Systems

arXiv:2606.11916v1 Announce Type: cross Abstract: This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric

hardwarearxiv-cs-ai

Agents

FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse

arXiv:2606.11290v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfyi

agentsarxiv-cs-ai

Tutorials

FOCUS: DLLMs Know How to Tame Their Compute Bound

arXiv:2601.23278v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high

tutorialsarxiv-cs-cl

Irresponsible AI: big tech's influence on AI research and associated impacts

arXiv:2512.03077v2 Announce Type: replace-cross Abstract: The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tec

OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments

arXiv:2606.11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study

arXiv:2606.12234v1 Announce Type: new Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved t

researcharxiv-cs-cl

On the Study of Biometric Spoofing Detection using Deep Learning

arXiv:2606.11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit

One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection

arXiv:2606.11202v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in domina

safetyarxiv-cs-cl

RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformers

model-releasesarxiv-cs-cv

arXiv:2512.14096v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) have achieved remarkable success in image generation, yet their deployment is hindered by high computational costs. We

Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Version

arXiv:2606.11399v1 Announce Type: new Abstract: Large Language Models (LLMs) are deployed across cultural contexts but often reflect homogenized values inherited from training data. Evaluations of cul

safetyarxiv-cs-cl

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

arXiv:2606.12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real d

To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending

arXiv:2606.11201v1 Announce Type: cross Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among

safetyarxiv-cs-ai

Towards Conditional Feature Alignment for Cross-Domain Counting

arXiv:2506.17137v3 Announce Type: replace Abstract: Object counting models often degrade under cross-domain deployment because density composition varies across domains and is itself task-relevant. St

safetyarxiv-cs-cv

An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks

arXiv:2606.10686v1 Announce Type: cross Abstract: The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach

Benchmarking Knowledge Editing using Logical Rules

arXiv:2606.10554v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge. However, retraining LLM

Can we trust our models? Epistemic calibration in second-order classification

arXiv:2606.10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings. However, classical calibration only assesses the relia

researcharxiv-cs-lg

Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark

model-releasesarxiv-cs-cl

arXiv:2606.10400v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed where answers must follow from what is in the image, yet they often answer from textual priors,

Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference

arXiv:2602.12542v2 Announce Type: replace-cross Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under

safetyarxiv-cs-ai

Agents

Goal-oriented Communication for Fast and Robust Robotic Fault Detection and Recovery

arXiv:2601.18765v2 Announce Type: replace Abstract: Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require lo

agentsarxiv-cs-ro

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers

arXiv:2606.09960v1 Announce Type: cross Abstract: We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedd

local-aiarxiv-cs-ai

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challen

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606.09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment. Existing methods rely predomi

local-aiarxiv-cs-ai

Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents

arXiv:2606.10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can ca

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606.10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as en

Locomotion analysis of a quadruped interacting with the lunar granular surface

arXiv:2606.10273v1 Announce Type: new Abstract: Deploying legged robots in extra-terrestrial environments includes many challenges due to complex terrain interactions, energy, and thermal constraints.

safetyarxiv-cs-ro

Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam?

arXiv:2606.10956v1 Announce Type: new Abstract: The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet their ability to navigate complex, professional-grade p

Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models

arXiv:2606.11074v1 Announce Type: cross Abstract: With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under c

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504.03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles. How