Search: “deployment”

BeLink: Biomedical Entity Linking Meets Generative Re-Ranking

arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy

applicationsarxiv-cs-cl

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Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems

DGX agent

arXiv:2605.22001v1 Announce Type: cross Abstract: Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives.

model-releasesarxiv-cs-cl

22 May 2026

Tutorials

Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions

arXiv:2605.22697v1 Announce Type: new Abstract: Robustness to domain changes is a key capability for effective deployment of human action recognition systems in real-world scenarios, where action cate

tutorialsarxiv-cs-cv

Hardware

Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins

arXiv:2605.21493v1 Announce Type: cross Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely

hardwarearxiv-cs-cv

FlyRoute: Self-Evolving Agent Profiling via Data Flywheel for Adaptive Task Routing

arXiv:2605.22057v1 Announce Type: new Abstract: Enterprise routers assign queries to expert agents, yet deployed profiles stay static while agents evolve (prompts, tools, models), and developers rarel

safetyarxiv-cs-cl

HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine

arXiv:2605.21496v1 Announce Type: cross Abstract: Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmar

Improving Viewpoint-Invariance and Temporal Consistency for Action Detection

arXiv:2605.22695v1 Announce Type: new Abstract: Viewpoint change invariance and action temporal consistency are critical aspects for the effective deployment of human action detection of untrimmed vid

researcharxiv-cs-cv

InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models

arXiv:2602.23200v2 Announce Type: replace-cross Abstract: When transformer-based language models are deployed for text generation, most of the inference time is spent in the decoding stage, where outp

LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications

arXiv:2603.27355v2 Announce Type: replace-cross Abstract: We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow. The system combines aut

Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light

arXiv:2605.22455v1 Announce Type: new Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven

safetyarxiv-cs-cv

Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook

arXiv:2605.20442v1 Announce Type: cross Abstract: Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, l

safetyarxiv-cs-ai

Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment

arXiv:2605.21401v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that make sequences of decisions over extended interactions in high-stakes

safetyarxiv-cs-ai

Open-World Evaluations for Measuring Frontier AI Capabilities

arXiv:2605.20520v1 Announce Type: new Abstract: Benchmark-based evaluation remains important for tracking frontier AI progress. But it can both overstate and understate deployed capability because it

Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?

model-releasesarxiv-cs-cv

arXiv:2605.22109v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchm

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks

arXiv:2605.22351v1 Announce Type: new Abstract: Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited

safetyarxiv-cs-cv

Agents

Stdlib or Third-Party? Empirical Performance and Correctness of LLM-Assisted Zero-Dependency Python Libraries

arXiv:2605.21405v1 Announce Type: cross Abstract: Third-party Python libraries introduce dependency management overhead, supply chain risk, and deployment friction in constrained environments. A natur

agentsarxiv-cs-ai

Training-Trajectory-Aware Token Selection

arXiv:2601.10348v2 Announce Type: replace Abstract: Efficient distillation is a key pathway for converting expensive reasoning capability into deployable efficiency, yet in the frontier regime where t

Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection

arXiv:2605.21977v1 Announce Type: new Abstract: AI-generated content (AIGC) is rapidly improving, creating an urgent need for detectors that generalize across data sources, deployment pipelines, and v

researcharxiv-cs-cv

Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction

arXiv:2605.21109v1 Announce Type: new Abstract: Reliable confidence estimates are important for safely deploying vision-based controllers in autonomous racing, where safety predictions must be derived

safetyarxiv-cs-ro

Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity

arXiv:2605.20269v1 Announce Type: new Abstract: Many bandit deployments (recommendation, clinical dosing, ad targeting) share two facts prior work handles only in isolation: rewards live on a low-dime

applicationsarxiv-cs-lg

DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions

model-releasesarxiv-cs-cv

arXiv:2605.20680v1 Announce Type: new Abstract: Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications. Practical deployments often involve low-light

Inference Time Policy Optimization for Offline RL with Differentiable World Models

arXiv:2603.22430v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without f

safetyarxiv-cs-lg

Agents

LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation

arXiv:2605.21007v1 Announce Type: new Abstract: Road segmentation is a fundamental perception task for autonomous driving and intelligent robotic systems, requiring both high accuracy and real-time in

agentsarxiv-cs-cv

Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs

arXiv:2605.20410v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in socially sensitive settings despite substantial documentation that they encode gender biases.

Memory-Efficient Partitioned DNN Inference on Resource-Constrained Android Crowds

model-releasesarxiv-cs-lg

arXiv:2605.20723v1 Announce Type: new Abstract: Deploying large deep neural networks on memory-constrained mobile devices is a central challenge in edge ML. While compression, pruning, and quantizatio

Mitigating Label Bias with Interpretable Rubric Embeddings

arXiv:2605.21455v1 Announce Type: new Abstract: Statistical decision algorithms are increasingly deployed in domains where ground-truth labels are hard to obtain, such as hiring, university admissions

safetyarxiv-cs-lg

OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization

arXiv:2605.20276v1 Announce Type: new Abstract: The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

arXiv:2605.20668v1 Announce Type: new Abstract: With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remai

Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search

arXiv:2605.21322v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without centralizing data. However, real-world deployments must simultaneously address stat

Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization

arXiv:2605.20295v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on mobile devices, where Neural Processing Units (NPUs) necessitate fully static quantization for

Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis

arXiv:2605.20277v1 Announce Type: new Abstract: Medical vision-language models (VLMs) have rapidly advanced as general-purpose multimodal assistants, yet their deployment in 3D Computed Tomography (CT

safetyarxiv-cs-cv

Retrospective Sparse Attention for Efficient Long-Context Generation

arXiv:2508.09001v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, i

researcharxiv-cs-cl

SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation

model-releasesarxiv-cs-lg

arXiv:2605.20189v1 Announce Type: cross Abstract: Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying in dynamic, real-world settings with prima

Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning

model-releasesarxiv-cs-lg

arXiv:2605.20803v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Re

CaptchaMind: Training CAPTCHA Solvers via Reinforcement Learning with Explicit Reasoning Supervision

arXiv:2605.19538v1 Announce Type: cross Abstract: CAPTCHAs are widely deployed as human verification mechanisms and frequently block intelligent agents from completing end-to-end automation in real-wo

Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation

arXiv:2605.19490v1 Announce Type: cross Abstract: Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based t

applicationsarxiv-cs-cv

Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds

arXiv:2605.18827v1 Announce Type: cross Abstract: Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely

Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers

arXiv:2603.29382v2 Announce Type: replace-cross Abstract: Lightweight cryptographic primitives are widely deployed in resource-constrained environments, particularly in Internet of Things (IoT) device

researcharxiv-cs-lg

Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South

arXiv:2605.19190v1 Announce Type: cross Abstract: Despite the global deployment of text-to-image (T2I) models, their safety frameworks are largely calibrated to a Western-centric default, creating sig

local-aiarxiv-cs-ai

GRASP: Deterministic argument ranking in interaction graphs

arXiv:2605.19141v1 Announce Type: cross Abstract: Large language models are increasingly deployed as automated judges to evaluate the strength of arguments. As this role expands, their legitimacy depe

local-aiarxiv-cs-ai

Learning Efficient Guardrails for Compliance

arXiv:2510.03485v2 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically undere

Tutorials

Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization

arXiv:2605.19113v1 Announce Type: cross Abstract: Many clinical risk scores are deployed as additive rules with nonnegative integer points assigned to relevant binary predictive features. These intege

tutorialsarxiv-cs-lg

Lost in Interpretation: The Plausibility-Faithfulness Trade-off in Cross-Lingual Explanations

arXiv:2605.19274v1 Announce Type: new Abstract: LLMs deployed multilingually are often audited via English explanations for non-English inputs. We evaluate extractive explanations ''where the model id

researcharxiv-cs-cl

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs

model-releasesarxiv-cs-cv

arXiv:2511.14159v2 Announce Type: replace Abstract: Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-wo

Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

arXiv:2512.01152v4 Announce Type: replace-cross Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. S

applicationsarxiv-cs-ai

PhyWorld: Physics-Faithful World Model for Video Generation

arXiv:2605.19242v1 Announce Type: cross Abstract: World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation mode

Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings

arXiv:2605.19207v1 Announce Type: cross Abstract: Deep learning models have shown strong performance in medical image analysis, but deploying them in low-resource clinical environments remains difficu

applicationsarxiv-cs-ai

Sampling-Based Safe Reinforcement Learning

arXiv:2605.19469v1 Announce Type: cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sa

safetyarxiv-cs-ai