Search: “deployment”

Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution

arXiv:2609.35855v1 Announce Type: cross Abstract: Optimizing deployed AI systems increasingly amounts to editing prompts, skills, harnesses, and code rather than model weights. Existing approaches com

researcharxiv-cs-ai

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Local Ai

Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging

DGX agent

arXiv:2609.38090v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-c

local-aiarxiv-cs-lg

30 Sept 2026

One Threshold Does Not Fit All Languages: Language-Conditional Deferral for Reliable and Efficient Low-Resource Text Classification

arXiv:2609.37861v1 Announce Type: new Abstract: In the Global South, the lower-income countries of Africa, Asia, and Latin America where most of the world's languages are spoken, a deployed text class

researcharxiv-cs-cl

OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization

model-releasesarxiv-cs-ai

arXiv:2609.37221v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamical

OTROPE: Optimal Transport-based Robust Off-policy Evaluation for Large Language Models

arXiv:2609.36264v1 Announce Type: new Abstract: Reliable evaluation of large language models (LLMs) is essential for their development and deployment, yet is often costly, risky, and difficult to perf

Local Ai

PreferenceFlow: Test-Time Guidance of Flow-Matching Robot Policies from Human Interventions

arXiv:2609.36872v1 Announce Type: new Abstract: Flow-matching policies can represent complex robot behaviors but remain susceptible to local errors under distribution shift at deployment. Many reinfor

local-aiarxiv-cs-ro

Preferent Compression Bounds Are Tight

arXiv:2609.36030v1 Announce Type: new Abstract: The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods. Sample compression

Safe-by-Design Learning via Energy-based Neural Networks

arXiv:2609.36942v1 Announce Type: cross Abstract: Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settin

Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination

model-releasesarxiv-cs-ai

arXiv:2609.37666v1 Announce Type: cross Abstract: Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground robots. However, conventional coverage methods typical

Skill-Space Shooting for Autonomous Robot Policy Improvement

arXiv:2609.38178v1 Announce Type: cross Abstract: Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this im

Agents

SoftSkill: Behavioral Compression for Contextual Adaptation

arXiv:2606.20333v2 Announce Type: replace Abstract: Natural-language skills let agents reuse task knowledge, yet deploying a long Markdown document makes the model interpret that knowledge anew on eve

agentsarxiv-cs-ai

Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs

model-releasesarxiv-cs-ai

arXiv:2604.03114v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy

ThinkingGuard: Decoding Implicit Hazards via Step-by-Step Risk Attribution in Multimodal Large Language Models

arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal imp

Agents

Training LLMs to Verbalize Evaluation Awareness

arXiv:2609.36316v1 Announce Type: cross Abstract: Evaluation awareness (EA) can cause large language models (LLMs) to behave differently during audits than in deployment, yet measuring and accounting

agentsarxiv-cs-ai

VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness

arXiv:2609.36870v1 Announce Type: new Abstract: Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rel

Local Ai

VLALight: A Vision-Language-Action Model for Traffic Signal Control

arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signali

local-aiarxiv-cs-ai

WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control

arXiv:2609.37922v1 Announce Type: new Abstract: Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlen

WISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation

arXiv:2609.37687v1 Announce Type: new Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying

researcharxiv-cs-ai

Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression

arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD-

A Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset

arXiv:2609.31677v1 Announce Type: new Abstract: Face recognition is widely deployed in surveillance, access control, and forensic workflows, yet accuracy degrades sharply once the face is occluded by

Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration

arXiv:2609.35415v1 Announce Type: cross Abstract: Frozen adaptive cruise control (ACC) policies can violate constraints when deployment dynamics differ from their training conditions. We propose resid

safetyarxiv-cs-cv

AI-driven Dispensing of Coral Reseeding Devices for Broad-scale Restoration of the Great Barrier Reef

arXiv:2509.01019v3 Announce Type: replace Abstract: Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral specie

Are Vision-Language-Action Models Robust to One-Step Observation Perturbations?

arXiv:2609.32550v1 Announce Type: cross Abstract: Understanding the safety risks of vision-language-action (VLA) models is essential for their deployment in the physical world. Existing safety researc

Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions

arXiv:2609.35569v1 Announce Type: cross Abstract: Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet

Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models

arXiv:2609.34765v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large vision-language models (LVLMs), but is typically calibrated on a small set while

safetyarxiv-cs-cv

CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering

arXiv:2609.34302v1 Announce Type: new Abstract: Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires

CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets

arXiv:2609.33310v1 Announce Type: new Abstract: Whole-body compliant control is essential for deploying heavy humanoids under high payload in human-centric environments. Most prior force-aware learnin

Elastic Selective Spectral Hybrids for Train-Once, Export-Many Budgeted Inference

arXiv:2609.32486v1 Announce Type: new Abstract: Deploying a language model under different computing and latency budgets calls for compact models with different quality-cost trade-offs. Towards this e

EntroPack: Fast and Accurate Entropy-Coded Weight Compression at Arbitrary Bitrates

arXiv:2609.34185v1 Announce Type: new Abstract: Weight compression helps large neural networks fit deployment memory budgets, but common fixed-width formats offer only coarse storage choices. Entropy

Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control

arXiv:2609.34018v1 Announce Type: cross Abstract: Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires

Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models

arXiv:2609.32069v1 Announce Type: new Abstract: Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve fr

FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents

arXiv:2609.34358v1 Announce Type: new Abstract: In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only thr

How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models

arXiv:2609.34514v1 Announce Type: cross Abstract: As large language models (LLMs) become increasingly widespread, preventing unsafe responses to harmful prompts is essential for their safe deployment.

Local Ai

Information Design Against Gaming and Learning Adversaries

arXiv:2609.31643v1 Announce Type: new Abstract: A principal who deploys a binary classifier with an abstention option must decide which queries the mechanism abstains on. The right choice depends on t

local-aiarxiv-cs-lg

Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation

arXiv:2609.35335v1 Announce Type: new Abstract: Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We inves

Last-Iterate Guarantees for Online Reinforcement Learning in Structured Constrained MDPs

arXiv:2609.32933v1 Announce Type: new Abstract: In safety-critical applications, deployment uses a single policy, whose performance and constraint satisfaction should hold directly rather than only fo

Applications

Learning to Generate Rigid Body Interactions with Video Diffusion Models

arXiv:2510.02284v4 Announce Type: replace Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond thei

applicationsarxiv-cs-cv

Less Sycophancy, Stronger Refusal? Lessons for AI Safety from Mechanistic Interpretability

arXiv:2609.35544v1 Announce Type: cross Abstract: Reliable refusal of harmful requests is essential to the safe deployment of language models. Because excessive eagerness to please users may undermine

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

arXiv:2609.31960v1 Announce Type: new Abstract: Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to c

Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction

arXiv:2609.34486v1 Announce Type: new Abstract: Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-base

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks

arXiv:2606.12876v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively

OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit

arXiv:2609.31631v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language models but face critical deployment challenges due to massive memory requirem

One Evaluation, Any Operating Point: Hypernetwork-Amortized MeanFlow for 3D MRI Reconstruction

arXiv:2609.31655v1 Announce Type: new Abstract: Generative priors reconstruct accelerated 3D MRI well but pay heavily at deployment: tens of network evaluations per volume, and protocol-specific hyper

Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks

arXiv:2609.32832v1 Announce Type: new Abstract: Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting

Pay for Hints, Not Answers: LLM Shepherding for Cost-Efficient Inference

arXiv:2601.22132v2 Announce Type: replace Abstract: Large Language Models (LLMs) deliver state-of-the-art performance on complex reasoning tasks, but their inference costs limit deployment at scale. S

Applications

Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling

arXiv:2603.18095v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is a practical path to deploy large diffusion models, but quantization noise can accumulate over the denoising traj

applicationsarxiv-cs-cv

Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation

model-releasesarxiv-cs-cv

arXiv:2609.33097v1 Announce Type: new Abstract: Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts a

Applications

RAEGL: Risk-Aware Evidence-Gated Learning for Selective Contextual Routing under Temporal Shift

arXiv:2609.33340v1 Announce Type: new Abstract: Contextual specialization can improve forecasting accuracy, but a correction selected on one historical interval may become unreliable under temporal di

applicationsarxiv-cs-lg