Search: “deployment”

Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap

arXiv:2609.16071v1 Announce Type: cross Abstract: Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control un

researcharxiv-cs-ai

16 Sept 2026

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Research

Shared-Prefix KV Reuse Across Standard LoRA Adapters: Quality and Serving Tradeoffs

DGX agent

arXiv:2609.17109v1 Announce Type: new Abstract: A common small-model deployment runs one shared backbone with several LoRA specialists that answer over the same context. Serving them naively re-prefil

researcharxiv-cs-ai

16 Sept 2026

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

arXiv:2609.17515v1 Announce Type: new Abstract: Pruning can reduce the deployment cost of large language models (LLMs), but its impact on context-grounded tool calling remains poorly understood. We sy

researcharxiv-cs-cl

16 Sept 2026

A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces

arXiv:2609.13170v1 Announce Type: cross Abstract: Blockchain-enabled Federated Learning (FL) marketplaces enable collaborative model training among mutually distrustful participants through smart cont

researcharxiv-cs-lg

Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model

arXiv:2606.13633v2 Announce Type: replace-cross Abstract: Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framew

tutorialsarxiv-cs-lg

Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments

arXiv:2609.15352v1 Announce Type: new Abstract: Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expe

safetyarxiv-cs-ro

Beyond Noise: Understanding and Overcoming Temperature Effects in Analog DNN Inference

arXiv:2609.15527v1 Announce Type: new Abstract: The energy efficiency of analog computing makes it one of the most promising candidates for deploying resource-intensive machine learning workloads on c

researcharxiv-cs-lg

Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments t

agentsarxiv-cs-ai

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fee

agentsarxiv-cs-lg

Decentralized Multi-Robot Task Allocation Under Degraded Communication: A Benchmark of Performance, Reliability, and Computation

model-releasesarxiv-cs-ro

arXiv:2609.13711v1 Announce Type: new Abstract: Selecting a decentralized Multi-Robot Task Allocation (MRTA) method for embedded deployment on autonomous platforms requires considering more than route

Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length

arXiv:2609.14595v1 Announce Type: cross Abstract: Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce r

safetyarxiv-cs-ai

Local Ai

DynSTEER: Dynamic Stage-wise Trajectory Evaluation and Execution-time Review for Agents

arXiv:2609.14637v1 Announce Type: new Abstract: Large language model agents are increasingly deployed for long-horizon task execution. However, current evaluation paradigms face three major limitation

local-aiarxiv-cs-ai

FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks

arXiv:2609.13580v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-

ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation

arXiv:2609.13737v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed, the generation of harmful content has become a critical safety concern. Existing safeguards o

safetyarxiv-cs-cl

Fusing Spectral Signatures and Activation Clustering for Backdoor Detection in Healthcare Imaging Models: Method, Implementation, and Evaluation

arXiv:2609.14290v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in healthcare imaging pipelines for diagnostic support, and training-time attacks against them are a

Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns

arXiv:2509.24988v2 Announce Type: replace-cross Abstract: Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains

tutorialsarxiv-cs-ai

Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision

arXiv:2609.13947v1 Announce Type: cross Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the log

researcharxiv-cs-ai

HGSQ: Heatmap-Guided Sparse Query Detector for Real-Time Aerial Small Object Detection

arXiv:2609.13306v1 Announce Type: new Abstract: Real-time aerial small object detection is an important visual signal and image processing problem, requiring a detector to preserve fine-grained locali

How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026

arXiv:2609.13679v1 Announce Type: new Abstract: How can robot policies learn more effectively from a fixed demonstration budget? The first Real-world Embodied AI Learning (REAL-I) Challenge at ICRA 20

tutorialsarxiv-cs-ro

Applications

Lightweight Generalized DeepFake Face Detection with WAVIE: Wavelet Augmented Vision Intermediate Embeddings

arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability

applicationsarxiv-cs-ai

MessyMem: Learning-from-Doing Memory for Mobile Manipulation

arXiv:2609.15976v1 Announce Type: new Abstract: Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an objec

tutorialsarxiv-cs-ro

Multimodal Foundation Models Adaptation based on Domain-Aware Relaxed Orthogonal Subspace for Remote Sensing

model-releasesarxiv-cs-cv

arXiv:2609.13654v1 Announce Type: new Abstract: Pretrained foundation models (FMs) have achieved remarkable success in computer vision, yet their high fine-tuning cost limits practical deployment. Par

OrchSLM: Probing the Dynamics of Small Language Model Orchestration

arXiv:2609.13470v1 Announce Type: new Abstract: Although large language models (LLMs) have demonstrated remarkable capabilities, their reliance on cloud-scale infrastructure poses fundamental challeng

agentsarxiv-cs-ai

Overflip: Repetition-Induced Label Flips in Guardrail Models

arXiv:2609.15013v1 Announce Type: new Abstract: Guardrail models are classifiers deployed to screen malicious prompts and responses in LLM-based services. To meet latency constraints, many lightweight

Parameter isolation with domain-specific experts for incremental audio classification

arXiv:2609.14730v1 Announce Type: cross Abstract: To successfully deploy a model in time-varying environments such as streaming data prediction and sensing control, domain-incremental learning (DIL) h

PC^2-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devices

arXiv:2609.14722v1 Announce Type: new Abstract: Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data.

PolicyMem: Geometric Policy Memory for LLM Governance

arXiv:2609.13734v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in real-world high-stakes applications, effective governance has become essential. Existing

safetyarxiv-cs-ai

Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo

arXiv:2507.19738v2 Announce Type: replace Abstract: While accurate LiDAR depth has been shown to improve stereo matching, high-end LiDAR remains costly and difficult to deploy at scale, motivating gui

tutorialsarxiv-cs-cv

Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection

model-releasesarxiv-cs-cv

arXiv:2609.14025v1 Announce Type: new Abstract: Forward-looking sonar object detection is essential for underwater perception, yet deployment on embedded platforms requires highly compact models. To a

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

arXiv:2609.13463v1 Announce Type: new Abstract: The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reli

Hardware

Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE

NVIDIA FLARE separates persistent federation services from dynamically launched job workers, allowing each participating site to deploy federated learning jobs on Docker, Kubernetes, or Slurm without

hardwarenvidia-developer

Self-Orchestrating Language Models: Leveraging Semantic Dependence for Efficient Inference

arXiv:2609.14850v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate impressive capabilities, but their deployment presents significant efficiency challenges. Autoregressive decodi

researcharxiv-cs-ai

Semantic Fibers and Cross-Gram Interference: A Calculus of Safety Drift in Overcomplete Representations

arXiv:2609.14861v1 Announce Type: cross Abstract: A deployed language model may refuse a harmful request in English yet comply with its faithful translation, revealing a cross-lingual safety failure t

safetyarxiv-cs-ai

Specifying Reward Functions for RL Without Environment Sampling

arXiv:2609.15544v1 Announce Type: cross Abstract: Enabling human stakeholders to specify reward functions that lead to their desired outcomes is a key challenge in deploying reinforcement learning age

agentsarxiv-cs-ai

SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing

arXiv:2609.15039v1 Announce Type: cross Abstract: User prompts provided to large language models (LLMs) may contain sensitive or private information that can be misused by remotely deployed models, su

The Router Within: Eliciting Native Skill Routing from a Frozen LLM

arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by prel

URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretraining

model-releasesarxiv-cs-cl

arXiv:2609.13899v1 Announce Type: cross Abstract: The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora,

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

arXiv:2609.13148v1 Announce Type: cross Abstract: Large language models are increasingly deployed as synthetic consumer panels, promising 97% cost reductions over traditional surveys. Yet aggregate va

When does a scaling result justify a different allocation? A critical review of resource-allocation evidence for AI systems

arXiv:2609.14500v1 Announce Type: new Abstract: AI scaling studies increasingly evaluate systems that combine a pretrained model with retrieval, search, verification, tools, and interaction. Yet a hig

When Tools Get in the Way: The Effect of Unnecessary Tool Availability on LLM Answering

arXiv:2609.14157v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed with external tools that extend what they can do beyond their own knowledge. Tools help on tasks

researcharxiv-cs-cl

X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control

arXiv:2609.15213v1 Announce Type: new Abstract: Scaling humanoid whole-body control toward general-purpose deployment requires large human motion corpora and training experience shared across robot bo

safetyarxiv-cs-ro

Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge

arXiv:2609.11939v1 Announce Type: cross Abstract: Traditional TinyML systems for edge devices achieve high accuracy by relying on fixed-depth models that require a constant number of multiply-accumula

Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning

arXiv:2609.12264v1 Announce Type: new Abstract: Designing effective chemotherapy regimens is hindered by tumor heterogeneity and drug resistance, which complicate the deployment of patient-specific mo

Hardware

Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference

arXiv:2511.15015v4 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying Mo

hardwarearxiv-cs-lg

Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning

arXiv:2609.11937v1 Announce Type: new Abstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of 'knowledge dominance,' where high-volume hubs natural

safetyarxiv-cs-lg

GRACE-DS: a Guarded Reward-guided Agent Correction Environment in Data Science

arXiv:2606.16000v3 Announce Type: replace Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agen

safetyarxiv-cs-cl

PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability

arXiv:2609.12225v1 Announce Type: cross Abstract: Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing O

Simulating Disengaged Students to Evaluate LLM-based Tutors

arXiv:2609.12331v1 Announce Type: new Abstract: Simulated students generated by computational models provide a practical way to evaluate tutoring strategies and pedagogical approaches used by human an