Search: “deployment”

arXiv:2610.03402v1 Announce Type: new Abstract: To deploy deep neural networks on edge hardware, highly efficient inference schemes are necessary that retain high accuracy. This work presents W16A16,

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Model Releases

Designing the Future of User Feedback for Generative AI

DGX agent

arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and feature

model-releasesarxiv-cs-ai

5 Oct 2026

DNAlign: Dynamic Null-Space Safe Alignment for LLMs

arXiv:2610.02844v1 Announce Type: new Abstract: Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either i

From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale

arXiv:2609.35149v2 Announce Type: replace Abstract: Deploying, migrating, or scaling an agent can change its model, harness, infrastructure, application, and intended users. We formulate agent calibra

Hardware

Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts

arXiv:2610.02241v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures allow frontier language models to scale to trillions of parameters, but their deployment is constrained by mass

hardwarearxiv-cs-ai

Hardware

Lightweight and Resource-Efficient Perception for Robotic Guide Dogs

arXiv:2610.03187v1 Announce Type: cross Abstract: Multi-camera streaming perception is increasingly deployed on heterogeneous edge platforms shared with co-resident workloads, yet accelerator placemen

hardwarearxiv-cs-cv

Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift

model-releasesarxiv-cs-ro

arXiv:2610.03249v1 Announce Type: cross Abstract: Deploying a pretrained controller exposes it to conditions that are absent from its training data. Sensor drift, outright sensor failure and accumulat

Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard

arXiv:2610.00651v1 Announce Type: new Abstract: Agent evaluations are increasingly used to compare LLMs and inform deployment decisions, yet ranks can reflect not only the model but also the effects o

ALFRED: Requirement-driven development of an open-source mobile manipulator for long-term plant monitoring

model-releasesarxiv-cs-cv

arXiv:2610.01477v1 Announce Type: cross Abstract: Tracking seasonal change in crops and forests requires observing the same plants repeatedly. Ground robots can do this at close range, and a manipulat

Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap

arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type,

safetyarxiv-cs-cv

HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control

arXiv:2610.00198v1 Announce Type: new Abstract: Recent advances in motion generation and whole-body tracking have enabled humanoid robots to execute increasingly diverse motions, yet the same motion c

researcharxiv-cs-ro

MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs

arXiv:2610.01058v1 Announce Type: cross Abstract: Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization wea

System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer

arXiv:2610.00253v1 Announce Type: cross Abstract: Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile descr

Tokenized Key-Gated Adapter Routing: A Secure Access Control Mechanism Against Private Data Leakage in LLMs

arXiv:2610.00309v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-critical domains (e.g., healthcare, finance, and government), but their propensity t

A Generalisation Signal Need Not Be a Model-Selection Signal

arXiv:2609.39099v1 Announce Type: cross Abstract: Model selection in computational biology often relies on validation data drawn from the training regime, even when deployment lies outside it. When va

researcharxiv-cs-ai

Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners

arXiv:2609.40208v1 Announce Type: new Abstract: Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challe

researcharxiv-cs-ro

It Takes Little to Rewrite Perception: Targeted Semantic Substitution in Vision-Language Models at epsilon leq 4/255

arXiv:2609.38298v1 Announce Type: new Abstract: Vision Language Models (VLMs) are widely deployed in safety-critical scenarios, and understanding to which extent they can be controlled by adversarial

safetyarxiv-cs-cv

Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation

arXiv:2604.02543v2 Announce Type: replace Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their

researcharxiv-cs-cv

Prediction is Better than Detection: Traffic Congestion Control using Drones

arXiv:2609.39637v1 Announce Type: new Abstract: A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scalin

safetyarxiv-cs-ro

Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners

arXiv:2609.38971v1 Announce Type: cross Abstract: Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments

safetyarxiv-cs-ro

An Empirical Study and Assessment of EU AI Act Compliance Checkers

arXiv:2609.36228v1 Announce Type: new Abstract: The EU AI Act introduces extensive compliance requirements for organizations that develop, deploy, or integrate AI systems. Many of these requirements a

Behavioral Capacity Certificates for Quantized Language Models

arXiv:2609.37887v1 Announce Type: new Abstract: Activation and key-value cache precision change what a quantized language model computes without altering its stored weights. Direct weight-code bounds,

researcharxiv-cs-lg

Calibrate the Decisions That Change the Future: On-Policy Post-Training Quantization for Multimodal Large Language Models

arXiv:2609.36828v1 Announce Type: new Abstract: Post-training quantization (PTQ) lowers deployment cost for multimodal large language models, but calibration typically reconstructs fixed sequences wit

Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

arXiv:2609.37972v1 Announce Type: cross Abstract: As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical s

Does the Unsafe Gradient Survive a Conversation? On the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue

arXiv:2609.36849v1 Announce Type: cross Abstract: Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns i

Evaluating the Effects of Prompt Perturbation on Bias and Hallucination in Large Language Models

arXiv:2609.35804v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, leading to their widespread deployment a

Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs

arXiv:2609.38106v1 Announce Type: cross Abstract: Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggreg

safetyarxiv-cs-cl

SafeCoEvo: Co-Evolving Safety Harnesses and Guards for LLM Agents at Test-Time

arXiv:2609.36580v1 Announce Type: new Abstract: LLM agents deployed in real-world environments continually encounter new tasks and safety risks, while execution feedback typically becomes available on

CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

arXiv:2609.33106v1 Announce Type: new Abstract: Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the o

safetyarxiv-cs-lg

CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning

arXiv:2607.17568v2 Announce Type: replace Abstract: Resource-constrained deployment requires sustaining large language model (LLM) capabilities as models scale under fixed memory and computation budge

researcharxiv-cs-lg

Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification

arXiv:2609.31658v1 Announce Type: new Abstract: Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts

researcharxiv-cs-cv

Cross-Material Support Transfer for Core-Loss Prediction Under Waveform Covariate Shift

arXiv:2609.31659v1 Announce Type: new Abstract: Power magnetic materials are characterized on the sinusoidal and triangular waveforms that excitation hardware conveniently produces, whereas deployed c

Tutorials

EdgeCraft: Automated Model Crafting for Edge IoT

arXiv:2609.35167v1 Announce Type: new Abstract: Machine learning (ML) increasingly powers Internet of Things (IoT) applications at the edge. Yet producing a deployable edge ML artifact for a specific

tutorialsarxiv-cs-lg

From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness

arXiv:2609.31656v1 Announce Type: new Abstract: Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources o

Same Probe, Different Numbers: Are Activation Probes Robust to Inference-Time Numerical Non-Determinism?

arXiv:2609.31796v1 Announce Type: new Abstract: Activation probes are increasingly used to monitor LLMs in deployment. A probe is typically trained under one inference configuration, then used under w

Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models

arXiv:2609.31397v1 Announce Type: cross Abstract: Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management po

28 Sept 2026

ScopeBench: Do Agents Preserve Engagement Boundaries Under Goal Pressure?

arXiv:2609.30325v1 Announce Type: new Abstract: Agents are increasingly deployed with real autonomy in web application and network penetration testing, where a single out-of-scope action can breach a

28 Sept 2026

Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement

arXiv:2609.29866v1 Announce Type: cross Abstract: Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontrolle

Combating Instruction Conflict via Energy-Driven Latent Conflict Detection

arXiv:2609.08646v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed with hierarchical instructions, yet they remain vulnerable to conflicts in which user directi

DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models

arXiv:2609.29092v1 Announce Type: cross Abstract: Vision-based legged locomotion methods assume clean depth at training time and rely on hand-tuned post-processing filters at deployment. However, filt

Encoded but Not Decoded: Layer-Localized Evidence for a Three-Level Gap in LLM Syntax

arXiv:2609.29848v1 Announce Type: new Abstract: A language model can fail a syntactic test in two distinct ways: by not encoding the relevant structure, or by encoding it but failing to use it at the

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

arXiv:2609.27900v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate

From Agent Output to Authorized Transition

arXiv:2609.28216v1 Announce Type: cross Abstract: Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturabl

Learning from Failures: Heterogeneous Graph Memory for Small Language Model Tool-Using Agents

arXiv:2609.28003v1 Announce Type: new Abstract: Small and medium-sized language models offer cost-effective executors for tool-using agents, making them attractive for local and large-scale deployment

safetyarxiv-cs-lg

Risk-Controlled KV-Cache Eviction: From Memory Budgets to Risk Targets

arXiv:2609.27981v1 Announce Type: new Abstract: KV-cache eviction is typically evaluated through average quality-memory trade-offs, yet a small average loss can hide requests whose utility degrades ma

Turning Safety into Competence: Minimally Exploitable Robot Policies via Safety-Filtered Reinforcement Learning

arXiv:2609.27312v1 Announce Type: cross Abstract: Robots deployed for competitive tasks must outmaneuver their opponents without sacrificing safety. Existing approaches, including safe reinforcement l

Agents

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

arXiv:2609.24515v1 Announce Type: cross Abstract: AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting become

agentsarxiv-cs-ai

23 Sept 2026

Topology-Aware Parameter-Efficient Adaptation for Cross-Dataset Retinal Vessel Segmentation