Search: “deployment”

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

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Model Releases

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

DGX agent

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

model-releasesarxiv-cs-ai

30 Sept 2026

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

Google is a Leader in the 2026 Gartner Magic Quadrant for Container Management

model-releasesgoogle-cloud-ai

We’re excited and proud to share that Gartner has recognized Google as a Leader for the fourth year in a row in the 2026 Gartner® Magic Quadrant™ for Container Management, based on its Completeness of

How growing Latin American midsize businesses are building in the AI era

model-releasesgoogle-cloud-ai

Latin America’s small and medium-sized businesses are the heartbeat of the region's economy — accounting for more than 60% of total employment in the region, according to United Nations estimates. And

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

model-releasesarxiv-cs-cv

arXiv:2609.26189v1 Announce Type: new Abstract: Retinal vessel segmentation in multi-domain deployment requires a source model to adapt to domains that differ in imaging conditions and annotation conv

23 Sept 2026

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by exper

safetyarxiv-cs-cv

Local Ai

ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence

arXiv:2609.24271v1 Announce Type: new Abstract: Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical inter

local-aiarxiv-cs-ro

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

arXiv:2603.00842v2 Announce Type: replace Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remain

New on Dedicated Model Inference: canary rollouts. Upgrade the model behind a live endpoint without downtime. Traffic moves from your curren…

model-releasestogether-ai--x

New on Dedicated Model Inference: canary rollouts. Upgrade the model behind a live endpoint without downtime. Traffic moves from your current deployment to the new checkpoint in gated steps (default 5

Agents

Rare Event Estimation via Iterative Unalignment

arXiv:2609.24969v1 Announce Type: new Abstract: As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic.

agentsarxiv-cs-lg

Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift

arXiv:2607.09175v2 Announce Type: replace-cross Abstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the

Hardware

Scout: Open-World Species Recognition on the Edge

arXiv:2609.22897v1 Announce Type: new Abstract: Large vision-language models (VLMs) enable recognition beyond a fixed class set, but their computational demands prevent them from running on many edge

hardwarearxiv-cs-cv

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

arXiv:2609.22216v1 Announce Type: cross Abstract: Quantization enables deployment of large language models on resource-constrained clinical edge devices, but its effect on clinical accuracy and safety

UniPoint: Unified Point-Level Sensor Fusion for Humanoid Locomotion Across Challenging Terrains

arXiv:2609.23666v1 Announce Type: new Abstract: Open-world deployment requires humanoid robots to cross highly heterogeneous terrain safely, with perception that simultaneously provides wide coverage,

vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

arXiv:2609.24274v1 Announce Type: new Abstract: Deploying language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between policy q

Available Guardrails: Certifying Selective Prediction across ML Systems

arXiv:2609.22048v1 Announce Type: new Abstract: A selective predictor acts as a safety gate: it returns an output only when the prediction appears sufficiently trustworthy. Deployments increasingly re

safetyarxiv-cs-lg

21 Sept 2026

Finally, a company brain that actually works! Codos starts with the people doing the work. It interviews employees to find which tasks can b…

Finally, a company brain that actually works! Codos starts with the people doing the work. It interviews employees to find which tasks can be automated, then deploys the automations and keeps learning

researchdair-ai--x

21 Sept 2026

Same World, Different Knowledge: When Isolated Audits Misjudge World-Model Repairs

arXiv:2609.21155v1 Announce Type: new Abstract: A repair favored under an isolated input fault can be inferior when deployed modules share the faulty information. We introduce an information-interface

21 Sept 2026

Cross-Architecture Foundation-Model Distillation for Edge Flood Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.20441v1 Announce Type: new Abstract: Geospatial foundation models can provide strong flood-segmentation performance, but their size limits deployment on memory-constrained edge hardware. We

D-Quant: Driftable Entropy Coding for KV Cache Quantization

arXiv:2609.19880v1 Announce Type: new Abstract: The KV cache has become a major bottleneck in deploying LLMs, as its memory footprint grows linearly with sequence length and batch size, imposing subst

researcharxiv-cs-cl

Elastic Spectral State Space Models for Train-Once Budgeted Inference

arXiv:2601.22488v3 Announce Type: replace Abstract: Modern sequence models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms wi

Applications

From Models to Systems: A Comprehensive Survey of Efficient Multimodal Learning

arXiv:2609.19445v1 Announce Type: cross Abstract: The rapid expansion of multimodal models has surfaced formidable bottlenecks in computation, memory, and deployment, catalyzing the rise of Efficient

applicationsarxiv-cs-ai

MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration

arXiv:2609.19683v1 Announce Type: cross Abstract: The deployment of Vision-Language Models (VLMs) on edge devices is severely bottlenecked by memory bandwidth, necessitating aggressive sub-8-bit quant

researcharxiv-cs-cv

Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2603.21785v2 Announce Type: replace Abstract: Visual odometry (VO) frontends are typically tuned offline by domain experts on pre-recorded datasets and then deployed with fixed hyperparameters.

RTK-Vision PPO for Autonomous Micro UAV Recovery on an Airborne Carrier

arXiv:2609.20629v1 Announce Type: new Abstract: Autonomous recovery of a micro unmanned aerial vehicle (UAV) onto a moving airborne carrier enables reusable deploy-mission-recover operation, but coupl

The DevFest Community Workshop Experience: Building Real Agents Together

model-releasesgoogle-cloud-ai

This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell