Search: “deployment”

Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency

arXiv:2602.01765v2 Announce Type: replace-cross Abstract: Diffusion models have been widely deployed in AIGC services, but their reliance on opaque training data exposes them to backdoor attacks. In p

local-aiarxiv-cs-ai

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Beyond the Harness: End-to-End Optimization of Context Artifacts for Enterprise Text-to-SQL

DGX agent

arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, a

model-releasesarxiv-cs-ai

25 Aug 2026

Buried in Textual Debt: Context Pruning with Visual Evidence Preservation for MLLM Agents

arXiv:2608.22963v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed as multi-step agents, where explicit reasoning supports task decomposition and tool c

safetyarxiv-cs-ai

Applications

Conformal Risk Minimization for Semi-Supervised Domain Adaptation via Optimal Transport

arXiv:2608.23153v1 Announce Type: new Abstract: In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, crea

applicationsarxiv-cs-lg

Environmental Injection Attacks against GUI Agents in Realistic Dynamic Environments

arXiv:2509.11250v3 Announce Type: replace-cross Abstract: Graphical User Interface (GUI) agents are increasingly deployed to interact with online web services, yet their exposure to open-world content

agentsarxiv-cs-cv

ExplainGuard: A Zero Trust Framework for Post-Hoc Explanation Integrity Guarantees in Blackbox XAI Models

arXiv:2608.21803v1 Announce Type: cross Abstract: As machine learning (ML) models are increasingly deployed in high-stakes environments, explainable AI (XAI) methods like SHAP and LIME have become ess

GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models

arXiv:2608.21652v1 Announce Type: new Abstract: Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive set

local-aiarxiv-cs-lg

How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network

arXiv:2608.22192v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in persistent social environments, where generated claims can be posted, replied to, remembered, and reused.

safetyarxiv-cs-cl

InjecMEM: Memory Injection Attack on LLM Agent Memory Systems

arXiv:2608.23471v1 Announce Type: cross Abstract: Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a question:

agentsarxiv-cs-ai

Learning from the Test: Self-Referential Differential Testing for Deep RL Agents

arXiv:2608.22284v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in re

safetyarxiv-cs-ai

M^3ISR: A Multi-Modal Multi-View Benchmark for 3D/4D Gaussian Splatting and Feedforward Compression

model-releasesarxiv-cs-cv

arXiv:2608.22465v1 Announce Type: new Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rema

Measuring Activation Control in Large Language Models

arXiv:2608.21664v1 Announce Type: new Abstract: Safe deployment of increasingly capable models will likely come to rely on latent-space monitoring as a complement to behavioral evaluations, especially

Models in the Same Family are NOT Trust-Equivalent

model-releasesarxiv-cs-cl

arXiv:2508.13533v2 Announce Type: replace Abstract: Within a model family, a smaller variant is often deployed as a drop-in replacement for a larger one when their performance is similar. However, per

Multimodal-Language-Model-Driven Interaction and Companionship for Service Robots in Elderly-Care Facilities

arXiv:2608.21387v1 Announce Type: new Abstract: Service robots are increasingly deployed in elderly-care facilities to alleviate caregiver workload and enhance the quality of daily care. However, most

safetyarxiv-cs-ro

NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching

model-releasesarxiv-cs-lg

arXiv:2608.22643v1 Announce Type: cross Abstract: Deploying large language models on edge devices is increasingly limited by a widening gap between model size and available memory. Existing approaches

No One Model Catches Every Harm: Benchmarking Content Moderation Across Safety Scenarios

arXiv:2608.21775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From advers

safetyarxiv-cs-cl

Privileged Critic Training Enables Sensor-Free Thruster Fault Adaptation in End-to-End RL

arXiv:2608.22976v1 Announce Type: new Abstract: Fault-tolerant navigation for thruster-actuated robots requires online adaptation to failures that are neither binary nor fully observable: thrusters ma

safetyarxiv-cs-ro

Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification

arXiv:2608.21874v1 Announce Type: cross Abstract: Deep learning has achieved strong performance in encrypted traffic classification (ETC), yet its computational cost limits deployment on resource-cons

PsychJail: Exploring Psychological Jailbreaks via Multi-Turn Persuasion of LLM Policies

arXiv:2608.23028v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in education, healthcare, policy advising, and other interactive settings, where users engage the

safetyarxiv-cs-ai

RACO: Reliability-Aware Coarse-Goal Optimization for Inspection-Oriented UAV Vision-Language Navigation

arXiv:2608.22678v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) is commonly evaluated as goal reaching, but inspection-oriented deployment requires the agent to stop within

agentsarxiv-cs-ai

Reinforcement Learning on Benign Facts Amplifies Leakage of Memorized Private Data

arXiv:2608.21727v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is deployed to make models better at reasoning tasks, but its side effect on what models will di

SA-RSQ: A Versatile Sparse Representation Framework for Multi-modal Recommender Systems

arXiv:2608.22979v1 Announce Type: new Abstract: Deploying high-dimensional multimodal features in industrial recommender systems incurs substantial storage and latency overhead. Hard quantization is c

Scalable quantum simulation of continuous-time generative models via tensor networks

arXiv:2608.21700v1 Announce Type: cross Abstract: Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein

Spine-Branch Coordination for Multi-agent Computer Use

arXiv:2608.22077v1 Announce Type: new Abstract: Computer use agents (CUAs) are increasingly deployed as multi-agent systems that decompose a task into multiple subtasks executed across parallel virtua

agentsarxiv-cs-cl

ST^2U: Stateful Test-Time Unlearning via Restricted Knowledge Boundary Control

model-releasesarxiv-cs-cl

arXiv:2608.23034v1 Announce Type: cross Abstract: Controlling restricted knowledge in large language models is essential for model alignment and safe deployment. Test-time unlearning avoids costly ret

SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation

arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fre

researcharxiv-cs-cv

Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency

arXiv:2608.23347v1 Announce Type: new Abstract: Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains du

researcharxiv-cs-lg

The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate

arXiv:2608.22152v1 Announce Type: new Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than act

agentsarxiv-cs-cl

Applications

The Illusion of Control: Why Bare Classifier Inversion Silently Fails in Concept-Bottleneck Text Generation

arXiv:2608.22956v1 Announce Type: new Abstract: Concept-bottleneck controllable generation routes multi-attribute control through a low-dimensional concept code that, at deployment, must be synthesise

applicationsarxiv-cs-cl

Thinking at the Right Size: Amortized Distillation Across Post-Trained LLMs

arXiv:2608.22854v1 Announce Type: new Abstract: Practical deployment of large language models (LLMs) requires families of post-trained variants---instruction-tuned, reasoning-tuned, and chat-style mod

safetyarxiv-cs-lg

aiXamine: Unified Black-Box Evaluation of Cross-Dimensional Trade-offs in LLM Safety, Security, and Privacy

arXiv:2608.20554v1 Announce Type: cross Abstract: The critical failure modes in deployed large language models (LLMs) are cross-dimensional: a model can score 99.3 in safety alignment while refusing o

safetyarxiv-cs-lg

Breaking High Confidence: Practical Face Impersonation under High-Security Thresholds

model-releasesarxiv-cs-cv

arXiv:2608.20884v1 Announce Type: new Abstract: Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport i

DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference

arXiv:2501.10375v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-

local-aiarxiv-cs-lg

Evaluation-as-Search: Adaptive Discovery of Grounding Failures in Meeting Assistants

arXiv:2608.20392v1 Announce Type: cross Abstract: LLM-powered meeting assistants are deployed at scale, yet systematic evaluation of their grounding fidelity remains limited to static benchmarks that

Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

arXiv:2608.21021v1 Announce Type: cross Abstract: Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations

Generating Multi-view Adversarial Examples for Visual Geometry Grounded Transformer

arXiv:2608.20748v1 Announce Type: new Abstract: The Visual Geometry Grounded Transformer (VGGT) enables unified feed-forward 3D reconstruction from multi-view images. However, deploying such a high-pe

safetyarxiv-cs-cv

Knowledge-Graph-Gated Defactualization for Style-Controllable and Fact-Preserving Generation in Agentic Conversational AI

arXiv:2608.20393v1 Announce Type: cross Abstract: Agentic large language models (LLMs) deployed in fact-sensitive applications such as customer support must simultaneously preserve factual correctness

Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound

arXiv:2608.20557v1 Announce Type: new Abstract: Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning

researcharxiv-cs-cv

Llama-Mobile: Efficient 2.7-Bit Quantization of VLMs

model-releasesarxiv-cs-cv

arXiv:2608.21134v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) on mobile devices is challenging due to their significant memory and compute requirements. We present a framewor

Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving

arXiv:2608.20874v1 Announce Type: new Abstract: Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety d

safetyarxiv-cs-cv

Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs

model-releasesarxiv-cs-lg

arXiv:2608.20873v1 Announce Type: new Abstract: Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint, a

Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs

arXiv:2608.20953v1 Announce Type: cross Abstract: Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and q

Robust Validation to Geometric Perturbations for Autonomous Pose Estimation

arXiv:2608.21066v1 Announce Type: new Abstract: Deploying autonomous systems in safety-critical domains demands guaranteed robustness against physically plausible geometric perturbations rather than a

safetyarxiv-cs-cv

Self-Speculation for Faster Reasoning Models

model-releasesarxiv-cs-cl

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance

SPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers

arXiv:2608.20802v1 Announce Type: new Abstract: Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and

Structure for Reading, Prose for Writing: Asymmetric Structural Conditioning in Multi-Agent Document Authoring

arXiv:2608.20786v1 Announce Type: new Abstract: Multi-agent pipelines that author formal documents must both read a requester's forms and write against them. We report a deployed tender-response syste

agentsarxiv-cs-ai

Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI

arXiv:2608.21172v1 Announce Type: new Abstract: Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address h

local-aiarxiv-cs-lg

Towards Traffic Modelling of Multi-Agent Systems: The Role of Coordination Topology

arXiv:2608.20494v1 Announce Type: cross Abstract: Multi-agent LLM systems are an emerging networked workload whose rapid deployment raises questions about the traffic patterns they generate. Compared