Search: “deployment”

actr: aligning thoughts and responses for multilingual safety in reasoning llms

arXiv:2609.37054v1 Announce Type: new Abstract: Ensuring the safety of reasoning large language models (LLMs) across languages is essential for their reliable deployment. However, when exposed to jail

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AnthroDial: Benchmarking LLM Anthropomorphism in Autonomous Social Interaction

DGX agent

arXiv:2609.37853v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or pers

agentsarxiv-cs-cl

30 Sept 2026

AnyAct: Universal Action for Self-Evolving Agents

arXiv:2609.37025v1 Announce Type: new Abstract: As large language models (LLMs) advance, AI agents are increasingly deployed in open-world environments to tackle complex sequential tasks (e.g., docume

AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks

model-releasesarxiv-cs-lg

arXiv:2609.37310v1 Announce Type: cross Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Yet

Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery

arXiv:2609.37263v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) achieve remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent

Hardware

Build a multi-agent music production pipeline on Amazon Bedrock AgentCore Runtime Instances

Amazon Bedrock AgentCore Runtime Instances gives multi-agent workflows AWS managed EC2 infrastructure with GPUs, persistent volumes, and multi-day sessions. In this post, we deploy a three-agent music

hardwareaws-ml-blog

Building production-ready AI agents with Atlas Agent Engine and Arize AX

Agents built and deployed on MongoDB’s Atlas Agent Engine can export OpenTelemetry traces to Arize AX, where teams can inspect each run, evaluate both the outcome and execution path, and test fixes be

agentsarize-ai

CPATTA: Conformal Supervision Allocation For Active Test-Time Adaptation

arXiv:2509.25692v2 Announce Type: replace-cross Abstract: Active Test-Time Adaptation (ATTA) improves model robustness under domain shift by selectively querying human annotations at deployment, but e

DeepForestVisionV2: Ecology-Driven Taxonomy Expansion for Camera-Trap Monitoring in African Tropical Forests

model-releasesarxiv-cs-cv

arXiv:2606.20223v2 Announce Type: replace Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Amo

DORA: Divergence-Oriented Data-Relay Algorithm for Partially Connected Robot Teams

arXiv:2609.36395v1 Announce Type: new Abstract: Teams of unmanned aerial vehicles (UAVs) deployed for search and monitoring missions frequently operate as partially connected networks, forcing each ro

researcharxiv-cs-ro

Hardware

FastVR: Efficient Streaming Video Restoration with One-Step Diffusion

arXiv:2609.36757v1 Announce Type: new Abstract: Diffusion-based video restoration recovers realistic details, but its practical deployment is limited by two efficiency bottlenecks: costly VAE encoding

hardwarearxiv-cs-cv

Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

arXiv:2602.12624v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited

From Concept Erasure to Style Purification: Contrastive Eigenbases for Artist Style Protection

arXiv:2602.08059v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety con

local-aiarxiv-cs-ai

I love decision models popularized by @typesafeai 's JEV but I also want to run it locally, especially for sensitive info. So as soon as @ol…

The user expresses enthusiasm for the decision‑modeling approach introduced by @typesafeai’s JEV tool, emphasizing a preference for local deployment to protect sensitive data. After learning that @oll

local-aiollama--x

Language Models Are 'Insecure' Reporters

arXiv:2609.36139v1 Announce Type: cross Abstract: As large language models are deployed in increasingly autonomous long-horizon tasks, manually auditing and verifying the actions, artifacts, and outpu

Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

arXiv:2609.35911v1 Announce Type: cross Abstract: Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guid

Learning to Prove, Not Just to Answer: Reinforcement Learning from Formal Verification for Natural-Language Logical Reasoning

arXiv:2609.37203v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for natural-language logical reasoning, where the final answer is easy to check but the proof beh

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

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

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

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

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

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

safetyarxiv-cs-lg

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

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

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

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

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

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

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-

researcharxiv-cs-lg

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

safetyarxiv-cs-lg

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

researcharxiv-cs-lg

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

model-releasesarxiv-cs-lg

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

model-releasesarxiv-cs-lg

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

safetyarxiv-cs-lg

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