Search: “deployment”

Liquid AI builds personal AI around device-level context

On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are r

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Hardware

LLA-MPPI: Rapidly Adaptive Whole-body Control of Legged Robots with GPU-Accelerated Parallel Simulations

DGX agent

arXiv:2610.10465v1 Announce Type: new Abstract: Real-time whole-body controllers for legged robots typically plan through a fixed nominal model and degrade when the deployed dynamics change. Adaptive

hardwarearxiv-cs-ro

8 Oct 2026

ProtocolMatch: Protocol-Dependent Model Selection for Scientific Dynamics Forecasting

arXiv:2610.10239v1 Announce Type: new Abstract: Scientific dynamics forecasting is often framed as an architecture choice, although deployment is also determined by observed history, rollout feedback,

local-aiarxiv-cs-lg

Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions

arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin

safetyarxiv-cs-cv

Q-PACE: Dynamic Precision Allocation for Quantization-Aware Training

arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final

Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs

model-releasesarxiv-cs-cl

arXiv:2610.09033v1 Announce Type: new Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment rout

Real-Time Joint Audio-Video Generation by Parallel Adapter Composition

arXiv:2610.10343v1 Announce Type: new Abstract: Deploying a joint audio-video diffusion transformer for real-time, interactive generation normally requires two essential modifications: block-autoregre

researcharxiv-cs-cv

REFIT: Recognize, Fix, and Test Wearable Sensor Placement Shifts without Labels

arXiv:2610.08991v1 Announce Type: new Abstract: We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training

Safe Meta-Policy Design with Risk Control

arXiv:2610.10393v1 Announce Type: cross Abstract: Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan polic

safetyarxiv-cs-lg

SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models

arXiv:2610.09498v1 Announce Type: new Abstract: Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce

researcharxiv-cs-cv

Stability and Diversity of Networked Self-Consuming Generative Ecosystems

arXiv:2610.09409v1 Announce Type: new Abstract: The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic d

TR-PTQ: High-Accuracy Integer-Only Transformer Post Training Quantization via Taylor Region Reformulation

arXiv:2610.09969v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment, yet transformer architectures remain challenging to quantize due to nonlinear layers. Whi

AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems

arXiv:2610.06971v1 Announce Type: new Abstract: Traditional data pipelines are notoriously brittle, often failing due to upstream schema drift, API contract changes, or website DOM modifications. Pres

agentsarxiv-cs-ai

Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction

arXiv:2610.08339v1 Announce Type: new Abstract: Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typical

agentsarxiv-cs-cv

Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks

arXiv:2603.04364v2 Announce Type: replace-cross Abstract: Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet the

safetyarxiv-cs-ai

Embedding and Rerankers models should be availabe on ckoud

I am sure people need it you know? There are plenty of embedding models on ollama, but only for local deployment. Which is a shame. I wish ollama would make embedding and rerankers available on ollama

local-air-ollama

Exact Unlearning via Quantized Sufficient Statistics

arXiv:2610.07197v1 Announce Type: new Abstract: Exact unlearning requires a deployed predictor to match one rebuilt without the information named by a deletion request. Existing general-purpose exact

local-aiarxiv-cs-lg

Intersectional Fairness via Mixed-Integer Optimization

arXiv:2601.19595v2 Announce Type: replace-cross Abstract: The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and tra

safetyarxiv-cs-ai

Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers

model-releasesarxiv-cs-ai

arXiv:2610.06883v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for time-dependent PDEs, but autoregressive deployment creates a refinement-allocation problem: prediction er

MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge

model-releasesarxiv-cs-ai

arXiv:2610.08669v1 Announce Type: cross Abstract: On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient

Multi-Robot Multi-Goal Motion Planning with Stochastic Skills

arXiv:2610.07784v1 Announce Type: new Abstract: As robots are increasingly deployed in groups and share workspaces to execute real-world tasks, planning their concurrent motions around complex manipul

PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation

arXiv:2610.08784v1 Announce Type: new Abstract: Pretrained robotic policies can suffer substantial performance degradation under out-of-distribution (OOD) conditions encountered during deployment, mot

Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pen

safetyarxiv-cs-lg

Seeing Through the Displaced Frame: Privileged Noise Distillation for Vision-Force Precision Assembly

model-releasesarxiv-cs-ro

arXiv:2610.07745v1 Announce Type: new Abstract: Pose error in precision assembly can corrupt not only what a robot observes but also the coordinate frame in which it acts. On the FORGE benchmark, the

Sensor-Layout-Agnostic Navigation via Geometric Observation Canonicalization

arXiv:2610.08306v1 Announce Type: new Abstract: Existing visual navigation policies are inherently bound to fixed camera configurations, creating a fundamental barrier to zero-shot deployment across h

ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?

model-releasesarxiv-cs-ai

arXiv:2610.07792v1 Announce Type: cross Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct

Silicon Language: A Robot-Native Knowledge Exchange Framework for Heterogeneous Robots

arXiv:2610.07650v1 Announce Type: new Abstract: Reusing a capability across heterogeneous robots still requires substantial human adaptation and verification: transferring a skill often means re-engin

SSR: Sparse Segment Reduction for Ternary GEMM Acceleration

arXiv:2610.08403v1 Announce Type: new Abstract: Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitig

Industry

Today @MeckaAI is announcing a $60M Series B from @sequoia to accelerate the advent of general purpose robotics This brings our total fundin…

Today @MeckaAI is announcing a 60M Series B from @sequoia to accelerate the advent of general purpose robotics This brings our total funding to over 120M to become the data and deployment layer for ph

industrysonya-huang--x

Towards In-Parameter Memory Augmentation for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2610.08630v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, us

Wiki-Talkie: Multilingual Benchmarking of Persona-Based Agents on Real-World Discussions

arXiv:2610.08513v1 Announce Type: cross Abstract: LLMs are increasingly deployed as autonomous agents in social environments, making it critical to study their ability to faithfully simulate human int

agentsarxiv-cs-ai

WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration

arXiv:2610.07860v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is dec

agentsarxiv-cs-ai

CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering

arXiv:2610.06260v1 Announce Type: new Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image generation, but their sampling cost limits deployment. Quantizing both weights and activati

researcharxiv-cs-cv

Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

arXiv:2610.06138v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-const

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

COVER: Learning to Accept More in Selective Sleep Staging

arXiv:2610.03911v1 Announce Type: new Abstract: Traditional sleep-staging methods apply the same model to every EEG epoch. Such uniform deployment expends computation on epochs that a smaller model co

DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents

arXiv:2610.04494v1 Announce Type: cross Abstract: Testing deep reinforcement learning (DRL) agents in cyber-physical systems aims to uncover diverse failures before deployment, but each execution can

agentsarxiv-cs-lg

DUET: Co-Evolving Solver and Grader Agents

arXiv:2610.04087v1 Announce Type: new Abstract: Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deploy

agentsarxiv-cs-lg

EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning

arXiv:2511.19935v3 Announce Type: replace Abstract: Deploying domain-specialized large language models on resource-constrained hardware motivates reducing both adaptation cost and the number of retain

Hardware

FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

arXiv:2508.12021v2 Announce Type: replace Abstract: Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challe

hardwarearxiv-cs-lg

FreeSpeed: Training-Free Speed Control for Generative Robot Policies

arXiv:2610.05734v1 Announce Type: new Abstract: Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constrain

From Transformers to Weighted Automata: Towards the Verification of Large Language Models

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

safetyarxiv-cs-lg

JASPER: Special Session on Joint Reliability And Security Assessment of SPlit Computing for Edge Robustness

arXiv:2610.04396v1 Announce Type: cross Abstract: Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers. How

KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models

arXiv:2610.03842v1 Announce Type: new Abstract: Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose unifo

Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations

arXiv:2610.05677v1 Announce Type: new Abstract: Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, re

Logit-Aware MIMO AirComp for Distributed Mixture-of-Experts LLM Inference over Wireless Edge Networks

arXiv:2610.03741v1 Announce Type: cross Abstract: Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks becaus

MemLeak: Cross-User Semantic Leakage in Multi-Tenant AI Agent Memory

arXiv:2610.04195v1 Announce Type: cross Abstract: Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface f

Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2610.05289v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaus