Search: “deployment”

Evaluating Model Retraining under Drift: Paired Comparisons of Cumulative Subgroup Disparity

arXiv:2609.09788v1 Announce Type: new Abstract: Choosing when to retrain a deployed classifier requires assessing subgroup error rates across the sequence of models used, including periods between upd

safetyarxiv-cs-lg

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios

DGX agent

arXiv:2609.09790v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-leve

model-releasesarxiv-cs-cl

10 Sept 2026

Local Ai

Privacy-Preserving Split Learning for Federated LLM Fine-Tuning

arXiv:2609.09794v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold

local-aiarxiv-cs-lg

SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction

arXiv:2609.06271v1 Announce Type: cross Abstract: Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or injecte

researcharxiv-cs-ai

We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation

arXiv:2609.07568v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains unders

Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

arXiv:2609.07664v1 Announce Type: new Abstract: Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluati

9 Sept 2026

All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs

arXiv:2609.06161v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment

9 Sept 2026

Power agent hubs or custom harnesses with the Antigravity SDK in one toolkit

model-releasesgoogle-cloud-ai

Enterprise agent adoption isn’t one-size-fits-all. While many teams will opt for managed commercial platforms, such as Gemini Enterprise Agent Platform for turnkey agent deployment and governance, dev

8 Sept 2026

An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders

arXiv:2406.02465v2 Announce Type: replace-cross Abstract: Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained f

applicationsarxiv-cs-ai

au^au-Bench: An Environment for End-To-End, Realistic Agent Construction

arXiv:2609.04611v1 Announce Type: new Abstract: LLM agents are rapidly becoming production software, deployed to handle customer service, adjudicate disputes, and operate internal systems. Notably, th

CoFreeVLA: Short-Horizon Collision-Free Dual-Arm Manipulation via Vision-Language-Action Model and Risk Estimation

arXiv:2601.21712v3 Announce Type: replace Abstract: Vision Language Action (VLA) models enable instruction-following manipulation, yet their deployment on coordinated dual-arm platforms remains severe

safetyarxiv-cs-ro

Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer

arXiv:2609.04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and

applicationsarxiv-cs-ai

How to Speculate about Uncertainty in Agentic Coding? A Draft-Model Gate Method

arXiv:2609.05274v1 Announce Type: new Abstract: LLM agents deployed for software engineering fail expensively: they act confidently wrong, and bad actions are recognized only after costly execution an

MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model

arXiv:2603.18892v2 Announce Type: replace-cross Abstract: Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in phys

ToPos: Automated Optimal Positioning on Topographic Manifolds using Constrained Geodesic Voronoi Decomposition

arXiv:2609.05084v1 Announce Type: new Abstract: Reliable autonomous mapping, environmental sampling, last-mile logistics, and infrastructure deployment depend on the optimal surface area-balanced dist

safetyarxiv-cs-ro

Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localization

model-releasesarxiv-cs-cl

arXiv:2609.04486v1 Announce Type: cross Abstract: Intent-based networking realization starts by translating high-level intents into low-level network configurations. Recent approaches have shifted tow

When Financial Fine-tuning Fails: A Three-Level Detectability Analysis of Numerical Hallucination in Domain-Adapted Language Models

arXiv:2609.04806v1 Announce Type: new Abstract: Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant

researcharxiv-cs-ai

Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

arXiv:2508.15038v2 Announce Type: replace-cross Abstract: Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneo

4 Sept 2026

Uncertainty Is Not a Safety Net for Clinical VQA, but Can It Anticipate Model Failure?

arXiv:2606.16583v2 Announce Type: replace Abstract: Safe deployment of clinical vision-language models (VLMs) requires reliable uncertainty estimation (UE): a signal indicating when predictions should

safetyarxiv-cs-cl

4 Sept 2026

A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference

arXiv:2609.01679v1 Announce Type: new Abstract: The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution

agentsarxiv-cs-lg

3 Sept 2026

Krea2 Turbo Distill 4 step LoRA - new checkpoint (chk60K) released (fine detail now above the 8-step teacher and clean of artefacts, best prompt-adherence and teacher-faithfulness scores so far)

model-releasesr-stablediffusion

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress) A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4. ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas. ⏱

3 Sept 2026

LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MR

arXiv:2609.02676v1 Announce Type: cross Abstract: Ultra-low-field MRI makes neonatal brain imaging deploy- able in low-resource settings, but its low SNR, lack of shielding, and long scan duration mak

applicationsarxiv-cs-cv

3 Sept 2026

A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies

arXiv:2609.00718v1 Announce Type: new Abstract: Many automobile and mobility companies deploy learned driving policies on embedded computers with limited memory and power. Pruning, knowledge distillat

Capability-Gated Language Models: Security Composes, Utility Does Not

arXiv:2609.00445v1 Announce Type: cross Abstract: Deployed language model safeguards (safety fine-tuning, filtering, unlearning) vary by principal only outside the model weights: filters are reconfigu

ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation

arXiv:2507.12674v3 Announce Type: replace-cross Abstract: Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through

researcharxiv-cs-ai

Runtime-Independent Persistent Agents: Preserving Identity, Memory, and Code Across Models, Harnesses, and Servers

arXiv:2609.00546v1 Announce Type: cross Abstract: Agent systems are commonly described by the model and harness that currently produce their behavior. That boundary is useful for one execution but und

agentsarxiv-cs-ai

Local Ai

Spawn Freely, Act Sparingly: Progressive Risk Vesting for Recursive LLM-Agent Trees

arXiv:2609.01035v1 Announce Type: new Abstract: Recursive LLM agents can broaden their search by spawning specialists. Some branches later request tools that send data or deploy code. When should a br

local-aiarxiv-cs-ai

Staged Linguistic Seeding: Grounded Query Expansion for Verified-Unit QA in AI Contact Centers

model-releasesarxiv-cs-cl

arXiv:2609.00844v1 Announce Type: new Abstract: Customer-service QA in an AI contact center (AICC) runs under deployment constraints that benchmark QA misses: tight voice-hotline latency and a high co

The Constitutional Coverage Trilemma in AI Governance

arXiv:2609.01275v1 Announce Type: cross Abstract: Frontier AI systems function as constitutional institutions: each deployed model encodes an implicit ranking among safety, helpfulness, honesty, auton

The Irreversibility Budget: Fleet-Level Risk Accounting and Admission Control for Agent Operating Systems

arXiv:2609.00275v1 Announce Type: new Abstract: Fleets of LLM agents now externalize effects that cannot be fully undone: they move money, deploy code, delete data, and disclose information. Current c

agentsarxiv-cs-ai

Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs

arXiv:2512.03994v4 Announce Type: replace Abstract: As organizations increasingly deploy LLMs in sensitive domains such as legal, financial, and medical settings, ensuring alignment with internal orga

safetyarxiv-cs-lg

ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents

arXiv:2608.30685v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions. R

Calibrating Small Language Models for Claim Check-Worthiness Detection

arXiv:2608.30731v1 Announce Type: cross Abstract: Assessing claim check-worthiness is an essential first step in automated fact-checking pipelines. This work is motivated by a real deployment challeng

applicationsarxiv-cs-ai

Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift

arXiv:2608.29465v1 Announce Type: cross Abstract: Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or switching costs. As operating conditions c

Efficient JPEG Restoration in the Wavelet Domain via Mean Flows

model-releasesarxiv-cs-cv

arXiv:2608.28730v1 Announce Type: cross Abstract: Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment.

Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

model-releasesarxiv-cs-cv

arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pres

Self-Evolving Skills via Surrogate-Guided Solve-and-Reproduce

arXiv:2608.28638v1 Announce Type: new Abstract: Agent skills are portable packages of instructions and resources an agent consults at deployment. Self-evolving them fails in two ways today. First, ski

Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security

arXiv:2608.29596v1 Announce Type: new Abstract: Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on co

Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts

arXiv:2511.11743v4 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantizati

Very special Training Data drops today: Making Cities Awesome ☺️ We haven’t featured an application company on Training Data in a while. The…

Very special Training Data drops today: Making Cities Awesome ☺️ We haven’t featured an application company on Training Data in a while. The diffusion of AI is the Most Important Thing, and the highes

safetysonya-huang--x

Local Ai

Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models

arXiv:2608.30649v1 Announce Type: new Abstract: Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely o

local-aiarxiv-cs-cl

Below the Noise Floor: Bimodal Seed Collapse and Distinct Failure Modes in Small-Model Knowledge Distillation

model-releasesarxiv-cs-cl

arXiv:2608.27729v1 Announce Type: new Abstract: Function routing -- selecting the correct API call from a fixed catalog given a natural-language request -- is a deployment problem where small students

CURA: Certified Runtime Alarms for Computer-Use Agents

arXiv:2608.27808v1 Announce Type: cross Abstract: Self-report is the cheapest oversight channel a deployer has, and on capable computer-use agents (CUAs) it fails precisely where oversight matters. On

researcharxiv-cs-cv

Generalized Gibbs Ensemble Weighting for Forecast Combination

arXiv:2608.28116v1 Announce Type: new Abstract: Forecast combination is a reliable way to improve predictive performance when several forecasting models are available. Simple aggregation rules such as

One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments

arXiv:2608.27628v1 Announce Type: new Abstract: Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental moni

agentsarxiv-cs-ro

A Contract-Centered Architecture for Scalable and Manageable Agentic Runtimes

arXiv:2608.27086v1 Announce Type: new Abstract: Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, secur

agentsarxiv-cs-ai

28 Aug 2026

Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification

model-releasesarxiv-cs-cv

arXiv:2608.26771v1 Announce Type: new Abstract: Automated tea leaf disease classification supports precision agriculture, yet deploying accurate models on edge devices remains challenging under tight

28 Aug 2026

Hardware

FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference