# LG AI Research — From Labeling to Deployment: VLM and Agentic AI-Based Autonomous Vision Inspection

- Company: LG AI Research (lgresearch.ai)
- Announced: 2026-09-02
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.lgresearch.ai/blog/view?seq=689
- Record: https://forck.live/items/7331-from-labeling-to-deployment-vlm-and-agentic-ai-based-autonomous-vision
- Subject: EXAONE

LG AI Research presents a zero-human vision inspection framework that uses Monte Carlo Dropout for uncertainty extraction and a Vision-Language Model (VLM) for auto-labeling, achieving 93.13% accuracy with a +4.84%p improvement over random sampling. The VLM is trained through a three-stage pipeline including base model training, domain-specific fine-tuning, and reasoning reinforcement learning, and a noise-robust learning policy based on embedding space similarity is employed to handle noisy labels.

## Evidence

Verbatim from https://www.lgresearch.ai/blog/view?seq=689:

> Applying this precise uncertainty sampling mechanism yielded an accuracy of 93.13%, representing a significant +4.84%p improvement compared to random sampling (88.29%) under identical data computational conditions.

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Record: https://forck.live/items/7331-from-labeling-to-deployment-vlm-and-agentic-ai-based-autonomous-vision
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