defect-detection

SkillSpec is a Hoare-style framework that uses intent-masked specification reasoning to detect defects in agent skills by aligning descriptions, instructions, and code, achieving 61.2% precision on real-world skills from SkillsBench and other repositories.

This paper proposes a multi-conditioned diffusion-based synthesis pipeline using Stable Diffusion XL and ControlNet to generate synthetic sand boil imagery for low-resource earthen-levee inspection, addressing the scarcity of annotated defect examples.

This paper introduces a Multi-Modal Agent framework for power distribution defect detection, evaluating foundation models on perception, reasoning, and tool usage capabilities, with a new domain-specific dataset and benchmark.

This paper proposes a large-scale multi-modal dataset (MMIO) for zero-shot industrial defect detection and introduces the Refined Text-Visual Prompt (RTVP) method, achieving state-of-the-art results on the benchmark.

Proposes AE-YOLO, an attention-guided autoencoder-enhanced YOLO framework for robust insulator defect detection in UAV transmission-line imagery, achieving 95.10% [email protected] and outperforming YOLO baselines by 5 points.

This paper introduces Structured Defect Grounding (SDG), a method that models text-to-image defects as structured (location, type, reason, importance) tuples and uses VLMs for detection, along with a 30K-image dataset SDG-30K and a diagnosis-to-alignment framework called BoxFlow-GRPO.

An independent researcher's study finds that a single LLM misses about half of code-review defects, while using multiple models from different providers significantly improves coverage, with the biggest gain from adding a second model. The paper seeks feedback and arXiv endorsement.

This paper introduces a lightweight multimodal LLM-based framework for cost-effective defect grading of power transmission equipment, using in-context learning and chain-of-thought to generate training data and fine-tuning Qwen3-VL-8B for state-of-the-art performance.

MIT researchers published a paper in 'Matter' describing an AI model that uses noninvasive neutron-scattering data to classify and quantify atomic defects in materials. The model can detect multiple defect types simultaneously, improving the characterization of semiconductors and other materials without damaging them.