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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.