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The article introduces a technique that extracts hidden states from an LLM at the last prompt token to perform classification without text generation, using a small MLP to read the model's internal decision, enabling fast and cheap zero-shot classifiers.
This paper introduces HRVConformer, a hybrid Convolution-Transformer architecture for classifying neonatal hypoxic-ischemic encephalopathy directly from raw heart rate signals, achieving an AUC of 83.23% and outperforming baseline models like ResNet50 and Transformer.
Trained a prompt injection classifier using ml-intern and DeepSeek V4 Flash, achieving 99% F1 with DistilBERT, optimized to ONNX int8 (~65MB) and deployable in the browser via Transformers.js v3.