Skip the Generation - Logit-Readout Architectures and OpenJEV

Explores a shift in AI architecture toward logit-readout paradigms, often referred to as "System One" decision models, which prioritize speed and structure over conversational text generation.

By analyzing the Typesafe Jev hosted baseline alongside several open-source implementations like OpenJEV and mini-JEV, the sources demonstrate how reading raw token probabilities in a single forward pass can reduce latency by 3x to 7x compared to traditional grammar-constrained generation.

While these methods effectively eliminate JSON syntax errors and provide high accuracy for categorical routing, the documentation notes that they struggle with probabilistic calibration and long-context efficiency.

The sources ultimately distinguish between four divergent OpenJEV repositories, ranging from MLX-based scorers for Apple Silicon to cross-encoders capable of playing video games.

This overview serves as a technical guide for developers choosing between low-latency logit extraction for discrete choices and generative decoding for complex, multi-field outputs.