djev
Jev-compatible decision server on a diffusion language model that reads a distribution per answer slot from one denoise step, with span extraction on top.
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About djev
djev is a decision server that works with diffusion language models to generate structured decisions. It reads probability distributions from a single denoising step and applies span extraction to identify relevant text segments. The tool implements Jev-style structured output compatibility, enabling developers to extract and process decisions in a standardized format from diffusion-based models. This approach combines the strengths of diffusion models with constraint-based output generation. The developer mmastrac created this tool, which is distributed under the Apache 2.0 license. It is implemented in Python and serves as an example implementation from a vLLM pull request, demonstrating how structured decision-making can be integrated with modern language model architectures.
The tool targets developers working with language models who need reliable, structured outputs rather than free-form text generation. By leveraging diffusion models and span extraction techniques, djev provides a mechanism for extracting specific information and decisions from model outputs in a consistent manner. This makes it useful for applications requiring deterministic decision extraction from neural network predictions.
Checked 2 November 2026.
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