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NandhaKishorM released vegaml on GitHub and PyPI: typed decisions from a frozen language-model backbone plus a small physics engine, returning choice/score/boolean answers with calibrated probabilities, conformal sets, and abstain—without generating text. Defaults are an 800M (Qwen3.5-0.8B frozen) build with a 14.3M engine; a 4B option is in the same checkpoint repo. Context is 73,728 tokens with read-once prefix caching, and an image path is supported. The README cites LocalLLaMA/typed-decisions test-split figures (for example soft accuracy 0.680 on 0.8B and 0.725 on 4B) and says inference on a single T4 is demonstrated in Colab.