Applied Compute's AC2 adds custom decision-model training with Jev-like heads
ypatil125 · x · 2026-10-10
Applied Compute announced its platform AC2 now supports decision-model training, letting teams build custom Jev-like models for use cases like request routing, content classification, and anomaly detection.
How it works: a small learned output layer (a "decision head") sits on top of a language-model backbone. The backbone processes the input, question, and answer choices; the head produces scores that are softmaxed into a probability distribution. The interface makes it easy to attach a variable-sized decision head to any frontier open-weight model and train with Brier loss or cross-entropy loss — training updates both the head and the language backbone.
Related event: AC2 Platform Opens Custom Decision Model Training(2 posts)→
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