decision models (the paper names Jev explicitly) do lightweight categorical reasoning for agents, but generation-oriented engines waste it on prefix recomputation or distort output probabilities. Flide's ForkAttention forks exact state inside a single flattened token stream — every query branch evaluates identically to an isolated forward pass — and the paper reports queries up to 9.5× faster than vLLM and SGLang while staying closest to a float32 reference. Directly relevant: this engine's own decision pass runs on Jev.