Liquid AI Releases d1: A Decision Model That Outputs Calibrated Probabilities With Zero Output Tokens

Liquid AI has released d1, a novel model architecture designed specifically for binary or multi-class decision tasks that returns calibrated probability scores without generating any output tokens. By eliminating the token generation step, d1 dramatically reduces latency and compute cost for classification and decision-making workloads where a probability score is the only needed output. This is a meaningful architectural departure from standard autoregressive models and is purpose-built for high-throughput production decision pipelines such as fraud detection, content moderation, or routing logic. Developers running large-scale inference on classification tasks should evaluate d1 as a drop-in efficiency improvement over prompting a generative model for the same output. Calibrated probability outputs also make d1 directly compatible with downstream systems that require uncertainty quantification.