A pretty important new primitive just landed in 🤗 Transformers: **decision models**. I think this could matter much more than adding support for any single model. Today, different decision models all approach "making a decision" differently: - @typesafeai’s Jev, the OG: typed decisions + calibrated probabilities, single forward - @AutoTrustAI’s JEV builds open System One models around a learned decision block, w/ multimodal. - @jaredpalmer’s KEV uses a pointer head over the options, sharing the computation of the state across questions. - @Cloudflare’s CLEF uses a joint schema head that routes evidence between the state, questions and options, scoring the whole schema together. - @perplexity_ai’s Decider uses a separate 255-way readout on top of Qwen, with non-causal attention in its full-attention layers. - @liquidai’s d1 is exploring small, multimodal decision models that can run all the way down to the edge. - And @XunzhuoLiu's Semantic Router team is pushing the serving side hard with System One + the D3 family: one typed interface for choice, score and yes/no decisions, with models from sub-1B to 27B. That experimentation is good, and needed! But without a common interface, every training framework, inference engine and evaluation tool has to understand every implementation separately. So Transformers is introducing a shared decision-model layer: `DecisionConfig` `pipeline("decision")` and, most importantly: `model.decide(...)` The idea is **not** to make every decision model work the same internally, but for them to speak the same language to the ecosystem around them. This means that, without running custom or remote code: - inference engines can serve them through one interface - training tools can build against common primitives. - the Hub can package them consistently. - evaluations can compare them without model-specific glue everywhere. This is the part of transformers I'm the most excited about: to grow alongside the ecosystem and the many ideas; and to make all of them more usable by everyone.