Thanks @LysandreJik and the @HuggingModels team for bringing decision models into Transformers—and highlighting AutoTrust AI’s JEV.
Agents don’t always need another generated paragraph. Sometimes they need to choose a tool, rank search results, or select the next action from a screenshot.
JEV’s System 1 path returns calibrated probabilities for explicit options in one forward pass, over text and images. Developers can use those probabilities to set thresholds, route uncertain cases for deeper reasoning, and avoid parsing free-form answers.
A shared model.decide(...) interface means less model-specific glue across inference, training and evaluation. That’s a meaningful step toward making open decision models easier to use.
Download our models and explore the demos:
huggingface.co/collections/au…
Agents don’t always need another generated paragraph. Sometimes they need to choose a tool, rank search results, or select the next action from a screenshot.
JEV’s System 1 path returns calibrated probabilities for explicit options in one forward pass, over text and images. Developers can use those probabilities to set thresholds, route uncertain cases for deeper reasoning, and avoid parsing free-form answers.
A shared model.decide(...) interface means less model-specific glue across inference, training and evaluation. That’s a meaningful step toward making open decision models easier to use.
Download our models and explore the demos:
huggingface.co/collections/au…
huggingface.co AutoTrust Decision Models - a autotrust Collection Calibrated probabilities for each question's answer options. 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.
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