Narrow Decision Models: A System-1 Architecture for Calibrated, Typed Decisions

Key points

• Jev is general-purpose: one set of weights, served as an API and adapted to a domain only through the request.1
• A Narrow Decision Model (NDM) offers the same interface but is trained for the decisions of one organization, is small enough to run on its own infrastructure, and has an open architecture whose properties can be stated and tested.1
• The architecture serializes a request as a prefix tree (the state, then each question, then each option) and runs a bidirectional encoder under a block attention mask with logical positions, so that each answer is, by construction, invariant to the order of its options and independent of the other questions asked.1
• A minimal validation on public and fictitious data shows that the invariances hold to rounding in a trained model, that a small instance routes on organization charts it has never seen well above lexical retrieval, and that, at equal compute, the block architecture reaches a lower loss than a pairwise cross-encoder.1

Sources (1)

• [1]Narrow Decision Models: A System-1 Architecture for Calibrated, Typed Decisions
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 09:49 PM

  System One models, introduced with TypeSafe's Jev, answer typed questions about a state with calibrated probabilities instead of generated text: choosing among options described in natural language (Choice), placing the state on an ordered scale (Score), or judging a yes/no statement (Noul).

  Jev is general-purpose: one set of weights, served as an API and adapted to a domain only through the request.

Extractive summary: sentences quoted from the sources.