KCAE.Profiles:2 - Typed assessors and generative readers

KCAE.Profiles:2.1 - A bounded Jev role

The TypeSafe interface takes a state and typed questions. It can fill a screening or classification role when inputs, meanings and subsequent operations are controlled. It does not replace source acquisition, explanation, deterministic calculation or final domain judgement. Model documentation describes English as the primary language and calls for testing other languages. Text-only inputs require an adequate prior transformation of a diagram or table. Treat version, limits, prices and availability as implementation inputs to verify at use, not durable properties of this framework.

The Jev 1.13 jaggedness account identifies sensitivities including negation, indirection, mathematical content, long irrelevant state and adversarial instructions. Give the assessor a focused state and explicit criterion, test the forms your cases use, and calculate exact arithmetic in code after semantic facts are obtained. A typed shape reduces parsing ambiguity; it is not an accuracy certificate. Keep retrieved instructions as untrusted source content, not a change to the assessor’s authority.

Use a generative reader when the task needs an explanation, missing-condition recovery, query construction or a new connection that the fixed questions cannot express. Use an appropriate specialist when interpretation or authority depends on that profession. Combine roles only when the receiving result and total cost justify them.

KCAE.Profiles:2.2 - What the cookbooks contribute, and what must change

The skill-suggestion cookbook supplies progressive selection: short descriptions for a wide ranking, fuller descriptions plus instruction openings for the shortlisted Choice, and full descriptions for per-candidate fits. Its Jev 1.12 demonstration uses synthetic requests. The inspected code gates on the maximum fits score, then returns the Choice winner; these can refer to different candidates. This is a static example defect, not a measured failure rate or a general vendor claim. KCAE.ASSESS:4.4 binds adequacy to the returned candidate. Method-library use also replaces the example’s action-oriented screen with the required contribution, including explanations, and reads complete necessary instructions before reliance.

The feature-discovery cookbook proposes natural-language questions, obtains numeric features, uses supervised-model errors to revise them and assesses held-out data. It demonstrates this on a bounded tasting-note prediction problem. KCAE.MEMORY adapts the error-to-question loop for recognition aids, while requiring actual permitted episodes and a separate test population. The adaptation is a proposed domain construction, not evidence that the cookbook already solves open-ended method noticing.

KCAE.Profiles:2.3 - Comparative evidence beyond the output type

The 2026 early empirical audit of typed decision models warns that interface specialization and comparison conditions can be confused with intrinsic accuracy gains. Typed decision coherence beyond calibration separates logical consistency among answers from probability calibration. These are bounded, early studies, useful as failure and comparison evidence rather than a final vendor ranking.

Accordingly, compare the actual typed and generative alternatives with matched evidence and required output. Test ranking, adequacy thresholds, calibration and any logical relationships separately. A model that selects the best available option can still need a distinct all-options-inadequate outcome. No scalar combines source authority, completeness, capability and permission into an automatic licence to act.