# KAI Language "Best Setup" — Layered Morphological Encoder + Grammar Feature Lattice

_Design + proven prototype (see KAI-morph-probe.py). Goal: Kai KNOWS word structure &
grammar, LEARNS smaller units from observation, and the lattice carries what the
modules need. Saved 2026-09-21._

## The five layers (bottom = robust/dumb, top = smart/grammatical)

- **L0 Character n-grams (2,3,4)** — universal fallback. Encodes ANY string incl.
  irregulars (buzz, smog, fridge), typos, names, any language. Always on.
- **L1 Learned sub-word vocabulary** — morphemes (roots/prefixes/suffixes) INDUCED
  from his own corpus by frequency (unsupervised morphology; BPE/Morfessor-style).
  Grows as he observes more text. PROVEN: found able/ed/ible/ing/ion/ly/tion +
  root "struct" with no hardcoded list.
- **L2 Allomorph + rule normalization** — a taught table + the 70 grammar rules map
  variants to ONE unit (ible->able), tag part-of-speech, mark affix type. PROVEN:
  able vs ible 0.500 -> 0.800; preferable vs impossible 0.166 -> 0.308.
- **L3 Grammatical features on each lattice cell** — every cell stores its
  decomposition (stem + affixes), POS, and features (number / tense / person /
  valency). This is the "built-in needs for the modules."
- **L4 Modules consume L3** — order_by_corpus / word_calculus read POS+features; a
  new agreement+argument-structure check gates assembly; grounded-generation composes
  under those constraints and the corpus-fit gate.

## What each of the owner's 70 rules maps to
- Morphology (prefix/suffix/root/inflection/allomorph) -> L1 + L2.
- Word classes / POS, agreement/concord, argument structure/valency -> L3 + the L4 check.
- Syntax / CFG / production / precedence -> the L4 assembler (upgrade of the carry math).
- Regex / n-grams / tokenization -> the INPUT (parse) side, sharpening L0/L1 segmentation.
- Phonology, medical, Semitic-specific -> not used at the current text stage.

## Migration reality (honest, non-negotiable)
1. Changing the encoder invalidates every existing cell vector. The whole lattice
   (~92k cells) must be RE-ENCODED once so old + new share one space. Batch job,
   done offline, verified before swap.
2. The morpheme vocabulary is LEARNED once from his corpora ([internal module],
   [internal module] exist) and thereafter updated as he observes.
3. All additive + flag-guarded + staged. Prototype first (done, in Python), then
   Rust port of the encoder, then the re-encode, then the L3 cell fields, then the
   L4 checks. Each step measured before the next.

## Proven so far (KAI-morph-probe.py, open test)
able=ible unified (0.80), suffix sharing recovered, roots found, irregulars safe,
control (cat/dog) stays 0. This validates L0-L2. L3/L4 are next.