L3 — Grounding: make Kai say TRUE things, fluently

Goal: the grammar-first decoder generates from a retrieved fact's meaning, not just the

prompt's words — so Kai is *correct*, not only fluent. Salad is fixed; this is the next tier.

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What already exists (leverage, don't rebuild)

• Retrieval: Universe::predictive_query / predictive_query_by_source returns ranked

QueryHits (each has text, a SparseVec, score, source, keywords). It can be

filtered to source == "truth-anchor" — i.e. retrieve facts, not chatter.

• A knowledge base — but a narrow one. [internal module] = 52 curated facts,

[internal module] = 108 truth-anchor claims. Honest catch: they are almost all about

Kai himself ("KAI is not an LLM", "KAI is a Rust system", "Universe is generative memory").

A grep for cat / vowel / two-plus-two / four = 0. So today he only *knows* himself.

• The fluent decoder: StatLexicon::generate_grammar_first(meaning, seed, params, mw, gw, info)

— transcripts nominate, meaning steers. This is what fixed the salad.

• A first grounding attempt: try_grounded_generation (behind [config flag]) re-orders a

retrieved cell's *words* with word_calculus::assemble. It reshuffles the fact rather than

*generating from* it — keep as a fallback, not the target.

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The core mechanism (the real change)

Wire retrieval → the grammar-first decoder so the *meaning spine is a fact*:

1. Retrieve. For the prompt, run predictive_query; prefer truth-anchor / high-score hits.

2. Ground the meaning. Build the decoder's meaning vector from the retrieved fact's text

(encode_sentence(fact)), optionally blended with the prompt. Meaning = the fact.

3. Seed + steer from the fact. Open on the fact's key noun; add a fact-cosine term to the

candidate scoring so the chain expresses the fact instead of drifting into corpus filler

("…anniversary gift…"). This is the fix for the mid-sentence drift you saw.

4. Confidence gate (honesty).

• Strong fact (score ≥ threshold) → generate grounded in it → fluent and correct.
• Weak / no fact → fall back to today's ungrounded grammar-first with a hedge

("From what I understand…"), or say he doesn't know. Never state a guess as fact.

Plugs into voice.rs generate_raw_thought, in the grammar-first block, behind a flag

(default OFF, byte-identical when off).

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Two tracks — and why data is half the battle

Track A — code (wire retrieval → generator). Buildable now. With the 52 self-facts it will

immediately make him answer self-questions correctly *and* freshly — "are you an LLM?",

"what are you?", "what is the Universe?" become grounded fluent sentences, not chatter. That's a

real, demonstrable win on day one.

Track B — data (grow the truth store). This is the true ceiling. Grounding can only make him

correct on topics that exist in the store. For "what is a cat" to be right, a cat fact must

be there. Path: curate world facts and --truth-import them (the pipeline exists). This is

DATA work, not decoder work — and it's what turns grounded-Kai from "knows himself" into

"knows things."

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Phases

• L3a — retrieval→meaning wiring. Feed the top fact into generate_grammar_first as meaning +

seed. Flag-gated. *Measure:* does grounded output contain the fact's content words?

• L3b — fact-steering. Add the fact-cosine term to candidate scoring so the chain stays on the

fact. *Measure:* topicality / factual-word retention vs L3a.

• L3c — confidence gate + honesty. Strong fact → state it; weak → hedge or defer. *Measure:*

zero confident-wrong answers on prompts with no fact.

• L3d — grow the KB. Curate + import world facts. *Measure:* correct answers to a held-out set

of general questions.

Measurement (extend --gen-probe)

Add a grounded A/B: for prompts that DO have a truth-claim (start with the self-questions), print

ungrounded vs grounded output + whether the fact's key terms survived. Same honest, deterministic

harness we used to prove the salad fix.

Honest bottom line

The mechanism is a clean, contained code change and it will work — I can prove it on his

self-knowledge immediately. Broad correctness is a data problem (52 facts, all about himself).

So L3 is ~40% decoder wiring, ~60% growing the truth store. Anyone who tells you the decoder alone

makes him "know things" is selling hopium.