A sub-1M-parameter, byte-level language model whose individual facts you can create, read, update, and delete one at a time — with provable per-edit locality — and that abstains when it isn't confident which fact you mean, instead of guessing. Runs on CPU, offline.

Status: research prototype. Everything here is small-scale and honestly scoped (see Caveats). It is a clean, reproducible demonstration — not a production system and not a state-of-the-art result.

Each fact lives in its own addressable atom (one column of an additive decoder). A prompt is routed to a fact by a frozen sentence embedding (so paraphrases reach the same fact), and the routed atom contributes the answer. Because facts are disjoint columns:

• UPDATE a fact = swap one decoder column (no retraining).
• DELETE a fact = zero its atom (others provably untouched).
• CREATE a fact = allocate a fresh atom.
• Locality is structural: editing fact A cannot change fact B's output (given no routing collision).
• Abstention: the routing confidence margin (top-1 minus top-2) is a calibrated "I don't know which fact you mean" signal — the model refuses low-confidence queries.

# 1) deps (CPU-only; sentence-transformers pulls all-MiniLM-L6-v2 on first run)
pip install -r requirements.txt

# 2) try the demo (routes a prompt, answers, or abstains; edits/deletes are live)
python demo.py --demo
python demo.py --prompt "the country of the Eiffel Tower, its capital is "
python demo.py --prompt "The capital of France is " --edit France=Lima
python demo.py --prompt "best pizza topping?"        # -> ABSTAIN (out of scope)

The router uses sentence-transformers/all-MiniLM-L6-v2 out of the box — no local paths or private packages required. Two optional environment variables exist for advanced use: YAZ_EMBEDDER_PATH (point at an alternative Embedder package; the bundled MiniLM is used if unset) and YAZ_TINYSTORIES_DIR (a TinyStories corpus, only needed for the optional bits-per-character side-checks).

A trained checkpoint (checkpoints/yaz_gen_semantic_v2.pt) ships with the repo; retrain with python scripts/train_gen.py configs/semantic_v2.json.

python scripts/scaling/s3_route_abstain.py     # abstention risk-coverage (AURC)
python scripts/scaling/s4_create_primitive.py  # the CREATE 4-condition battery
pytest -q                                       # smoke test asserting a headline number

All runs are deterministic (seed 2026), CPU. Results write to results/.

• First-byte editor. Edits reliably set the answer's first byte; multi-byte generation is not faithful (full-word transfer ≈ 0.05). Treat the first character as the signal.
• Routing degrades on hard clues (≈0.85 on famous entities → ≈0.50 on oblique, name-free ones).
• Locality is structural — it holds while no two facts route to the same atom; collisions can occur at larger fact counts.
• Tiny, synthetic scope — 50 country→capital facts, single seed, CPU. Not validated at scale or on open-vocabulary knowledge.
• Not novel-by-defensibility. The mechanisms (sentence-embedding-keyed editing, selective prediction) are individually present in the published literature. Yaz is a clean, reproducible prototype, not a unique capability.

yaz/            model + semantic router
scripts/        training, eval, and reproduction scripts
data/           synthetic country→capital facts + paraphrase probes
checkpoints/    trained model(s)
results/        result JSON written by the scripts
demo.py         the CLI demo
tests/          smoke test

MIT — see LICENSE.