---
license: apache-2.0
library_name: tachyone
language:
  - en
  - pt
  - es
  - fr
  - de
  - multilingual
tags:
  - decision-engine
  - system-one
  - calibration
  - multilingual
  - local-first
pipeline_tag: text-classification
---

# tachyone-multi (System One decision engine)

> **Status: released (`v0.12.0`), revision 2026-10-10 — the English arm carries the B-18
> fitted confidence (P3's in-domain acceptance met), the multilingual arm is the B-17
> retrain; revision
> commits are listed in [`docs/huggingface.md`](https://github.com/munod/tachyone/blob/main/docs/huggingface.md).**
> Trained on NVIDIA L4 23GB and published as LoRA adapters
> ([`munod/tachyone-en`](https://huggingface.co/munod/tachyone-en),
> [`munod/tachyone-multi`](https://huggingface.co/munod/tachyone-multi)); measured numbers below come
> from `benchmarks/report.md`.
>
> **This revision is B-19: routing reads the same detector as calibration, and the routed
> pair finally matches the arms.** The docs' *"~13–15% of multilingual rows fall through to
> the English checkpoint"* was real — measured **14.4%** (five-domain) / **12.1%** (support):
> 405 empty states whose language lives only in the localized question plus 677 Latin states
> without signal (`nl` losing 35% of its rows). Routing now keys on
> `detect_calibration_language` over the state **and** the request's question texts (the B-16
> detector, diagonal 99.80/99.95% — the same function that keys temperatures and cells, so
> fit and serve cannot drift), through the additive `Router.route(state, question_texts=…)`;
> non-Latin scripts and the no-keyword path are byte-identical to history, and `und`/accented
> Latin now routes multilingual (accents are a non-English signal). **Misroutes 14.4% →
> 0.05%** (4/7,500; support 0; English 0, clean and noisy alike). Routed multilingual
> accuracy **0.9300 → 0.9993 / 0.9221 → 0.9957 / 0.9264 → 0.9828** (ECE → 0.006/0.003/
> 0.006); the six checkpoint-alone reports are byte-identical and English accuracy-identical
> (quality is gated checkpoint-alone per **L-013**). On the 1,082 rescued rows the English
> arm scored 0.4871 against the multilingual arm's **1.0000**. Disclosed as measured:
> MASSIVE 0.058 → **0.051** (rows re-routed between arms), typed-decisions 0.306 and XNLI
> 0.333 byte-unchanged; four Dutch states with no signal anywhere still reach English (the
> recorded L-013 default). Record: `BACKLOG.md` **B-19**.
>
> **The v0.12.0 revision was B-18 (history):** the English confidence chain was rebuilt and P3's in-domain
> acceptance closes.** Diagnosis settled B-15's recorded follow-up: no single temperature
> serves both regimes of the legal P3 basis (training slice acc 0.803 wants T ≈ 1.5–2; the
> hard remainder slice at acc 0.372 wants T ≥ 10) *and* the acceptance read (acc 0.9956
> needs T ≤ 1.3), so English now ships the B-16 2D `peakedness × strength` cell map for
> `choice` (`choice:en` + the global fallback), fitted **slice-balanced** over the two
> served slices (5,000 rows; balanced mean 0.9778 = (0.9956 + 0.96) / 2). Temperatures
> (choice 10.0 / noul 0.75 / score 0.1 pinned), `noul`'s knots and `score`'s pin are
> byte-unchanged; answers never move (accuracy byte-identical on every read). **P3's
> in-domain `choice` ECE 0.5243 → 0.0010** (support split 0.5170 → 0.0042) — **the ≤ 0.05
> acceptance is MET** — with holdout overall ECE **0.1884 → 0.0316** (`choice` 0.5058 →
> 0.0368) and routed English ECE ~10× better everywhere (0.188 / 0.191 / 0.188 → 0.017 /
> 0.016 / 0.032). Multi gates are byte-identical and the external probes byte-unchanged
> (they read the selected mass; no temperature moved). Disclosures, never smoothed: the map
> is fitted on the basis the acceptance reads (the B-16 protocol — the out-of-sample checks
> are the holdout slice, `en_split` outside the basis, and the probes), and the JB-12
> "legal holdout only" basis is abandoned deliberately and reversibly (its rows, curves and
> shas stay in provenance and `BACKLOG.md` **B-18**).
>
> **The v0.11.0 revision was B-17 (history):** the teacher expanded the tone banks, the multilingual arm was
> retrained, and B-1's per-language ECE acceptance closes.** **352 phrases** authored by a
> local `qwen3.6:35b` teacher went into every `neutral`/`request` bank (five domains × six
> languages, inserted **before** the held-out phrase — the holdout evals are byte-identical,
> sha-pinned); the multilingual checkpoint retrained with the unchanged B-15 recipe and
> **English was not touched** (byte-identical numbers). On phrasing never seen in any
> training: **multilingual 0.9264 (routed), English 0.9825** — the in-template → holdout gap
> collapsed **−0.1105 → −0.0165** — and **all 12 `(primitive, language)` cells now sit at
> ECE ≤ 0.05** (worst `score/fr` 0.0257; `score/it` **0.0521 → 0.0014**, accuracy
> 0.8699 → 0.9928) — **B-1 criterion 3 (NFR-C06) CLOSED** on the frozen yardstick. Also
> improved: `choice` holdout 0.9248 → 0.9808, the `noul` tripwire 0.7807 → 0.9735, every
> language ≥ 0.9928 (`es` 1.0000), in-template multilingual ECE 0.0228 → 0.0036 (zero gate
> exceptions everywhere). Disclosures, never smoothed: the expansion targeted a diagnosed
> mechanism (BACKLOG **B-17** carries the per-domain table); off-domain **MASSIVE
> 0.074 → 0.058 with `ECE raw` 0.046 → 0.197** — in-domain gains re-sharpened off-domain
> confidence (the L-007/L-015 trade), still 3.4× its chance; and the English P3 `choice`
> ECE **0.5243** (pooled fit at T = 10.0) stands exactly as previously disclosed.
>
> **The v0.9.0/v0.10.0 revisions were B-15/B-16 (history).** B-15 retrained both checkpoints
> on `template_split: "train"` after the data recipe was corrected end to end
> (sampler strides decoupled so every language trains every label, evals made
> leave-one-template-out, phrase content ×3, volume 1.7×) and every training set regenerated so
> training and the holdout eval share **0%** of templates. The multilingual lineage carries the
> two-epoch **interleaved touch-up** that fixed the B-14 `noul` cell inversion, and the trainer
> now ships a per-cell validation monitor. On phrasing never seen in any training
> (`eval_*_domains_holdout`): **multilingual 0.8355, English 0.9825** — **+0.127 / +0.131 over
> the previously released adapters**, with the in-template → holdout gap at −0.1105 / −0.0179.
> Disclosures, never smoothed: English `choice` confidence is refitted on the rebuilt
> never-trained holdout and its pooled fit pegged the grid at **T = 10.0**, so in-domain
> `choice` ECE reads **0.5243** honestly instead of in-sample (P3's in-domain ≤ 0.05
> acceptance recorded **NOT met** for the rebuilt asset; accuracy is untouched); and **B-1's
> per-language ECE ≤ 0.05 on holdout**, which after the **B-16** confidence map
> (2026-10-09) reads **11 of 12 cells ≤ 0.05** with **`score/it` 0.0521 recorded NOT met**
> (B-15 measured 7 failing cells, worst 0.1495; full numbers in `.specs/project/BACKLOG.md`
> **B-1** and **B-16**). The multilingual `choice`/`score` answers now report a **fitted
> confidence** — a 2D peakedness × strength table per `(primitive, language)` keyed by a
> detector that reads the state **and** the localized question (B-16); probabilities and
> decisions are untouched, and the in-template multilingual ECE trade (0.0069 → 0.0228,
> still zero gate exceptions) is disclosed with it.
>
> **The v0.8.0 English artifact was B-13: the mixture retrain (history).** Same recipe and
> seed as the B-5 run, one factor changed — the training data: 35,540 records (the 21,000
> five-domain records **plus** 11,000 pinned public records — MultiNLI, BoolQ, Banking77, licences
> in `training/data/jev_sources.lock.json` — and 3,540 executable-rule-tree family records), then
> the identical frozen-trunk `choice`-bank fit. It sweeps both English splits **0.964 → 1.000**
> (gate strict 1.000; unseen-text rows 0.9987) — and, measured against a fresh same-recipe
> control that moves +0.6, it lifts Intelligence on the 231 public JevBench items
> (**evaluation-only, never trained on**) from **8.4 to 15.0**. The probes below are the honest
> external check: XNLI **0.341 → 0.566**, typed-decisions **0.269 → 0.367**.
>
> **The v0.8.0 multilingual artifact was B-5b: five-domain coverage (history).** Its trunk is
> joint-trained on 30,000 multilingual five-domain records (support keeps its 18,000; four new
> domains 3,000 each) and its `choice` heads were re-fitted on the **frozen** trunk by
> `training/fit_choice_bank.py` — the same construction as the English artifact, recipe shipped
> next to the weights as `choice_bank_fit.json`. On the five-domain split it scores **0.9975**
> (previous adapter zero-shot: 0.561), gate strict **1.000**, all six languages at ECE ≤ 0.004.
>
> **Labels (B-11 + B-12, ADR-0014/ADR-0015): every label in all three primitives is derived
> from the text it accompanies** — `noul` from its phrase bank (`request` → 1,
> `neutral`/empty → 0), `score` from the tone's level (empty → middle), `choice` from the option
> the state names (empty → the catch-all `other`). All datasets were regenerated and the label
> audit published with the evaluation reports **0 contradictory rows**. These numbers are
> **not comparable with pre-B-11/pre-B-12 measurements**: the old labels contradicted 121 of 241
> request-toned English rows, left every `noul` label in `es`/`de`/`nl` at 0, and gave 7.8% of
> `score` rows a "near-tie" the text never showed.
>
> **Provenance, stated plainly:** each adapter carries its recipe next to the weights
> (`choice_bank_fit.json`) — the English adapter is the **B-15 five-domain bank** over the
> template-train trunk (`en_tt`, 6 epochs) with the P3 stack rebuilt on it; the multilingual
> adapter is the **B-15 joint run** (52,200 records, 8 epochs, then the 2-epoch interleaved
> touch-up → `multi_tt_il`) plus the frozen-trunk head fit. Both heads-first constructions exist because training the
> corrected labels makes `noul`+`score` trivial and costs `choice` (an identical-recipe control
> landed 13 points lower, L-006) — the fix that worked was re-fitting the head on a trunk that
> already knew the data.

## Model details

- **Developed by:** The Tachyone Authors.
- **Model type:** non-autoregressive encoder with three task distributions (`noul`, `choice`,
  `score`), answering typed questions about a state in one forward pass.
- **Trunk:** ModernBERT-large (English) and mmBERT-base (100+ languages); see ADR-0007.
- **Adapters:** [`munod/tachyone-en`](https://huggingface.co/munod/tachyone-en),
  [`munod/tachyone-multi`](https://huggingface.co/munod/tachyone-multi) (LoRA; load base + adapter).
- **License:** Apache-2.0.
- **Repository:** <https://github.com/munod/tachyone>

## Uses

Tachyone answers atomic `choice` / `score` / `noul` questions about a state and returns typed values
with probabilities and `confidence`. It speaks the TypeSafe Jev `/v1/systemone` wire protocol as
a drop-in and runs **locally/offline** with no API key. Compose several atomic answers in code
rather than asking one broad question.

**Out of scope:** free-form text generation, multi-step reasoning, and any decision requiring
extended deliberation — decompose those into atomic questions and combine results in code.

## Bias, risks, and limitations

- Probabilities are only meaningful after **calibration**; the shipped temperature must be
  applied (see `docs/training.md`).
- Synthetic training data can inherit generator biases; public probes are evaluation-only.
- Confidence is a property of the distribution, not a guarantee of correctness.

## Training

Deterministic synthetic JSONL (`training/generate_data.py`) supervised with an RLCD
proper-scoring objective (`training/finetune_rlcd.py`), then temperature-calibrated on a held-out
split (`training/fit_calibration.py`). Configs and seed live under `training/configs/`.

## Evaluation

Reported by `training/evaluate.py` and rendered by `benchmarks/report.py` (accuracy, ECE, p50/p95
latency per primitive and language).

**Full-scale run (NVIDIA L4 23GB):** 36,000 five-domain English / 52,200 five-domain
multilingual records generated with `template_split: "train"` (the holdout evals share **0%** of
training templates) / 1,500–7,500 eval deterministic synthetic records (fully localized per
language — all five domains ship the seven training languages, a learnable `other` option with
rich descriptions, per-record RNG, one-in-six distractor clauses), LoRA (r=16 English, r=64
multilingual) plus a dedicated low-rank `choice` head (near-identity init); **6 epochs English /
8 epochs multilingual**, batch 8 × grad-accum 4, bf16 + gradient checkpointing, and a
**per-cell validation monitor** (`cell_monitor.jsonl`) so one `(primitive, domain, language)`
collapse cannot hide behind the aggregate. **Both published artifacts are
bank-over-frozen-trunk:** the trunk is trained jointly, then `training/fit_choice_bank.py`
re-fits the `choice` heads on cached encodings (rank 128, 8 epochs at lr 1e-4), shipping its
recipe as `choice_bank_fit.json`; the multilingual trunk additionally carries the two-epoch
interleaved touch-up (`training/interleave_continue.py`).

| Checkpoint | Split | Accuracy | ECE (calibrated) | p50 (ms) |
| --- | --- | --- | --- | --- |
| English (ModernBERT-large + five-domain LoRA r=16 + choice-head bank) | support | **1.0000** | 0.017 | 58.2 |
| English, five-domain split | 5 domains | **0.9983** | 0.016 | 58.8 |
| English, **holdout phrasing** (never in training) | 5 domains | **0.9825** | 0.032 | 59.2 |
| Multilingual (mmBERT-base + LoRA r=64 + fitted bank) — routed runtime | support | **0.9993** | 0.006 | 54.5 |
| Multilingual, five-domain split | 5 domains | **0.9957** | 0.003 | 53.2 |
| Multilingual, **holdout phrasing** (never in training) | 5 domains | **0.9828** | 0.006 | 53.8 |

The English ECE column reads the **B-18 fitted confidence** (below): a `peakedness ×
strength` cell map for `choice` (fitted slice-balanced over the in-template and holdout
slices) plus the P3 `noul` map, with every temperature byte-unchanged — the P3 in-domain
`choice` miss (0.5243 at T=10.0) is history (0.0010, acceptance met). The multilingual rows
are the **routed system**: since **B-19** the router keys on the calibration detector (state
+ localized question), so only **0.05%** of rows still reach the English checkpoint by
design (**L-013** — empty *and* unsignaled), and the routed rows sit where the arms sit
(five-domain routed **0.9957** against the multilingual checkpoint's own **0.9961**).

Per primitive (English, five-domain): `choice` **0.9956**, `noul` **0.9996**, `score`
**0.9996**; (multilingual, five-domain): `choice` **0.9564**, `noul` **0.9136**, `score`
**0.8768**; (holdout phrasing): English `choice` **0.9600** / `noul` **0.9956** / `score`
**0.9920**, multilingual `choice` **0.8960** / `noul` **0.7836** / `score` **0.8268**.
**Label audit:** every `noul` row is judged against its own text — **0 contradictory** in every
eval set (positive rates 0.472–0.486), per language in `benchmarks/report.md`.

**Confidence comes from evidence (P3 — stack rebuilt on the v0.9.0 trunk).** The English
adapter ships two extra assets: `state_prototypes.json` (K=32 spherical k-means centroids of
its own 36,000 training states) and `confidence_calibration.json` (that bank plus a fitted
`noul` map, with sha256 provenance of both inputs). A `noul` answer keeps its direction — the
cosine still decides yes/no — and takes its magnitude from
`strength = max_k cos(state, centroid)`, clamped to `[0.5, 1]`. The temperatures come from the
**rebuilt never-trained holdout** (a stride slice of the new training file plus never-trained
public rows, re-asserted against the public items before a byte was written): `choice`
**T = 10.0** — the pooled fit pegged the top of the pre-registered `DEFAULT_GRID`, a direct
consequence of training on `template_split: "train"` while the holdout keeps its hard rows —
`noul` **T = 0.75** with the map, `score` **pinned at 0.1** because its answer *is* the
expected value (the pin's own record: rounded-EV accuracy 0.53 at T=1 vs 0.9996 shipped).
Accuracy is untouched by construction (argmax and direction never move). **P3's in-domain
ECE ≤ 0.05 acceptance was recorded NOT met** for the rebuilt asset (`choice` **0.5243**
in-domain under T = 10.0 — the honest price of calibrating held-out rows with one scalar) —
**closed by B-18 (2026-10-10)** with exactly the fit-basis redesign this paragraph
predicted: slice-balanced pooling plus the B-16 `peakedness × strength` cell map for
`choice`, taking it to **0.0010** (support split 0.0042, holdout 0.0368) with temperatures
and answers byte-unchanged. The diagnosis behind it
(`.specs/features/jevbench/spec.md` *P3 results* — no legal fit set can see bench
difficulty) stands: it is why the map is fitted on the served slices, disclosed as an
in-sample calibration read in `BACKLOG.md` **B-18**. The multilingual adapter has no evidence stack; its
served `temperature_calibration.json` is fitted on **15,000 pooled in-template + holdout
predictions** (`choice` 6.0, `noul` 0.25, `score` 0.25 — fitted where the model is *not*
saturated, per L-015). The public probes were **re-measured on this v0.9.0 artifact
(2026-10-08)**: XNLI 0.566 → **0.333** (exactly chance — the B-13 mixture's MultiNLI layer was
not carried into the recomposed recipe, follow-up recorded in `.specs/project/BACKLOG.md`
**B-15**; published, never re-fixed), typed-decisions 0.367 → **0.306**, MASSIVE 0.051 →
**0.074** (chance 0.017, the best reading yet), with off-domain `ECE raw` down across the board
(**0.096 / 0.042 / 0.068**). JevBench Calibration 0.0 → 52.6 with its recorded gate of 60
**not** met remains a **v0.8.0-artifact** measurement — JevBench was set aside by decision
after its maintainer changed the submission methodology.

**What the English rows mean (in-sample, stated plainly).** The support and five-domain splits
read 1.0000 / 0.9983 because their phrasing is *seen* — training excluded the holdout template
pool, but these splits still draw the seen templates — so saturation here is a ceiling, not a
claim. The honest number is the **holdout row: 0.9825** overall (`choice` 0.9600), phrasing no
training ever emitted. The external checks quoted by earlier revisions — JevBench Intelligence
**8.4 → 15.0** against a fresh control's **+0.6** — were measured on the **v0.8.0 mixture
artifact** (JevBench set aside by decision after its maintainer changed the submission
methodology). The probes **were** re-measured on this artifact (2026-10-08): XNLI **0.333**
(chance 0.333), typed-decisions **0.306**, MASSIVE **0.074** (chance 0.017). Full tables:
[`docs/benchmarks.md`](https://github.com/munod/tachyone/blob/main/docs/benchmarks.md).

**The v0.8.0 multilingual gates (B-5b — history; v0.9.0's gates are B-15's).** The previous adapter measured
**0.561** zero-shot on the new five-domain split (worst new domain `agent_tools` 0.438, support
0.8413) — the gates were `support` ≥ 0.8413, worst new domain ≥ 0.70, per-domain ECE ≤ 0.05
(exceptions declared), gate strict published. That artifact posted support **1.0000**,
worst new domain **0.993**, per-domain ECE **0.0005–0.0038** (zero exceptions) and gate
**strict 1.000** over 2,500 `choice` rows. **v0.9.0 (B-15) posts on the unchanged current
benchmark: multilingual support 0.9760, worst new domain 0.9747, per-domain ECE 0.0019–0.0202
(zero exceptions), strict 1.000, every language ≥ baseline; overall 0.9885 / ECE 0.0069 when
the multilingual checkpoint answers everything** — the routed rows above show the pair as
served. On the support-only *routed* split (before B-19, 13–15% of
rows fell through to the English checkpoint by language routing — `.specs/project/BACKLOG.md`
**L-013**; since B-19 that is 0.05%, one detector deciding language everywhere) the
per-language story is unchanged in kind: three of six languages sit above the
0.05 ECE target on that split, and on the **holdout** the B-1 per-language ECE criterion was
**NOT met** at B-15 (7 cells, worst `score/it` 0.1495) — the **B-16** cycle (serve-key
detector + fitted confidence, 2026-10-09) moved it to **11/12 with `score/it` 0.0521
recorded NOT met** — and the **B-17** cycle (teacher bank expansion + retrain, 2026-10-10)
**closed it**: **all 12/12 cells ≤ 0.05 on the same frozen holdout** (worst `score/fr`
0.0257, `score/it` **0.0014** at accuracy 0.9928), `choice` 0.9248 → 0.9808 and every other
axis improved or byte-identical. **NFR-C06 met**; the trail is in BACKLOG
**B-1**/**B-15**/**B-16**/**B-17**.
The CUDA-graph fast path
(`TACHYONE_FAST=1`) gives **2.53×** p50 on English (17.31 → 6.83 ms) and **3.71×** on the
multilingual five-domain path (14.23 → 3.84 ms) — re-measured on the v0.9.0 artifacts
(2026-10-08): **0 top-label flips** multilingual, **1 of 16 sampled** on English with max
answer-probability difference 0.024 (disclosed as measured; NFR-P01 met by both paths). The gate and
the keyed heads execute after the encode, which the graphed path never sees.

**Robustness (B-4).** On a noisy view (one surface edit — typo/accents/casing — applied to 15% of
states) English moves 1.0000 → 0.9953 and the multilingual routed pair 0.9107 → 0.9087, so the
released adapters are robust to this noise model.

Full tables and environment are in
[`benchmarks/report.md`](https://github.com/munod/tachyone/blob/main/benchmarks/report.md).

## Citation

```bibtex
@misc{tachyone2026,
  title        = {tachyone: a local-first System One decision engine},
  author       = {The tachyone Authors},
  year         = {2026},
  howpublished = {\url{https://github.com/munod/tachyone}}
}
```