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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 (v0.4.0), revision 2026-09-29. Trained on a single RTX 3060 12GB and published as LoRA adapters (munod/tachyone-en, munod/tachyone-multi); measured numbers below come from benchmarks/report.md.

This revision is 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: the English adapter carries the B-11 weights and the multilingual adapter the B-12 retrain — each is the best measured checkpoint of its recipe (training on the corrected labels makes noul+score trivial and costs choice; an identical-recipe control landed 13 points lower, L-006).

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, 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 (single RTX 3060 12GB): 21,000 five-domain English / 18,000 multilingual train / 1,500 eval deterministic synthetic records (fully localized per language, 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); 8 epochs for multilingual, batch 16, bf16 + gradient checkpointing. The English artifact is the B-5 bank (ADR-0016): run 5's six-epoch trunk kept frozen while the choice head was re-fitted as a bank — shared + one head per domain, rank 128, 8 epochs at lr 1e-4 — by training/fit_choice_bank.py, whose recipe ships next to the weights as choice_bank_fit.json.

Checkpoint	Accuracy	ECE (calibrated)	p50 (ms)
English (ModernBERT-large + five-domain LoRA r=16 + choice-head bank)	0.964	0.023	54.4
Multilingual (mmBERT-base + LoRA r=64 + choice head)	0.743	0.089	16.0

Per primitive (English): choice 1.000, noul 0.946, score 0.946; (multilingual): choice 0.468, noul 0.892, score 0.870. Label audit: every noul row is judged against its own text — 0 contradictory in both eval sets (positive rates 0.482 / 0.486), per language in benchmarks/report.md.

What this English row trades (published in full, not summarized away). On the support-only split the previous artifact scored 0.972 — noul 0.992, score 0.978, choice 0.946. The five-domain bank scores 0.964 there: choice becomes 1.000 while noul/score give up 4.6 and 3.2 points, and in exchange the adapter covers four domains it could not answer at all before — on the five-domain split the previous artifact scores 0.511 overall (worst domain 0.328) against this one's 0.964 (worst domain 0.963). The gate that routes each choice question to its domain head scores strict 1.000 (0 to shared, 0 wrong domain) over 2,500 rows, and accuracy on the 473 rows whose text never occurs in training is 0.998. Full tables: docs/benchmarks.md.

choice is the weak primitive on the multilingual side (0.468) and it is a training trade, not a label problem: choice labels never changed, and retraining on the corrected labels makes noul (1.000) and score (0.998) trivial — the same tone detector — while the shared trunk starves choice (an identical-recipe English control landed at 0.841, the five-domain retrain's choice collapsed to 0.303). The English side did not retrain the trunk to escape that: it re-fitted the choice head on a frozen trunk (B-5 / ADR-0016), which lifts choice to 1.000 and leaves noul/score where the trunk already had them — the cost and the gain in the table above are one artifact, not two runs (provenance: choice_bank_fit.json next to the weights). One of six languages meets ECE ≤ 0.05 (es 0.045); pt 0.062, fr 0.101, de 0.118, nl 0.192 and it 0.197 remain above target (NFR-C06), and multilingual choice/noul ECE (0.099 / 0.108) is declared with them. The CUDA-graph fast path (TACHYONE_FAST=1) gives a 2.65× p50 speedup (8.97 → 3.39 ms) with 0 top-label flips.

Robustness (B-4). On a noisy view (one surface edit — typo/accents/casing — applied to 15% of states) English drops only 0.964 → 0.963 and multilingual 0.743 → 0.744, so the released adapters are robust to this noise model.

Full tables and environment are in benchmarks/report.md.

Citation

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