THX-01

THX-01 is a non-autoregressive, multilingual decision model developed by HAL-X AI. Given a state (a message, ticket, e-mail, document, JSON record or agent trace) and one or more typed questions written in natural language, it returns a calibrated answer to every question in a single forward pass of about 10 ms on one GPU.

THX-01 is trained with large-scale Reinforcement Learning for Calibrated Decisions (RLCD): the reward is a strictly proper scoring rule, so reporting honest probabilities is the only way to maximise it. Beyond choosing among options, THX-01 can return numbers stated in a document, verbatim excerpts and supporting citations, through the same interface.

Parameters	322M (307M encoder, 15M decision head)
Encoder	mmBERT-base, 22 layers, 256k vocabulary
Input	up to 1,024 tokens per question (question, options and state); longer states are truncated
Post-training	eight large-scale RLCD stages, about 2.1 million training decisions
Languages	post-trained in 18 languages with emphasis on Azerbaijani; 100+ supported
Latency	about 10 ms per request, about 1 ms per decision when batched
License	Apache 2.0

Contents

1. Question types
2. Quickstart
3. Benchmarks
4. Method
5. Training
6. Languages
7. REST API
8. Limitations
9. Citation

Question types

type	returns	criteria
choice	one of N options with a probability for each	{"key": "description", ...} or a list
noul	P(yes)	optional
score	an ordinal level and its distribution	ordered list of levels
number	a numeric value stated in the document, or null if it is not stated	optional min, max, precision
excerpt	a verbatim span of the document with its character offsets	none
"cite": true (any question)	the parts of the document that support the answer, with probabilities	none

number, excerpt and citations are native THX-01 capabilities. They are not part of the TypeSafe question types (choice, noul, score), where they have to be emulated in the service layer with repeated choice calls. THX-01 also answers such emulation calls (value-range buckets and document chunks) through its native lookup, so existing service layers work unchanged.

number returns values exactly as written in the document, normalised (1,2 mln becomes 1200000). It performs no unit or currency conversion: a question about kilometres when the document states miles, or about euros when it states US dollars, is answered with null. excerpt cuts its answer out of the document, so it cannot contain invented text.

Quickstart

import thx01

agent = thx01.load("doofz/THX-01")              # GPU if available

doc = ("Northwind Corp. reported Q3 2026 results. Revenue for the quarter was $48.3 million, up 14%. "
       "Net income was $6.2 million, compared with $4.1 million a year ago. "
       '"We will open two offices in Baku," said CEO Laura Chen.')

result = agent.decide(doc, {
    "kind":    {"type": "choice", "question": "What is this document?",
                "criteria": {"earnings": "earnings report", "complaint": "customer complaint", "invoice": "invoice"}},
    "revenue": {"type": "number", "question": "What was the revenue, in US dollars?", "cite": True},
    "quote":   {"type": "excerpt", "question": "What did the CEO say?"},
})
# kind -> earnings, revenue -> 48300000 (+ the supporting sentence),
# quote -> verbatim span starting "We will open two offices in Baku"

Install from PyPI (the weights download from this repository on first use):

pip install thx01                 # library
pip install "thx01[server]"       # plus the REST server

Benchmarks

Support-ticket classification

Fifteen categories (billing, refund, login, technical bug, outage, delivery, order change, product question, account change, security and fraud, data privacy, complaint, feature request, integration and API, contract and legal), four test sets with 2,843 tickets in Azerbaijani, Russian, English and Turkish: Clean, Corrupted (transliteration, removed diacritics, typos, noise), Messy (written messy on purpose) and Independent (written by GPT-6-Luna, never used for training).

model	Clean	Corrupted	Messy	Independent	Avg	ECE	latency
THX-01	99.2	97.7	98.8	97.9	98.4	0.003	~10 ms
Claude Sonnet 5.5 †	98.5	98.0	98.5	99.0	98.5	–	1.5 s
Wahoo 1.5	99.8	96.3	98.0	97.2	97.8	–	145 ms
GPT-6-Luna	98.6	97.4	96.7	98.2	97.7	–	1.9 s
TypeSafe Jev 1.13	99.2	95.0	96.2	99.0	97.4	0.007	331 ms
Kev-4B	96.1	88.4	90.8	95.8	92.8	0.202	830 ms

† Evaluated on a stratified subset of 200 tickets per set. ECE is measured on the Independent set; LLMs return no probabilities. THX-01 latency on one GPU; other models through their APIs.

Extraction and citation

Held-out multilingual documents (earnings reports, invoices, customs declarations, contracts, news, e-mails): 1,824 number questions and 1,357 excerpt questions. Number tasks report exact-value accuracy, excerpt tasks token F1, citation the accuracy of the top supporting part.

task	previous checkpoint	THX-01
Number lookup	69.6	93.4
Number via range buckets (service-layer emulation)	0.0	95.1
Excerpt extraction (F1)	20.1	84.1
Excerpt via document chunks (F1)	8.9	83.8
Reference citation	12.7	94.0

On number lookup over the same documents, TypeSafe Jev 1.13 reaches 96.6%.

Calibration and selective automation

Because confidence is calibrated, a threshold turns it into an operating policy: THX-01 handles about three quarters of all tickets automatically without a single error on any of the four sets, and 90% of tickets at an accuracy of at least 99.5%.

Multilingual decision suite

Thirty-nine held-out tasks: LLM routing, held-out synthetic schemas in 16 languages, MASSIVE scenarios and intents, SIB-200 topic classification in ten languages, AG News, Banking77, SMS spam, DAIR Emotion and Azerbaijani app reviews. Mean accuracy rises from 58.3% at initialisation to 84.2%, and mean ECE falls from 0.204 to 0.066. Hand-written LLM routing reaches 100%.

All 39 tasks

task	initialisation	THX-01	ECE
LLM routing (hand-written)	30.9	100.0	0.060
Synthetic routing (held-out schemas)	33.2	95.6	0.020
Synthetic decisions (held-out schemas)	53.4	87.8	0.025
Synthetic guard (held-out schemas)	66.9	96.8	0.024
Synthetic taxonomy (held-out schemas)	36.0	90.6	0.035
Synthetic emotion (held-out schemas)	58.6	96.4	0.051
AG News (4)	94.1	92.3	0.018
DAIR Emotion (6)	43.4	44.6	0.284
Banking77 (77)	51.7	65.9	0.087
SMS spam (yes/no)	73.5	87.3	0.048
AZ app-review sentiment	73.5	87.0	0.072
MASSIVE-intent@20 (az)	36.3	89.7	0.038
Intent yes/no (az)	65.2	95.2	0.017
MASSIVE-intent@20 (en)	61.9	93.5	0.041
Intent yes/no (en)	66.7	95.3	0.017
MASSIVE-scenario (en)	69.4	90.8	0.049
MASSIVE-scenario (az)	41.6	88.8	0.052
MASSIVE-scenario (ru)	57.6	89.8	0.039
MASSIVE-scenario (tr)	50.2	87.4	0.055
MASSIVE-scenario (de)	56.4	88.2	0.050
MASSIVE-scenario (fr)	59.8	90.4	0.044
MASSIVE-scenario (es)	55.0	87.8	0.040
MASSIVE-scenario (ar)	43.8	82.2	0.050
MASSIVE-scenario (hi)	46.2	85.4	0.037
MASSIVE-scenario (zh-CN)	61.0	87.2	0.060
MASSIVE-scenario (fa)	46.6	89.0	0.052
MASSIVE-scenario (ka)	16.8	78.0	0.060
MASSIVE-scenario (ja)	59.2	91.8	0.043
MASSIVE-scenario (ko)	48.8	86.4	0.039
SIB-200 (az)	67.2	73.5	0.111
SIB-200 (en)	78.4	78.4	0.068
SIB-200 (ru)	75.5	76.0	0.082
SIB-200 (tr)	74.0	73.5	0.126
SIB-200 (de)	75.5	79.9	0.080
SIB-200 (ar)	73.0	76.5	0.076
SIB-200 (hi)	67.2	69.6	0.132
SIB-200 (zh)	77.9	77.9	0.082
SIB-200 (kk)	68.6	68.1	0.158
SIB-200 (uz)	58.3	70.1	0.145

Speed

workload	time on one GPU
one question	about 9 ms
three questions about the same state	about 10 ms
150 requests batched	about 156 ms
peak throughput	about 3,000 decisions per second

Method

Every question is serialised as [CLS] question [MASK] option 1 [MASK] option 2 ... [SEP] state. The encoder reads the whole sequence at once; a two-layer decision head scores each option at its own [MASK] position, and a softmax with a temperature fitted per question type and option count gives calibrated probabilities. All questions of a request are scored in one batched pass. Questions with more than 24 options are decided by a two-round tournament.

Reinforcement Learning for Calibrated Decisions

Each question is a one-step game: the policy reports a distribution q over the options, the outcome y is revealed, and the reward is

R(q, t) = sum_k t_k log q_k + 0.5 * (sum_k t_k q_k) / ||q||_2 - 1[ordinal] * RPS(q, t)

a combination of the logarithmic, spherical and ranked-probability scores. Because the reward is strictly proper, its expectation is maximised only when the reported distribution equals the true one. THX-01 optimises the expected reward with an exact, zero-variance gradient. Soft targets teach two further behaviours: a uniform target when no option applies (so the model reports uncertainty instead of a confident wrong answer) and near-miss credit on ordinal scales.

Training

stage	focus	decisions per epoch
S1	broad multilingual RLCD: synthetic decisions in 16 languages, MASSIVE in 14 languages, Azerbaijani sentiment	157,912 (x2)
S2	spatial and control decisions	53,040
S3	LLM and agent routing	48,000
S4	generic skills: bare-key options, identifier lookup, no-good-option honesty	77,000
S5	robustness and domain: 600 new business taxonomies, confusable pairs, transliteration and noise, support tickets	213,961 (x2)
S6	boundary cases between confusable categories, three teacher models	284,207
S7	weight interpolation and temperature refit	–
S8	extraction and citation: numbers, excerpts, supporting parts, currency equivalence, new hard decisions, full replay	493,442

Training data combines curated data from the HAL-X data team, public benchmarks' training splits and verified synthetic data. Synthetic items are generated label-first by teacher models (Wahoo 1.5, GPT-6-Luna, Claude Sonnet 5.5) and kept only when a blind second pass agrees; test items and their near-duplicates are excluded from training. Optimisation uses 8-bit AdamW, bf16, length-bucketed batches, random option order and option subsets, and a frozen token-embedding matrix that preserves the encoder's coverage of more than 1,800 pretraining languages.

Languages

Post-training covers Azerbaijani, English, Russian, Turkish, German, French, Spanish, Arabic, Hindi, Chinese, Persian, Georgian, Japanese, Korean, Ukrainian, Kazakh, Uzbek and Italian, with Azerbaijani the largest language in every synthetic component. A dedicated curriculum covers the way text is actually typed in the region: Azerbaijani without its letters (e for the schwa, s for s-cedilla, c for c-cedilla), Russian in Latin transliteration, and code-mixing of Azerbaijani, Russian and English. Through its encoder THX-01 supports more than 100 languages.

model	Independent az	ru	en	Corrupted az	ru	en
THX-01	97.1	98.3	98.3	97.9	96.6	97.9
Claude Sonnet 5.5 †	98.5	100.0	98.5	97.6	100.0	97.5
Wahoo 1.5	96.2	95.8	99.6	94.5	95.0	98.9
GPT-6-Luna	97.9	97.5	99.2	97.2	95.0	98.6
TypeSafe Jev 1.13	98.8	98.3	100.0	92.9	93.3	98.2
Kev-4B	94.2	95.4	97.9	83.1	89.1	94.3

REST API

pip install "thx01[server]"
THX01_MODEL=doofz/THX-01 THX01_API_KEY=your-key python -m thx01.server --port 8095

POST /v1/decide (alias POST /v1/systemone, TypeSafe-compatible) takes {"state": ..., "questions": {...}} and returns {"answers": {...}, "latency_ms": ...}; POST /v1/decide/batch (alias /v1/systemone/batch) takes {"items": [...]}. The state may be a string or an object with a document field; the question text may be given as instructions or question. Requests are micro-batched on the GPU.

Limitations

• Fine-grained intent sets with many near-synonymous labels remain hard: Banking77 (77 intents) reaches 65.9%.
• Emotion with six overlapping classes (DAIR Emotion) reaches 44.6%; the model reports correspondingly low confidence.
• number and excerpt locate text that is present in the document; they do not perform arithmetic, unit conversion or paraphrase.

Citation

@techreport{thx01_2026,
  title       = {THX-01: Large-Scale Reinforcement Learning for Calibrated Decisions in 100+ Languages},
  author      = {Aghayev, Farid and Ahmadbayli, Elturan},
  institution = {HAL-X AI},
  year        = {2026}
}

License

Apache License 2.0. See LICENSE and NOTICE.