Laya

Open-source, non-autoregressive System 1 decision model. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with calibrated probabilities in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate.

This repo holds the English checkpoint and serves as the hub for the family.

checkpoint	encoder	params	context	use it for
convaiinnovations/laya (this repo)	ModernBERT-large	421M	512	English
convaiinnovations/laya-multilingual	mmBERT-base	322M	1024	100+ languages, ~2x faster
convaiinnovations/laya-typed-decisions	ModernBERT-large	421M	1024	the typed-decisions workflows

question type	returns
choice	selected option, probability per option, confidence
score	expected level on your ordinal rubric, distribution, confidence
noul	calibrated probability P(true)

Quickstart

pip install laya

import laya

agent = laya.load("convaiinnovations/laya")
result = agent.predict(
    {"subject": "Duplicate charge on invoice 4411",
     "body": "We were billed twice for March. Please refund the duplicate."},
    {"department": {"type": "choice", "instructions": "Which team should handle this?",
                    "criteria": {"billing": "invoices, payments, refunds",
                                 "technical": "bugs and outages", "sales": "pricing"}},
     "urgency": {"type": "score", "instructions": "How urgent is this?",
                 "criteria": ["not urgent", "soon", "blocking"]},
     "churn_risk": {"type": "noul", "instructions": "Does the user threaten to cancel?"}},
)
print(result["answers"]["department"]["choice"])

Routing between the three checkpoints

from laya import Router

router = Router()                       # lazy-loads only what a request needs
router.predict({"body": "I was charged twice"}, questions)          # -> laya
router.predict({"body": "मुझसे दो बार शुल्क लिया गया"}, questions)   # -> laya-multilingual
router.predict(state, questions, model="typed-decisions")           # explicit

Router keeps one checkpoint resident by default (max_loaded); raise it for mixed-language traffic so it does not reload on every language switch.

If laya.load() hangs: transformers probes for TensorFlow at import, and when TF is installed its abseil runtime can deadlock model construction. Run with USE_TF=0.

Architecture

• Backbone ModernBERT-large (395M, bidirectional, fully fine-tuned) + a decision head trained from scratch: 2 transformer layers, an option-marker scorer, and an act/escalate head. 421M total.
• Option markers every option is scored at its own [MASK] token, then softmaxed over that question's options. The answer space is defined at request time, so new schemas need no retraining.
• Budget 512 tokens per question (question + options + state).
• Batching every question in a call is answered in one forward pass.

Training

RLCD (Reinforcement Learning for Calibrated Decisions). The policy reports a distribution; exploration adds zero-mean Gaussian noise to the logits; the reward is a strictly proper scoring rule (log + spherical, plus ranked probability score for ordinal questions). Expected reward is maximised only by reporting honest probabilities. Updates are REINFORCE with a group-mean baseline (GRPO-style). Multi-turn conversations use TD(λ=1.0) over prefix slices.

7,313 updates, 1 epoch, ~1.96 h. Fitted temperatures [1.637, 1.251, 1.983] with per-option-count scaling.

Benchmarks

Measured on a Tesla T4; every checkpoint answered byte-identical questions in the same run.

Speed

questions/call	laya	laya-multilingual
1	39.5 ms	32.8 ms
10	158.6 ms (15.9 ms/q)	72.3 ms (7.2 ms/q)
50	771 ms	337 ms (6.8 ms/q)

103–332 questions/sec batched. TypeSafe Jev has been independently measured at 236–276 ms p50 (AbdelStark, nibzard), so Laya answers a single question roughly 6–7× faster.

Against Jev

Jev figures are published by third parties, not measured here — no TypeSafe API access. Sample sizes and prompts differ; treat as indicative, not a controlled head-to-head.

dataset	Jev	Laya
typed-decisions (2,000 decisions)	0.727	0.766	laya-typed-decisions
AG News (4 labels)	0.910	0.950	laya
DAIR Emotion (6)	0.480 · Brier 0.846 · NLL 5.588	0.595	laya, held out
ECE	0.246	0.081	after temperature fitting

On DAIR Emotion, Jev assigned zero probability to the true label on 16% of examples.

typed-decisions, all three checkpoints

400 cases, 2,000 decisions, four workflows — measured here.

model	accuracy	soft acc	Brier	ECE	score MAE
laya-typed-decisions	0.766	0.471	0.062	0.213	0.242
laya	0.362	0.332	0.316	0.175	0.694
laya-multilingual	0.342	0.326	0.439	0.285	0.687
Jev 1.13.0 (published)	0.727	0.580	0.148	0.144	0.391
teacher ceiling	0.735
majority class	0.461

The fine-tuned checkpoint clears the teacher ceiling and wins all four workflows: invoice processing 0.804, security incidents 0.766, customer service 0.764, agent-trace observability 0.730. By primitive: noul 0.857, choice 0.733, score 0.723.

The base checkpoints sit below the majority-class baseline here — the capability on this benchmark comes from fine-tuning, which is what the fine-tuning notebook is for.

English tasks

task	laya	laya-multilingual
AG News	0.947	0.937	in training mix
BoolQ	0.830	0.787	in training mix
DAIR Emotion	0.573	0.513	held out
prompt-injections	0.698	0.578	held out, n=116
SST-5 (ordinal)	0.372	0.282	held out

Languages — use laya-multilingual outside English

Across 51 languages on MASSIVE intent (20 options, random = 0.050), this checkpoint macro-averages 0.227 with macro ECE 0.733, clearing 3× random on only 23 of 51. Khmer scores 0.000 accuracy at 0.952 confidence.

	laya	laya-multilingual
MASSIVE intent, English	0.783	0.657
MASSIVE intent, 13 others	0.306	0.451
XNLI, English	0.860	0.843
XNLI, 14 others	0.521	0.731

The confidence score gives no warning when the input is unreadable, so the choice has to be made before the forward pass — that is what Router is for.

Limits

• Near chance on typed-decisions zero-shot — 0.362 here and 0.352 for multilingual, against a 0.318 random and 0.461 majority-class baseline. The 0.766 belongs to the checkpoint fine-tuned on that benchmark's own training split. Laya is a fast base to specialise, not a zero-shot decision engine.
• Keep choice questions under ~20 options. Options share a fixed head_max_len budget (192 tokens here), so a very large label space leaves only a few tokens per label and accuracy falls off sharply. Split into a coarse choice then a fine one.
• Ordinal score questions are the weakest primitive (SST-5 0.372).
• Ships over-confident: refitting one temperature per (question type, option count) moves mean ECE 0.466 → 0.081. Do this on your own data before trusting the probabilities.
• English only. Use laya-multilingual for anything else.

Links

• GitHub https://github.com/NandhaKishorM/laya
• PyPI https://pypi.org/project/laya/
• Demo https://huggingface.co/spaces/convaiinnovations/laya-demo
• Write-up dev.to

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