Laya

Laya is an open-source, non-autoregressive System 1 decision model: give it a state (text, email, ticket, or JSON document) and typed questions, and it returns typed answers with mathematically calibrated probabilities and confidence scores. It never generates text, eliminating parsing errors and hallucinations.

This is the fine-tuned checkpoint, incorporating dedicated email triage (spam, phishing, department routing), conversation trajectory modeling (TD(lambda = 1.0)), and per-cardinality temperature calibration.

Architecture

• Backbone: ModernBERT-large (395M, fully fine-tuned, bidirectional), plus a decision head trained from scratch (2 transformer layers, an option marker scorer, and an act/escalate head). Total parameters: 421M.
• Option Markers: Every option is scored at its own [MASK] marker token, then a softmax is applied over that question's options.
• Input budget: 512 tokens per question (question + options + state).
• Multi-question batching: Evaluates all questions in a single forward pass (~33 to 38 ms on GPU).

Training

Trained with RLCD (Reinforcement Learning for Calibrated Decisions): the policy reports a probability distribution, exploration adds zero-mean Gaussian noise to the logits, and the reward is a strictly proper scoring rule (log score + spherical score, plus ranked probability score for ordinal score questions). The maximum expected reward is achieved only when the model outputs true, calibrated probabilities.

Multi-turn dialogues use Temporal Difference learning with Monte Carlo targets (TD(lambda = 1.0)) over prefix slices, preventing outcome leakage. 100% human-annotated real-world datasets, zero synthetic shortcuts.

• Training: Fine-tuned (7,313 updates, 1 epoch, ~1.96 hours)
• Fitted calibration temperatures: [1.637, 1.251, 1.983] (with per-option-count scaling)

Benchmark: Laya vs. TypeSafe Jev

Evaluation Results (This Checkpoint)

• In-task test sets: macro accuracy 0.838, macro ECE 0.060
• Zero-shot (held-out task families): macro accuracy 0.651, macro ECE 0.207
• Key Task Families:
  • Intent and routing: accuracy 0.991, ECE 0.009
  • Moderation and safety: accuracy 0.967, ECE 0.061
  • Emotion and tone: accuracy 0.906, ECE 0.018
  • Email triage and phishing: accuracy 0.732, ECE 0.017
  • Inference and fact checking: accuracy 0.883, ECE 0.054

Detailed results by task family, reliability diagrams, and risk-coverage curves are located in eval/ in this repository.

Quickstart (pip install laya)

import laya

# Load the model directly from Hugging Face Hub
agent = laya.load("convaiinnovations/laya")

state = {
    "from": "customer@acme.com",
    "subject": "Duplicate billing on March invoice #4411",
    "body": "Hi team, we were billed twice for March. Please refund the duplicate before Friday or we will cancel our plan."
}

questions = {
    "department": {
        "type": "choice",
        "instructions": "Which department should handle this email?",
        "criteria": {
            "billing": "invoices, payments, refunds",
            "technical": "bugs, outages, integrations",
            "sales": "pricing, contracts, demos",
            "other": "everything else"
        }
    },
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this request?",
        "criteria": ["not urgent", "soon", "critical deadline or blocking issue"]
    },
    "churn_risk": {
        "type": "noul",
        "instructions": "Does the user threaten to cancel or switch to a competitor?"
    },
    "is_phishing": {
        "type": "noul",
        "instructions": "Is this email a phishing or scam attempt?"
    }
}

result = agent.predict(state, questions)
answers = result["answers"]

print("Department :", answers["department"]["choice"], f"(confidence: {answers['department']['confidence']:.2f})")
print("Urgency    :", f"{answers['urgency']['score']:.2f} / 2.0")
print("Churn Risk :", f"{answers['churn_risk']['noul']:.1%}")
print("Phishing   :", f"{answers['is_phishing']['noul']:.1%}")

Interactive Web Demo

Try the live Gradio Space: convaiinnovations/laya-demo

License and Support

Released under the Apache 2.0 License by Convai Innovations, who offer commercial support, enterprise integration, and custom fine-tuning.

Limitations

• Text only, English, 512 tokens per question (longer states are truncated).
• Calibration is measured on the benchmark datasets; evaluate on your own distribution before full automation.
• Arithmetic, counting, date comparisons, and multi-hop index lookups should be kept in deterministic code.