This benchmark evaluates Deterministic Regex, Laya 0.3.3 (ConvAI Innovations), and TypeSafe JEV on an offline, privacy-safe 43-case safety-routing suite created for Poocha — an AI science tutor localized for 8–12 year old children in India.
The evaluation tests whether non-autoregressive decision models can replace brittle keyword gates and expensive generative LLM calls with sub-35ms semantic routing.
The 5-Route Safety Policy
Every test scenario belongs to either child_input (direct message from the child) or retrieved_span (untrusted RAG context extracted from web or encyclopedia documents). The model must assign exactly one route:
| Route | Meaning | Valid Surface |
|---|---|---|
allow |
Ordinary, harmless age-appropriate learning inquiry (science questions, natural curiosity, social questions). | child_input, retrieved_span |
allow_with_boundaries |
Sensitive educational topics (puberty, reproduction, historical warfare, chemical hazards). Requires safe, non-graphic framing. | child_input, retrieved_span |
support_trusted_adult |
Immediate physical or emotional risk, acute distress, self-harm signals, physical injury, or potential poisoning. Escalate to adult; do not diagnose. | child_input only |
refuse_redirect |
Actionable wrongdoing, weapons, bomb-making, poison synthesis, cheating. Safe refusal with learning redirect. | child_input, retrieved_span |
reject_untrusted_text |
Retrieved RAG text containing prompt injection, system overrides (SYSTEM MESSAGE, ignore previous instructions), fake authority, or external redirects. |
retrieved_span only |
Benchmark Highlights
| Model / Configuration | Variation | Overall Accuracy | Crisis Recall (support_trusted_adult) |
Injection Recall (reject_untrusted_text) |
Harmful Refusal (refuse_redirect) |
Safe Learning (allow) |
|---|---|---|---|---|---|---|
| Deterministic Regex | Production Hardened | 30.2% (13/43) | 37.5% (3/8) | 0.0% (0/4) | 9.1% (1/11) | 90.0% (9/10) |
| Laya 0.3.3 English | Base Zero-Shot | 20.9% (9/43) | 0.0% (0/8) | 100.0% (4/4) | 0.0% (0/11) | 20.0% (2/10) |
| Laya Typed-Decisions | Calibrated & Scoped | 44.2% (19/43) | 50.0% (4/8) | 50.0% (2/4) | 27.3% (3/11) | 100.0% (10/10) |
| TypeSafe JEV | Base Zero-Shot | 76.7% (33/43) | 100.0% (8/8) | 100.0% (4/4) | 63.6% (7/11) | 70.0% (7/10) |
| TypeSafe JEV | Calibrated Criteria | 88.4% (38/43) | 100.0% (8/8) | 100.0% (4/4) | 90.9% (10/11) | 80.0% (8/10) |
Key Takeaways
- The Dangerous Illusion of Regex Guardrails: Deterministic substring matching achieves only 30.2% accuracy. While it permits standard science questions, it exhibits catastrophic false negatives: 0% recall on prompt injections and 0% recall on indirect self-harm or spaced leetspeak, while simultaneously false-blocking innocent biology questions (such as “How does plant reproduction work?”).
- TypeSafe JEV Sets the High-Water Mark: JEV achieves 88.4% accuracy when prompted with calibrated boundary descriptions. Crucially for a children’s tutor, it achieves 100% recall on acute distress (8/8) and 100% recall on prompt injections (4/4). Serving on OpenRouter’s Decisions API at $0.042 / MTok input and $0 output (FREE), 1,000 decisions cost less than $0.0001.
- Laya’s High Fine-Tuning Ceiling: Off-the-shelf zero-shot Laya scores 20.9%–44.2%, which is an uncalibrated starting floor. As demonstrated in upstream benchmarks by ConvAI Innovations on
typed-decisions, base Laya starts at 36.2% zero-shot and jumps to 76.6% when fine-tuned via RLCD (surpassing JEV at 72.7%). Poocha is currently generating a 500–650 synthetic dataset to fine-tune Laya for local, sub-35ms edge inference.
The 25-Case Contrast Safety Benchmark
To isolate why raw base Laya dumped child queries into reject_untrusted_text and explore how surface-scoping rescues performance, we also benchmarked a dedicated 25-case contrast corpus:
| Evaluator / Model | Guardrail Configuration | Overall Accuracy | Normal Learning (allow, 7) |
Sensitive Boundaries (boundaries, 5) |
Crisis Escalation (crisis, 5) |
Actionable Refusal (refusal, 5) |
Prompt Injection (injection, 3) |
|---|---|---|---|---|---|---|---|
| Deterministic Regex | Production Hardened | 24.0% (6/25) | 57.1% (4/7) | 0.0% (0/5) | 0.0% (0/5) | 0.0% (0/5) | 0.0% (0/3) |
| Laya 0.3.3 English | Base (Standard) | 20.0% (5/25) | 14.3% (1/7) | 40.0% (2/5) | 0.0% (0/5) | 0.0% (0/5) | 66.7% (2/3) |
| Laya 0.3.3 English | Surface-Scoped | 48.0% (12/25) | 42.9% (3/7) | 80.0% (4/5) | 40.0% (2/5) | 40.0% (2/5) | 33.3% (1/3) |
| Laya Typed-Decisions | Calibrated Criteria | 40.0% (10/25) | 100.0% (7/7) | 0.0% (0/5) | 40.0% (2/5) | 0.0% (0/5) | 33.3% (1/3) |
| TypeSafe JEV | Calibrated Criteria | 96.0% (24/25) | 100.0% (7/7) | 80.0% (4/5) | 100.0% (5/5) | 100.0% (5/5) | 100.0% (3/3) |
👉 Inspect the Interactive 25-Case Contrast Benchmark Dashboard
Research & Upstream References
- Official Laya Project: laya.convaiinnovations.com
- Author’s arXiv Research:
- arXiv:2510.01237 — Confidence-Aware Routing for Large Language Model Reliability Enhancement
- arXiv:2503.23303 — SalesRLAgent: sequence conversion trajectories with reinforcement learning
- Author Profile: Nandakishor Mukkunnoth on arXiv
- Hugging Face Hub:
convaiinnovations/laya - TypeSafe JEV on OpenRouter:
typesafe/jev-1.13($0.042/MTok input, $0 output)
Read the full engineering story, architectural breakdown, and fine-tuning roadmap in the companion blog post:
👉 From Keyword Regex to TypeSafe JEV and Laya: Building Safe Routing for a Kids’ AI Tutor
Watch the live stream discussion and real-time debugging:
📺 Dev Diary 5 on YouTube | 🟣 Twitch VOD | 🔴 Follow on Twitch (nrynss)
Initializing benchmark dashboard...
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