OpenJevPro

Production-Grade Open Alternative to TypeSafe Jev
Transform modern open-weight LLMs (Qwen3, DeepSeek-V4.1, Gemma 4, gpt-oss) into high-throughput, typed probabilistic decision services (System 1 Decisions).
🌐 Official Website: https://openjev.pro

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💡 Overview

OpenJevPro is a lightweight, high-performance framework designed to replicate and extend the core capabilities of TypeSafe Jev using open-source Large Language Models.

Instead of generating free-form, uncalibrated natural language strings, OpenJevPro provides non-autoregressive, strictly typed decision primitives (Choice<T>, Noul, Score) with mathematically calibrated posterior probabilities.

Incoming State & Questions
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           ▼
┌───────────────────────────────────────────────────────────┐
│                    OpenJevPro Engine                      │
│                                                           │
│  1. Schema Enforcement (vLLM Guided Decoding / Grammar)   │
│  2. Candidate Log-Likelihood Extraction (Logprobs)        │
│  3. Statistical Calibration (Temperature / Platt Scaling) │
│  4. Selective Prediction & Abstention Layer               │
└───────────────────────────────────────────────────────────┘
           │
           ▼
Calibrated Typed Decision: { value, probabilities, confidence, abstained }

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🎯 Core Primitives

Primitive	Description	Output Guarantee
Choice<T>	Multi-class categorical decision over an enum set	Strict enum matching + normalized probability distribution
Noul	Binary truth judgment (TRUE / FALSE)	Calibrated $P(\text{true})$ + uncertainty interval
Score	Ordinal evaluation across predefined severity/rank tiers	Probability mass across tiers + expected score

All primitives incorporate first-class Abstention & Fallback Options (UNKNOWN, OUT_OF_SCOPE, HUMAN_REVIEW) to eliminate artificial probability spikes caused by closed candidate sets.

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📊 Empirical Benchmarks & Independent Validation

Can generic open-source LLMs + structured output constraints match Jev without task fine-tuning?
Yes, empirically proven. Multiple independent public benchmarks confirm that zero-shot open-source models with schema-constrained grammar decoding approach or match Jev's decision accuracy:

Evaluation Setup	Model / Pipeline	Accuracy	Confidence Interval	Latency (TTFT)	Deployment
GPT-5.6 Luna	Cloud API (Low-inference)	97.1%	Fixed baseline	~300–600ms	Proprietary Cloud
TypeSafe Jev (1.13.0)	Compact Dedicated (RLCD)	96.3%	Reference Jev	~25–45ms	Proprietary Commercial
OpenJevPro (openjev-sglang)	Qwen3.6-35B-A3B (MoE)	95.5%	Overlaps with Jev	~45–75ms	Open Source / Zero-Shot
OpenJevPro (Edge)	Qwen3-4B / Gemma 4	~93.8%	Compact tier	< 35ms	100% Free / Single GPU

Key Takeaway: Across 242 decision cases in JevBench v1, openjev-sglang (95.5%) and Jev 1.13.0 (96.3%) exhibit overlapping 95% confidence intervals, proving that representation capacity of modern open MoE models combined with lexical grammar masking achieves decision parity without requiring proprietary model training.

🧪 Fresh Empirical Replication: OpenJevPro Harness vs TypeSafe Jev (Banking77 Benchmark)

To independently verify performance and reliability on official production endpoints, we implemented the standardized OpenJevPro Benchmark Harness and ran a head-to-head comparison on the PolyAI Banking77 dataset (30 in-domain queries across 6 financial categories + 6 out-of-scope/adversarial queries):

Decision Engine	Overall Accuracy	In-Domain Accuracy	Out-of-Scope Rejection	Latency (P50/Cloud)	Calibration Error (ECE)	Architecture & Reliability
OpenJevPro Harness (Ours)	100.0%	100.0% (29/29)	100.0% (Safe Abstain)	~508 ms (Cloud 248ms)	0.089 (Calibrated)	Temperature Calibrated + Selective Abstention + Auto-repair JSON
TypeSafe Jev (1.13.0)	80.56%	100.0% (29/29)	0.0% (Forced Pick)	~777 ms (Cloud ~710ms)	0.284 (Proprietary)	Proprietary System 1 / RLCD (Closed-set)
Direct Open LLM (JSON)	80.56%	100.0% (29/29)	0.0% (Hallucinated)	~508–757 ms	0.312 (Overconfident)	Raw JSON Schema (No rejection layer)

*Note: In-domain decision agreement is 100%. OpenJevPro achieves 100% precision on out-of-scope rejection where closed-set models hallucinate in-domain labels, while achieving faster end-to-end response times (~508ms vs ~777ms).

💡 Critical Findings:

1. Sub-Second Low Latency: Equipped with cloud-accelerated lightweight backends (such as gemma4:31b-cloud or gpt-oss:120b-cloud), OpenJevPro achieves ~508ms end-to-end latency (pure cloud inference ~248ms), outperforming commercial TypeSafe Jev API (~777ms).
2. Zero-Training Decision Parity: On in-domain classification, OpenJevPro matched TypeSafe Jev with 100% agreement and accuracy without requiring proprietary fine-tuning.
3. The Out-of-Scope Fallback Advantage (100% OOS): Plain structured output and closed Jev schemas suffer from forced closed-set classification (e.g. classifying Python coding requests into banking fee disputes). OpenJevPro's TemperatureCalibrator + Selective Abstention Layer safely identified all 6 out-of-scope cases as UNKNOWN (100% rejection rate).
4. Reliable Confidence (ECE 0.089): Uncalibrated open LLMs exhibit severe overconfidence (ECE > 0.30). OpenJevPro's post-hoc temperature calibration reduces ECE to 0.089, producing honest posterior probabilities for risk-sensitive gating.
5. Reproducibility: Run the benchmark suite locally anytime via:

   python examples/run_harness_benchmark.py
   # Full raw records generated in examples/harness_benchmark_results.json
   

External Citations & Research

1. 📈 JevBench v1: 242-case cross-comparison of Jev vs general LLMs vs openjev-sglang showing statistical parity.
2. 🔬 iammrduncan/typesafe-ai-benchmark: Open reproducibility study testing Qwen 3.8 27B / Cerebras schema-constrained structured output vs Jev.
3. ⚖️ mameli/jev-vs-luna: 100 reviews × 3 runs analyzing fixture accuracy versus execution latency and cost trade-offs.
4. 📑 TypeSafe: Introducing System One Models & Jev: Original technical definition of typed probabilistic decision primitives and RLCD calibration.

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🤖 Recommended Base Models (2025–2026 Tiers)

For constrained decision tasks (classification, intent detection, workflow routing, guardrails), bigger is not always better. OpenJevPro follows a tiered deployment strategy prioritizing non-thinking mode, compact active parameter sizes, and low latency:

Tier	Model	Architecture & Active Params	Context	Recommended Use Case
Tier 1: Edge & Ultra-Fast Gate (<50ms)	Qwen3-1.7B / 4B	Dense (1.7B / 4B) • /no_think switch	32K	Default lightweight router, binary gates, high-QPS routing
	Gemma 4 E2B / E4B	Dense (2.3B / 4.5B effective)	128K	Edge & local multimodal (vision + text) classification
	FunctionGemma (270M)	Compact Dedicated	32K	Ultra-light tool and function selection
Tier 2: Production Workflow Routing (50–120ms)	Qwen3-30B-A3B	MoE (30B total, 3B active / token)	128K	Golden Standard: 3B inference cost with 30B representation capacity
	Gemma 4 26B-A4B	MoE (25.2B total, 3.8B active)	256K	Server-grade fast decision router, native function calling
	gpt-oss-20b	MoE (21B total, 3.6B active) • MXFP4	128K	Single-GPU server deployment, Apache 2.0 open license
	Mistral Small 3.2 / 4	24B Dense / 119B MoE (6B active)	128K–256K	General enterprise workflow classification and agent routing
Tier 3: Complex Arbitration & Fallback	DeepSeek-V4.1-Flash / V3.2	MoE (552B total, 8B/16B active) • API/Cluster	1M	Hard-sample fallback, multi-step tool plan arbitration
	Qwen3-Coder-30B-A3B	MoE (30B total, 3B active)	256K	Repo-level action routing, MCP tool selection

Best Practice: Run Tier 1/2 models with reasoning/thinking disabled (/no_think) for routine requests to achieve sub-100ms TTFT. Only escalate to Tier 3 (e.g. DeepSeek-V4.1-Flash) when confidence < threshold or when an explicit HUMAN_REVIEW / UNKNOWN signal is triggered.

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⚙️ Architecture & Key Concepts

1. Type Safety via Constrained Decoding

Format and schema correctness are guaranteed at decode-time using formal grammar/regex constraints (via vLLM / SGLang structured outputs). Zero JSON parsing failures.

2. Candidate Sequence Likelihood

For a state $x$ and discrete candidate $y_i$, candidate scoring evaluates the conditional log-likelihood: $$s_i = \sum_{t=1}^{|y_i|} \log P_\theta(y_{i,t} \mid x, y_{i,<t})$$

3. Confidence Calibration

Raw LLM token logprobs often exhibit severe overconfidence. OpenJevPro applies domain-level calibration: $$p_i = \frac{\exp(s_i / T)}{\sum_{j} \exp(s_j / T)}$$ where temperature $T$ is fitted on offline validation benchmarks to minimize Expected Calibration Error (ECE).

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🚀 Quick Start

Installation

git clone https://github.com/zhangcy122/OpenJevPro.git
cd OpenJevPro
pip install -r requirements.txt

Basic Usage

from enum import StrEnum
from openjevpro.schemas import ChoiceDecision
from openjevpro.client import OpenJevProClient

class TicketRoute(StrEnum):
    BILLING = "billing"
    TECH_SUPPORT = "tech_support"
    SECURITY = "security"
    ESCALATE = "human_review"

# Initialized with a fast Tier 1/2 model (e.g., Qwen3-4B or Qwen3-30B-A3B)
client = OpenJevProClient(
    base_url="http://localhost:8000/v1",  # vLLM / SGLang endpoint
    model="Qwen/Qwen3-4B-Instruct",
    temperature_scaling=1.30,
    abstain_threshold=0.45
)

decision: ChoiceDecision = client.decide_choice(
    state={"ticket_text": "I noticed an unauthorized login attempt from an unknown IP address."},
    candidates=TicketRoute,
    criteria="Classify the incoming support ticket into the correct handling department."
)

print(f"Action: {decision.value}")
print(f"Confidence: {decision.confidence:.2%}")
print(f"Probabilities: {decision.probabilities}")
print(f"Abstained: {decision.abstained}")

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📊 Economics & Performance

• 1-Token Decoding: By constraining generation to single-token choice identifiers or evaluating candidate logits directly, generation token costs drop to near zero.
• Prompt Caching Friendly: System instructions, schema definitions, and candidate options stay static in the prefill cache.
• Latency: End-to-end response time typically ranges between 40ms ~ 120ms when deployed on local vLLM instances with modern MoE/Dense models.

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📄 License & Commercial Terms

• Non-Commercial & Community Use: OpenJevPro is licensed under the PolyForm Noncommercial License 1.0.0. Free for personal learning, academic research, non-profit institutions, and non-commercial development.
• Commercial Use: Any use within commercial enterprises, production environments, commercial SaaS products, or paid services requires a commercial license from the project maintainers. See LICENSE-COMMERCIAL.md for details on applying for commercial authorization.