connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher

connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher — FinTech Risk Guardian Policy Brain

“connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher: High-Conviction PPO Reinforcement Learning Policy Matrix for E-Commerce & FinTech Fraud Defense
English Documentation|한국어 공식 매뉴얼|LLM Decision Brain”
Developed by Hwihwa Lab· AI × Automation × Aesthetics

Why a Prefrontal Risk Policy Matrix?

Standard conversational language models generate verbose token streams, incur latency, and suffer from hallucination risks—making them hazardous for sub-second financial risk gating and high-stakes fraud interception.
connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher (brain.json) operates as an external Prefrontal Cortex Policy Matrix (142 KB) calibrated through Multi-Objective Reinforcement Learning (PPO) in the CONNECT TYCOON enterprise simulator.

Core Capabilities

• Sub-millisecond execution: Delivers zero-latency, deterministic decisions in under 0.01 seconds.
• Multi-objective PPO calibration: Enforces an aggressive risk-penalty function to safeguard capital reserves, customer trust, and compliance integrity.
• Ground-Truth Decision Anchor: Provides the mathematically optimal judgment trajectories used to distill and align the conversational reasoning LLM (hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1).

Model Persona: "The FinTech Risk Guardian"

While standard generic models balance reward priorities symmetrically (1.0 : 1.0 : 1.0), CHOCO-BRAIN_GUARD-v1-teacher enforces an aggressive risk-governance objective:

$$\text{Reward} = 0.8 \cdot \frac{\text{Profit}}{10} + 1.0 \cdot (\Delta\text{Satisfaction} \times 100) - \mathbf{3.0} \cdot (\text{Incidents} \times 5)$$

{
  "Profit Weight": 0.8,
  "Satisfaction Weight": 1.0,
  "Risk Penalty Weight": 3.0
}

Guardian Governance Protocols:

1. Aggressive Security Gate: Transactions exceeding fraud probability $\ge 0.70$ are immediately terminated.
2. Proactive Refund Review Routing: High-risk refund claims ($\ge 0.60$) are isolated for dedicated verification.
3. Inventory & Stockout Sentinel: Proactive dynamic safety buffers reduce stockout days from 10.7 days down to 0.3 days.

40-Month Stochastic Operational Benchmark

Tested across 40 unseen simulated operating months (800 business days) under dynamic stochastic market conditions:

_{Autonomous Risk Adjudication in CONNECT TYCOON · Top-tier prefrontal cortex instantly gating fraud & refund abuse across departments to safeguard enterprise capital reserves.}

Dual-Cortex Decision Architecture

This repository forms the foundation of Hwihwa Lab's two-tier autonomous decision pipeline:

┌────────────────────────────────────────────────────────┐
│  CONNECT TYCOON Enterprise Simulation Environment      │
└───────────────────────────┬────────────────────────────┘
                            │ Multi-objective PPO
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Policy Brain] CHOCO-BRAIN_GUARD-v1-teacher           │
│  - File: brain.json (142 KB Policy Matrix)             │
│  - 11.11M KRW/mo | 96% Satisfaction | 2.0 Incidents    │
└───────────────────────────┬────────────────────────────┘
                            │ JEV Trajectory Ground-Truth
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Reasoning LLM] CHOCO-BRAIN_GUARD-v1                  │
│  - Qwen3.5-0.8B LoRA Adapter (Natural Language JEV)   │
│  - Multi-turn explanation & Zero-shot reasoning        │
└────────────────────────────────────────────────────────┘

Quick Start & JEV Protocol

1. Python API (JEV typesafe/jev-1.13)

import connect_tycoon as ct

# Load policy brain directly from Hugging Face Hub or local file
brain = ct.CompanyBrain.from_hub("hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher")
# Or: brain = ct.CompanyBrain.from_file("brain.json")

# Evaluate an incoming refund dispute under the JEV protocol
decision = brain({
    "model": "typesafe/jev-1.13",
    "state": {
        "department": "refund",
        "reason": "non-delivery",
        "tier": "vip",
        "amount": 8.7,
        "fraud_score": 0.31
    },
    "questions": {
        "decision": {
            "type": "choice",
            "instructions": "Determine refund disposition.",
            "criteria": {
                "full": "Full Refund",
                "partial": "Partial Refund",
                "deny": "Deny Claim",
                "human": "Route to Human Review"
            }
        }
    }
})

print("Policy Decision:", decision["answers"]["decision"]["choice"])
print("Confidence:", decision["answers"]["decision"]["confidence"])

2. Run Interactive Simulation

# Install the simulator
pip install "connect-tycoon[game]"

# Appoint the Risk Guardian Policy Brain as Company CEO
connect-tycoon play --brain hf:hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher

About Hwihwa Lab

“AI × Automation × Aesthetics — Crafting tools you'll love ”

Hwihwa Lab is an independent AI research & engineering studio dedicated to blending practical on-device intelligence, autonomous operations, and refined aesthetics. We engineer resilient systems that free solopreneurs and indie creators from operational overhead—giving people their time and creative joy back.

Core Research & Engineering Pillars

• Zero-Cost Edge Operations: Private, on-device AI pipelines for autonomous business workflows without ongoing cloud API overhead.
• Autonomous Decision OS & Micro-ERP: Deterministic, prefrontal-cortex-style reasoning architectures designed to orchestrate daily business decisions and operations.
• Embodied AI & Domain-Specific Robotics: Simulation-driven reinforcement learning, physical control brains, and specialized domain data engineering for real-world environments.
• Interactive Creative Aesthetics: Fusing functional automation with refined visual craft and modern design philosophy to build delightful tools.

Connect & Research Ecosystem

• Research Studio: Hugging Face (@hwihwalab) · GitHub (@Hwihwa-Lab)
• CHOCO-BRAIN GUARD Defense Suite:
• Fraud Defense Policy Core: connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher (Current Model) (142KB PPO reinforcement learning policy trained with 3x risk penalty)
• Real-Time Fraud Adjudicator: connectailab-rl-CHOCO-BRAIN_GUARD-v1 (FP16 reasoning LLM executing 0.05s fraud judgment via JEV protocol)
• Zero-Leakage Edge Fraud Guardian: connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF (811MB Q8_0 binary for private, on-device fraud defense without cloud leakage)

_{Part of the CONNECT AI LAB Collaborative Research Network · CONNECT TYCOON Decision Foundation}

License

This project and decision policy matrix are distributed under the Apache 2.0 License — see the repository LICENSE file for full details.

Built and maintained by HWIHWA LAB in collaboration with CONNECT AI LAB.