Most large language models are conversational agents designed to generate verbose text. connectailab-rl-CHOCO-BRAINGUARD-v1-GGUF is an External Prefrontal Cortex Decision Engine for Autonomous E-Commerce & FinTech Defense — powered by the JEV (Judgment-Evaluation-Verification) protocol and calibrated via Deep Reinforcement Learning (PPO), this on-device executive cortex transforms high-risk payment fraud, suspicious refund exploitation, and compliance incidents into structured, zero-latency Action Verdicts in 0.05 seconds on edge hardware without hallucinations. Calibrated with an aggressive risk penalty function to prioritize business resilience and capital preservation: Evaluated against…
Independent publisher
Hwihwa Lab
hwihwalab
Generative AI, Multi-Agent Systems, Creative Tech, Generative Design
Models in Library3
Datasets in Library0
Models on Hugging Face15
Followers2
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Published tasks: Reinforcement learning.
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Models
Standard conversational LLMs are prone to hallucinations, verbosity, and latency—rendering them unsuitable for mission-critical financial safety audits. connectailab-rl-CHOCO-BRAINGUARD-v1 is an External Prefrontal Cortex Decision Engine for Solopreneurs & E-Commerce — powered by the JEV (Judgment-Evaluation-Verification) protocol and calibrated via Deep Reinforcement Learning (PPO), this on-device executive cortex neutralizes malicious fraud attacks, high-risk refund exploitation, and compliance crises in 0.05 seconds with zero latency. While standard generic models maintain balanced weights (1.0: 1.0: 1.0), CHOCO-BRAINGUARD-v1 is mathematically tuned with an aggressive risk penalty…
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-BRAINGUARD-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. While standard generic models balance reward priorities symmetrically (1.0: 1.0: 1.0), CHOCO-BRAINGUARD-v1-teacher enforces an aggressive risk-governance objective: $$\text{Reward} = 0.8 \cdot \frac{\text{Profit}}{10} + 1.0 \cdot…