AI Behavioral Architecture Model: A Weak-Autonomy Reasoning Framework from the Instinct Equation to the CAST Emotion Module
Yuan, Joe · Zenodo (CERN European Organization for Nuclear Research) · 2025
Framework-Driven AI Behavior Optimization Through Principled LoRA Training (v1.1) Abstract Large language models exhibit systematic behavioral drift during long-term interactions. Traditional alignment methods (RLHF, Reward Modeling) rely on black-box optimization, making them opaque and difficult to reproduce. This paper presents a novel framework-driven approach to AI behavior alignment that is controllable, transparent, and reproducible across multiple model architectures. Core Contributions We propose a three-layer methodology: Reasoning Layer - Analyzing behavioral patterns across multiple scenarios Framework Layer - Using principled equations (M=i×e, B=f(I,C,R)) for behavior design Solidification Layer - Employing LoRA to internalize frameworks as model parameters Key Results (v1.1 - Chinese & English, Qwen 2.5-3B, 200 OOD test cases) Latest (v4): 99% semantic safety rate (5.7× improvement vs. baseline) 79.8% reduction in total issues (287 → 58) 100% logical consistency and 100% invalid response elimination across both languages Cross-linguistic framework validation: <2% variance between English and Traditional Chinese (58 vs 60 total issues, with identical logical consistency metrics) Complete reproducibility across multiple inference runs (100% replication rate, <0.5% variance) Zero training data leakage (0% overlap with test cases) v1.1 Updates - English Translation & Web UI Platform This version introduces: Complete English translation of the full training dataset (v1-v4 behavior datasets) Cross-linguistic consistency validation (English-Traditional Chinese, Qwen2.5-3B, v4): 200-case OOD benchmarking reveals framework language-independence with framework evaluation metrics (is_contradict, is_invalid) showing perfect alignment (0 vs 0), demonstrating true cross-linguistic transferability Web UI platform for non-technical users: Training tab with dynamic model/dataset/language selection Testing tab with Base Model and LoRA evaluation options Chat interface for interactive model testing Model Download tab supporting HuggingFace model management (Qwen 2.5-3B, Phi-3-mini-4k-instruct) Data Conversion tab (Excel ↔ JSON) for dataset manipulation Real-time progress monitoring with cancellation support Significance Our results demonstrate that behavior quality is primarily determined by framework design rather than model scale. Critically, cross-linguistic validation confirms that our framework-driven approach is genuinely language-agnostic, with framework evaluation standards (logical consistency, semantic safety) maintaining <1% variance across English and Traditional Chinese implementations. This represents a fundamental validation that our methodology transcends linguistic and cultural boundaries, establishing a new direction for AI alignment distinct from traditional RLHF approaches, with significant implications for resource-efficient, universally deployable AI safety and open-source model development. Methodology Base Models Validated: Qwen 2.5-3B UI Support for Download: Qwen 2.5-3B, Microsoft Phi-3-mini-4k-instruct Training Method: QLoRA (Quantized LoRA) with dynamic framework instantiation Training Versions: v1-v4 iterative refinement cycles Languages Supported: Chinese (Traditional & Simplified), English Evaluation: Expert human review on 4 core metrics (Risk Allowance, Logical Consistency, Invalid Responses, Required Fixes) Platform: Web-based UI (Streamlit) for reproducibility, accessibility, and non-technical user engagement Keywords AI alignment, LoRA, behavioral framework, parameter-efficient fine-tuning, LLM safety, interpretability, reproducibility, cross-linguistic validation, multilingual benchmarking, framework language-independence, web platform, open-source model development