Enhanced MVRA: A Hierarchical Validation Framework for Reliable AI Reasoning with Adaptive Conflict Resolution and Performance Optimization

This work is governed by a dual-licensing policy. For detailed terms regarding both non-commercial (CC BY-NC-SA 4.0) and commercial use, please consult the full Legal and Operational Framework: https://zenodo.org/records/16044774 ----------------------------------------- This repository implements the Enhanced Modular and Validated Reasoning Architecture (MVRA), a prototype framework for automated, multi‑tier content validation. Building on a hierarchical “judge” design, it applies three levels of evaluation—objective constraints (syntax and fact checks), semi‑objective metrics (logical coherence and completeness), and subjective assessments (style and creativity)—to produce a single, conflict‑resolved quality score. A lightweight MetaJudge component detects and resolves score discrepancies using weighted averages and dynamic penalties, while an ErrorMemory module records recurring error patterns to drive adaptive reliability updates for each judge. Key features include: Parallelized evaluation via Python’s ThreadPoolExecutor, reducing end‑to‑end latency to milliseconds. Conflict resolution with tunable thresholds and penalty schemes that adapt judge weights based on past performance. Performance monitoring, tracking costs, throughput, latency, accuracy, and conflict rates for each validation run. Adaptive learning, where judge reliability scores evolve according to error history and context similarity. Included sample runs demonstrate the system’s ability to reject poor content early, validate technical and creative inputs with > 90 % accuracy, and update judge confidences over time. Future work will integrate real LLM‑based judges, expand fact‑checking resources, and scale across distributed environments.