This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does Directional Preference Alignment affect code generation pass@1 scores on HumanEval when models are subjected to adversarial syntax perturbations compared to standard RLHF. Abstract---Large Language Models (LLMs) suffer from inherent stochasticity, limiting their utility in high-stakes enterprise environments where determinism and auditability are required. This paper introduces the MFOUR Vibe Framework (MVF), a platform-agnostic architectural. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: How does Directional Preference Alignment affect code generation pass@1 scores on HumanEval when models are subjected to adversarial syntax perturbations compared to standard RLHF?Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.