Quantitative Logic & AI Veracity Research | Independent Developer
TextRLHF
Engineered and executed a framework for auditing the algorithmic analysis of text-based data and mitigating risk in AI model outputs. Designed Anti-Sycophancy Gates to identify factual inconsistencies and prevent alignment failures in large language models. Developed real-time Infrastructure Desync Detectors for telemetry audit supporting model performance stability.
• Applied objective audit protocols to assess and validate AI-generated responses.
• Refined mechanisms to detect and flag sycophantic tendencies in language models.
• Evaluated system synchronization to safeguard against data or logic drift.
• Focused on upholding factual veracity across all AI outputs.
Engineered and executed a framework for auditing the algorithmic analysis of text-based data and mitigating risk in AI model outputs. Designed Anti-Sycophancy Gates to identify factual inconsistencies and prevent alignment failures in large language models. Developed real-time Infrastructure Desync Detectors for telemetry audit supporting model performance stability. • Applied objective audit protocols to assess and validate AI-generated responses. • Refined mechanisms to detect and flag sycophantic tendencies in language models. • Evaluated system synchronization to safeguard against data or logic drift. • Focused on upholding factual veracity across all AI outputs.
2024 - Present