Dynamic Accountability Frameworks presents a visual governance architecture for the emerging era of autonomous research systems and self-driving laboratories. As artificial intelligence increasingly designs experiments, directs robotic labs, and accelerates scientific discovery, traditional research governance models become insufficient. This presentation introduces a structured accountability framework designed for AI-driven experimentation environments. It outlines how oversight, verification, and institutional memory can be integrated directly into the autonomous research pipeline. Through a series of diagrams and system models, the presentation explains how accountability can be embedded across five layers of scientific infrastructure: data integrity, AI decision systems, experiment governance, capital allocation, and institutional knowledge. Key concepts explored include autonomous experiment cycles, accountability injection points, governance feedback loops, capital accountability, experimental portfolio management, and knowledge graph–based institutional memory. The framework also proposes mechanisms for real-time oversight, anomaly detection, and strategic learning loops that connect scientific discovery with funding and policy decisions. Designed primarily as a visual conceptual map, this document aims to help researchers, policymakers, AI developers, and research institutions think more clearly about governance architectures for autonomous science. The future of research will increasingly rely on AI-driven experimentation systems. Ensuring accountability within these systems will be essential for maintaining scientific integrity, transparency, and long-term knowledge accumulation. Author: Nabal Kishore Pande ORCID: 0009-0007-3325-9966 - Download as a PDF or view online for free