A systematic investigation into alignment strategies, existential risk mitigation, and the architectural foundations of safe artificial intelligence systems. Form follows function.
Analysis
Interpretability breakthroughs in transformer architectures have revealed systematic feature decomposition patterns, enabling researchers to map over 4,000 distinct behavioral circuits across frontier language models.
Constitutional AI methods demonstrate measurable reduction in harmful outputs (37% improvement on adversarial benchmarks) while preserving task performance within 2% of unconstrained baselines.
Scalable oversight protocols using recursive reward modeling show diminishing returns beyond three iteration layers, suggesting fundamental limits to current alignment-via-feedback architectures.
Multi-agent coordination failures emerge predictably at system scales above 10,000 concurrent agents, with misalignment propagation following power-law distributions in simulated environments.
Spanning 2019 to 2026, across 42 institutions worldwide
Highest recorded score using combined RLHF and constitutional training
Annual investment as of 2025
Mechanistic interpretability has accelerated exponentially. The number of fully documented neural circuits doubled every 8 months between 2023 and 2026, establishing a robust empirical foundation for alignment verification.
Research Areas
Alignment
Training paradigms that leverage iterative human preference signals to shape model behavior toward intended objectives while minimizing reward hacking.
Interpretability
Reverse-engineering neural network computations to identify and catalog the specific circuits responsible for distinct model behaviors and capabilities.
Governance
Policy architectures for multilateral cooperation on AI development standards, compute governance, and shared safety evaluation infrastructure.
Robustness
Systematic stress-testing methodologies designed to discover failure modes, jailbreaks, and unexpected emergent behaviors before deployment.
Ethics
Formal approaches to encoding human values under conditions of deep moral disagreement, drawing on decision theory and moral philosophy.
Forecasting
Predicting when qualitatively new capabilities will emerge during training, and developing early-warning metrics for dangerous capability thresholds.