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V. AI, Learning & Information Theory (111–140)
- Formal definition of Artificial General Intelligence.
- Provable alignment framework for superintelligence.
- Exact generalization law for deep neural networks.
- Implicit bias of optimization theory.
- Mathematical theory of emergence in AI systems.
- Information bottleneck closed-form solution.
- AI interpretability via information geometry.
- Robustness bounds under adversarial attack.
- Neural-symbolic reasoning unification.
- Formal verification of AI-generated code.
- Universal attention architecture theory.
- Self-improving AI stability constraints.
- Learning under distribution shift limits.
- Energy efficiency bounds of AI systems.
- Multi-agent equilibrium theory.
- Collective intelligence emergence modeling.
- AI epistemic uncertainty formalization.
- Lifelong learning theoretical limits.
- Human-AI co-evolution dynamics.
- AI governance mechanism formalization.
- Ethical logic formal systems for AI.
- Computational theory of creativity.
- AI-driven scientific discovery automation theory.
- Intelligence scaling law completion.
- Causal representation learning theory.
- Reinforcement learning global convergence proof.
- Information-theoretic limits of reasoning systems.
- Machine reasoning formal completeness.
- General-purpose cognitive architecture theory.
- Algorithmic consciousness modeling.