AI Development Framework (AIDF)

AIDF is a mathematically grounded framework that combines formal reasoning (logic/semantics) with optimization and governance mathematics. It ensures correctness by construction, runtime verification, and traceable decisions tied to formal invariants. Built on sequent calculus, operational semantics, denotational semantics, and lifecycle calculus, AIDF transforms AI development from ad-hoc implementation to mathematically verified systems with provable guarantees.

Mathematical Foundations

• Sequent Calculus: Requirements → Assurance transitions with consistency guarantees
• Operational Semantics: Deterministic state transitions and termination guarantees
• Denotational Semantics: Category theory mappings and compositional design
• Master & Lifecycle Calculus: KKT conditions, duality theory, and optimization
• Policy/Governance & Assurance: Gate soundness, compliance closure, safety/liveness

Key Features

• Correctness by Construction: Systems proven correct at design time
• Runtime Verification: Via obligations and monitors
• Traceable Decisions: All decisions tied to formal invariants
• Standards Integration: ISO/IEC 42001, NIST AI RMF, IEEE standards
• Assessment Engine: AI maturity assessments with scoring and recommendations

For detailed technical documentation, see the AIDF repository documentation.