Mathematical Autopsy Deep Dive
The complete six-step process from ambiguous intent to deterministic execution.
The Complete Pipeline
Mathematical Autopsy decomposes complex natural language instructions into a deterministic, inspectable, and replayable series of logical transformations. Each step builds upon the previous, creating a complete audit trail from intent to implementation.
The Six Steps
intent.md
The Original Vision
The original human vision or instruction in natural language. This is where every Mathematical Autopsy begins—capturing the raw intent before any transformation.
- Natural language description of the desired outcome
- Initial requirements and constraints
- Success criteria and acceptance conditions
- Context and background information
conversation.md
Exploring Edge Cases
Simulated dialog exploring edge cases and clarifications. This step ensures we understand the full scope and handle ambiguity before formalization.
- Q&A session to clarify ambiguous requirements
- Edge case identification and handling
- Scope refinement and boundary conditions
- Stakeholder alignment on expectations
calculus.md
Formal Symbolic Logic
Formal symbolic logic describing the mathematical solution. This is where natural language transforms into precise mathematical statements.
- Mathematical formulation of the problem
- Symbolic representation of operations
- Invariant definitions and constraints
- Formal specification of behavior
calculus_trace.md
Validation Trace
Validation trace proving behavior correctness. This step provides mathematical proof that the calculus satisfies the original intent.
- Step-by-step proof of correctness
- Validation against original requirements
- Edge case verification
- Mathematical guarantees and bounds
calculus_map.md
Symbol to Code Mapping
Mapping of mathematical symbols to code artifacts. This bridges the gap between mathematical abstraction and concrete implementation.
- Symbol-to-code artifact mapping
- Implementation strategy documentation
- Data structure and algorithm selection
- Performance and complexity analysis
code.py
Executable Implementation
Final executable code derived from proven mathematics. Every line of code can be traced back to a mathematical statement.
- Implementation matching the calculus
- Code comments referencing mathematical steps
- Test cases validating mathematical properties
- Documentation linking code to proofs
Why This Process Works
Explainability
Every output grounded in an intermediate symbolic step. Complete transparency from intent to code.
Testability
Each transformation validated independently. Mathematical proof at every stage.
Replayability
Any result regenerated identically from original intent. Deterministic and reproducible.
Prove Your AI's Reasoning
Mathematical Autopsy transforms black-box AI into transparent, provable systems. Every decision traced, every output verified.