6 papers
Negative and Fractional Types in the Fidelity Framework
Houston Haynes
Our Native Type Universe (NTU) has been detailed through five previous papers establishing the substrate our framework's compilation pipeline targets across multiple hardware platf…
Fixed-Point Scaffolding in the Clef Programming Language
Houston Haynes
For fans of Gabriel's "Worse is Better" it may be ironic that C++, by way of MLIR, serves as the scaffold for compiling an ML-family language whose correctness properties are struc…
Decidable By Construction: Design-Time Verification for Trustworthy AI
Houston Haynes
A prevailing assumption in machine learning is that model correctness must be enforced after the fact. We observe that the properties determining whether an AI model is numerically…
Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
Houston Haynes
Prevailing AI training assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relative to inference, optimizer complexity, and str…
The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation
Houston Haynes
The Program Semantic Graph (PSG) introduced in prior work on Dimensional Type Systems and Deterministic Memory Management encodes compilation-relevant properties as binary edge rel…
Dimensional Type Systems and Deterministic Memory Management: Design-Time Semantic Preservation in Native Compilation
Houston Haynes
We present a compilation framework in which dimensional type annotations persist through multi-stage MLIR lowering, enabling the compiler to jointly resolve numeric representation…