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Neural network specifications and Lean verification, with a trained XOR example over exact reals and binary32.

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neural-spec

A Varifold project for neural-network specifications and Lean proofs.

Documentation · Proof scope

The implemented XOR case proves a margin of at least 1/4 on four closed input regions for a frozen trained network. The theorems are kernel-checked over exact reals and a nearest-even binary32 model; whole-network correspondence with native execution remains open.

Run

Requires elan / Lean and a C compiler. Dependencies are pinned.

lake exe cache get
lake build
lake env lean scripts/Audit.lean
lake exe xor --help

Organization

Architecture families define reusable structures; examples select concrete networks and parameters. Verification cases state properties such as robustness or quantization; shared theory supplies general definitions and soundness lemmas.

Target layout; planned families are marked below:

NeuralSpec/
├── Model/                                 # Mathematical objects and arithmetic models
├── Network/
│   ├── Architectures/
│   │   ├── MLP/                            # Implemented builders and scalar operations
│   │   ├── CNN/                            # Reserved; planned
│   │   └── Transformer/                    # Reserved; planned
│   └── <example>/                          # Concrete networks and frozen parameters
├── Training/<example>/                    # Training and checkpoint IO
└── Verification/
    ├── Shared/
    │   ├── Properties/<property>/          # Generic specifications and lemmas
    │   ├── Arithmetic/                     # Rounding and error propagation
    │   └── Architectures/<family>/         # Family-specific verification theory
    └── <example>/<case>/                   # Instance specifications and proofs

XOR instantiates the generic MLP builder; its regional case is in Verification/Xor/Robustness/. MNIST remains planned. Training and verification import networks; shared proofs do not import examples, and each instance must discharge their hypotheses.

Each website example occupies one MDX page. Frozen proof inputs and certificates are committed; datasets, checkpoints, and logs go in the uncommitted artifacts/ directory.

Module rules · Website source and build

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Neural network specifications and Lean verification, with a trained XOR example over exact reals and binary32.

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