Can this skill help with adversarial attacks?

Yes, it can generate formal certificates that prove a model is mathematically robust against adversarial perturbations within a defined epsilon range.

What is formal verification in the context of AI?

Formal verification is the use of mathematical proofs to guarantee that an AI model behaves correctly according to specific properties, going beyond standard empirical testing.

Which theorem provers does this skill support?

The skill provides interfaces for industry-standard solvers including Z3, Lean, and Coq to encode and solve logical formulas representing model behaviors.

What is categorical correctness in AI?

It involves using category theory to ensure that when AI models are composed or transformed, their core logical properties and constraints are preserved.

How does interval arithmetic improve AI reliability?

It propagates input ranges through a network to determine absolute output bounds, which is critical for certifying safety and preventing out-of-bounds errors.

Formal Verification AI

Name: Formal Verification AI
Author: plurigrid

byplurigrid

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Data Science & ML

Ensures the reliability and mathematical correctness of AI models through automated theorem proving, interval arithmetic, and categorical proofs.

Formal Verification AI provides a rigorous framework for validating neural networks and AI systems against strict mathematical specifications. By integrating automated theorem provers like Z3, Lean, and Coq with advanced methods such as interval arithmetic and functorial analysis, it allows developers to generate certified bounds on outputs and prove adversarial robustness. This skill is indispensable for safety-critical applications where empirical testing is insufficient and formal guarantees of fairness, safety, and compositional correctness are mandatory.

Key Features

01Adversarial robustness certification and defense verification

028 GitHub stars

03Categorical proof verification for compositional network correctness

04Encoding of safety-critical properties for neural network models

05Automated theorem proving with Z3, Lean, and Coq integration

06Interval arithmetic for certified output bounds and stability analysis

Use Cases

01Ensuring compositional correctness in modular neural architectures

02Verifying adversarial robustness in safety-critical autonomous systems

03Proving fairness and bias-free properties in automated decision models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi formal-verification-ai

For use in Claude.ai and ChatGPT

Download Skill