ai-logic-audit-framework Automated testing harness that detects logical inefficiencies and structural faults in AI-generated algorithms. Where systems fail LLMs frequently produce algorithmically correct but computationally inefficient solutions, such as defaulting to O(n²) nested iteration for lookup tasks. Detection Mechanism • Dynamic complexity analysis via input scaling (10^2 -> 10^5) • Automated execution time profiling • Deterministic Big-O threshold flagging False Positive Safeguards • Does not flag nested loops when the inner iteration scope is strictly constant (O(1)). • Ignores trivial O(n²) signatures on explicitly bounded, micro-scale datasets where memory overhead of hashing outweighs compute costs. Quick Start: Live Demo Run the live evaluator to see the system catch a quadratic bottleneck in real-time. Why this matters Prevents production-level latency bottlenecks and compute waste in AI-assisted CI/CD pipelines, ensuring scaling infrastructure remains resilient.