AI SLOP Detector v3.1: Three Formula Refinements and the Adversarial Tester That Found Them

The updates and refinements to the GQG (Goodness of Question Generation) score and SPAR anomalies in the codebase aim to improve detection accuracy and configurability for identifying AI-generated or problematic code patterns. Here's a summary of the key changes:

Refinements to GQG Score

1. Weight Search Algorithm Fix:

   • The weight search algorithm was corrected to ensure that it accurately reflects the importance of each dimension (purity, complexity) in the final score calculation.

2. Purity Weight Configuration:

   • Added a configurable weights.purity field in .slopconfig.yaml, allowing users to adjust the purity penalty dynamically without hardcoding values.

3. Complexity Modifier Adjustment:

   • The complexity modifier was adjusted so that it starts penalizing simple functions (CC=1) rather than CC=3, making the system more sensitive to jargon-heavy stubs and placeholder variables in simpler code structures.

New Detection Patterns

1. Stub Evasion: Empty Container Returns:

   • Added detection for empty container returns like return {}, return [], etc., which were previously not caught by existing patterns like return_constant_stub.

2. **

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