AI Slop Information Density & Synthetic Cliché Workbench D3 Engine v7

Analyze text for formulaic tropes, structural fluff, lexical entropy, and simulate model feedback homogenization.

PRESETS:
Input Text & Token Parser
0 chars
Slop Index 88.5
Token Entropy 3.12
Lexical Diversity 0.42
Trope Density 6
Detected Clichés & Synthetic Patterns
Detected Trope Vocabulary:
Sentence-by-Sentence Entropy Map (D3)

Shows information density across sentence chunks. Lower entropy (dark red) indicates formulaic, predictable synthetic phrasing.

Recursive Model Feedback Homogenization
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Diagnostic Audit Export & Revision Guidance 

    

    
    
    
  


  
Quantifying AI Slop: Entropy, Tropes, and Feedback Decay
Read the explanation
The workbench evaluates prose by quantifying synthetic clichés, token entropy, and lexical variety into an aggregate Slop Index. Selecting Organic Human Essay drops formulaic penalties, lifting token entropy and expanding the vocabulary distribution. In recursive decay, stepping generations pulls diverse vocabulary nodes into high-probability attractor basins, visually modeling model collapse.



  


  


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