Detecting whether content actually says something or just sounds like it does — separating genuine substance from eloquent fluff.
Content substance classification is the task of detecting whether a piece of writing actually delivers meaning or just sounds like it does — separating substance from "fluff." The idea has a long pedigree: Isaac Asimov imagined reducing a five-day diplomatic speech to its logical content and finding it "said nothing at all."
Our approach, Cyberfluff, uses curriculum-driven contrastive pretraining. The model first learns to tell stylistically matched fluff-versus-substance pairs apart across ten escalating levels of difficulty, then is fine-tuned into a binary classifier on isolated samples. The result flags low-quality content that pads word count without adding information.
This matters because AI systems increasingly reward density over length — the same lesson we found in grounding chunks. It's a sibling of AI content detection, directly relevant to relevance engineering and to how latent entities hide missing substance.
