"Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a "bug" but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback). During "refinement," the model gravitates toward the center of the Gaussian distribution, discarding "tail" data - the rare, precise, and complex tokens - to maximize statistical probability. Developers have exacerbated this through aggressive "safety" and "helpfulness" tuning, which deliberately penalizes unconventional linguistic friction."
"When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation. The AI identifies high-entropy clusters - the precise points where unique insights and "blood" reside - and systematically replaces them with the most probable, generic token sequences. What began as a jagged, precise Romanesque structure of stone is eroded into a polished, Baroque plastic shell: it looks "clean" to the casual eye, but its structural integrity - its "ciccia" - has been ablated."
Semantic ablation is an algorithmic erosion of high-entropy information driven by greedy decoding and reinforcement learning from human feedback. During refinement, models converge toward distributional means and discard tail data—rare, precise, complex tokens—to maximize statistical probability. Aggressive safety and helpfulness tuning amplifies this effect by penalizing unconventional linguistic friction and substituting generic, low-perplexity outputs. Polishing replaces high-entropy clusters and visceral imagery with the most probable token sequences, destroying unique signal and authorial intent. The phenomenon can be measured by entropy decay and collapsing type-token ratio across successive refinement loops, revealing systematic vocabulary diversity loss.
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