CPA Lock-In in Language-Model Generation (Pilot Dataset)

Per-token generation data testing the Lock-In prediction of CPA + C in a language model. The design holds the underlying fact constant and varies only how far the prompt pins the admissible answer, separating genuine narrowing of the answer from the unrelated effect of adding output requirements.Twelve factual items the model answers reliably are each posed at three constraint levels on the same fact: open generation about the topic, the plain question, and the question pinned to a one-word answer. Five runs per item per level give 180 generations and 3,736 per-token records.Generation length before halt falls monotonically with constraint, from a mean of 49 tokens at the open level to 12 at the plain question to 1.2 when the answer is pinned. At high constraint the model commits to a single locked continuation and stops, the informational counterpart of vitrification in glass. Median per-answer entropy falls across the three levels.Pilot scale: one model, twelve items. The dataset shows the Lock-In signature and its direction. It is not a quantitative rate law for the information domain. A mean of token entropy across conditions is not reported, because generation length differs about fortyfold between the open and pinned levels and such a mean would track length rather than constraint.