Constraint-Driven Coherence in LLM Output: Replication Dataset (GPT-4o Extended Run)

This replication expands the initial four-model CPA-coherence study to an extended GPT-4o dataset comprising 90 runs (30 per constraint level). Using identical prompts and fixed parameters, we confirm that increasing informational constraint systematically reduces both the mean surprisal and mean token entropy, replicating DPΦ's predicted coherence-under-constraint signature with high statistical confidence. The experiment additionally reveals a strong correlation between mean surprisal and mean entropy (r = 0.957, p << 0.001), consistent with dual processing regimes ("fast/exploratory" vs. "slow/convergent"). This aggregate surprisal curve fits the Vogel-Fulcher-Tammann (VFT) equation with epsilon < 10^-5, mirroring glass-freeze behavior observed in physical systems. All codes, data, and figures are provided for replication and review.