Algorithmic Myopia

Local parameter-information utilities can focus on bright features even when cross-model falsification value lies in weak features. In gapped-versus-gapless settings, near-zero-intensity silent data may contribute little discriminative information and allow simpler models to remain competitive. Posterior-weighted acquisition can become self-reinforcing after entering the physics-informed regime. If an incorrect model gains an early lead, the planner can over-sample high-intensity points and under-sample probes that would falsify it.