Algorithmic Myopia

Cross-source consensus on Algorithmic Myopia from 1 sources and 4 claims.

1 sources · 4 claims

How it works

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