dailyai.report

A new arXiv paper argues that scientific discovery functions like gradient descent, often stalling in local optima. Historical contingency and institutional lock-in create cognitive path dependence, preventing a shift toward more accurate global truths. This framework suggests that current paradigms are merely the most tractable options. Researchers must now account for these systemic biases when designing AI discovery tools.