Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the \emph{trajectory} of scientific discovery is rarely examined as an optimization problem in its own right. This paper argues that the body of scientific knowledge, at any given historical moment, represents a \emph{local optimum} rather than a global one--that the frameworks, formalisms, and paradigms through which we understand nature are substantially shaped by historical contingency, cognitive path dependence, and institutional lock-in. Drawing an analogy to gradient descent in machine learning, we propose that science follows the steepest local gradient of tractability, empirical accessibility, and institutional reward, and in doing so may bypass fundamentally superior descriptions of nature. We develop this thesis through detailed case studies spanning mathematics, physics, chemistry, biology, neuroscience, and statistical methodology. We identify three interlocking mechanisms of lock-in--cognitive, formal, and institutional--and argue that recognizing these mechanisms is a prerequisite for designing meta-scientific strategies capable of escaping local optima. We conclude by proposing concrete interventions and discussing the epistemological implications of our thesis for the philosophy of science.

翻译：科学被广泛视为人类揭示自然世界真理最可靠的方法。然而，科学发现的"轨迹"本身却极少被作为优化问题加以审视。本文论证，在任何历史时刻，科学知识体系所代表的是局部最优解而非全局最优解——我们理解自然所依托的框架、形式体系和范式，在很大程度上受到历史偶然性、认知路径依赖与制度锁定的塑造。通过类比机器学习中的梯度下降法，我们提出科学遵循着可解性、经验可及性和制度激励的局部最陡梯度，在此过程中可能绕过对自然更优越的基本描述。我们通过涵盖数学、物理学、化学、生物学、神经科学与统计方法论的详细案例研究来阐述这一论点。我们识别出三种相互耦合的锁定机制——认知锁定、形式锁定与制度锁定——并论证认识这些机制是设计能够摆脱局部最优解的元科学策略的前提条件。最后，我们提出具体干预措施，并讨论本论点对科学哲学的认识论启示。