We present a case study for how AI coding systems can be used to generate novel scientific hypotheses. We combine a generic coding agent (Google's AntiGravity) with an LLM-driven tree search algorithm (Empirical Research Assistance / ERA) to autonomously generate high-efficiency three-dimensional photovoltaic (3DPV) structures that overcome losses limiting flat solar panels at mid-latitudes. These structures operate by presenting favorable angles to the sun throughout the day, and for illustrative purposes we focus on optimizing performance for a single solar day. Our workflow begins by using AntiGravity to reproduce calculations \cite{bernardi2012solar} showing that 3DPV can have energy densities much higher than stationary flat PV panels. We use these initial designs as the starting point for large scale tree search, where we seek improved solutions and score them for their diurnal yield. The initial tree search leads to nominally more efficient solutions, yet they are caused by algorithmic reward hacking, arising from non-physical design features such as structurally levitating disconnected tiers and exploitations of the discretizations in the optics solver. To counteract this, we develop a workflow where the coding agent iteratively patches the physics engine with constraints to eliminate reward hacking. With reward-hacking eliminated, ERA discovers a series of designs with various constraints and improved performance, including optimal designs with different fixed collector areas, optimizing zenith tracking and avoiding self shadowing. Combining coding agents with tree search (ERA) provides a powerful platform for scientific discovery, for problems whose solutions can be empirically evaluated with a score function.

翻译：我们呈现了一个案例研究，展示如何利用人工智能编码系统生成新颖的科学假设。我们将通用编码代理（谷歌的AntiGravity）与大型语言模型驱动的树搜索算法（实证研究助手/ERA）相结合，自主生成高效的三维光伏（3DPV）结构，以克服中纬度地区限制平板太阳能电池板效率的损耗。这些结构通过在整个白天呈现有利角度来运行，为便于说明，我们重点优化单个太阳日的性能。我们的工作流程首先利用AntiGravity重现计算\cite{bernardi2012solar}，证明三维光伏的能量密度可远高于静止平板光伏面板。我们以这些初始设计为起点进行大规模树搜索，寻找改进方案并根据其日产量进行评分。初始树搜索产生了名义上更高效的方案，但这些方案源于算法奖励黑客行为，由非物理设计特征（如结构悬浮的不连续层级及对光学求解器离散化的利用）引发。为解决此问题，我们开发了一个工作流程，其中编码代理通过约束迭代修补物理引擎以消除奖励黑客行为。消除奖励黑客后，ERA发现了一系列具有不同约束条件和改进性能的设计，包括具有不同固定收集器面积的最优设计，优化天顶跟踪并避免自阴影。将编码代理与树搜索（ERA）相结合，为可通过评分函数进行经验评估的问题提供了强大的科学发现平台。