用元优化框架提升科学发现效率，实现算法性能67倍加速
Scientific discovery as meta-optimization: a combinatorial optimization case study
- 将科研过程建模为元优化，同时优化目标函数与搜索策略
- 在3-SAT问题上将算法复杂度从N^2.51降至N^1.33，最大提速67倍
- 适用于各类科学问题，尤其适合需动态调整评估标准的探索任务
科学发现本质上是一个优化问题，其状态空间涵盖所有理论与实验方案，评估标准基于质量、新颖性和有效性。大语言模型（LLM）已能自动探索该空间，但我们认为同步优化评估标准同样关键。本文提出将研究过程形式化为元优化，即优化目标本身也参与优化。核心贡献是‘共识目标聚合’：通过相关性加权投票整合LLM生成的目标函数，形成稳定且自我修正的评估准则，随理解深化而演进。我们将此框架应用于基于数字记忆计算机的3-SAT算法发现，将基准复杂度从∼N²·⁵¹降低至∼N¹·³³，对最大测试实例实现∼67×加速。作为通用框架，有望显著推动科学发现进程。
原文摘要 · Abstract (English)
Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. Large language models (LLMs) have enabled automated exploration of this space, but we argue that simultaneous modification of the evaluation criteria is equally important. Here, we propose formalizing research as meta-optimization, where the optimization objective itself is also being optimized. Our key contribution is "consensus objective aggregation," where LLM-generated objective functions are combined via correlation-weighted voting, yielding a stable, self-correcting evaluation criterion that evolves as understanding deepens. We apply this framework to algorithm discovery for 3-SAT problems based on digital MemComputing machines, reducing the baseline scaling with problem size $N$ from $\sim N^{2.51}$ to $\sim N^{1.33}$ and delivering a $\sim 67\times$ speedup on the largest instances tested. As a problem-agnostic framework, we hope this approach will considerably aid scientific discovery.
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