QuotientPO：面向基因组尺度代谢模型修复的商空间探索方法
Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair
一篇挺有意思的方法论文：把等价的修复方案归到同一个机制里再去做探索，在2212个代谢模型上把成功率从17.93%提到20.10%，思路值得做科学发现方向的人看看。
论文研究基因组尺度代谢模型（GEM）修复问题：同一表型可由多个反应编辑解释，输出空间的多样性不等于科学假设的多样性。作者提出 QuotientPO，将等价修复折叠为规范机制，直接在商空间上做探索，并推导出核化 Rényi 估计器来度量不同修复核心之间的分级拥挤。在 2,212 个 held-out GEM 上，QuotientPO 把 Success@32 从 17.93% 提升到 20.10%，相对提升 12.1%，且在相同采样预算下发现更多不同的成功核心。
Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair
Repairing scientific models from functional observations differs fundamentally from supervised prediction: feedback may certify a solution without revealing which structural correction is responsible. We study this setting for genome-scale metabolic model (GEM) repair, where multiple reaction edits can explain the same phenotypes and many apparently distinct edits correspond to the same biological mechanism. This many-to-one structure creates a hidden failure mode for conventional exploration: diversity in the output space need not translate into diversity of scientific hypotheses. We introduce QuotientPO, which collapses equivalent repairs into canonical mechanisms and optimizes exploration directly over the resulting quotient space. To make quotient exploration informative under finite rollouts, we derive a kernelized Rényi estimator that resolves graded crowding among distinct repair cores beyond coarse exact-match counts. On 2,212 held-out GEMs, QuotientPO improves Success@32 from 17.93% to 20.10% (+12.1% relative) while consistently increasing distinct successful-core discovery under the same sampling budget. These results establish quotient-space exploration as a principled approach to mechanism-level discovery under verifier-induced equivalence.