arXiv：cs.LG（机器学习，全量分类）· Zahra Khodagholi, Niloofar Yousefi·· 1 天前AI 评分34
一个池，多个靶点：一种守恒层及档案数据能识别什么
One pool, many targets: a conservation layer and what archival data can identify
AI 导读
研究提出一种可微分的标量平衡层，用单一守恒方程刻画有限 guide-loaded RISC 池，并给出唯一正根与精确隐式梯度。基于档案脱靶数据的审计显示，校正后的热力学亲和力与实测抑制方向一致但关联较弱，配对置换检验与 construct-cluster bootstrap 未能证明耦合带来额外预测价值；剂量拟合异质且常违反模型隐含的超线性指数约束，因此这些数据无法识别竞争参数。代码已公开。
正文
Abstract:Pairwise guide--transcript scores do not enforce conservation of a finite guide-loaded RISC pool when they are interpreted independently as occupancies. We formulate a differentiable scalar equilibrium layer: one conservation equation with a unique positive root and exact implicit gradients. It yields a redistribution theorem, a qualified high-resource limit, an analysis of the retrieval approximation, and a conditional rank-invariance result: within one construct at one dose, rankings by fractional occupancy cannot distinguish equilibrium from independent scoring. We therefore audit the two experiments that proposition leaves open, dose and cross-context, on archival off-target data. Corrected thermodynamic affinities associate weakly with measured repression in the direction a working predictor requires, but a paired permutation test and a construct-cluster bootstrap do not establish added predictive value from the coupling: what survives their differing permutation-null baselines is \GapNet{}, a descriptive \GapNetOverSE{} of the equilibrium association's cluster standard error. The dose fits are heterogeneous and frequently violate the model-implied exponent constraint, which is superlinear rather than sublinear, so these data do not identify the competition parameter. A saturable compression of the competitor set holds both accuracy targets on held-out guide families but is not faster at the size measured. The contribution is a reusable conservation operator and the experimental information needed to test it. The code for this study is available at this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00445 [cs.LG] |
| (or arXiv:2610.00445v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00445 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zahra Khodagholi [view email]
[v1]
Wed, 30 Sep 2026 17:50:12 UTC (2,061 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org