# Improving online FDR procedures via online analogs of e-closure and compound e-values

arXiv: https://arxiv.org/abs/2603.24792 · HTML: https://mlsanity.com/paper/2603.24792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors](https://mlsanity.com/paper/2609.27051/) (2026)
- [Evidence-Gated Research: Statistically Controlled Model Adoption in Adaptive Search](https://mlsanity.com/paper/2610.01751/) (2026)

## Abstract (arXiv)

In many scientific applications, hypotheses are generated and tested continuously in a stream. We develop a framework for improving online multiple testing procedures with false discovery rate (FDR) control under arbitrary dependence. Our approach is two-fold: we construct methods via the online e-closure principle, as well as a novel formulation of online compound e-values that is defined through donations. This yields strict power improvements over state-of-the-art e-value and p-value procedures while retaining FDR control. We further derive algorithms that compute the decision at time t in O(log t) time, and we demonstrate improved empirical performance on synthetic and real data.