# Interpretable Memory Models for Spaced Repetition

arXiv: https://arxiv.org/abs/2610.10548 · HTML: https://mlsanity.com/paper/2610.10548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning of Marked Temporal Point Processes](https://mlsanity.com/paper/1805.09360/) (2018)
- [DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills](https://mlsanity.com/paper/1905.06873/) (2019)
- [Unbounded Human Learning: Optimal Scheduling for Spaced Repetition](https://mlsanity.com/paper/1602.07032/) (2016)
- [Adaptive Forgetting Curves for Spaced Repetition Language Learning](https://mlsanity.com/paper/2004.11327/) (2020)
- [Evolvable Psychology Informed Neural Network for Memory Behavior Modeling](https://mlsanity.com/paper/2408.14492/) (2024)

## Abstract (arXiv)

Spaced repetition software schedules reviews with a memory model fit to review logs. Accuracy on a test set is not sufficient evidence of quality since available data are produced by existing schedulers, and new solutions must extrapolate beyond them. A model also needs a simple mechanistic interpretation. We present SBD, a model that is more interpretable and 80% smaller than the current state of the art at nearly the same accuracy.