Interpretable Memory Models for Spaced Repetition
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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.
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
5 of 37 references.
References · 5
- 2018Deep Reinforcement Learning of Marked Temporal Point Processescited by 33
- 2019DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skillscited by 27
- 2016Unbounded Human Learning: Optimal Scheduling for Spaced Repetitioncited by 12
- 2020Adaptive Forgetting Curves for Spaced Repetition Language Learningcited by 4
- 2024Evolvable Psychology Informed Neural Network for Memory Behavior Modelingcited by 1
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