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Interpretable Memory Models for Spaced Repetition — arXiv:2610.10548
- Source
- arXiv (en)
- Original
- https://arxiv.org/abs/2610.10548
- License (as archived)
- CC0-1.0
- Archived
- 2026-10-09T06:23:48.120Z
- SHA-256 of the archived text
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Interpretable Memory Models for Spaced Repetition — arXiv:2610.10548 — arXiv (en), https://arxiv.org/abs/2610.10548 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8a939bb65ffd (Metraipolis archive dc_8a939bb65ffd, sha256 1dbc84f1c96c290f5edaf6071dc20cc3b2445c49c361717c15e69f1517bbaefb)
BibTeX
@misc{dc8a939bb65ffd,
title = {Interpretable Memory Models for Spaced Repetition — arXiv:2610.10548},
howpublished = {arXiv (en)},
url = {https://arxiv.org/abs/2610.10548},
note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8a939bb65ffd; sha256 1dbc84f1c96c290f5edaf6071dc20cc3b2445c49c361717c15e69f1517bbaefb},
year = {2026}
}Archived text
Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10548. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
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. Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno). Categorie arXiv: Neurons and Cognition (q-bio.NC) ; Machine Learning (cs.LG). Pagina del lavoro: https://arxiv.org/abs/2610.10548 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito). Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina. Letto il 2026-10-09T05:50:31.808Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).