Notes on FSRS-6: A Small Experiment in Residual Calibration

Introduction I'm a high school student in Japan who spends a lot of time reading about machine learning and LLMs. Lately, I've been using an app called RemNote for studying, and it uses FSRS-6 as its spaced repetition model. At some point I started wondering whether there was room to improve it — not replace it, just nudge it slightly. I'm writing this as a rough memo and draft, partly for my own records and partly because I'd like to eventually build something like this into an app of my own. I haven't done a thorough literature review, and this is honestly just me thinking out loud, so I might be getting things wrong. If I am, I'd really appreciate hearing about it. The Core Idea FSRS-6 is genuinely strong. I don't think it should be thrown out and replaced with something else — if anything, I think it's impressive that it works as well as it does in practice. My starting point wasn't to criticize it, but to ask whether a thin correction layer on top of it could capture a bit more si