ran3000

Just watched the video, great work!

Interesting, I've been surprised to see how many language learning apps already include some of the ideas I've discussed in the blog post!

How far along are you in developing the system?

You're right, it might work by restricting to just AnKing data. My concern was around other, possibly personal, cards making their way into the dataset.

Reading the thread, I definitely overlooked language learning solutions. Thanks for sharing!

This specific problem gave us lots of headache while building https://rember.com We don't have a good solution yet. My hope is that something like content-aware memory models solve the problem at a lower level, so we don't have to worry about it at the product level.

It would be awesome to work on that data. I'm afraid of the privacy implications though.

Amazing work! In https://rember.com the main unit is a note representing a concept or idea, plus some flashcards associated to it, hsrs would fit perfectly! I'll look more deeply into it.

I would be valuable for shared decks, like the one you mentioned. As far as I can tell, the majority of Anki users are medical school students or language learners. Both groups benefit from shared decks. So I think it's a good idea to pursue.

My personal interest is more on conceptual knowledge, like math, cs, history or random blog posts and ideas. It's often the case that, on the same article, different people focus different things, so it would be hard to collect even a small number of reviews on a flashcard you want to study.

Phrasing looks amazing!

There's a lot of UX work to do for SRS. Do you have a sense of how well the ideas behind Humane SRS translate outside of language learning? I imagine the main challenge would be identifying a steady influx of new cards.

I agree that gains in scheduling accuracy are fairly imperceptible for most students. That's why, over the past few years building https://rember.com, we've focused on UX rather than memory models. People who review hundreds of card a day definitely feel the difference, doing 50 fewer reviews per day is liberating. And now that LLMs can generate decent-quality flashcards, people will build larger and larger collections, so scheduler improvements might suddenly become much more important.

Ultimately, though, the biggest advantages is freeing the SRS designer. I'm sure you've grappled with questions like "is the right unit the card, the note, the deck or something else entirely?" or "what happens to the review history if the student edits a card?". You have to consider how review UX, creation/editing flows, and card organization interact. Decoupling the scheduler from these concerns would help a ton.

Being easy to integrate is an underappreciated feature of FSRS.

Using decks to draw semantic boundaries is likely overly constraining. I think we want to account for finer differences between cards. Decks are coarse and people differ in the ways they use them, some people recommend having just one global deck. Notes are too fine. We explored something in between: a note capturing an idea or concept, plus an associated set of cards. Turns out it's hard to draw idea boundaries. That's why I think it's easier to relate cards by semantic embeddings or more rigid but clearer structures, like the DAG of dependencies suggested elsewhere in this thread.

Ah, I totally missed this, thanks for sharing it.

Since in Anki the "note" is the editing unit, that works for some cloze deletions but not for QA cards (only for double-sided QA cards). A content-aware memory model would allow you to apply "disperse siblings" to any set of cards, independently of whether they were created together in the same editing interface.

Yes, that reminds me of knowledge tracing and methods like 1PL-IRT.

I think you can do both and get even better results. The main limitation is that the same flashcards must be studied by multiple students, which doesn't generally apply.

I also love the idea of the market, you could even extend it to evaluate/write high-quality flashcards.

I'd love to see it.

What I like about your approach is that it circumvents the data problem. You don't need a dataset with review histories and flashcard content in order to train a model.

I explored memory models for spaced repetition in my master's thesis and later built an SRS product. This post shares my thoughts on content-aware memory models.

I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.

Yes, I do this sometimes for math proofs.

The point of writing atomic flashcards is to prevent the loss of resolution reviewing questions about wholes (sentences in your case). Mind that atomic does not mean that it has to be about details, I usually create flashcards for each abstraction, that is, besides asking for details, I also write a question for the full sentence. This is one way to prevent the loss of resolution, but sometimes it is time consuming to write all those flashcards (think about a long proof). Another way to do it, is to write down the answer pen-and-paper, that way you are forced to focus on the details, not just the big picture: no loss of resolution.