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Putting stuff you have learned into a markdown file is a very "shallow" version of continual learning. It can remember facts, yes, but I doubt a model can master new out-of-distribution tasks this way. If anything, I think that Google's Titans[1] and Hope[2] architectures are more aligned with true continual learning (without being actual continual learning still, which is why they call it "test-time memorization").

[1] https://arxiv.org/pdf/2501.00663

[2] https://arxiv.org/pdf/2512.24695

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I have had it master tasks by doing this. The first time it tries to solve an issue it may take a long time, but it documents its findings and how it was able to do it and then it applies that knowledge the next time the task comes up.
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