Experiments
What we ran, in plain words, with the numbers behind each call.
The idea
Not a run. Anchor, sticky-note memory, what we test, and what we do not claim.
Read the ideaHow the model works
Not a run. Snapshot, notepad, and the write network Adam actually trains.
Read the methodImportant things to note
Not a run. Mixed results, the next steps in order, the baselines, and which datasets can support a claim.
Read the notesRuns
MIND keep the notepad, confirmation
We reran the keep-the-pad test on a university RTX 4080 SUPER. Same MIND-small setup, one seed, validation only. Keeping the notepad again beat wiping it. This time the run finished: a checkpoint was written, and the keep-versus-wipe score check completed. Test split stayed sealed.
OpenMIND keep the notepad
On MIND-small we trained the snapshot-plus-notepad model two ways: wipe the notepad and rebuild it from the last twenty clicks, or keep it across visits like a live app. Keeping the pad ranked better on validation. One seed. Training stopped early after the score peaked.
OpenMIND snapshot and notepad
On MIND-small news ranking we freeze each user's first training clicks as a stable snapshot, then let a small notepad of fast weights adapt on later clicks. Across three model seeds, snapshot plus notepad beat a moving history mean. Validation only. The held-out test split stayed sealed.
OpenAmazon snapshot and notepad
On Amazon reviews we used the same four-row design with frozen product embeddings. The notepad still added a large ranking gain. A moving profile beat the earliest-product snapshot when memory was on. One seed only. A stale shopping snapshot may be the wrong slow state for this task.
Open