This is a really neat result. A few things that stood out to me reading the card:

The 60.6% embedding dominance is striking — at 12,096 of 19,969 params, the shared input/output matrix does most of the "work" in parameter-count terms. That's a very different ratio from typical GPT-2-style models where the embedding is maybe 20–40% of the total. The word-level 378-token vocab is clearly what makes this ratio possible; a subword vocab would blow up that number fast.

The 4-pass recurrent depth with the value-residual mixing (passes 2–4 blending current with pass-1 values) is a clever way to buy extra compute without extra parameters. Curious whether you ablated the pass count — does 2 or 3 passes degrade the "all four criteria" rate noticeably, or is 4 just where it plateaued?

65/100 meeting all four criteria at 20K params is a solid data point for the "how small can you go" question. The Ouro lineage makes sense as a base.

Congrats on shipping it — the card is well-structured and the quick-start is clean.

Cool!😎