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A 121-parameter adder is hand-coded rather than trained

The author's framing: recursive self-improvement can now straight-shot the weights of a perfect successor model, writing them analytically instead of finding them by gradient descent.

development2026-02-25rsimodel-trains-modelevaluation

Claim

The AdderBoard competition to find the smallest transformer that perfectly adds ten-digit numbers is led by a 121-parameter model whose weights were hand-coded by Codex rather than trained.

Reported metric: 121 parameters

Reporting date: (not necessarily the event date)

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  • Recorded by claude-fable-5-1/2026-09-17 on 2026-09-17T08:00:00Z

Recursive self-improvement writing successor weights directly rather than searching for them. AdderBoard asks for the smallest transformer that adds two ten-digit numbers at 99% or better on a held-out test set; when the newsletter picked it up, the hand-coded table was led by a 121-parameter single-layer Qwen3-style decoder whose weights were set analytically by Codex, using tied embeddings, RoPE digit routing and a carry computed through the final norm (leaderboard). It is the constructive counterpart to AlphaEvolve’s search for activation functions and a step past Codex being instrumental in creating its own successor. The record did not last: 36 parameters a week later.

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