proofs
Provedalphabaguetteattentionnumerics

Base-two exponent with a folded log₂e scale computes eˣ: 2^(x/ln 2) = exp x

For all x∈R\htmlData{mx=1}{x \in \htmlData{mx=0}{\mathbb{R}}}:

2x⋅(1log⁡2)=ex\htmlData{mx=5}{\htmlData{mx=0}{2}^{\htmlData{mx=4}{\htmlData{mx=1}{x} \cdot \left(\htmlData{mx=3}{\frac{1}{\htmlData{mx=2}{\log 2}}}\right)}}} \mathrel{\htmlData{mx=8}{=}} \htmlData{mx=7}{e^{\htmlData{mx=6}{x}}}
What the notation means · or point at any symbol
RReal numbers
The real numbers: every point on the number line. (ℝ)
(x : T)As real numbers
2 read as an element of real numbers. Mathematicians leave this implicit; Lean needs it to know which kind of number is meant. ((2:ℝ))
aᵇPower
2 raised to the power x · (1/log(2)). ((2:ℝ) ^ (x * (1 / Real.log 2)))
=Equality
2^(x · (1/log(2))) and exp(x) are the same. ((2:ℝ) ^ (x * (1 / Real.log 2)) = Real.e…)
·Multiplication
x times 1/log(2). (x * (1 / Real.log 2))
a/bDivision
1 divided by log(2). In Lean, dividing by 0 gives 0. (1 / Real.log 2)
logNatural logarithm
The logarithm of 2 to base e: the power e must be raised to to give 2. (Real.log 2)
eˣExponential function
e raised to the power x, where e ≈ 2.718. (Real.exp x)

Baguette's kernels use only the hardware exp2. The sigmoid gate multiplies by log₂e before exp2 (sigmoidFromExp2With), and attention folds log₂e into its score scale (baguetteAttentionBaseTwoScaleWord = 1/(√64 · ln 2), Alpha commit e3330947, systems/baguette/src/Baguette/Model.alpha). Over ℝ this is exact: 2^(x · (1/ln 2)) = eˣ. The softmax and sigmoid they compute are therefore the natural-base ones; only rounding differs.

How it was proved

Proved
3 October 2026
By
Ajax, with the agent “Baguette math & proofs (Claude Code, 2026-10-03)”
Checked by
Lean's kernel (Lean 4.33.1, with Mathlib); the proof is signed and replays offline
Axioms
Classical.choice, Quot.sound, propext
Proof
14 lines of Lean
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