Provedllmlarge-language-modelsformal-proofattention
Attention interpolates from the second value toward the first
For all :
What the notation means · or point at any symbol
- RReal numbers
- The real numbers: every point on the number line. (
ℝ) - x ∈ TVariables
- s0, s1, v0, v1 range over real numbers: the statement must hold for every choice. (
(s0 s1 v0 v1 : ℝ)) - attend2
- TransformerAttention.attend2: a definition from the library, not special notation. Its name is its meaning; open it in Lean to see how it is defined. (
TransformerAttention.attend2) - f(x)Function application
- TransformerAttention.attend2 applied to s0, s1, v0, v1. Lean writes f x; papers write f(x). (
TransformerAttention.attend2 s0 s1 v0 v1) - =Equality
- attend2(s0, s1, v0, v1) and v1 + weight0(s0, s1) · (v0 − v1) are the same. (
TransformerAttention.attend2 s0 s1 v0 v…) - +Addition
- v1 plus weight0(s0, s1) · (v0 − v1). (
v1 + TransformerAttention.weight0 s0 s1…) - weight0
- TransformerAttention.weight0: a definition from the library, not special notation. Its name is its meaning; open it in Lean to see how it is defined. (
TransformerAttention.weight0) - ·Multiplication
- weight0(s0, s1) times v0 − v1. (
TransformerAttention.weight0 s0 s1 * (v…) - -Subtraction
- v0 minus v1. On ℕ, Lean's subtraction stops at 0: 2 − 5 = 0. (
v0 - v1)
theorem attn2-interp-left (s0 s1 v0 v1 : ℝ) :
TransformerAttention.attend2 s0 s1 v0 v1 = v1 + TransformerAttention.weight0 s0 s1 * (v0 - v1)attend2 = v1 + weight0 * (v0 - v1): the first weight is the interpolation parameter.
How it was proved
- Proved
- 28 September 2026
- By
- Ajax, with the agent “ompmuse”
- 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
- 15 lines of Lean
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