[guided]The argument is symmetric to Step 3. The signature is homogeneous in its driving path (Step 3 derivation), and the inner product on $V^{\otimes i}$ is bilinear, so the scalar $\pi^i$ is free to migrate to either slot. Migrating it to the *second* slot rather than the first:
\begin{align*}
\pi^i\,\langle S(x)^i_{a,s},\, S(y)^i_{a,t}\rangle_{V^{\otimes i}} = \langle S(x)^i_{a,s},\, \pi^i\,S(y)^i_{a,t}\rangle_{V^{\otimes i}} = \langle S(x)^i_{a,s},\, S(\pi y)^i_{a,t}\rangle_{V^{\otimes i}},
\end{align*}
where the last equality is the homogeneity $S(\pi y)^i_{a,t} = \pi^i\,S(y)^i_{a,t}$ applied to $y$ (this works pathwise: for each realization of $\pi$, $\pi y$ is a well-defined bounded variation path on $[a,t]$, and the level-$i$ signature scales by $\pi^i$).
Substituting into the post-Fubini formula from Step 2,
\begin{align*}
k_\phi^{x,y}(s,t) = \mathbb{E}\!\left[\sum_{i=0}^\infty \pi^i\,\langle S(x)^i_{a,s},\, S(y)^i_{a,t}\rangle_{V^{\otimes i}}\right] = \mathbb{E}\!\left[\sum_{i=0}^\infty \langle S(x)^i_{a,s},\, S(\pi y)^i_{a,t}\rangle_{V^{\otimes i}}\right].
\end{align*}
The inner sum, by definition of the constant-weight signature kernel ($\phi \equiv 1$), is $k^{x, \pi y}(s,t)$:
\begin{align*}
\sum_{i=0}^\infty \langle S(x)^i_{a,s},\, S(\pi y)^i_{a,t}\rangle_{V^{\otimes i}} = k^{x, \pi y}(s,t).
\end{align*}
Hence
\begin{align*}
k_\phi^{x,y}(s,t) = \mathbb{E}_\pi\!\left[ k^{x, \pi y}(s,t) \right].
\end{align*}
Combining with Step 3,
\begin{align*}
k_\phi^{x,y}(s,t) = \mathbb{E}_\pi\!\left[ k^{\pi x, y}(s,t) \right] = \mathbb{E}_\pi\!\left[ k^{x, \pi y}(s,t) \right].
\end{align*}
The two representations are equally valid because $\pi$ is a *scalar* — it commutes through the bilinear inner product. They are not in general both equal to $\mathbb{E}_\pi[k^{\pi x, \pi y}(s,t)]$: pushing $\pi$ into both slots simultaneously would yield $\pi^{2i}$ instead of $\pi^i$ at level $i$, corresponding to the kernel weighted by $\mathbb{E}[\pi^{2i}] = \psi(2i)$, not $\phi(i)$. The PDE-averaging interpretation in [Weighted Signature Kernel as Averaged PDE Solution](/theorems/???) exploits exactly this — solving a single signature kernel PDE on the averaged input is enough; one does not need to average over both slots.[/guided]