from numpy import*
k=kron
u=lambda x:fromiter(base_repr(x,3),int).reshape(4,4)-1
s=k(u(25952008),u(27752297))+k(u(21543856),u(18501071))
t=k(u(21169798),i:=eye(4,4,0,object))
a=b=2*k(i,i)
for c in input():b=[s@t,s,s@t@t@t][-ord(c)%4]@b//2
print(all(a*a==b*b))
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How it works
Uses only big integer linear algebra. This is probably not the golfiest solution method, but OP said they’re interested in this approach.
The rigid motions of the hyperbolic plane are isomorphic to \$\mathrm{PSL}(2, \mathbb R)\$, which can be represented by 2×2 matrices acting on \$\mathbb R^2\$.
\begin{gather*}
S = \begin{bmatrix}
\frac1{\sqrt 2}(-\sqrt φ + φ + \sqrt φ^3) & 0 \\
0 & \frac1{\sqrt 2}(\sqrt φ + φ - \sqrt φ^3)
\end{bmatrix}, \\
T = \begin{bmatrix}
\frac1{\sqrt2} & -\frac1{\sqrt2} \\
\frac1{\sqrt2} & \frac1{\sqrt2}
\end{bmatrix}, \quad L = ST, \quad R = ST^3.
\end{gather*}
However, in the hyperbolic plane, floating-point errors accumulate exponentially with distance, so we need a way to do the same calculation exactly. For our purposes, we only need to work in the 16-dimensional sublattice \$M\mathbb Z^{16} ⊂ \mathbb R^2\$ generated by the columns of
$$M = \left[\begin{smallmatrix}
1 & \sqrt φ & φ & \sqrt φ^3 &
\frac{1}{\sqrt 2} & \frac{\sqrt φ}{\sqrt 2} & \frac{φ}{\sqrt 2} & \frac{\sqrt φ^3}{\sqrt 2} &
0 & 0 & 0 & 0 &
-\frac{1}{\sqrt 2} & -\frac{\sqrt φ}{\sqrt 2} & -\frac{φ}{\sqrt 2} & -\frac{\sqrt φ^3}{\sqrt 2}
\\
0 & 0 & 0 & 0 &
\frac{1}{\sqrt 2} & \frac{\sqrt φ}{\sqrt 2} & \frac{φ}{\sqrt 2} & \frac{\sqrt φ^3}{\sqrt 2} &
1 & \sqrt φ & φ & \sqrt φ^3 &
\frac{1}{\sqrt 2} & \frac{\sqrt φ}{\sqrt 2} & \frac{φ}{\sqrt 2} & \frac{\sqrt φ^3}{\sqrt 2}
\end{smallmatrix}\right].$$
In order for \$L, S, R\$ to act on this lattice, we need it to be closed under multiplication by \$\sqrt2\$ and \$\sqrt φ\$, which it is; for example:
$$
\sqrt2 \begin{bmatrix}1 \\ 0\end{bmatrix} = \begin{bmatrix}\frac1{\sqrt 2} \\ \frac1{\sqrt 2}\end{bmatrix} - \begin{bmatrix}-\frac1{\sqrt 2} \\ \frac1{\sqrt 2}\end{bmatrix}, \quad
\sqrt φ\begin{bmatrix}\sqrt φ^3 \\ 0\end{bmatrix} = \begin{bmatrix}1 \\ 0\end{bmatrix} + \begin{bmatrix}φ \\ 0\end{bmatrix}.
$$
We also need to multiply some points by \$\frac12\$, but it turns out that as long as we use \$2I\$ as the initial matrix rather than \$I\$, those points always have even coefficients. The resulting actions are given by
\begin{gather*}
SM = \frac12 M\left[\begin{smallmatrix}
0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & -1 & 0 \\
0 & 0 & 0 & 0 & -1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & -1 & -1 \\
0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & -1 & 0 & -1 & -1 \\
0 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & -1 & -1 & 0 & -1 \\
0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 0 & 0 & 0 & 0 \\
-1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & -1 & 1 & 0 & 0 & 0 & 0 \\
1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & -1 & 0 & 0 & 0 & 0 \\
1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 \\
0 & 0 & 0 & 0 & 1 & 0 & -1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & -1 & 1 \\
0 & 0 & 0 & 0 & 1 & 0 & 1 & -1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & -1 \\
0 & 0 & 0 & 0 & -1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 1 \\
0 & -1 & -1 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 0 & 0 & 0 & 0 \\
1 & 0 & -1 & -1 & 0 & 0 & 0 & 0 & 1 & 0 & -1 & 1 & 0 & 0 & 0 & 0 \\
-1 & 0 & -1 & -1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & -1 & 0 & 0 & 0 & 0 \\
-1 & -1 & 0 & -1 & 0 & 0 & 0 & 0 & -1 & 1 & 0 & 1 & 0 & 0 & 0 & 0
\end{smallmatrix}\right], \\
TM = M\left[\begin{smallmatrix}
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 \\
1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0
\end{smallmatrix}\right],
\end{gather*}
which we compress slightly using the Kronecker product.