First, in this form of binary representation of a floating number, the exponent
is typically offset by a pre-agreed amount. For simplicity, let's say the offset
is $(100)_2$. We can expand the two bit patterns as two numbers.
$$
\require{enclose}
\def\xD{{}_{10}}
\def\xH{{}_{16}}
\def\xB{{}_{2}}
\newcommand{\xP}[2][black]{\enclose{box}{\color{#1}{\small\verb/#2/}}}
\begin{array}{rcl}
\xP[red]{1}\xP[orange]{100}\xP[green]{1100} &+&
\xP[black]{0}\xP[blue]{101}\xP[magenta]{1011}\\
&\Downarrow\\
\color{red}{-}(1.\color{green}{1100})\xB \times 2^{(\color{orange}{100})
\xB-(100)\xB}
&+& (1.\color{magenta}{1011})\xB \times 2^{(\color{blue}{101})\xB - (100)\xB}
\\
&\Downarrow\\
\underbrace{-\left(1+\frac12+\frac14\right) \times 2^0 }_{= -\frac74} &+&
\underbrace{\left(1 + \frac12 + \frac18 + \frac{1}{16}\right)\times 2^1}_{= \frac{27}{8}}
\end{array}
$$
The bit pattern on the left corresponds to the number $-\frac{7}{4}$, while
the one on the right corresponds to $\frac{27}{8}$. Their sum equals to $\frac{13}{8}$. Since
$$\frac{13}{8} = (1 + \frac12 + \frac18) \times 2^0 = (1.\color{gold}{1010})\xB \times 2^{(\color{orange}{100})\xB - (100)\xB}$$
The bit pattern for the result is $\xP{0}\xP[orange]{100}\xP[gold]{1010}$.
You can start from a different offset. However, the two numbers at hand
and their sum all have compatible magnitude. There is no issues of underflow or overflow. Independent of what offset you choose, you will get the same final bit pattern.