Remarkable limit involving $m_p=\log_p(p^{x_1} + \cdots + p^{x_n})-\log_p(n)$ It is easy to prove that
$\lim_{p\rightarrow 1} m_p = (x_1 + \cdots + x_n)/n$. The following fact about the derivative of $m_p$ with respect to $p$ is also elementary:
$$m'_p =\frac{dm_p}{dp}
=\frac{1}{p \log p}\cdot\Big[\frac{x_1p^{x_1}+\cdots+x_np^{x_n}}{p_1^{x_1}+\cdots+p_n^{x_n}}-m_p\Big].$$
My interest in this is to create an alternative to the power mean, called the exponential mean: see here and here. The limit I am interested in is $\lim_{p\rightarrow 1} m'_p$. Using WolframAlpha, I computed the limit for $n=2,3,4,5$ (see here) and the following remarkable pattern emerges:
$$\lim_{p\rightarrow 1} m'_p=\frac{1}{2n^2}\sum_{1\leq i<j\leq n}(x_i-x_j)^2.$$
How do you go about formally proving this fact? It does not sound elementary to me. Also, it sounds like $m_p$ is a strictly increasing function of $p$ (its derivative beeing positive everywhere, with $m'_0 =+\infty$ and $m'_\infty =0$) unless all the $x_i$'s are identical.
Update
In short, $m_1$ is the arithmetic mean and $m'_1$ is half the empirical variance of $x_1,\cdots,x_n$. I tried to see if such simple formulas existed for the power mean $M_p$, but I could not find anything interesting other than the well known fact that $M_1=m_1$ is the arithmetic mean. It would be interesting to see how the second and third derivatives of $m_p$ at $p=1$ are linked to the higher empirical moments of $x_1,\cdots,x_n$.
 A: $\newcommand\bar\overline$
Letting $t:=\ln p$, we see that the limit in question is the limit of
$$d(t):=\frac1t\Big(\sum_1^n x_j e^{tx_j}\Big/\sum_1^n e^{tx_j}-m_{e^t}\Big)$$
as $t\to0$.
Next, letting
$\bar x:=\frac1n\,\sum_1^n x_j$, $\bar{x^2}:=\frac1n\,\sum_1^n x_j^2$, and $s^2=\bar{x^2}-\bar x^2$, we have
$$\sum_1^n x_j e^{tx_j}=\sum_1^n x_j (1+tx_j+o(t))
=n(\bar x+t\bar{x^2})+o(t),$$
$$\sum_1^n e^{tx_j}=\sum_1^n (1+tx_j+o(t))
=n(1+t\bar x)+o(t),$$
$$m_{e^t}=\log_{e^t}\Big(\frac1n\,\sum_1^n e^{tx_j}\Big) \\
=\log_{e^t}(1+t\bar x+t^2\bar{x^2}/2+o(t^2)) \\
=\tfrac1t\,\ln(1+t\bar x+t^2\bar{x^2}/2+o(t^2)) \\
=\bar x+ts^2/2+o(t).$$
So,
$$d(t)=\frac1t\Big(\frac{\bar x+t\bar{x^2}}{1+t\bar x}+o(t)-\bar x-ts^2/2\Big) \\
=\frac1t\Big((\bar x+t\bar{x^2})(1-t\bar x)+o(t)-\bar x-ts^2/2\Big) \\ 
=s^2/2+o(1). $$
So, the limit in question is
$$s^2/2
=\frac1{4n^2}\sum_{1\le i,j\le n}(x_i-x_j)^2 \\ 
=\frac1{4n^2}\sum_{1\le i,j\le n,\ i\ne j}(x_i-x_j)^2 \\ 
=\frac1{2n^2}\sum_{1\le i<j\le n}(x_i-x_j)^2,$$
as conjectured.

Details on the first equality in the last three-line display: The left-hand side of that equality is $\frac12\,Var\,X$, where $X$ is any random variable whose distribution is $\frac1n\,\sum_1^n\delta_{x_j}$, where $\delta_a$ is the Dirac probability measure at point $a$. The right-hand side of that equality is $$\frac14\,E(X-X')^2=\frac14\,Var(X-X')=\frac12\,Var\,X,$$
where $X'$ is an independent copy of $X$.
Of course, that equality can also be checked by straightforward algebraic calculations.
