deletions | additions       diff --git a/experim.tex b/experim.tex  index aa23119..f313ef1 100644  --- a/experim.tex  +++ b/experim.tex 
...
For each benchmark we analyze CPU time performing 10 trials preceded by an initial warm-up iteration; reported confidence intervals are stated at 95\% confidence level.  \subsection{Results}  \label{ss:experim-results}  We now present the main results of our experimental evaluation, aimed at addressing the following questions:  %\begin{itemize}  %\item {\bf Message 1}: how much does a never-firing OSR point impact code quality? We run a program with one or more OSR points, and we measure the slowdown given by factors such as cache effects (due to code bloat), register pressure, etc. due to the presence of the OSR points.  diff --git a/prev-eval-sol.tex b/prev-eval-sol.tex  index 69ddab0..c7143b9 100644  --- a/prev-eval-sol.tex  +++ b/prev-eval-sol.tex 
...
%\item Guard computation in $f'$ can be rather expensive, as it may require checking many parameters.  %\end{enumerate}  Our approach combines the flexibility of OSR-based specialization with the efficiency of the JIT-based method, answering an open question raised by Lameed and Hendren~\cite{lameed2013feval}. Similarly to OSR-based specialization, it does not place restrictions on the functions that can be optimized. On the other hand, it works at IIR (rather than IR) level as in JIT-based specialization, which makes it possible to perform type inference on the specialized code. Working at IIR level eliminates the two main sources of inefficiency of OSR-based specialization: 1) we can replace generic istructions with specialized instructions, and 2) the types of $g$'s arguments do not need to be cached or guarded as they are statically inferred. These observations are confirmed in practice by experiments on real MATLAB programs, as we will show in \mysection\ref{}.  %Furthermore, since the dynamic code optimization works at IIR (rather than IR) level, type inference can replace generic istructions with specialized instructions. %, removing the main source of inefficiency of OSR-based specialization.