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\section{Research Directory Overview}

This directory index was the navigation hub for the \texttt{docs/06-research/}
subtree before the 2026-06-16 documentation reorganisation. It is preserved
as a historical reference; canonical research content has been promoted into
the formal volume.

\subsection{Key Research Contributions}

Three primary contributions are identified.

\textbf{Physics-grounded self-adaptive runtime.}
StarForth demonstrates a novel approach to VM optimisation: an execution
heat model grounded in thermodynamic metaphor, a rolling window of truth
for deterministic metric seeding, and an inference engine for adaptive
parameter tuning.

\textbf{Formally proven deterministic behaviour.}
Zero percent algorithmic variance across ninety experimental runs.
Determinism is established independently for heat decay (Loop~\#3),
window-width inference (Loop~\#5), and pipelining metrics (Loop~\#4),
and reproduces across platforms.

\textbf{Design of Experiments methodology.}
A rigorous factorial DoE framework drives all experimental work, including
ANOVA and Levene's-test statistical validation, heartbeat-driven data
collection, and reproducible protocols.

\subsection{Publication Materials}

A comprehensive peer-review submission package was assembled under
\texttt{archive/phase-1/Reference/physics\_experiment/PEER\_REVIEW\_SUBMISSION/},
including a main paper draft, formal verification interpretation, variance
analysis summary, and supplementary materials.

A DARPA SSM proposal and a provisional patent application were filed during
this period.

\subsection{Experimental Reproducibility}

All experiments are reproducible via:

\begin{lstlisting}[language=bash]
make fastest
./build/amd64/fastest/starforth --doe
\end{lstlisting}

Results should demonstrate 0\% algorithmic variance.