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%% SOURCE: docs/working/architecture/03-architecture/adaptive-systems/loop-5-sketch.md
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%% FITS: dev-guide/ch-physics
%% EDITORIAL: lifted — prose rewritten to press voice

\section{Loop \#5: Context-Aware Window Tuning --- Design Sketch}

This section presents a design sketch for Loop \#5, the context-aware window
tuner. The motivating gap is that the rolling window shrinks on pattern diversity
alone, with no evidence that shrinking helps or harms pipelining prediction. The
sketch closes that gap by driving window size from the actual prefetch hit rate
through a binary-chop search.

\subsection{Intended Behavior}

Loop \#5 measures, triggers, adapts, and records. It tracks global pipelining
metrics across all words --- total speculative prefetch attempts, successful
hits, and their ratio. Periodically, when the window is warm, it computes
prediction accuracy at the current window size and asks the binary-chop routine
for the next size to try. It then shrinks or grows the effective window according
to the accuracy trend: if a smaller window improves accuracy it keeps shrinking;
if accuracy degrades it grows back; and it converges on the size that maximizes
prediction accuracy. Each size tried is recorded with its observed accuracy,
building a history that accelerates convergence.

\subsection{Required Metrics}

A new aggregate structure carries the global pipelining state alongside the
existing per-word transition metrics.

\begin{lstlisting}[language=C]
typedef struct {
    uint64_t prefetch_attempts;       /* total speculative prefetch calls */
    uint64_t prefetch_hits;           /* word was actually looked up next */
    uint64_t window_tuning_checks;    /* tuning checks performed */
    uint32_t last_checked_window_size;
    double   last_checked_accuracy;
    uint32_t suggested_next_size;     /* binary-chop recommendation */
} PipelineGlobalMetrics;
\end{lstlisting}

The prefetch counter increments when a speculative promotion fires; the hit
counter increments when a subsequent lookup matches a word that was recently
promoted speculatively. Accuracy is the ratio of hits to attempts.

\subsection{The Binary-Chop Suggestion}

The tuning routine proposes the next window size from the accuracy trend. With no
data it holds. On the first check it shrinks by a quarter. Thereafter it compares
current accuracy to the last recorded accuracy: an improvement above a one
percent threshold continues shrinking, a degradation below minus one percent
grows the window by roughly a third, and a plateau holds the current size --- all
bounded by the configured minimum and maximum.

\begin{equation}
\textit{accuracy} = \frac{\textit{prefetch\_hits}}{\textit{prefetch\_attempts}},
\qquad
\Delta = \textit{accuracy}_{\text{current}} - \textit{accuracy}_{\text{last}}
\end{equation}

The suggestion is consulted from the adaptive-shrink check, which already runs on
the execution path; when warm and with pipelining enabled, it polls the tuner
every thousand executions and applies any change, logging the transition and the
accuracy that drove it.

\subsection{Design Decisions}

Four choices shape the loop. \emph{Accuracy measurement} counts speculative
promotions that were actually used, which requires distinguishing a predicted
lookup from a natural one --- by flagging speculatively promoted entries, by
matching predicted word identifiers against the next lookup, or by reusing the
per-word prefetch metrics already collected. \emph{Tuning frequency} is every
thousand warm executions, made configurable at build time. \emph{Search strategy}
is greedy shrinking with backoff on degradation, converging by oscillating around
the sweet spot. \emph{Build knobs} gate the loop behind the pipelining flag and
expose the tuning frequency and accuracy threshold.

\subsection{Validation and Risks}

Validation would run the design-of-experiments harness across a baseline, a
cache-only configuration, a window-tuning-with-pipelining configuration, and a
combined configuration, collecting the converged window size, the final prefetch
accuracy, and throughput against baseline. The known risks are the overhead of
periodic accuracy checks, oscillation under noisy accuracy, workload-dependent
optima, and the possibility that a speculative lookup does not actually
correlate with a prediction. The sketch estimates roughly 150 lines of code
across the VM struct, the interpreter, the pipelining-metrics module, and the
rolling-window check.

%% PATENT: the accuracy-driven binary-chop window controller is patent-adjacent;
%%   no claims drafted here.

%% TODO(bob): confirm whether Loop #5 has since been implemented; if so, promote
%%   the converged metrics from the validation matrix into the chapter.
