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\hypersetup{
  pdftitle={Program--value separability: the structural precondition for compilation{,} caching{,} and dense journaling in a DSL runtime},
  pdfauthor={Alvaro Rivera},
  pdfkeywords={\xmpquote{program-value
separability}, \xmpquote{code-as-data}, \xmpquote{script-form
persistence}, \xmpquote{executable journal}, \xmpquote{DSL
runtime}, \xmpquote{dual compilation}, \xmpquote{journal
density}, \xmpquote{partial
evaluation}, \xmpquote{CQRS}, \xmpquote{actor model}, \xmpquote{event
sourcing}, \xmpquote{puppeteer framework}},
  hidelinks,
  pdfcreator={LaTeX via pandoc}}

\title{Program--value separability: the structural precondition for
compilation, caching, and dense journaling in a DSL runtime}
\author{Alvaro Rivera}
\date{2026-06-17}

\begin{document}
\maketitle
\begin{abstract}
This paper is an analytic theory contribution in the sense of Gregor's
(2006) \emph{theory for analyzing} (Type I): it introduces a structural
construct --- \emph{program-value separability} --- and the binary
taxonomy it induces, states a necessary-condition claim about the
runtime consequences that follow from it, and presents one realization
in production as an existence proof that the construct is realizable ---
not the design-science evaluation of an artifact (Hevner, March, Park,
\& Ram, 2004), which a companion case-study paper is the venue for.
Compilation of a domain-specific language program to native code,
caching of compiled programs across invocations, and dense journaling of
operations rather than their effects are commonly treated as independent
runtime optimizations. This paper argues that they are not --- that all
three are downstream consequences of a single structural property of DSL
programs, which we name \emph{program--value separability}: the
syntactically decidable condition that a program declares its parameters
explicitly rather than embedding values in its body. The property itself
is long known --- partial evaluation and related compilation techniques
exploit the same static/dynamic separation --- but where that tradition
uses the split to specialize execution and then discards it, this paper
repositions it as the precondition for persistence and identity: the
separated program becomes the durable, replayable unit of state, and
compilation is only one of four consequences that follow. Three of them
--- compilation, caching, and dense journaling --- are developed and
measured here as projections of this single precondition; a fourth,
replication-bounded entropy, follows from the same precondition and is
taken up in a companion paper. We characterize one concrete realization
in a CQRS + Actor + Event Sourcing runtime --- the same Puppeteer
framework introduced in the prior paper of this series --- and report
empirical magnitudes from two independent open-source DDD aggregates
(dotnet/eShop's Order and kgrzybek/modular-monolith's
SubscriptionPayment): a compiled-versus-interpreted speedup that scales
with DSL-bound work (roughly 1.5× to 3.1×); a cold compile cost
amortized within several hundred invocations; and a journal that stored,
on each of the two aggregates, about 99.8\% of parametric entries as
compact action references --- 3.5× and 5.6× denser than the equivalent
literal-script storage. The journal-density values are exact per-host
counts and the speedups carry run-to-run variation; all are reported as
illustrations of the structural property on the cases measured, not as
estimates of its magnitude in general. The principle generalizes beyond
any one runtime: program-value separability characterizes the structural
condition any DSL runtime must satisfy to admit compilation, caching,
and dense persistence at all.
\end{abstract}

\section{Program--value separability}\label{programvalue-separability}

\subsection{TL;DR}\label{tldr}

\begin{quote}
Compilation, caching, and dense journaling are \textbf{not} features
layered atop interpretation. They \textbf{are} downstream consequences
of a single structural commitment: values live outside the script, not
embedded within it.

The transition often described as ``adding compilation'' inverts the
causal order. A DSL whose programs embed their values has no stable
identifier, no template to cache, no separable sub-program to compile
ahead of time --- interpretation is not a stylistic choice but a
structural necessity. Once values move outside the script, the program
becomes a function awaiting its arguments --- \emph{F(x₁, x₂, \ldots,
xₙ)}, as the runtime documents itself. And it is now the \emph{same}
program on every call, whatever arguments it receives: it has acquired
an identity of its own, independent of its values. That identity is the
pivot. The runtime decisions below are not consequences of separability
directly so much as consequences of a program's having a stable identity
--- and separability is what confers it.

From that single act of separation, four runtime decisions cohere.
Scripts with externalized parameters are eligible for compilation to IL
via Expression trees, cache as reusable programs with action
identifiers, persist to the journal as compact action entries, and
replicate with entropy bounded by argument vectors. Scripts with
hardcoded values are not cached, run once, and persist as their literal
text. The four are not parallel design choices; they are projections of
a single precondition. This paper develops and measures the first three
(compilation, caching, dense journaling); the fourth,
replication-bounded entropy, follows from the same precondition and is
developed in a companion paper --- it is named here, not claimed as a
result of this one.

This precondition operates on the substrate characterized in
\href{01-anti-porosity.md}{the previous paper of this series} ---
Puppeteer's journal as \emph{code-as-data}: programs persisted in their
own executable text. The prior paper develops this as \emph{script-form
persistence} (the \emph{executable journal}) and is careful to claim it
in a sense narrower than Lisp's strong homoiconicity, which it
explicitly disclaims; we adopt the label \emph{code-as-data} from this
point on, with that formalism standing as established there. The
compactness of these entries is not merely syntactic: each names an
operation substantially richer than its surface signature, with the
asymmetry scaling with the domain library's depth (§5.5).

\textbf{This is the principle of program--value separability: a DSL
program becomes identifiable, compilable, cacheable, and persistable
densely only when it is separable from its values. Externalized
parameters are the structural precondition under which compilation,
caching, and dense journaling become possible at all.}
\end{quote}

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\subsection{Claims this paper makes}\label{claims-this-paper-makes}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  \textbf{Causal inversion: program--value separation is a necessary
  condition for the stable-identity faces.} Compilation, caching, and
  dense journaling in the runtime described here are not optimizations
  layered atop interpretation. They are downstream consequences of
  \emph{program--value separability} --- the structural property that
  values live outside the script body, not embedded within it. A DSL
  program embedding its values has no stable identifier under which to
  reuse a specialization across invocations, no template to reference in
  the journal rather than copy, no bounded argument stream to replicate.
  The separation is necessary for these faces, not optional --- though
  it need not be \emph{declared} rather than recovered by analysis
  (§1.2), and it is not claimed that caching of every kind requires it
  (memoization of results does not). \emph{(Verification: structural,
  within this class of runtimes --- a script embedding its values
  produces a different cache key per invocation, so cross-invocation
  reuse, reference-based journaling, and bounded replication collapse
  together; the argument and its two standard objections are made
  precise in §1.2. The runtime documents the model itself: ``Un script
  funciona como F(x1,x2,\ldots,xn)'' (\texttt{ActorHandler.cs:1085}) ---
  F is the program; the xᵢ are its externalized values.)}
\item
  \textbf{DSL programs admit a structural binary taxonomy.}
  \emph{Parametric scripts} are separable from their values: cacheable
  as reusable programs with action identifiers, persistable as compact
  action entries in the journal, and economically compilable.
  \emph{Literal scripts} are non-separable: never cached, ephemeral, and
  persisted as their literal text. Whether either class is compiled or
  interpreted at runtime is governed by a separate per-actor policy
  that, by default, follows the parametric/literal split.
  \emph{(Verification: ActorHandler.cs:1091-1093 documents the rule
  verbatim. Implementation: PrepareCommandProgram
  (ActorHandler.cs:1097-1148) decides via parameters.HasUserParameter()
  and assigns one of three JournalEntry values --- IsScript (literal),
  IsNewAction (parametric, first invocation), IsExistingAction
  (parametric, cached invocation). Compilation flag set per program by
  AdjustCompilationMode (Program.cs:134-150) under the
  CompilationModePolicy enum.)}
\item
  \textbf{Interpretation and compilation are strategies over a shared
  AST substrate.} They are not separate execution paths over translated
  representations. Both walk the same AST: interpretation walks it
  directly; compilation specializes it into IL via Expression trees.
  Both paths pay the same parsing cost; only the compiled path pays the
  compilation cost. \emph{(Verification: Program.Execute()
  (interpretation) and Program.ExecuteExpression() →
  ProgramExpression().Compile() (compilation) operate over the same
  Statement tree produced by Parser.Parse().)}
\item
  \textbf{Journal density emerges from separability, not from a
  compression scheme.} A stable F(x₁, \ldots, xₙ) collapses repeated
  invocations into (actionId, values) tuples, with the script definition
  persisted once and referenced thereafter. The compactness is
  structural, not designed. \emph{(Verification:
  Diary.WriteNewActionEntry (definition + first invocation values) vs
  Diary.WriteActionEntry (actionId + values only on subsequent
  invocations).)}
\item
  \textbf{Verb richness: surface vs depth.} A single DSL verb may invoke
  orchestrations richer than its surface signature suggests. This is not
  the result of translation between layers; the DSL is the invocation
  language for domain operations implemented directly in the host
  language. Journal density is therefore the asymmetry between small
  persisted tokens and the live-domain operations they represent.
  \emph{(Verification: §4 develops the construct and §5.5 measures it on
  two open-source DDD aggregates --- eShop's \texttt{Order} purchase
  verb dispatches α = 9 host-language invocations with a 24-method
  static closure (β/α ≈ 2.7×); Grzybek's \texttt{SubscriptionPayment}
  verb dispatches α = 2 with a 73-method static closure (β/α ≈ 36×). α
  is exact; β is a crude static lower bound on reachable host surface
  (§5.5), and at n=2 the 2.7×-vs-36× gap is not attributable to any
  single cause. The supported claim is the qualitative one --- the
  persisted token is far smaller than the behavior it names --- carried
  by α and by journal density (§5.4), not by the β/α magnitude.)}
\item
  \textbf{Hot-loaded DSL programs over a stably-loaded domain.} The
  runtime supports hot-loaded DSL programs against a stably-loaded
  domain library: the assemblies configured as the actor's domain
  libraries are reflectively cached at first use; new DSL scripts can
  combine the types they carry in new ways without assembly reload. This
  enables ad-hoc procedure invocations against a live, stateful actor
  --- without redeploying endpoints or altering the domain library.
  \emph{(Verification: DomainLibraries.GetOrLoad(params Assembly{[}{]})
  caches public types statically per deduplicated assembly set;
  ActorV2.Using(scriptForChk, scriptForCmd) introduces new scripts per
  invocation against that cached surface area.)}
\end{enumerate}

\subsection{When NOT to use this
approach}\label{when-not-to-use-this-approach}

The principle of program--value separability has limits, and the
implementation pattern that realizes it has narrower limits still. We
name six regimes in which the analysis presented here either does not
apply, applies only partially, or invites overreach.

\subsubsection{A. This paper is not advocating for compiling all DSL
programs
unconditionally}\label{a.-this-paper-is-not-advocating-for-compiling-all-dsl-programs-unconditionally}

The structural taxonomy described here distinguishes parametric from
literal scripts; both are first-class. Literal scripts --- ad-hoc oracle
invocations against a live actor, exploratory queries against a stateful
runtime, configuration-time experiments --- pay zero amortization cost
because they incur no compilation. Treating them as legacy or transient
would discard a structurally valid mode of interaction.

\subsubsection{B. This paper does not address consistency under
concurrent
actors}\label{b.-this-paper-does-not-address-consistency-under-concurrent-actors}

The runtime described operates with strict per-actor ordering and
isolation. Multi-actor consistency, reaction propagation across actor
boundaries, and the contract under which observable state remains
coherent are treated formally in a companion paper of this series.

\subsubsection{C. This paper does not claim program--value separability
is sufficient for the four-fold
coherence}\label{c.-this-paper-does-not-claim-programvalue-separability-is-sufficient-for-the-four-fold-coherence}

Separability is necessary but not sufficient. SQL prepared statements
achieve compilation and caching while exhibiting only two of the four
faces --- the persisted artifact is row data, not the parameterized
statement itself, so the journal is not code-as-data (script-form
persistence does not emerge). Other realizations may exhibit further
subsets. Puppeteer demonstrates one realization where all four faces
emerge by structural commitment; the converse is not entailed by
separability alone.

\subsubsection{D. This paper does not extend separability to
general-purpose programming
languages}\label{d.-this-paper-does-not-extend-separability-to-general-purpose-programming-languages}

The principle is formulated for DSL runtimes --- domain-specific
languages whose programs are short, structured, and amenable to
identification by their textual form. General-purpose programming
languages introduce variables captured from enclosing scope,
side-effecting state in the host, and identity that exceeds the
parameter contract. Whether separability admits a meaningful formulation
in that broader setting is a separate question, and we do not take it up
here.

\subsubsection{E. This paper does not claim that hot-loaded DSL programs
replace traditional
deployment}\label{e.-this-paper-does-not-claim-that-hot-loaded-dsl-programs-replace-traditional-deployment}

Claim 6 articulates a runtime capability, not a deployment
recommendation. Hot-loaded DSL programs against a stably-loaded domain
enable ad-hoc invocation; redeployment of the domain library remains the
appropriate move when the implementation itself changes. The two are
complementary, not alternatives.

\subsubsection{F. This paper does not address security, sandboxing, or
resource bounds for hot-loaded
scripts}\label{f.-this-paper-does-not-address-security-sandboxing-or-resource-bounds-for-hot-loaded-scripts}

Claim 6 grants a running actor the ability to accept and execute DSL
programs formed at call time. The structural argument made here is
silent on the operational concerns that capability raises in production:
sandboxing of the DSL evaluator, per-script resource limits (CPU,
memory, wall-clock), authentication and authorization over which callers
may submit ad-hoc invocations, and the trust model under which the
supplied script is admitted at all. These concerns require separate
machinery --- out of scope for the conceptual contribution offered here,
but unavoidable for any deployment that exposes hot-loaded invocation
beyond a closed boundary.

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\subsection{1. Introduction}\label{introduction}

This paper makes an analytic theory contribution. It identifies a
structural property of DSL programs that prior literature has touched
but not isolated as one --- the construct: \emph{program-value
separability}; derives the runtime consequences that follow when the
property is held --- the principles: compilation, caching, and dense
journaling, developed and measured here, together with a fourth,
replication-bounded entropy, that follows from the same precondition and
is developed in a companion paper; and presents an instantiation --- a
system in which those principles have been realized in production --- as
confirmation that the construct is realizable. The contribution is
conceptual; the instantiation is the existence proof of realizability,
not the substance of the claim. The genre is the one Gregor (2006) names
\emph{theory for analyzing} (Type I): it introduces constructs and a
taxonomy that let the phenomenon be described and classified, and states
a necessary-condition claim about their consequences, with empirical
evaluation supplementary. The Hevner-style design-science
\emph{evaluation} of the artifact (Hevner, March, Park, \& Ram, 2004)
--- bundling artifact assessment with construct introduction --- is
deferred to a companion case-study paper; the Type I frame separates the
two cleanly, and the instantiation here serves only as an existence
proof of realizability.

The structural property at the center of this paper is not, in itself,
new, and §7 documents its lineage. Partial evaluation, lambda lifting,
closure conversion, and template instantiation all turn on a separation
between a program and the values it will receive; each predates this
work by decades. What is new is the dependent variable. In those
traditions, separability is instrumental to \emph{execution}: a
binding-time or free-variable analysis recovers the static/dynamic
split, a transformation consumes it to emit a faster residual program,
and the split is discarded once the specialized code exists. This paper
repositions the same property as the precondition for \emph{persistence
and identity}. The separated program is not consumed by a transformation
but persisted as the durable, replayable, replicable unit of state ---
written once as a definition, referenced thereafter by a stable
identifier and an argument vector, and reconstructed by deterministic
replay. Compilation becomes one face among four, and not the
load-bearing one. The claim this paper defends is that compilation,
caching, dense journaling, and replication-bounded entropy are not four
independent optimizations but four projections of this single
precondition once the persisted artifact is the program rather than its
effects --- a connection the execution-specialization literature has no
occasion to draw, because it never treats the program as state.

\subsubsection{1.1 Calibrating the
surface}\label{calibrating-the-surface}

You already understand this taxonomy. A SQL prepared statement is
parameter-separable: the database parses it once, builds a query plan
once, and reuses both for thousands of invocations with different bind
values. An ad-hoc query offers no such surface --- each is a fresh
string, parsed and planned afresh. The same structural distinction
governs DSL programs in any runtime that aspires to compilation,
caching, and dense persistence. What changes between SQL and the runtime
described here is not the principle, but the surface to which it
applies. The principle, in other words, is neither new nor Puppeteer's:
it is one every database programmer already relies on locally, each time
a prepared statement is reused. What is new is the recognition that the
same condition, taken as general rather than local, is what underwrites
compilation, caching, dense journaling, and replication alike --- and
that a runtime can be built to honor it everywhere, not only at the
query boundary.

That surface tends to be unfamiliar in a specific way. The professional
reader of this paper has very likely spent a career persisting
application state in relational tables. Where event sourcing has
appeared in their work, it has typically appeared in tabular form:
events as rows, schemas as projections, queries as joins against
materialized views. The implicit assumption --- that every
representation of state ultimately resolves into something table-shaped
--- is not a failure of imagination but the horizon that industrial
practice has produced over several decades. A runtime in which the
persisted artifact is the program itself, in which density emerges from
separability rather than from compression of rows, has had no canonical
place in that landscape.

Calibration begins by separating two layers that the dominant paradigm
tends to collapse into one. The first layer is the implementation:
domain types, methods, business rules --- written in the host language,
indistinguishable in form from any well-designed object-oriented or
functional library. The second layer is the invocation: the language by
which a running actor is commanded and queried. In runtimes of the kind
described here --- of which Puppeteer, the framework introduced in the
prior paper of this series, is one --- that invocation language is a
small DSL whose programs read as scripts, persist as journal entries,
and execute as either compiled lambdas or interpreted walks over their
own AST. The two layers are not translations of one another. The host
language carries the implementation; the DSL carries the contract by
which the implementation is reached. Conflating them --- assuming the
DSL must duplicate, transcribe, or shadow the host code --- is where
most readings of the architecture begin to diverge from its actual
structure.

Three readings should be pre-empted at the outset: this is not double
programming, not a translation layer, not a mapping specification. The
domain implementation lives once, in the host language. The DSL is the
language by which that implementation is invoked. The relationship is
closer to that between SQL and a relational engine than to that between
a model and a generated DTO: SQL does not duplicate the engine's logic;
it names operations that the engine performs. A short SQL statement can
trigger joins, locks, and execution plans far heavier than its surface
text. The DSL of an actor runtime carries the same kind of leverage ---
small surface, deep behavior --- and the persisted journal records not
the unfolding of that behavior but the invocations that produced it.
With the surface calibrated, the formal question can now be asked: what
does it take, structurally, for a DSL program to be compilable,
cacheable, and amenable to dense persistence?

\subsubsection{1.2 A formal
characterization}\label{a-formal-characterization}

The question can be sharpened. Compilation requires a stable artifact to
specialize. Caching requires a stable identifier under which to file the
result of that specialization. Dense journaling --- persistence that
does not balloon with the size of every invocation --- requires a stable
description of the operation that can be referenced rather than copied.
Each of these three demands the same property of the program: that it
admit, at the moment it is named, a separation between the program
itself and the values it will receive.

This property is \textbf{Program--Value Separability}. A DSL program is
separable when its textual form names an operation parameterized by
values supplied externally at invocation, rather than carrying those
values within its body. Separability is structural, not stylistic: it is
a property the program either has or does not have. It is decidable
syntactically at preparation time, by inspection of the parameter
declarations on the program's surface. The principle that follows admits
a compact statement: program--value separation is a necessary condition
for the \emph{stable-identity} faces --- caching a specialization under
a key reused across invocations, journaling the program by reference
rather than by copy, and replicating it as a bounded argument stream.
Two scopings keep this from collapsing into a definition. The necessity
is of the separation itself, not of any particular way of obtaining it:
a runtime may have the programmer declare parameters, as the one
described here does, or recover the separation by analysis. And the
necessity is of \emph{these} faces, not of caching in general ---
caching of other objects, such as memoized results, requires no
separation at all. Necessity is the strong claim; its scope and the two
natural objections to it are made precise shortly.

What separability buys, in a word, is \emph{identity}. A program whose
values are supplied from outside has a textual form that is stable
across invocations --- it is the same program the second time and the
thousandth --- and so it can be named; a program that embeds its values
is a different string on every call, and no two invocations are the same
program at all. Identity is the pivot on which the rest turns. Once a
program has an identity independent of its values, caching has something
to file under, compilation has a stable artifact to specialize, the
journal has a reference to record in place of a copy, and replication
has a unit to ship. The faces traced in this paper are best read not as
four properties of separability but as consequences of the \emph{program
identity} that separability confers --- and it is the absence of that
identity, not the presence of embedded values as such, that makes a
value-embedding script uncacheable, uncompilable-for-reuse, and undense
in the journal.

Separability admits a binary structural taxonomy of DSL programs. A
\textbf{parametric script} is separable from its values: it is filed
under a stable identifier in a runtime cache and persisted to the
journal as a compact reference plus an argument vector. A
\textbf{literal script} is non-separable: it is not cached, runs once as
written, and persists as its literal text. Whether either class is
compiled or interpreted at runtime is governed by a separate per-actor
policy that, by default, follows the parametric/literal split --- but
admits override. Under that default policy, the runtime documents the
joint regime in its own comments: scripts without user parameters are
interpreted, uncached, and persisted as Script entries; scripts with
user parameters are compiled, cached with an ActionId, and persisted as
Action entries. The two strict properties --- caching and journal entry
format --- are encoded as values of a single enum decided at the moment
a program enters preparation. The taxonomy is not analytical but
operational: the runtime acts on it.

Necessity is not sufficiency. A program that admits separability gains
the structural possibility of being cached, persisted as a compact
reference, and amortizing its compilation --- but does not by that fact
alone exhibit dense journaling. SQL prepared statements illustrate the
gap: they are separable, they cache, they compile --- yet the persisted
artifact in the database is row data, not the parameterized statement
itself. The third face --- a journal in which the named operation is the
persisted entry, not its downstream effects --- does not emerge from
separability alone. It requires the further commitment, code-as-data,
that the runtime treat its programs as the persisted artifact: what is
recorded in the journal is the program parameterized and the values it
received, not the state changes those values produced. The sections that
follow trace how separability, joined to code-as-data, yields the three
faces in turn --- and where a runtime that holds separability without
the second commitment falls short.

Necessity, made precise. Two objections test the claim, and locating
where each lands sharpens it rather than defeats it. The first is
\emph{memoization}: a runtime can cache the result of an invocation
keyed on its inputs with no parameters declared anywhere. This is
genuine caching, but of a different object --- it caches outputs, not
the program. A memoized value-embedding script still hits only on
identical re-invocation, never across distinct argument vectors, and
yields neither a reusable specialization, nor a reference-based journal
entry, nor a bounded replication stream. Memoization caches effects; the
faces at issue cache the operation. The second objection is stronger: a
runtime can \emph{recover} a separation the programmer never declared
--- canonicalizing literal scripts and abstracting their literals into a
template after the fact, the inverse of constant folding. This does not
refute the claim; it relocates it. What such a runtime recovers
\emph{is} program--value separation, obtained by analysis instead of by
declaration. The separation remains the precondition; canonicalization
is merely another route to it, one that pays an analysis cost and yields
a separation that is provisional and runtime-derived rather than stable
and surface-given. So the claim is not that a programmer must declare
parameters --- it is that the four-fold coherence requires the
separation to exist, however reached, and that a \emph{stable,
syntactically decidable} separation is what makes the separated program
serviceable as the durable identity under which journaling and
replication operate, where an analysis-recovered one is not. Throughout,
the definition of separability is syntactic --- a property of program
text --- while the claim concerns that property's consequences; the two
are kept apart precisely so the claim is not true by definition.

The DSL program characterized above --- the textual artifact with
declared parameters --- is the unit of separability. When the term
\emph{program} appears at the substrate level in subsequent papers of
this series (the unit that is recorded, replicated between nodes, and
replayed against state), it denotes the pair: a \textbf{domain library}
(the compiled classes and verbs against which the DSL is interpreted,
loaded statically and identical across replicas) and a \textbf{journal
of invocations} (the ordered sequence of action references with
parameter bindings that the runtime has applied). Replay is the
deterministic re-execution of the journal against the library; what
persists, replicates, and reconstructs state is this pair. A DSL program
in the sense of §1.2 is the unit; the substrate-level program is the
cumulative state-producing artifact built from many DSL invocations
executed against a stable library. The two readings are not in tension:
separability of the unit is what makes the cumulative artifact dense;
the cumulative artifact is what makes the unit's separability
operationally consequential.

The central claim can now be stated without hedging. It is not that
separability improves compilation --- compilation is merely its most
visible consequence, and the paper would stand were it removed, on
caching, dense journaling, and replication-bounded entropy alone. The
claim is that compilation, caching, dense journaling, and
replication-bounded entropy are four operational projections of one
structural condition: a program's having an identity independent of its
values. Nor is the condition peculiar to this runtime or this DSL. No
system can give a program a \emph{reusable} identity --- and therefore
none of the consequences that depend on one, a cached specialization, a
reference recorded in place of a copy, a replayable unit --- while its
values remain embedded in its representation. Separability is the
general precondition; the runtime of §3 is one case study in what
follows once it is met.

\subsection{2. Consequences of
separability}\label{consequences-of-separability}

\subsubsection{2.1 Compilation as
specialization}\label{compilation-as-specialization}

The first consequence of separability is that the program admits
ahead-of-time specialization. A separable program is, in form, a
function awaiting its arguments --- its body refers to values that will
arrive at invocation, not to literals fixed at write time. That
structural shape is precisely what a specializer requires. Given the
program once, the runtime can resolve its references, lower its abstract
syntax tree into a typed expression tree, and emit the executable form
in advance of any specific call. The values are bound later; the work of
preparing the program is done once.

The pipeline that produces this specialized form proceeds in three
stages. The parser yields an abstract syntax tree in which user
parameters appear not as values but as named slots --- placeholders to
be supplied at invocation. The runtime then traverses that tree and
emits a typed expression: each named slot is bound to a parameter
expression of the host runtime, and each operation of the script is
bound to the corresponding host-language method or property access.
Compilation of that expression yields an executable delegate over the
parameter list --- a function that takes the values supplied at
invocation and runs the program against them.

Specialization, considered alone, is a one-time cost paid against an
indefinite number of invocations. The parser runs once for the program;
the expression tree is constructed once; the compiler emits the delegate
once. Each subsequent invocation supplies new values into the existing
delegate and runs through host-language code that has already been
resolved, typed, and emitted. The amortization is structural: a
separable program admits compilation that pays for itself across
repeated invocation, while a non-separable program offers nothing to
amortize against. The work, however, is only economical if the
specialized form persists between invocations.

\subsubsection{2.2 Caching as
identification}\label{caching-as-identification}

Persistence between invocations requires an identifier under which to
file the work that has been done. Separability supplies that identifier
directly: the script string of a separable program is stable across
invocations because its values are external to it. The same
parameterized program text, encountered a second time, is recognizable
as the same program --- and the cached specialization belongs to it. A
non-separable program has no such anchor; every invocation produces a
different string, and no two invocations are the same program at all.
Caching is not an addition; it is what becomes possible once the program
has a stable name.

The mechanism is direct. On first encounter, the runtime parses the
script, prepares its specialized form, and stores both under an action
identifier --- a stable handle assigned to the script when it is first
encountered. The script string serves as the lookup key; the action
identifier serves as the persistent reference under which the program's
specialization lives. On subsequent encounters with the same script, the
lookup succeeds, and the runtime retrieves the specialization without
repeating the work that produced it. The cache is not a memoization of
values; it is a registry of programs.

On a cache hit, the work that has been preserved is the program itself:
the resolved tree, the typed expression, the compiled delegate. The
values arriving at this invocation are bound into the delegate's
parameter slots and the program runs against them. From the program's
standpoint, nothing has changed between the first invocation and the
thousandth --- the script names the same operation, parameterized by the
same slots, executed against the same domain. What differs across
invocations is only the values supplied; what persists is the program.

\subsubsection{2.3 Dense journaling as
reference}\label{dense-journaling-as-reference}

The third consequence of separability is that persistence becomes
reference rather than duplication. The program, having earned a stable
identifier in the cache, can be written to the journal once --- as a
definition, with the script's text and its parameter declarations
recorded in full. Each subsequent invocation is recorded not by
duplicating the program text but by referring back to that definition
under its identifier, accompanied only by the values supplied at that
invocation. Where a non-separable runtime must record every invocation
as a complete script --- body, values, and all --- a separable runtime
records the program once and its invocations as compact references
against it.

The mechanism distinguishes three kinds of journal entries. When a
parametric program is encountered for the first time, the runtime writes
a definition entry: the script's text, its parameter declarations, and
the values supplied at this first invocation, all recorded together
under the program's newly assigned identifier. Subsequent invocations of
the same program write only a reference entry --- the identifier of the
definition, plus the values for that call. A non-separable program,
having no stable identifier, is written each time as a script entry: its
text in full, since it cannot be referred back to anything that came
before. Three entry kinds, decided mechanically at preparation time from
the program's separability, encode the program's relation to its
identity. The distinction is, at bottom, ontological: the entry type
records whether the thing persisted \emph{is} a program with an identity
of its own --- an Action, referable and replayable --- or merely a
script that ran once and has none. The journal is, in this sense, a
ledger of which operations possess operational identity and which do
not; separability is the test it applies, and the entry type is the
verdict it records.

The density follows. For a parametric program invoked many times, the
journal's storage scales with the cumulative size of the argument
vectors, not with the cumulative size of the script's body --- the body
has been recorded once, and every later invocation costs only the bytes
of its values. This is what makes the journal dense: nothing is copied
that has been preserved by reference, and what survives is the operation
that took place, not the state that resulted. The persisted artifact is
the program --- the script as text, parameterized, with its invocation
values --- rather than the downstream effects of running it. In
code-as-data terms, the program lives in the journal as itself, not as a
record of what it produced. Compilation, caching, and dense journaling
are thus not independent optimizations but successive consequences of
the same structural property: once a program is separable from its
values, it becomes nameable; once nameable, it becomes cacheable; once
cacheable, it becomes referable in persistence. The mechanism by which
the runtime accomplishes this --- the pipeline that compiles, the cache
that retains, the journal that references --- is the subject of §3.

\subsection{3. Realization in a concrete
runtime}\label{realization-in-a-concrete-runtime}

\subsubsection{3.0 Origins of the
instantiation}\label{origins-of-the-instantiation}

The realization described in this section was not engineered to
demonstrate the principle articulated above. The principle was
identified by inspecting a runtime that had, over years of independent
development for production use, come to satisfy it. The genealogy is
structural rather than programmatic --- first the artifact, then the
principle that explains why the artifact cohered. What follows describes
the runtime as it stands; the relationship between its mechanisms and
the principle of §1.2 is a recognition, not a derivation.

The artifact is open to inspection, and the claims that follow are meant
to be checked rather than taken on trust. The runtime is public at
\texttt{github.com/alvaroNCubo/puppeteer}, archived in Software Heritage
at the commit cited under \emph{Code provenance}; every
\texttt{file.cs:NN} reference in this section resolves against that
public source. The benchmark harness of §5 is likewise public (this
paper repository's \texttt{labs/} and the runtime's
\texttt{tests-local/}), and the host aggregates it exercises are public
MIT projects. The existence proof offered here is therefore verifiable
in the sense that matters for the claim: a reader can read the cited
code, build it, and re-run the measurements. The earlier, internal
lineage of the runtime is not part of the proof --- only the public
snapshot is --- and where the public snapshot differs from the internal
development line, the public snapshot governs every reference and figure
in this paper.

\subsubsection{3.1 Compilation pipeline}\label{compilation-pipeline}

The mechanisms described here are not independent engineering decisions
but direct realizations of the separability principle established in the
preceding sections. In the runtime described, the compilation pipeline
runs from text to executable in three well-defined stages. A
\texttt{Parser.Parse()} call lowers the script into an abstract syntax
tree of \texttt{Statement} and \texttt{AstExpression} nodes. Each
\texttt{Statement} carries an \texttt{ExecuteExpression} method that,
when traversed, emits a \texttt{System.Linq.Expressions} node bound to
the host parameters and the host-language operations the script names.
The runtime composes these into a single \texttt{Expression.Lambda} and
calls \texttt{.Compile()} to produce an executable delegate. Three
stages --- parsing, lowering, and compilation --- produce the artifacts
on which the runtime operates: the AST, the expression tree, and the
compiled delegate.

The mechanism is straightforward to follow in the source.
\texttt{PrepareCommandProgram} (\texttt{ActorHandler.cs:1097-1148})
orchestrates the first encounter: it rents a parser from a pool, parses
the script, and decides --- by parameter presence --- whether to enter
the parametric path. On the parametric path, the program's
\texttt{ProgramExpression} method (\texttt{Program.cs:182-244}) walks
the AST: for each Statement, it calls the Statement's
\texttt{ExecuteExpression}, which returns an \texttt{Expression} node
bound to the runtime's parameter and output expressions. These nodes
accumulate into a \texttt{BlockExpression}, which
\texttt{ProgramExpression} wraps in
\texttt{Expression.Lambda\textless{}Func\textless{}Parameters,\ Output,\ string\textgreater{}\textgreater{}}
(\texttt{Program.cs:244}). The compilation itself is one line:
\texttt{\_executable\ =\ programExpression.Compile()}
(\texttt{Program.cs:163}). The compiled delegate lives in the program's
\texttt{\_executable} field; subsequent invocations skip the compile
step and call the delegate directly with the values supplied at the call
site.

The same mechanism applies one level deeper, to a feature the runtime
calls \emph{Eval parameters}. A parameter declared with the
\texttt{Eval} modifier carries its own sub-script, which the runtime
treats as a separable program in miniature: parsed once, compiled to its
own delegate, and cached against the parameter's name. The cache
structure is a dictionary keyed by parameter name to a tuple of the eval
script and its compiled executable (\texttt{Program.cs:305}). On each
invocation, the runtime compares the parameter's current script to the
cached one; if they differ, the entry is invalidated and the sub-program
is re-parsed and re-compiled (\texttt{Program.cs:323-331}). The
principle scales: any region of the program that has a stable textual
identity admits the same treatment as the whole.

\subsubsection{3.2 Interpretation
retained}\label{interpretation-retained}

The runtime preserves interpretation as a first-class execution mode,
not as a legacy fallback. When a script presents itself without user
parameters, or when a per-actor policy directs the runtime to interpret
regardless of parameter presence, the program executes by walking the
AST directly: each Statement carries an \texttt{Execute} method that
runs against the program's current state, with no expression tree built
and no delegate compiled. The interpreted path costs zero compilation
but pays the cost of tree traversal at every invocation. Its place in
the runtime is precisely the inverse of the compiled path's: useful
where invocation is one-shot or unanticipated, useless where the program
will be reused.

The decision is governed by a per-actor \texttt{CompilationModePolicy}
enum with three values --- \texttt{Automatic}, \texttt{AlwaysCompiled},
and \texttt{AlwaysInterpreted} --- declared on the actor and defaulted
to \texttt{Automatic} (\texttt{Actor.cs:8-13}). On the parametric path,
\texttt{PrepareCommandProgram} calls \texttt{AdjustCompilationMode} with
\texttt{useInterpretedMode:\ false}; on the literal path, with
\texttt{useInterpretedMode:\ true} (\texttt{ActorHandler.cs:1117-1131}).
Under \texttt{Automatic}, the policy honors that hint: parametric
programs compile, literal ones interpret. Under \texttt{AlwaysCompiled}
or \texttt{AlwaysInterpreted}, the hint is overridden in favor of the
policy's name (\texttt{Program.cs:134-150}). Execution itself is
dispatched by another switch on the same policy: \texttt{Perform} calls
\texttt{ExecuteExpression} if the program is in compiled mode and
\texttt{Execute} otherwise (\texttt{ActorHandler.cs:1955-1968}).

Caching applies asymmetrically to the two principal kinds of DSL
invocation. For commands --- programs that mutate actor state and
persist to the journal --- the runtime caches only when the script
declares user parameters (\texttt{ActorHandler.cs:1117}). For queries,
checks, and emit invocations --- programs that read state without
persistence --- the runtime caches when the script declares user
parameters or when no parameters are supplied at all
(\texttt{ActorHandler.cs:1405}). The asymmetry is operational. Commands
run under a write lock and serialize, so caching pays only when the same
parametric program is invoked many times. Queries run under a read lock
and parallelize, so a cache entry amortizes regardless of parametric
reuse --- a frequently-emitted query gains from caching even when its
parameter set is fixed. One final observation: cosmetic variation in the
script's text across releases generates distinct cache identifiers, but
the runtime's reaction mechanism --- treated in a companion paper ---
matches behavior against semantic patterns rather than identifier
equality, so coherence is preserved across textual variants.

\subsubsection{3.3 Hot-loaded DSL programs over a stably-loaded
domain}\label{hot-loaded-dsl-programs-over-a-stably-loaded-domain}

The third element of realization is the runtime's support for hot-loaded
DSL programs against a stably-loaded domain library. The phrase deserves
care, because it is easy to misread. The hot element is the DSL: at any
time during the actor's life, a new script --- never seen before by this
runtime instance --- can be supplied, parsed, prepared, and executed
without restart, redeployment, or reflection over a re-loaded assembly.
The stable element is the domain library: the host-language types and
methods that the DSL invokes are loaded once, by reflection over the
assemblies configured as the actor's domain libraries, and held in a
cache for the lifetime of the process. Hot-loaded programs combine
stable domain elements in new ways; they do not bring new domain
elements into being. The runtime is hot at one layer and stable at the
layer beneath it.

The mechanism is brief in the source. When an actor is constructed, the
public types of its configured library assemblies are walked once by
reflection and cached against a deduplicated key:
\texttt{DomainLibraries.GetOrLoad(params\ Assembly{[}{]})}
(\texttt{DomainLibraries.cs:77-115}, with both a single-assembly and a
multi-assembly overload), invoked from \texttt{ActorHandler}
(\texttt{ActorHandler.cs:61}) with the actor's
\texttt{LibraryAssemblies}. The cache survives the lifetime of the
process; the same assembly set, loaded by a second actor, reuses the
same domain dictionary. Against that fixed surface, new DSL scripts
arrive through the actor's fluent interface ---
\texttt{ActorV2.Using(scriptForChk,\ scriptForCmd)}
(\texttt{ActorV2.cs:32}) --- at any time the actor is running. There is
no AppDomain reload, no \texttt{AssemblyLoadContext} unload, no
file-watcher over the assembly: the surface area is fixed at first load
and dynamics live entirely above it.

The operational consequence is direct: a running actor can be queried or
commanded with a script formed at call time, against any combination of
the domain operations the configured library assemblies carry. A
configuration adjustment, a one-off audit query, a derivation that the
published endpoints do not anticipate --- each can be expressed as a
fresh DSL program and executed against live actor state without
intermediate deployment. The relationship resembles gRPC in its
directness --- a procedure call against the live system rather than a
navigation of REST resources --- but without a pre-declared interface
contract: the script itself is the contract, formed at call time. What
has been shown in this section is that separability is not merely a
formal property of DSL programs but one that a concrete runtime can
recognize, act upon, and encode into its execution, caching, and
persistence behavior. The realization characterized here is one of an
extensible family: other actor runtimes --- Akka, Orleans, Erlang/OTP
--- admit, in principle, the same construction, since a parameterized
DSL surface above a host-language domain library would yield the same
four-fold coherence under the same separability commitment. The
expressive reach of such on-the-fly programs depends on the richness of
the verbs the domain library exposes, which the next section takes up.

\subsection{4. Verb richness: surface vs
depth}\label{verb-richness-surface-vs-depth}

Separability makes the program nameable; verb richness determines how
much domain meaning that name carries. The verbs that a DSL invokes are
not method calls on data structures. A verb in this setting names an
operation against a live, stateful actor --- an orchestration that the
host language has been written to perform on behalf of the domain. A
single verb may invoke many internal methods, traverse domain
relationships, evaluate derived properties, trigger downstream
behaviors, and write to multiple regions of state, all in the course of
executing one DSL statement. Where a setter assigns a field, an actor
verb concludes a transaction --- moving the actor from one consistent
state to another. The asymmetry between the surface --- the few
characters that name the verb in the script --- and the depth --- the
volume of domain computation that runs when it is called --- is
structural, not incidental.

Consider a verb that any reader from an e-commerce or supply-chain
background will recognize: the \texttt{Confirm} of a purchase order. The
script that names this verb at the DSL surface is short --- only a
handful of tokens. What runs when the script is invoked is substantially
larger: a verification that the order's reservations are still valid, a
hold-to-allocation conversion across one or more inventory ledgers, a
tax computation that depends on the order's line items and their fiscal
contexts, a posting to the financial ledger, and a set of reaction
triggers that propagate the confirmation to subscribed views and
downstream actors. Each of these steps is itself a tree of host-language
method calls; the leaf operations include arithmetic, predicate checks,
persistence writes, and outbound messages. The runtime gives the
consistency boundary that separates this depth from its surface
first-class support: a check script and a command script can be supplied
together via \texttt{ActorV2.Using(scriptForChk,\ scriptForCmd)} and
dispatched as a single invocation through
\texttt{PerformCheckThenCommand} (\texttt{ActorV2Invocation.cs:69}). The
check runs against the actor's current state under a read lock; if its
assertions hold, the command runs under a write lock and is persisted to
the journal. If the check fails, no state change is applied and no
journal entry is written --- the actor's consistent state is preserved
by construction.

Confirm is one example among many. The same asymmetry obtains for any
verb that names a domain transition rather than a field assignment ---
Settle, Allocate, Reconcile, Release, and a hundred others that a
well-designed domain library will expose. Verb richness is not an
artifact of this particular runtime; it is a property of how domain
operations are conceived and named. The implication folds back into the
journal density argument of §2.3: when each persisted entry refers to a
domain operation that may run dozens to hundreds of internal method
calls, the journal's compactness in bytes is matched by an enormous
compactness in semantic information per byte. The journal does not
record state changes; it records named transactions, each of which
carries its full operational meaning by reference to the domain library
that knows how to run it. The value of separability would be modest if
the verbs it named were shallow. It becomes profound when each name
stands for a full domain transaction.

Under porous substrates, the asymmetry inverts entirely. There, every
domain element must persist into a relational schema, and deep domain
logic becomes a serialization burden --- each derived state, each
accumulator update, each new field propagates as schema complexity, ETL
transforms, and projection overhead. Under a code-as-data substrate, the
same depth is rewarded, not penalized: the domain library can be made
arbitrarily expressive without expanding what the journal must record.
Complex abstractions compose without friction: no DTOs, ORM annotations,
or projection layers stand between the domain operation and its
persistence. Porosity makes deep domains expensive to represent;
anti-porosity makes them economical to compose. The two directions
reinforce one another: the richer the domain, the more meaning each
Action carries per persisted byte; and because the journal pushes no
persistence concern back into the domain, the domain is free to grow
richer still. Density and domain depth are not in tension --- each makes
room for the other, the plain structural consequence of recording
operations instead of their effects. This asymmetry is why journal
density matters at all. The journal is not compressing syntax ---
shaving bytes from a line of text; it is preserving a reference to an
operation whose semantic volume exceeds its textual surface. An ActionId
stands not for a line but for a domain transaction: were a verb a mere
setter, density would be a trivial saving, but because a verb can name a
whole transaction, the compact entry preserves something large by
reference rather than copying it. This is the point the prior paper
makes from the other side --- the substrate stays dense because what it
records are operations, not their unfolded effects. The empirical
magnitude of these numbers --- how many operations a typical verb
dispatches, how many bytes a typical journal entry occupies, how the
ratio behaves at scale --- is the subject of §5.

\subsection{5. Empirical results}\label{empirical-results}

\subsubsection{5.1 Methodology}\label{methodology}

The measurements that follow span two complementary axes. The first axis
is \emph{program complexity}: a synthetic straight-line arithmetic
kernel parametric on integer inputs (depth 5 to 100 statements); a
DSL-rich kernel exercising control flow (for, if), arithmetic, and
parameter binding (\textasciitilde500 dispatched operations per
invocation); and a multi-item purchase command operating against an
external rich-domain aggregate. The first two tiers isolate runtime
overhead independent of host-language work; the third locates that
overhead within a representative end-to-end transaction.

The second axis is \emph{host codebase}. The production-verb
measurements are replicated against two independent open-source
MIT-licensed DDD aggregates that share a structural shape (rich
behavioral methods on aggregate roots) but represent disjoint business
domains: \texttt{dotnet/eShop}'s \texttt{Order} aggregate (e-commerce
ordering with multi-item cart and a four-step state machine), and
\texttt{kgrzybek/modular-monolith-with-ddd}'s
\texttt{SubscriptionPayment} aggregate (subscription billing with a
payment-lifecycle verb). The dual-codebase design is deliberate: if the
measured properties of compilation, caching, and journaling are
structural consequences of the runtime rather than artifacts of a
particular domain, the same property should appear on both codebases.
The selection criterion was structural similarity (DDD aggregates with
rich behavior methods rather than CRUD entities) over an unrelated
business domain. This criterion selects on the dependent variable, and
we are explicit about what that does and does not buy: choosing two
rich-behavior aggregates tests whether the measured properties survive a
change of domain \emph{within} that class --- it does not probe where
the mechanism stops paying off. To probe the floor rather than the
favourable case, §5.2 and §5.5 add an adversarial \emph{flat-CRUD} verb
(a single field setter), and §5.6 reads the result: the CRUD verb is
itself separable, so all four faces still obtain. What varies with the
domain is not whether they hold but the \emph{magnitude} of the speedup
(which persists even on a trivial verb) and the separate, non-face
property of \emph{verb richness} (which collapses to \textasciitilde1×
when the domain is shallow). Both aggregates are reachable as public
source for reproduction; Appendix A lists the per-section datasets and
the commit SHA.

Two hosts are not a statistical sample, and no claim of generality is
rested on them. The structural claims of this paper are carried by the
argument of §1--§4; the two codebases serve a narrower, falsificationist
purpose --- to test whether the predicted properties are artifacts of
one domain. Were they domain-specific, they would not appear on a
second, independently-authored aggregate from a disjoint business
domain. That they appear on both, with the same structure and differing
only in magnitude, removes domain-specificity as an explanation; it does
not, and is not offered to, estimate a population parameter. The two
hosts are a robustness replication, not a survey.

Measurements were produced against the public runtime commit
\texttt{b42d0f7} (Appendix A), built in \textbf{Release}, on a
13th-generation Intel Core i9-13900 (24 physical / 32 logical cores), 64
GB RAM, Windows 11 (build 26200), .NET 9.0.14 (RyuJIT, AVX2). Two
classes of measurement are reported and they differ in kind.
\emph{Deterministic} measurements --- journal entry counts and payload
bytes (§5.4), DSL dispatch counts and static call-graph closure (§5.5)
--- are exact, independent of build configuration and of any timing
instrument, and reproduce bit-identically across runs; they carry the
structural claims and are verified identical between Debug and Release.
\emph{Timing} measurements --- the compiled-versus-interpreted speedup
(§5.2), cold compile cost (§5.2), and eval-cache hit/miss (§5.3) --- use
the instrument each regime requires. Steady-state throughput (the
speedup) uses \textbf{BenchmarkDotNet} (v0.14): 8 warm-up iterations and
15 measured iterations of 20,000 invocations each, with tiered
compilation disabled (\texttt{DOTNET\_TieredCompilation=0}) so the
dynamically-emitted compiled delegate is fully optimized from the first
measured invocation rather than averaged across a Tier-0→Tier-1
transition; means are reported with their 99.9\% confidence
half-interval. One-shot costs (a program's cold compile, an eval
sub-program's recompile) are isolated at their exact call site by a
runtime instrumentation hook and timed with Stopwatch --- the
appropriate instrument for a single hundreds-of-microseconds event, not
a tight steady-state loop. Cache and pool hit rates use Interlocked
counters; journal-density measurements parse the runtime's binary
journal format directly. The pool-hit-rate figures (§5.3) and the
cache-footprint figures (§6.3) are such counter and GC measurements ---
insensitive to build configuration and to the exact commit --- taken on
the same public runtime line; they are reported as measured rather than
separately re-pinned to \texttt{b42d0f7}, whose pool and cache code
paths they exercise unchanged. The bench follows a
bootstrap-then-measurement pattern: a non-parametric bootstrap script
establishes the facade in the actor's persistent symbol table once, and
subsequent invocations bind per-iteration values via the runtime's
parameter API while the script string stays constant, so the
compiled-program cache hits on every call --- the regime the
amortization argument names. Raw per-iteration data, the BenchmarkDotNet
environment reports, and the commit SHA are in the companion
\texttt{data/} directory (Appendix A).

\subsubsection{5.2 Compilation
amortization}\label{compilation-amortization}

Compiled execution shows a steady-state speedup over interpreted
execution that varies with where the program's work resides. For a
synthetic arithmetic kernel of depth 100 --- wholly DSL-bound --- the
speedup is ≈3.1× (interpreted 2.99 µs, compiled 0.97 µs). For a DSL-rich
kernel of \textasciitilde500 dispatched operations exercising for-loops,
conditionals, and parameter binding, it is ≈2.2× (interpreted 16.0 µs,
compiled 7.3 µs). For a multi-item purchase verb against the eShop
\texttt{Order} aggregate --- one DSL dispatch cascading through the
\texttt{Order} constructor, three \texttt{AddOrderItem} calls, and a
four-step state-machine walk to Shipped --- it is ≈1.5× (interpreted
1.97 µs, compiled 1.31 µs). These are BenchmarkDotNet means from a
single run with tiered compilation disabled; the within-run 99.9\%
confidence half-interval is a few percent, but the ratios carry
process-to-process variation of order ±0.2 (the variation caveat in §5.6
applies), so they are read to about one significant figure. The pattern
is monotonic: the more of the per-invocation work is DSL-bound, the
larger the speedup; the more it is domain-bound, the smaller. The
runtime amortizes only the AST-traversal overhead --- the host-language
code the verb dispatches runs identically in both modes, so a verb
dominated by host work shows the compiler's contribution as a smaller
fraction. (Under Debug builds, with neither path optimized by the JIT,
the same curve spans 1.49×--4.10×; the Release figures here are the
defensible ones and the curve's direction is unchanged.)

A deliberately adversarial datapoint sharpens what the curve measures. A
flat-CRUD verb --- a single field setter invoked as
\texttt{o\ =\ c.SetValue(v)}, with α = 1 host dispatch --- does
\emph{not} collapse to a 1× speedup: it measures ≈1.9× (interpreted 0.63
µs, compiled 0.34 µs). The speedup does not track domain richness; it
tracks the fraction of per-invocation time spent in the DSL dispatch
that compilation removes --- reflection-based method resolution and
parameter binding --- and that cost is present even when the host method
is trivial. What lowers the speedup is host work, not domain simplicity:
the production verb, one dispatch into a heavy aggregate, sits lowest
(≈1.5×) precisely because its host work dilutes the fixed DSL overhead,
and an I/O-bound verb would approach 1× from below. The flat-CRUD case
is genuinely adversarial only for a \emph{separate} property --- verb
richness, which collapses to β/α = 1× there (§5.5) and which is not one
of the four faces --- and for none of the faces themselves: the CRUD
verb is separable, so it still caches, journals densely, replicates with
bounded entropy, and compiles with amortization. The adversarial case
maps the \emph{magnitude} of the speedup and the \emph{depth} a verb
names; it does not turn any face off.

Specialization is paid once, at first encounter, not at every
invocation. Compiling a single program scales about linearly with its
statement count: for straight-line arithmetic kernels the cold compile
cost, isolated at the \texttt{Compile()} call site, runs from 0.56 ms
(p50, depth 5) to 4.30 ms (p50, depth 100) --- a slope of roughly 39 µs
per statement. The eShop purchase verb is a single dispatch and
therefore a small expression tree; it compiles cold in 380 µs at p50
(516 µs at p95; N=100 distinct script variants forcing cache misses).
Against the 0.70 µs per-invocation steady-state saving for that verb,
the cold compile recovers itself after roughly 540 invocations ---
within the first moments of any actor that outlives its warm-up. These
numbers are not performance claims but empirical confirmation of the
structural amortization predicted by separability; the comparison is
internal (compiled vs.~interpreted on the same runtime), not a
competitive benchmark against external systems (§5.6).

\subsubsection{5.3 Cache amortization}\label{cache-amortization}

The same amortization pattern applies recursively to sub-programs. A
parameter declared with the Eval modifier carries its own sub-script
that the runtime treats as a separable program in miniature; on a cache
hit, the sub-program pays only the cost of invoking its cached delegate,
while on a cache miss --- when the sub-script's text differs from the
cached entry --- it re-parses, rebuilds, and re-compiles. Across three
sub-program complexities (a two-term arithmetic expression, a fifty-term
arithmetic kernel, and a method call against a facade-bound counter on
the eShop side), the cache-hit cost is 0.5--0.6 µs at p50 while the
cache-miss cost is 214--243 µs at p50. The miss-to-hit ratio is roughly
400× regardless of sub-program complexity --- a separation of nearly
three orders of magnitude that confirms the principle extends to any
region of the program with a stable textual identity.

Allocation pressure is amortized at a finer granularity by parser and
parameter pools. Three workloads measured under AlwaysCompiled policy:
1,000 single-thread invocations against a stable parametric script
recorded a 100\% parameter-pool hit rate; 1,000 single-thread
invocations against distinct cold-cache scripts recorded a 100\% hit
rate on both pools; 5,000 invocations across 8 parallel threads against
distinct scripts recorded 99.90\% on parsers and 99.86\% on parameters
--- twelve total misses across the parallel workload's
\textasciitilde10,000 pool rents. Allocation of new Parser and
Parameters instances is incurred at startup and under brief
thread-fanout transients only; under steady state, both pools service
the rent without allocation.

\subsubsection{5.4 Journal density}\label{journal-density}

In a parametric purchase workload against the eShop \texttt{Order}
aggregate --- a nine-line DSL script cascading through order
construction, four \texttt{AddOrderItem} calls, and a four-step
state-machine walk --- 99.8\% of journal entries land as compact action
references: 1,001 invocations produce 1,001 action entries plus a single
Define entry that carries the script body once. Each action entry's
payload averages 115 bytes --- the argument vector for seventeen
parameters (user identity plus four products' details plus shared
modifiers). The Define entry's payload is 642 bytes --- the script body
itself, persisted once. Had each invocation stored the literal script
text instead, the action payload would be 5.6× larger. The runtime's
choice to persist named operations rather than their textual content is
what makes this density possible. This density does not arise from
compression techniques but from reference instead of duplication.

The same compaction structure appears on the second host. Against
Grzybek's \texttt{SubscriptionPayment} --- a two-statement DSL surface
(\texttt{NewWaitingPayment} followed by \texttt{MarkAsPaid}) over a
payment-lifecycle aggregate --- N=1,000 parametric invocations produce
1,001 Action entries (99.8\% of the journal) plus a single Define entry
of 207 bytes carrying the script body. Action payloads average 60 bytes.
The literal-script storage would be 3.5× larger. The ratio is more
modest than eShop's because both the script body and the argument vector
are smaller on a narrower verb; the structural property --- arguments
scaling with invocations while the body is persisted once --- is
identical.

The magnitude of the ratio is a function of how the host's domain API
surfaces its data, not of the compaction mechanism. eShop's
\texttt{AddOrderItem} carries the full product specification per item
--- pid, name, price, discount, picUrl, units --- so per-iteration
argument vectors are dense (115 B). Grzybek's \texttt{NewWaitingPayment}
accepts five primitive parameters and \texttt{MarkAsPaid} takes none, so
the argument vector is short (60 B). Hosts whose verbs identify catalog
entries by short stable references compound the ratio further; hosts
whose verbs string together longer parametric sequences also compound
it. Either way the structural property holds: arguments scale with
invocations; the script body does not.

\subsubsection{5.5 Verb richness}\label{verb-richness}

For the eShop purchase verb the DSL script dispatches \textbf{α = 9}
host-language invocations per DSL invocation --- an exact, deterministic
count, reproduced without variance across 1,000 runs. To gauge the
host-language surface those 9 entry points reach, a syntactic Roslyn
walker computes the \emph{static forward closure} of the call graph,
excluding trivial accessors and cutting off at the project boundary:
\textbf{β = 24} methods within
\texttt{dotnet-eShop/src/Ordering.Domain}. For the Grzybek
\texttt{SubscriptionPayment} verb, α = 2 (its facade folds value-object
construction into one host call) and β = 73 within
\texttt{Payments.Domain} plus the shared \texttt{BuildingBlocks/Domain}
(275 declarations indexed, 105 trivial accessors filtered). The β/α
ratios are 2.7× (eShop) and \textasciitilde36× (Grzybek).

What β does and does not measure must be stated plainly. β is a
\emph{static lower bound on reachable declared surface}, not a count of
operations executed: it over-counts (branches never taken at runtime
remain in the closure) and under-counts (it does not follow virtual
dispatch, delegates, or reflection). It is also sensitive to assembly
partition --- moving code across \texttt{.csproj} boundaries moves the
cutoff and changes β --- so it measures reachable surface \emph{under a
chosen boundary}, not an invariant of domain depth. The exact, faithful
quantity is α: one DSL token dispatches several host operations. β
corroborates only the coarse direction the surface-vs-depth claim of §4
needs --- that the host neighborhood a single verb names is much larger
than the verb's textual surface --- and nothing finer.

The 2.7×-versus-36× gap, in particular, should not be read as the
measured effect of a single cause. With two hosts, author coding style,
aggregate granularity, and persistence-driven design pressure are fully
confounded, and the data cannot apportion the difference among them.
Both \texttt{Ordering.Domain} and \texttt{Payments.Domain} are
RDBMS-anchored DDD aggregates --- \texttt{private\ set} properties,
EF-Core owned-entity annotations, \texttt{int?} foreign keys instead of
object references, little polymorphism on the roots --- and it is
\emph{plausible} that this representational pressure flattens a graph a
richer domain would deepen. But that is a hypothesis these two points
illustrate, not a relationship they establish; and the very features a
richer domain would add --- polymorphism, delegates --- are exactly what
the static walker cannot follow, so the conjecture that β would grow by
orders of magnitude on such a domain is not one this instrument can
confirm. What the measurement does support is the modest, exact claim of
§4: a single persisted DSL token names a host-language operation
substantially larger than itself, so the journal records far less than
the behavior it commands. That observation rests on α and on the
journal-density result (§5.4), both exact --- not on the β/α magnitude
or any causal reading of it.

An adversarial floor case makes the dependence explicit. A flat-CRUD
verb --- a single field setter that calls nothing --- has α = 1 and β =
1, so β/α = 1× and the surface-vs-depth asymmetry vanishes entirely.
Verb richness is therefore a property of the host domain's depth, not of
separability or the runtime, and it is not one of the four faces: where
the domain is shallow there is simply no depth to name. Its collapse
disables none of the faces --- the flat-CRUD verb, being separable,
still caches, journals densely, replicates with bounded entropy, and
compiles with amortization; the journal entry is simply a compact
reference to a verb that happens to name little.

\subsubsection{5.6 Limitations and threats to
validity}\label{limitations-and-threats-to-validity}

Several boundaries qualify the magnitudes above. \emph{Sample of hosts.}
Two aggregates are a robustness replication against domain-specificity
(§5.1), not a sample from which a population magnitude can be estimated;
the generality this paper claims is structural (§1--§4), and the
empirical role of eShop and Grzybek is to show the predicted properties
are not artifacts of one domain. \emph{Single environment.} All timings
come from one machine and one runtime build, so absolute microsecond
figures are machine-specific. The portable quantities are the ratios ---
speedup, miss-to-hit, density --- each a comparison of two paths under
identical conditions, where machine-constant factors cancel. The speedup
ratios additionally vary by order ±0.2 across process launches (ordinary
microbenchmark reality), so they are read to about one significant
figure of confidence --- their ordering and direction, not their third
digit. The journal-density and dispatch-count figures, being
deterministic counts, do not carry this variation. \emph{Steady state
versus production regime.} The speedup is measured with tiered
compilation disabled, which isolates the fully-optimized steady state of
both paths and removes the Tier-0→Tier-1 transition that otherwise makes
the dynamically-emitted compiled delegate bimodal; under the default
tiered regime a long-lived actor reaches the same steady state, with
wider variance during warm-up. \emph{Synthetic kernels.} The arithmetic
and DSL-rich kernels isolate runtime overhead independent of host work;
they are not stand-ins for whole applications, and the production verb
is included precisely to locate that overhead inside a representative
end-to-end transaction.

What none of these qualifications touch is the structural result,
because it rests on the \emph{deterministic} measurements rather than
the timings. The journal-density and verb-richness figures (§5.4--§5.5)
are exact counts and bytes, identical across builds and machines, and
they carry the paper's central empirical observation: that the journal's
expressiveness per byte rises as the host domain grows richer --- each
persisted action names a deeper orchestration --- while the persisted
footprint per invocation does not. The relationship runs both ways. A
richer domain library makes each named action carry more meaning per
stored byte; and the DSL, carrying no persistence concern of its own,
lets the domain be modeled as richly as its designers wish without
expanding what the journal must record. The timing results confirm only
the \emph{direction} of the amortization argument --- that compilation
pays for itself, within a few hundred invocations --- not a universal
constant; the structural claim does not depend on their magnitude.

\emph{No external baseline, by design.} The speedup of §5.2 is an
internal, controlled comparison --- compiled versus interpreted
execution of the same DSL program, against the same host domain code,
under the same journaling, on the same runtime --- so the only variable
is the one separability is about: the AST-traversal overhead that
compilation removes. It is deliberately not a competitive benchmark
against Orleans, a hand-written compiled expression tree, or a SQL
stored procedure. Those alternatives differ from the runtime here on
several axes at once --- language, persistence model, domain encoding,
transport --- so a latency comparison would measure a confounded mixture
rather than the separability effect, and this paper makes no claim that
the runtime is faster than any of them. The external comparison the
thesis does require is structural rather than chronometric: whether a
system exhibits the four-fold coherence at all. On that axis SQL
prepared statements (§6.4, §7.6) and Truffle/GraalVM (§7.2) are compared
explicitly --- they share separability and its execution-side
consequences but, persisting rows or volatile machine code rather than
the named operation, exhibit only a subset of the four faces. The
speedup confirms that compilation amortizes as the principle predicts;
it is not offered as evidence of competitive performance, and
``compiling beats interpreting one's own AST'' is indeed its expected,
modest content --- the load-bearing empirical results are the
deterministic density and dispatch measurements, not the timings.

\emph{Selection of hosts, and the adversarial case.} The two production
hosts were chosen for structural similarity (rich-behavior DDD
aggregates), which is selection on the dependent variable: on its own it
can only show the mechanism surviving a change of domain within that
class, not where it stops paying off. The flat-CRUD verb of §5.2 and
§5.5 is the corrective, and it locates the limits precisely --- but the
limits are not where a first reading might place them. The CRUD verb is
itself separable, so all four faces still obtain: it caches, journals
densely, replicates with bounded entropy, and compiles with
amortization. What the adversarial case varies is not \emph{whether} the
faces hold but two quantities that are not faces at all: the
\emph{magnitude} of the compiled speedup (a gradient set by how much
per-invocation time is DSL dispatch versus host work --- ≈1.9× even for
the trivial verb, falling toward 1× only as host or I/O work dominates)
and \emph{verb richness} (β/α → 1× when the verb is a bare setter), a
property of the host domain's depth that the journal-density argument
uses but that separability does not entail. So this does not qualify the
title's claim. Separability remains the precondition for the four faces,
and they obtain wherever a program is separable; the adversarial case
maps how much each consequence delivers across domains, not a boundary
where the consequences disappear. The boundary where separability holds
but a face does \emph{not} is a different case entirely --- a separable
runtime lacking code-as-data, exhibiting caching and compilation but not
dense journaling --- and it is SQL prepared statements (§6.4), the
worked example of necessity without sufficiency.

\subsection{6. Counter-arguments}\label{counter-arguments}

\subsubsection{\texorpdfstring{6.1 \emph{``This is just JIT
compilation''}}{6.1 ``This is just JIT compilation''}}\label{this-is-just-jit-compilation}

A reviewer familiar with managed runtimes might object that the runtime
described here is just-in-time compilation in disguise. The objection
conflates two distinct compilation events. The .NET CLR's JIT translates
IL bytecode into native machine code at first invocation of any method;
this happens identically whether the runtime executes its DSL by walking
an AST or by invoking a compiled delegate. What the runtime adds is a
separate, prior compilation: from DSL script to typed Expression tree to
IL --- the work that produces the delegate the JIT will eventually
translate. The speedup measured in §5.2 is a function of this DSL→IL
stage, not of the IL→native stage. Both modes deliver IL to the JIT in
the same way, so JIT effects cancel; what remains is the AST traversal
overhead that the DSL→IL stage amortizes away. Calling this ``just JIT''
would erase the layer of specialization that separability makes
possible.

\subsubsection{\texorpdfstring{6.2 \emph{``Why retain interpretation?
Just compile
everything''}}{6.2 ``Why retain interpretation? Just compile everything''}}\label{why-retain-interpretation-just-compile-everything}

A second objection: if compilation is so much faster, why retain
interpretation at all? The answer is that compilation is economical only
when the program will be reused. A script seen once and never again ---
an ad-hoc query against a live actor, a one-off configuration
adjustment, an exploratory invocation formed at call time --- provides
no future invocations against which to amortize the compilation cost.
Forcing such scripts through the compilation pipeline pays a cold
compile cost (§5.2) --- hundreds of microseconds to a few milliseconds,
depending on program size --- and discards the result; the compiled
delegate is never invoked a second time, and the journal records the
script literally rather than as a referenced action. The runtime's
\texttt{AlwaysCompiled} policy permits this on demand, but the default
policy correctly recognizes that not every script is amortizable.
Separability is necessary for compilation to make sense; reuse is the
condition under which compilation is economical.

\subsubsection{\texorpdfstring{6.3 \emph{``Dual paths add memory
cost''}}{6.3 ``Dual paths add memory cost''}}\label{dual-paths-add-memory-cost}

A third objection: maintaining a compiled delegate per parametric
program inflates memory at scale, and the dual-path arrangement
compounds the cost. The measurement disagrees. In a single-actor cache
holding 100, 1,000, 10,000, and 100,000 distinct parametric programs
under \texttt{AlwaysCompiled} policy, the per-entry footprint stabilizes
at approximately 6 KB across all four scales: 6,039 bytes per entry at
100 programs, 5,981 at 1,000, 5,977 at 10,000, and 5,930 at 100,000 ---
no super-linear overhead, no hash-table degradation. The marginal memory
of an invocation against an already-cached program is effectively zero:
100,000 invocations against a single cached program retain 104 bytes
total. A production actor caching dozens of distinct parametric programs
and invoking them at scale incurs a memory cost on the order of hundreds
of kilobytes. The cache scales linearly with distinct programs, not with
invocations --- well below the threshold at which the dual-path
arrangement would be a structural concern.

\subsubsection{\texorpdfstring{6.4 \emph{``This is just SQL prepared
statements''}}{6.4 ``This is just SQL prepared statements''}}\label{this-is-just-sql-prepared-statements}

A final objection: this is just SQL prepared statements rebranded ---
the same parametric-template-plus-bind-values pattern that relational
databases have used for decades. The pattern is indeed the same; what
differs is what the runtime persists. A SQL prepared statement is
parameter-separable and admits compilation and caching: the database
parses it once, builds a query plan once, and reuses both for many
invocations with different bind values. But what the database persists
is row data --- the projections produced by executing the statement
against the underlying tables. The parameterized statement itself is not
the persisted artifact; the rows it touches are. The runtime described
here persists the operation rather than its row-level effects. Two of
the four faces of separability --- compilation and caching --- are
present in both systems; the journal density that follows from
persisting the named operation is what SQL prepared statements do not
achieve. Separability is necessary; pairing it with code-as-data is what
produces the dense journal.

\subsubsection{\texorpdfstring{6.5 \emph{``A verb dispatching dozens or
hundreds of host methods produces an opaque
runtime''}}{6.5 ``A verb dispatching dozens or hundreds of host methods produces an opaque runtime''}}\label{a-verb-dispatching-dozens-or-hundreds-of-host-methods-produces-an-opaque-runtime}

A reviewer might object that a verb whose static call-graph closure
reaches dozens or hundreds of host-language methods (§5.5) produces an
opaque runtime, and that debugging across such depth is intractable. The
objection misreads the architecture. The depth lives in the host
language: domain classes, methods, business rules --- debuggable with
the standard tools the host already provides. The DSL surface only names
what the host invokes; it does not introduce a separate execution layer
that the host debugger fails to penetrate. Standard practice in the
runtime described here proceeds by test-driven development with
end-to-end test cases, with breakpoints and step-through performed in
the host language; once a script is moved to a runtime endpoint, the
same breakpoints continue to apply against the domain library it
invokes. More consequentially, the journal makes post-mortem debugging
tractable in a way porous substrates do not allow. When a production
failure surfaces at journal entry id N, that entry refers --- by name
--- to the exact script that produced the defect, parameterized by the
exact values the script consumed. A breakpoint at the journal entry, a
step-through of the named operation, and the defect reproduces; the
failure is then replicated in a small end-to-end test case, fixed, and
released. The dense journal is not the opacity it might appear to be on
a superficial reading; it is among the runtime's most powerful debugging
artifacts, because the persisted artifact is the operation that ran, not
a downstream trace of its effects.

Each objection treats compilation, caching, and journaling as
independent engineering choices. The argument of this paper is that they
are consequences of a single structural property; the objections
dissolve when that property is made explicit.

\subsection{7. Related work}\label{related-work}

\subsubsection{7.1 Partial evaluation}\label{partial-evaluation}

The structural condition this paper formalizes has a close ancestor in
\textbf{partial evaluation}, as developed by Jones, Gomard, and Sestoft
(1993) and the Futamura projections (Futamura, 1971/1999). Partial
evaluation specializes a program with respect to a subset of inputs
known in advance, producing a residual program that depends only on the
dynamic arguments --- and it requires, structurally, that the program's
static and dynamic inputs be separable. Where the partial-evaluation
literature applies the technique to general-purpose host languages, this
paper applies the same separability principle to a domain-specific
language whose programs are short, parametric by construction, and
stored as journal entries; in this setting, separability is syntactic,
decidable from the program's surface rather than from static analysis.
Two differences are load-bearing. First, partial evaluation
\emph{recovers} the static/dynamic split by binding-time analysis and
\emph{consumes} it: the residual program is the artifact of interest,
and the separation is discarded once specialization is complete. Here
the split is declared on the program's surface and \emph{persisted} ---
it survives into the journal as an (identifier, argument-vector) pair
and into replication as the unit that replays to state. Second, the
further consequences traced in §2 --- caching as identity, dense
journaling, replication-bounded entropy --- fall outside the
partial-evaluation tradition's concern, which is execution speed rather
than persistence; they follow from pairing separability with
code-as-data persistence. Partial evaluation is the closest ancestor of
the compilation face alone; on the other three it is silent.

\subsubsection{7.2 Self-optimizing AST interpreters
(Truffle/GraalVM)}\label{self-optimizing-ast-interpreters-trufflegraalvm}

The most direct comparison is not a classical technique but a production
system: the Truffle framework and the GraalVM compiler (Würthinger et
al., 2012, 2017). Truffle hosts a language as a self-optimizing abstract
syntax tree whose interpreter nodes rewrite themselves under runtime
profiling, and GraalVM applies partial evaluation to the interpreter
specialized to a given AST --- realizing the first Futamura projection
in practice, from interpreter-plus-program to machine code, cached per
call target and guarded by deoptimization. On the compilation face
(§2.1, §3.1), Truffle is more sophisticated than the runtime
characterized here: it profiles observed types and values, speculates
aggressively, inlines across a polyglot boundary, and deoptimizes when a
speculation fails. A reviewer is right to ask what program--value
separability adds once Truffle is on the table. The first half of the
answer is that Truffle already turns on it: its specialization holds the
AST constant while frame arguments vary, and its compilation amortizes
only when the same AST recurs across invocations --- so it
\emph{presupposes} program--value separation. A program embedding its
values would present a fresh AST per call and defeat Truffle's reuse
exactly as §2.2 describes it defeating the cache. Truffle is thus not a
counterexample to the necessity claim but an independent witness to it,
operating on the same precondition.

The second half is that Truffle never leaves the execution axis. Its
specialization is volatile execution machinery --- machine code keyed to
an in-process call target, provisional under deoptimization, discarded
at process exit, bound to no stable name, replicated to no other node.
There is no journal in which the specialized program is the persisted
entry, no identifier under which it is referenced across time, no
replication of the program as the unit of state. The separation Truffle
exploits is operational and implicit, recovered and re-validated by the
runtime; the separation this paper formalizes is declared on the
program's surface, decided once at preparation time without profiling or
speculation, and elevated to a durable identity --- a content-derived
action reference that is at once the cache key, the journal entry, and
the replication unit. This makes the necessity claim of §1.2 precise:
declared separability is not necessary for fast execution --- Truffle
shows a profile-driven runtime can compile and cache without it --- but
it is necessary for the persistence faces, because a provisional,
in-memory, profile-derived boundary cannot serve as a stable journal
reference or a replication anchor. The mechanism follows the same
divide: Truffle partially evaluates a host-language interpreter against
the AST; the runtime here lowers the DSL AST directly into a typed
expression tree and a delegate over the declared parameter vector, with
no interpreter to specialize and no speculative guard to maintain.

\subsubsection{7.3 Lambda lifting}\label{lambda-lifting}

\textbf{Lambda lifting} (Johnsson, 1985) is a program transformation
that rewrites locally-defined functions whose free variables capture an
enclosing scope into top-level functions that receive those variables as
explicit parameters. The transformation makes the function's
dependencies syntactically visible --- and, by doing so, makes the
function amenable to ahead-of-time compilation, separate compilation,
and uncluttered call-graph analysis. The structural insight is the one
this paper generalizes: dependencies that remain implicit in the
program's body cannot be specialized over, but the same dependencies
promoted to the surface can. Where lambda lifting transforms programs
that were already written and externalizes their captured environment,
the runtime described here requires programs to declare their values as
externalized parameters from the start. Lambda lifting opens
compilation; this paper extends the same principle to caching, dense
journaling, and replication-bounded entropy by joining separability with
code-as-data persistence.

\subsubsection{7.4 Closure conversion}\label{closure-conversion}

\textbf{Closure conversion} (Appel, 1992) is a compilation technique
that translates a function with captured lexical environment into an
explicit closure record --- a pair of function pointer and environment
data --- that the runtime carries as a single value. Closure conversion
makes implicit lexical dependencies explicit, but preserves them as
runtime data: the closure travels with its environment record, retained
in memory, and re-bound on each invocation. Lambda lifting and the
principle of this paper sit on the other side of the same choice: rather
than carry the environment, eliminate it by promoting the variables that
would have been captured into top-level parameters supplied at the call
site. The runtime described here makes that choice mandatory and
declarative --- values must be supplied at invocation, not captured from
a surrounding scope --- which is what permits the journal to record the
program by name without any captured-environment record traveling
alongside it.

\subsubsection{7.5 Template instantiation}\label{template-instantiation}

\textbf{Template instantiation} in general-purpose languages ---
exemplified by C++ templates (Stroustrup, 1994; Vandevoorde, Josuttis,
\& Gregor, 2017) and the broader tradition of generic programming ---
applies the separability principle in another setting: a template body
names operations parameterized by types or compile-time constants, and
the compiler instantiates each template with concrete parameters at
compile time, producing specialized code per instantiation. The
structural pattern is the same as the one described here: a program that
admits parametric reuse must be written so its parameters are separable
from its body. Template instantiation operates over types and
compile-time values; the runtime described here applies the same
principle over runtime values supplied at invocation. The distinction is
operational: instead of monomorphizing the program once per parameter
tuple, the runtime compiles the program once and rebinds its arguments
on every call. Both arrangements presuppose what this paper formalizes.

\subsubsection{7.6 Prepared statements}\label{prepared-statements}

\textbf{Prepared statements} in relational database systems (SQL
standard ISO/IEC 9075:2023; Hellerstein, Stonebraker, \& Hamilton, 2007)
are the closest operational analog to the runtime described here. A
prepared statement carries a parametric template (with placeholders for
bind values), the database parses and plans it once, caches the plan,
and reuses both for many invocations with different argument vectors ---
the same parametric-template-plus-bind-values pattern §1.1 used as
pedagogical anchor for the principle this paper formalizes. The two
systems share separability and its first two consequences: compilation
(the query plan) and caching (the plan cache). They diverge on what the
runtime persists. A relational database persists row data; the prepared
statement itself is not the persisted artifact. The runtime described
here persists the named operation rather than its row-level effects,
completing the four-fold coherence with journal density and
replication-bounded entropy. The principle this paper formalizes
generalizes beyond any one runtime --- it characterizes the structural
condition any DSL runtime must satisfy to admit compilation, caching,
and dense persistence at all.

These traditions arise in different domains --- compilers, functional
languages, generic programming, database engines --- and they share one
structural observation: programs whose dependencies are syntactically
separable from their bodies admit forms of specialization that programs
with embedded values cannot. What they do not share is what this paper
adds. In every case above, separability is instrumental to execution and
the separation is consumed by a transformation or a query planner; in
none is the separated program the persisted, replayable, replicable unit
of state. The contribution here is not to observe separability again but
to relocate it --- from an enabler of execution specialization to the
precondition of a persistence regime --- and to show that compilation,
caching, dense journaling, and replication-bounded entropy are one
precondition's four projections rather than four independent techniques.
Prepared statements make the gap concrete: they supply the closest
operational analog and still exhibit only two of the four faces, because
they persist rows rather than the named operation. Separability is
necessary for the four-fold coherence; code-as-data persistence is what
carries it past the two faces the prior art already reaches.

\subsection{8. Conclusion}\label{conclusion}

The argument of this paper can be stated compactly. Program--value
separability --- the syntactically decidable property that a DSL program
declares user parameters rather than embedding values in its body --- is
the structural precondition under which compilation, caching, and dense
journaling become economically meaningful. The four runtime faces traced
here are not parallel design choices: they are downstream consequences
of separability, paired with code-as-data persistence in the case of
dense journaling. The runtime characterized in §3 realizes the principle
concretely; the magnitudes reported in §5 confirm the predicted
structural amortization across two open-source DDD aggregates --- a
compiled-versus-interpreted speedup that scales monotonically with
DSL-bound work (roughly 1.5× on the eShop purchase verb to 3.1× on a
synthetic arithmetic kernel), a cold compile cost (≈380 µs for the eShop
purchase verb) that recovers itself within several hundred invocations,
and a journal in which ≈99.8\% of parametric entries are compact action
references producing a footprint several-fold denser than the equivalent
literal-script storage. These magnitudes are drawn from two
RDBMS-anchored DDD aggregates; whether host domains without that
representational pressure would widen the surface-vs-depth gap further
is a plausible conjecture, not a result that two data points --- or a
static call-graph walker blind to polymorphism and delegates --- can
establish.

The principle takes its place alongside the analysis of the prior paper
of this series, \href{01-anti-porosity.md}{\emph{Anti-porous
Architecture}}, which characterized porosity as a representational
sparsity problem and density preservation as the operational consequence
of an event-sourced DSL journal. Where the prior paper named the defect
that domain representations on tabular substrates accumulate, this paper
names the condition under which that defect can be avoided.

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}ll@{}}
\toprule\noalign{}
Prior paper & This paper \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Problem: porosity & Condition for avoiding it \\
Density preserved & Program--value separability \\
Executable journal & Stable program identifier \\
Operations vs state & Functions vs instances \\
\end{longtable}
}

The two papers describe the same phenomenon from opposite sides: density
preservation is what is achieved; separability is what makes it
achievable. Together, they argue that density in domain representation
is not an optimization but a structural property that follows from how
programs relate to their values.

Stated as a design space, the two papers occupy a cell that little else
does. Two properties are at work, and each is common on its own.
\emph{Separability} --- values external to the program --- is
everywhere: every prepared statement, every parameterized query has it.
\emph{Persisting the operation} rather than its effects is also well
known: event sourcing is built on it. What is rare is their conjunction.
SQL is separable but persists rows, not the statement; event-sourced
logs persist operations but their events are command-shaped \emph{data},
not separable programs with an identity of their own; ordinary CRUD has
neither. A runtime that is \emph{both} separable \emph{and} persists the
operation produces what neither tradition has alone: a persistent
operation carrying a stable identity independent of its values --- an
\emph{Action}. Separability (this paper) supplies the identity;
code-as-data persistence (Paper 1) makes the identified operation the
durable artifact. The Action is precisely what falls out when both hold
at once, and it is the corner of the design space --- separable
\emph{and} operation-persisting --- that this series occupies and that
the surrounding traditions, each holding one property without the other,
do not reach.

The principle is not bound to the runtime characterized in §3. In an
Orleans- or Akka-style actor framework with a parameterized command DSL
above a host-language domain library, the same precondition would
predict the same four-fold coherence; in a service that uses prepared
statements over an event-sourced log of named operations, three of the
four faces are already available, and the fourth --- replication-bounded
entropy --- follows once the persisted artifact is the operation rather
than its row-level effects. The realization in §3 is one example of a
construction the principle admits, not the only path to it.

Two extensions of the principle remain to be developed in subsequent
work. The fourfold coherence described here does not exhaust the
operational consequences of an externalized-parameter, locality-bound
substrate: a persistent local write buffer with asynchronous remote
replication, for instance, is structurally enabled by the same
commitments --- the actor's isolation guarantees that locally-buffered,
not-yet-replicated entries are invisible to other contexts by
construction, not by convention. The persistent buffer and its
zero-downtime implications are treated in a companion paper. A second
companion paper takes up the reaction mechanism whose
semantic-pattern-matching has been mentioned in passing throughout this
argument, and the consistency contract under which observable state
remains coherent across actors. In each case the same structural
observation continues to apply --- what makes the journal compact, what
makes deep domains compose without friction, is the separation of the
program from its values. Porosity makes deep domains expensive to
represent; anti-porosity makes them economical to compose.

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\subsection{Code provenance}\label{code-provenance}

Source-code references in this paper resolve against the public
Puppeteer repository at commit
\href{https://github.com/alvaroNCubo/puppeteer/tree/b42d0f76b4278c36a45c221cc1453e3ee8ffb3de}{\texttt{b42d0f7}}
(2026-05-26) --- the same public snapshot cited by the prior paper of
this series. The empirical measurements of §5 were produced against this
commit, built in Release (Appendix A, §5.1). The snapshot is archived in
Software Heritage under the following persistent identifier:

\begin{verbatim}
swh:1:dir:177e2d61486bdbdfd2d5e774fcf392a45406e60d;
  origin=https://github.com/alvaroNCubo/puppeteer;
  anchor=swh:1:rev:b42d0f76b4278c36a45c221cc1453e3ee8ffb3de
\end{verbatim}

The core files cited inline (\texttt{ActorHandler.cs},
\texttt{Program.cs}, \texttt{Actor.cs}, \texttt{ActorV2.cs},
\texttt{ActorV2Invocation.cs}, \texttt{DomainLibraries.cs}) are
byte-identical between this commit and the prior public snapshot
\href{https://github.com/alvaroNCubo/puppeteer/tree/2f31f9674a5de816bdf1bf9d8360ff218a02e4da}{\texttt{2f31f96}}
(2026-05-18); the line numbers below resolve against either.

Inline references of the form \texttt{file.cs:NN} (e.g.,
\texttt{ActorHandler.cs:38}) resolve against this snapshot. A reader can
construct a per-file SWHID by adding the qualifiers
\texttt{;path=\textless{}path\textgreater{};lines=\textless{}NN\textgreater{}}
to the directory SWHID above. Future commits to the repository may
renumber lines; the SWHID preserves the cited state independently of any
future change to the repository or its hosting.

\subsection{Acknowledgments}\label{acknowledgments}

The author used large language models (including Claude and ChatGPT) as
editorial assistants for language refinement, structural feedback, and
literature navigation. All original ideas, terminology, theoretical
constructs, and technical content presented in this work are solely the
author's.

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\subsection{References}\label{references}

Appel, A. W. (1992). \emph{Compiling with continuations}. Cambridge
University Press.

Futamura, Y. (1999). Partial evaluation of computation process --- an
approach to a compiler-compiler. \emph{Higher-Order and Symbolic
Computation}, \emph{12}(4), 381--391.
https://doi.org/10.1023/A:1010095604496 (Original work published 1971 in
\emph{Systems, Computers, Controls}, \emph{2}(5), 45--50.)

Gregor, S. (2006). The nature of theory in information systems.
\emph{MIS Quarterly}, \emph{30}(3), 611--642.

Hellerstein, J. M., Stonebraker, M., \& Hamilton, J. (2007).
Architecture of a database system. \emph{Foundations and Trends in
Databases}, \emph{1}(2), 141--259.

Hevner, A. R., March, S. T., Park, J., \& Ram, S. (2004). Design science
in information systems research. \emph{MIS Quarterly}, \emph{28}(1),
75--105.

International Organization for Standardization. (2023). \emph{ISO/IEC
9075:2023 Information technology --- Database languages --- SQL}.

Johnsson, T. (1985). Lambda lifting: Transforming programs to recursive
equations. In J.-P. Jouannaud (Ed.), \emph{Functional programming
languages and computer architecture} (pp.~190--203). Springer-Verlag.
(Lecture Notes in Computer Science, Vol. 201)

Jones, N. D., Gomard, C. K., \& Sestoft, P. (1993). \emph{Partial
evaluation and automatic program generation}. Prentice Hall.

Rivera, A. (2026a). Anti-porous architecture: a unified design principle
for CQRS + Actor + Event-Sourcing systems. \emph{Puppeteer Papers
Series}, Paper 1. Zenodo. https://doi.org/10.5281/zenodo.20404863

Stroustrup, B. (1994). \emph{The design and evolution of C++}.
Addison-Wesley.

Vandevoorde, D., Josuttis, N. M., \& Gregor, D. (2017). \emph{C++
templates: The complete guide} (2nd ed.). Addison-Wesley.

Würthinger, T., Wöß, A., Stadler, L., Duboscq, G., Simon, D., \& Wimmer,
C. (2012). Self-optimizing AST interpreters. In \emph{Proceedings of the
8th Symposium on Dynamic Languages (DLS '12)} (pp.~73--82). ACM.
https://doi.org/10.1145/2384577.2384587

Würthinger, T., Wimmer, C., Humer, C., Wöß, A., Stadler, L., Seaton, C.,
Duboscq, G., Simon, D., \& Grimmer, M. (2017). Practical partial
evaluation for high-performance dynamic language runtimes. In
\emph{Proceedings of the 38th ACM SIGPLAN Conference on Programming
Language Design and Implementation (PLDI 2017)} (pp.~662--676). ACM.
https://doi.org/10.1145/3062341.3062381

\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}

\subsection{Appendix A --- Code
references}\label{appendix-a-code-references}

The references below cite source locations in the Puppeteer codebase as
\texttt{file:line} pairs against the public commit \texttt{b42d0f7} (see
\emph{Code provenance}); the cited core files are byte-identical at the
prior public snapshot \texttt{2f31f96}. The benchmark harness that
produced §5 is public: the MSTest cold-compile, eval, journal-density,
and dispatch-count benches live in the runtime repository under
\texttt{tests-local/} at commit \texttt{b42d0f7}, while the
BenchmarkDotNet speedup harness, the synthetic compile/eval sweeps, and
the Roslyn call-graph walkers live in this paper repository's
\texttt{labs/} directory (\texttt{lab01-bdn-speedup/},
\texttt{lab05-eshop-roslyn/}, \texttt{lab05-grzybek-roslyn/}). Datasets
cited in §5 --- raw per-iteration CSVs, the BenchmarkDotNet environment
reports, and per-lab summaries --- are stored in the companion
\texttt{data/} directory of this paper repository, each stamped with the
commit SHA. The measurement environment is recorded in §5.1.

\subsubsection{§1.2 --- Formal
characterization}\label{formal-characterization}

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.5000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Location
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it shows
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Runtime self-documentation as \texttt{F(x1,...,xn)} &
\texttt{ActorHandler.cs:1085} & Comment articulating the parametric
program model \\
Parametric/literal regime documented in code &
\texttt{ActorHandler.cs:1091-1093} & Comment encoding the binary
taxonomy: scripts without user parameters are interpreted, uncached,
persisted as Script entries; scripts with user parameters are compiled,
cached with an ActionId, persisted as Action entries \\
\end{longtable}
}

\subsubsection{§3.1 --- Compilation
pipeline}\label{compilation-pipeline-1}

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.5000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Location
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it shows
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Orchestration on first encounter & \texttt{ActorHandler.cs:1097-1148} &
\texttt{PrepareCommandProgram} rents a parser, parses the script,
decides parametric vs literal path \\
AST traversal & \texttt{Program.cs:182-244} & \texttt{ProgramExpression}
walks \texttt{Statement}s, accumulates \texttt{Expression} nodes into a
\texttt{BlockExpression} \\
Lambda composition & \texttt{Program.cs:244} &
\texttt{Expression.Lambda\textless{}Func\textless{}Parameters,\ Output,\ string\textgreater{}\textgreater{}}
wraps the \texttt{BlockExpression} \\
Compilation step & \texttt{Program.cs:163} &
\texttt{\_executable\ =\ programExpression.Compile()} \\
Eval parameter cache structure & \texttt{Program.cs:305} & Dictionary
keyed by parameter name to \texttt{(EvalScript,\ Compiled)} tuple \\
Eval recompile on script change & \texttt{Program.cs:323-331} & Cache
lookup, invalidation, re-parse, re-compile \\
\end{longtable}
}

\subsubsection{§3.2 --- Interpretation
retained}\label{interpretation-retained-1}

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.5000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Location
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it shows
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
\texttt{CompilationModePolicy} enum & \texttt{Actor.cs:8-13} & Three
policy values (\texttt{Automatic}, \texttt{AlwaysCompiled},
\texttt{AlwaysInterpreted}); default is \texttt{Automatic} \\
Policy hint passed at call site & \texttt{ActorHandler.cs:1117-1131} &
\texttt{AdjustCompilationMode(useInterpretedMode,\ policy)} ---
\texttt{useInterpretedMode:\ true} for literal path, \texttt{false} for
parametric \\
Policy resolution switch & \texttt{Program.cs:134-150} & Sets
\texttt{IsCompiledMode} from policy + hint \\
Execution dispatch & \texttt{ActorHandler.cs:1955-1968} &
\texttt{Perform} calls \texttt{ExecuteExpression} if compiled,
\texttt{Execute} otherwise \\
Commands caching rule & \texttt{ActorHandler.cs:1117} & Caches only when
\texttt{parameters.HasUserParameter()} is true \\
Queries caching rule & \texttt{ActorHandler.cs:1405} & Caches when
\texttt{EMPTY\_PARAMETERS} \emph{or} \texttt{HasUserParameter()} is true
(asymmetry vs commands) \\
\end{longtable}
}

\subsubsection{§3.3 --- Hot-loaded DSL
programs}\label{hot-loaded-dsl-programs}

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.5000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Location
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it shows
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Domain library loading & \texttt{DomainLibraries.cs:77-115} &
\texttt{GetOrLoad(params\ Assembly{[}{]})} reflects public types and
caches them once per deduplicated assembly set; a single-assembly
overload remains for the back-compat path \\
Library binding to actor & \texttt{ActorHandler.cs:61} &
\texttt{libraries\ =\ DomainLibraries.GetOrLoad(LibraryAssemblies)} \\
Fluent script invocation & \texttt{ActorV2.cs:32} &
\texttt{Using(scriptForChk,\ scriptForCmd)} introduces new scripts at
runtime against the cached domain \\
\end{longtable}
}

\subsubsection{§4 --- Verb richness}\label{verb-richness-1}

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.2500}}
  >{\raggedright\arraybackslash}p{(\linewidth - 4\tabcolsep) * \real{0.5000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Reference
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Location
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it shows
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
Check-then-command fluent dispatch & \texttt{ActorV2Invocation.cs:69} &
\texttt{PerformCheckThenCommand()} --- read-lock check + write-lock
command + journal write, with rollback on check failure \\
\end{longtable}
}

\subsubsection{§5 --- Empirical datasets}\label{empirical-datasets}

The datasets that produced the magnitudes reported in §5 are stored in
the companion \texttt{data/} directory of the paper repository. The
production-verb measurements are produced against two independent
open-source aggregates:

\begin{itemize}
\tightlist
\item
  \textbf{eShop} ---
  \href{https://github.com/dotnet/eShop}{\texttt{dotnet/eShop}} (MIT),
  \texttt{src/Ordering.Domain/AggregatesModel/OrderAggregate/Order.cs}.
  Multi-item purchase via 10-arg constructor plus four
  \texttt{AddOrderItem} calls plus a four-step state-machine walk to
  Shipped.
\item
  \textbf{Grzybek} ---
  \href{https://github.com/kgrzybek/modular-monolith-with-ddd}{\texttt{kgrzybek/modular-monolith-with-ddd}}
  (MIT),
  \texttt{src/Modules/Payments/Domain/SubscriptionPayments/SubscriptionPayment.cs}.
  Subscription payment lifecycle via \texttt{Buy} factory plus
  \texttt{MarkAsPaid}.
\end{itemize}

The synthetic kernels in §5.2--§5.3 (arithmetic, DSL-rich) carry no
external-codebase dependency; their datasets are in
\texttt{data/paper2-synthetic/}.

{\def\LTcaptype{none} % do not increment counter
\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\linewidth - 8\tabcolsep) * \real{0.2000}}
  >{\raggedright\arraybackslash}p{(\linewidth - 8\tabcolsep) * \real{0.2000}}
  >{\raggedright\arraybackslash}p{(\linewidth - 8\tabcolsep) * \real{0.2000}}
  >{\raggedright\arraybackslash}p{(\linewidth - 8\tabcolsep) * \real{0.2000}}
  >{\raggedright\arraybackslash}p{(\linewidth - 8\tabcolsep) * \real{0.2000}}@{}}
\toprule\noalign{}
\begin{minipage}[b]{\linewidth}\raggedright
Section
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Dataset directory
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Lab
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Host
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
What it contains
\end{minipage} \\
\midrule\noalign{}
\endhead
\bottomrule\noalign{}
\endlastfoot
§5.2 (compilation speedup) & \texttt{data/lab01-eshop/} & Lab 1 & eShop
+ synthetic & BenchmarkDotNet compiled-vs-interpreted (Release,
\texttt{DOTNET\_TieredCompilation=0}); GitHub-markdown + CSV reports
incl.~the BDN environment block \\
§5.2 (compile cold cost, production verb) & \texttt{data/lab02-eshop/} &
Lab 2 Tier 3 & eShop & Cold compile cost per distinct script variant,
N=100, isolated at the \texttt{Compile()} call site \\
§5.2 (compile cost by depth) & \texttt{data/paper2-synthetic/} & Lab 2
synthetic & synthetic & Cold compile cost vs statement count (depths
5--100), 50 variants per depth \\
§5.3 (eval cache hit vs miss) & \texttt{data/lab03-eshop/} & Lab 3 Tier
C & eShop & Stable vs mutating eval text; per-iteration end-to-end and
isolated eval-compile ticks \\
§5.3 (eval cache by complexity) & \texttt{data/paper2-synthetic/} & Lab
3 synthetic & synthetic & Hit vs miss for 2-term and 50-term eval
sub-programs \\
§5.4 (journal density) & \texttt{data/lab04-eshop/} & Lab 4 & eShop &
Action / Script / Define entry counts and payload bytes parsed directly
from BinaryEventCodec journal\_*.bin \\
§5.4 (journal density, replication) & \texttt{data/lab04-grzybek/} & Lab
4 & Grzybek & Same entry-type analysis applied to the
SubscriptionPayment lifecycle verb \\
§5.5 (verb richness) & \texttt{data/lab05-eshop/} & Lab 5 & eShop & α
DSL dispatch counter (runtime) plus β Roslyn forward closure over
\texttt{Ordering.Domain} \\
§5.5 (verb richness, replication) & \texttt{data/lab05-grzybek/} & Lab 5
& Grzybek & α + β over \texttt{Payments.Domain} plus shared
\texttt{BuildingBlocks/Domain} \\
\end{longtable}
}

Each dataset directory contains a \texttt{headline.md} summary, raw
CSVs, and a Git SHA stamping the runtime version against which the lab
was run. The Roslyn walker source for the β half of §5.5 lives at
\texttt{labs/lab05-eshop-roslyn/} and
\texttt{labs/lab05-grzybek-roslyn/} --- both are self-contained .NET 9
console projects that can be re-executed against the public source trees
of the respective aggregates.

\end{document}
