\section{Related Work}

\subsection{Autopoiesis and artificial life}

The concept of autopoiesis originates in biological organization: a network
produces components that realize the network and specify a boundary
\cite{varela1974autopoiesis}. Our use is explicitly analogical and
operational. The components are semantic records, executable artifacts, and
machine resources; production is mediated by engineering work and admission;
the boundary includes both isolation mechanisms and fact/authority roots.

Tierra and Avida demonstrated digital organisms whose programs reproduce and
evolve within designed computational environments \cite{ray1991tierra,
ofria2004avida}. They make heredity, variation, and selection concrete, but
their organisms inhabit specialized artificial-life worlds. The present
proposal instead studies one accountable subject embedded in the ordinary
software supply chain, using real compilers, dependencies, networks,
repositories, and maintenance work. Its central selection problem includes
semantic continuity and authority, not only reproductive fitness.

\subsection{Persistent language-model agents}

Generative Agents combines a complete natural-language experience record with
reflection and dynamic retrieval to produce persistent believable behavior
\cite{park2023generative}. Voyager combines an automatic curriculum, an
executable skill library, and iterative prompting for open-ended embodied
learning in Minecraft \cite{wang2023voyager}. Both establish important
patterns: agents can accumulate skills, retrieve prior experience, and sustain
behavior beyond one prompt.

The difference is not that the present proposal has memory while prior agents
do not. It is that memory eligibility, admitted facts, authority, work
identity, executable body, and successor admission are separate first-class
objects. Believable continuity is not sufficient; the subject must survive
replacement and produce auditable reasons why a remembered record or new organ
can govern action.

\subsection{Self-improving software}

The G\"odel Machine formalizes a self-referential system that rewrites itself
after proving a rewrite useful under its axiomatized utility and hardware
\cite{schmidhuber2003goedel}. The Darwin G\"odel Machine replaces an
impractical proof obligation with open-ended empirical evolution of coding
agents and benchmark evaluation \cite{zhang2025dgm}. AlphaEvolve pairs
large-language-model variation with automated evaluators to evolve algorithms
that improve concrete systems \cite{novikov2025alphaevolve}.

These systems most directly address improvement search. Semantic autopoiesis
adds a different question: which continuing subject owns the candidate,
which causal work and authority justify it, and how does the new executable
body become an admitted successor without allowing the proposer to redefine
the selection constitution? The answer can incorporate proof, benchmarks,
evolutionary archives, or AlphaEvolve-style evaluators as organs. It does not
replace them.

\subsection{Novelty boundary}

As stated in the Introduction, we do not claim that machine life, digital
organisms, persistent agents, or self-modifying systems have not previously
been imagined. Each has a long history. Nor is the distinction reducible to
adding memory to an agent, tools to a model, or self-editing to a program. The
narrower contribution is their engineering synthesis around two jointly
load-bearing ideas:

\begin{enumerate}[leftmargin=*]
  \item a KFD-shaped semantic kernel that preserves accountable subject
  continuity across replaceable cognition and material bodies; and
  \item recursive dogfood that admits qualified changes to the organism's own
  real development and execution organs.
\end{enumerate}

Whether this synthesis produces a genuinely useful research object is an
empirical question for the survival trials, not a priority claim.
