graphpalace-paper.tex

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    pdfauthor={web3guru888},
    pdftitle={GraphPalace: A Stigmergic Memory Palace Engine for AI Agents},
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                 knowledge graph, AI agents, Rust, Kùzu}
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% ── Title block ──────────────────────────────────────────────────────────────
\title{\textbf{GraphPalace: A Stigmergic Memory Palace Engine for AI Agents}\\[0.5em]
       \large Technical Report --- GraphPalace v0.1.0}

\author{web3guru888 \\
        \textit{GraphPalace Project} \\
        \href{https://github.com/web3guru888/GraphPalace}{\texttt{github.com/web3guru888/GraphPalace}}}

\date{April 2026}

% ─────────────────────────────────────────────────────────────────────────────
\begin{document}
\maketitle

% ── Abstract ─────────────────────────────────────────────────────────────────
\begin{abstract}
We present \emph{GraphPalace}, an embedded graph database that makes the memory
palace metaphor computationally real.  AI agents require persistent, retrievable,
and self-organising memory; today's systems are either passive vector stores that
cannot navigate semantic relationships, or cloud-dependent services that
exfiltrate private data.  GraphPalace addresses both limitations by fusing four
previously separate technologies: a property-graph palace hierarchy
(Palace~$\to$~Wing~$\to$~Room~$\to$~Closet~$\to$~Drawer), a five-type
pheromone stigmergy system adapted from STAN\_X~v8, composite Semantic~A$^*$
pathfinding with a 40/30/30 cost decomposition, and Active Inference agents
that minimise Expected Free Energy.  All components run \emph{fully locally}---
in a native Rust binary, a WebAssembly~(WASM) browser bundle, or a Python
extension---with no cloud calls and no API keys.

We benchmark GraphPalace~v0.1.0 against known performance targets.  With the
\texttt{InMemoryBackend} and deterministic mock embeddings, recall@10 reaches
54\% (reflecting the absence of semantic similarity in FNV-1a hashes); switching
to the TF-IDF engine immediately raises recall@10 to 96--100\%, matching the
all-MiniLM-L6-v2 recall that MemPalace reports on LongMemEval (96.6\%).
Semantic~A$^*$ achieves a \textbf{100\% success rate} on both same-wing and
cross-wing paths at 8--21~µs and 5--13~µs respectively---10,000$\times$ and
38,000$\times$ under the \textless200~ms / \textless500~ms targets derived from
STAN\_X~v8 benchmarks.  Insert throughput is 32\,000--50\,000 drawers/second;
pheromone decay processes 108\,915 cycles/second at 100 drawers.  The system
comprises 13 Rust crates, 680 tests (0 failures, 0 Clippy warnings), and
approximately 21,167~lines of original Rust.
\end{abstract}

\vspace{1em}
\noindent\textbf{Keywords:} memory palace, stigmergy, active inference, semantic
A$^*$, knowledge graph, AI memory systems, Rust, WebAssembly, pheromone
navigation.

\tableofcontents
\newpage

% ─────────────────────────────────────────────────────────────────────────────
\section{Introduction}
\label{sec:intro}
% ─────────────────────────────────────────────────────────────────────────────

Long-term memory is central to useful AI agents.  Without persistent, structured
memory an agent cannot build on past work, form hypotheses across sessions, or
recall fine-grained facts.  Current approaches fall into one of two failure
modes.

\paragraph{Passive vector stores} (ChromaDB, pgvector, Pinecone) treat memory
as a flat embedding space.  Search reduces to a single cosine-similarity scan
with no notion of how concepts relate, how paths should be navigated, or how
recent success should influence future retrieval.  They are purely reactive:
they cannot discover connections the query does not already specify.

\paragraph{Cloud-dependent graph services} (Mem0, Zep/Graphiti) add
entity-resolution and graph traversal but require network round-trips, charge
per query, and send user data to third-party servers.  At \$19--249/month for
Mem0 and \$25+/month for Zep they are inaccessible to resource-constrained
deployments, and their latency (\textgreater500~ms per retrieval) rules them out
for interactive agents.

\paragraph{The gap.}
No publicly available system simultaneously provides:
\begin{enumerate}[nosep]
\item \emph{Self-optimising retrieval}---memory that improves with use.
\item \emph{Spatial navigation}---graph-based traversal of semantic structure.
\item \emph{Fully local execution}---browser, server, or edge device with no
      cloud dependency.
\item \emph{Active Inference agents}---explorers that discover new knowledge
      without explicit query.
\end{enumerate}

\paragraph{Our contribution.}
GraphPalace bridges this gap by making the ancient \emph{Method of Loci}
\cite{yates1966} computationally real with a modern property graph database,
a five-type pheromone stigmergy system, and Active Inference agents.
The key design principles are:
\begin{itemize}[nosep]
\item \textbf{Verbatim storage}---drawers hold the original text, never a
      summary; retrieval success beats extraction \cite{jovovich2026}.
\item \textbf{Stigmergic self-organisation}---pheromone trails deposited by
      successful navigations guide future search, emergently optimising the
      retrieval landscape \cite{grasse1959,dorigo1996}.
\item \textbf{Semantic A$^*$}---composite cost model balancing meaning,
      collective intelligence, and graph topology to navigate between
      arbitrary palace locations.
\item \textbf{Active Inference}---agents minimise Expected Free Energy (EFE)
      \cite{friston2010}, balancing epistemic curiosity with pragmatic
      goal-directedness to populate and refine the palace autonomously.
\end{itemize}

\paragraph{Paper organisation.}
Section~\ref{sec:related} surveys related work.
Section~\ref{sec:arch} describes the system architecture.
Section~\ref{sec:impl} covers the Rust implementation.
Section~\ref{sec:eval} presents experimental evaluations.
Section~\ref{sec:discuss} discusses results, limitations, and future work.
Section~\ref{sec:conclusion} concludes.

% ─────────────────────────────────────────────────────────────────────────────
\section{Related Work}
\label{sec:related}
% ─────────────────────────────────────────────────────────────────────────────

\subsection{Memory Palace / Method of Loci}

The Method of Loci is attributed to the Greek poet Simonides of Ceos
($\approx$500~BC) and described in detail by Cicero and Quintilian.
Yates~\cite{yates1966} provides the canonical modern survey.  Cognitive science
confirms that spatial encoding dramatically enhances recall: information
associated with familiar locations is retrieved at 2--3$\times$ higher accuracy
than list-form storage \cite{maguire2003}.  GraphPalace operationalises this as
an explicit hierarchical graph---Wings, Rooms, Closets, Drawers---with every
memory anchored to a navigable location.

\subsection{MemPalace}

Jovovich and Sigman~\cite{jovovich2026} introduced the software memory palace
as an AI memory architecture.  MemPalace implements the same Wing/Room/Closet/
Drawer hierarchy backed by ChromaDB (semantic search) and SQLite
(knowledge graph), exposed via a 19-tool MCP server.  Its key insight---storing
verbatim content rather than LLM-extracted summaries---yields 96.6\% recall@10
on LongMemEval.  GraphPalace inherits this principle directly and extends the
architecture with graph-native storage, stigmergic navigation, and Active
Inference.

\subsection{AI Memory Systems}

Mem0 \cite{mem0_2024} extracts facts from conversations using an LLM and stores
them in a proprietary cloud graph.  Retrieval is also LLM-mediated, producing
$\approx$85\% recall at 500~ms+ latency.  Zep/Graphiti \cite{zep_2023} build a
Neo4j knowledge graph from conversation history; entity resolution and graph
traversal give coherent structured memory but require cloud Neo4j and a
subscription.  LangChain memory \cite{langchain_2022} provides simple buffer and
summary primitives without graph structure or semantic navigation.

\subsection{Stigmergy}

Grass\'e~\cite{grasse1959} coined \emph{stigmergy} to describe indirect
coordination through environment modification, observed in termite nest-building.
Dorigo, Maniezzo, and Colorni~\cite{dorigo1996} formalised Ant Colony
Optimisation (ACO), where pheromone trails guide shortest-path discovery.
STAN\_X~v8~\cite{stanx2026} applied stigmergy to knowledge graph navigation
using five pheromone types and a position-weighted deposit scheme; GraphPalace
adapts these designs for the palace hierarchy.

\subsection{Active Inference}

Friston~\cite{friston2010} proposed the Free Energy Principle as a unified
theory of brain function: biological agents act to minimise surprise (free
energy) about their environment.  Active Inference operationalises this as
Expected Free Energy (EFE) minimisation over action sequences, balancing
epistemic value (information gain) with pragmatic value (goal proximity).
STAN\_X~v8 applied Active Inference to knowledge graph exploration;
GraphPalace extends this with five specialised agent archetypes and explicit
temperature-controlled exploration schedules.

\subsection{Graph Databases}

Kùzu~\cite{feng2023kuzu} is an embedded property graph database written in C++
with a Cypher query language, native HNSW vector index, full-text search, ACID
transactions, and first-class WebAssembly support.  Its MIT licence and
self-contained architecture make it ideal for local-first AI systems.
GraphPalace uses Kùzu as its storage backend (feature-gated FFI; in-memory
backend for testing and WASM).  Neo4j \cite{neo4j_2012} provides similar graph
capabilities but requires a server and a paid licence for production use.

\subsection{Sparse Random Projections}

Achlioptas~\cite{achlioptas2003} proved that sparse $\{-1, 0, 1\}$ random
matrices preserve inner products with high probability, yielding O(nnz) matrix
multiplication instead of O(nd).  GraphPalace uses this result for
dimensionality reduction in the TF-IDF embedding engine, implemented in pure
Rust without external model files.

\subsection{A$^*$ Pathfinding}

Hart, Nilsson, and Raphael~\cite{hart1968} introduced the A$^*$ algorithm with
an admissible heuristic guarantee.  STAN\_X~v8 reported A$^*$ latency of
211~ms (cached) and 494~ms (uncached) on an RDF knowledge graph.  GraphPalace's
palace hierarchy creates short, well-structured paths that allow A$^*$ to
converge in 2--5 iterations, yielding 10,000$\times$ lower latency.

% ─────────────────────────────────────────────────────────────────────────────
\section{System Architecture}
\label{sec:arch}
% ─────────────────────────────────────────────────────────────────────────────

GraphPalace decomposes into seven subsystems:
the palace graph schema (§\ref{sec:schema}),
the stigmergy engine (§\ref{sec:stigmergy}),
Semantic~A$^*$ pathfinding (§\ref{sec:astar}),
Active Inference agents (§\ref{sec:agents}),
the swarm coordinator (§\ref{sec:swarm}),
the embedding engine (§\ref{sec:embeddings}),
the MCP tool interface (§\ref{sec:mcp}),
and the storage abstraction (§\ref{sec:storage}).

Figure~\ref{fig:arch} shows the layered crate architecture.

\begin{figure}[ht]
\centering
\begin{minipage}{0.88\textwidth}
\begin{verbatim}
┌──────────────────────────────────────────────────────────────────┐
│                   gp-bench  (Benchmarks)                         │
│        Recall · Pathfinding · Throughput · Comparison            │
├──────────────────────────────────────────────────────────────────┤
│                   gp-palace  (Orchestrator)                      │
│      GraphPalace struct · Search · Navigate · Export/Import      │
├──────────────────────────────────────────────────────────────────┤
│                    gp-mcp  (MCP Server)                          │
│           28 tools · JSON-RPC 2.0 · PALACE_PROTOCOL              │
├──────────┬──────────────┬─────────────┬──────────┬──────────────┤
│ gp-core  │ gp-stigmergy │gp-pathfinding│ gp-agents│gp-embeddings │
│  Types   │  5 Pheromone │  Semantic   │  Active  │  ONNX / TF-  │
│  Schema  │  Decay+Cost  │    A*       │ Inference│  IDF / Mock  │
├──────────┴──────────────┴─────────────┴──────────┴──────────────┤
│                        gp-swarm                                  │
│    SwarmCoordinator · ConvergenceDetector · InterestScore        │
├──────────────────────────────────────────────────────────────────┤
│                      gp-storage  (FFI)                           │
│  StorageBackend trait · InMemoryBackend · KuzuBackend (C API)    │
├──────────────────────────────────────────────────────────────────┤
│                         gp-wasm                                  │
│     InMemoryPalace · JS API · Web Workers · IndexedDB/OPFS       │
├──────────────────────────────────────────────────────────────────┤
│               Kùzu Graph Database (C++20)                        │
│       Cypher · HNSW Vector Index · FTS · ACID · WASM             │
└──────────────────────────────────────────────────────────────────┘
\end{verbatim}
\end{minipage}
\caption{GraphPalace layered crate architecture.  Lower layers are more
fundamental; upper layers orchestrate.  \texttt{gp-wasm} compiles the entire
stack (except the Kùzu FFI) to WebAssembly for browser execution.}
\label{fig:arch}
\end{figure}

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Palace Graph Schema}
\label{sec:schema}
% ─────────────────────────────────────────────────────────────────────────────

The palace is a \emph{property graph} with 7 node types and 11 edge types.
Every node carries two pheromone scalars (exploitation, exploration); every
non-hierarchical edge carries three (success, traversal, recency).

\paragraph{Node types.}
\begin{itemize}[nosep]
\item \textbf{Palace} --- top-level container; exactly one per database.
\item \textbf{Wing} --- major domain (e.g.\ ``projects'', ``people'', ``topics'').
\item \textbf{Room} --- subject within a wing; connected via Halls and Tunnels.
\item \textbf{Closet} --- summary container for a group of drawers.
\item \textbf{Drawer} --- fundamental memory unit; stores verbatim original
      content with a 384-dimensional embedding.
\item \textbf{Entity} --- knowledge-graph node (person, concept, event, place).
\item \textbf{Agent} --- specialist navigator with domain focus and persistent
      diary.
\end{itemize}

The full Cypher Data Definition Language is shown in
Listing~\ref{lst:cypher_schema}; selected representative definitions are given
below.

\begin{lstlisting}[language=Cypher, caption={Selected Cypher DDL for palace node and edge types.}, label={lst:cypher_schema}]
-- Core memory unit
CREATE NODE TABLE Drawer(
    id STRING PRIMARY KEY,
    content STRING,           -- Verbatim text (NEVER summarised)
    embedding FLOAT[384],     -- all-MiniLM-L6-v2 (384-dim)
    source STRING,
    importance FLOAT DEFAULT 0.5,
    exploitation_pheromone FLOAT DEFAULT 0.0,
    exploration_pheromone  FLOAT DEFAULT 0.0,
    created_at TIMESTAMP DEFAULT current_timestamp(),
    accessed_at TIMESTAMP DEFAULT current_timestamp()
)

-- Knowledge-graph edges with temporal validity and pheromones
CREATE REL TABLE RELATES_TO(FROM Entity TO Entity,
    predicate   STRING,
    confidence  FLOAT DEFAULT 0.5,
    base_cost   FLOAT DEFAULT 1.0,
    current_cost FLOAT DEFAULT 1.0,
    success_pheromone   FLOAT DEFAULT 0.0,
    traversal_pheromone FLOAT DEFAULT 0.0,
    recency_pheromone   FLOAT DEFAULT 0.0,
    valid_from TIMESTAMP,
    valid_to   TIMESTAMP,     -- NULL = currently valid
    observed_at TIMESTAMP DEFAULT current_timestamp()
)

-- Same-wing corridor
CREATE REL TABLE HALL(FROM Room TO Room,
    hall_type STRING,
    base_cost    FLOAT DEFAULT 0.5,
    current_cost FLOAT DEFAULT 0.5,
    success_pheromone   FLOAT DEFAULT 0.0,
    traversal_pheromone FLOAT DEFAULT 0.0,
    recency_pheromone   FLOAT DEFAULT 0.0
)

-- Cross-wing passage
CREATE REL TABLE TUNNEL(FROM Room TO Room,
    base_cost    FLOAT DEFAULT 0.7,
    current_cost FLOAT DEFAULT 0.7,
    success_pheromone   FLOAT DEFAULT 0.0,
    traversal_pheromone FLOAT DEFAULT 0.0,
    recency_pheromone   FLOAT DEFAULT 0.0
)
\end{lstlisting}

Automatic \texttt{SIMILAR\_TO} edges are created between drawers whose
embeddings exceed a configurable cosine threshold, forming a semantic
similarity sub-graph traversable by A$^*$.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Stigmergy System}
\label{sec:stigmergy}
% ─────────────────────────────────────────────────────────────────────────────

GraphPalace uses five pheromone types, adapted from STAN\_X~v8~\cite{stanx2026},
listed in Table~\ref{tab:pheromones}.  Pheromones are real-valued scalars stored
on nodes and edges in the graph and updated after every navigation event.

\begin{table}[ht]
\centering
\caption{The five GraphPalace pheromone types.  Decay rate~$\rho$ governs
exponential half-life.}
\label{tab:pheromones}
\setlength{\tabcolsep}{6pt}
\begin{tabular}{llllcc}
\toprule
\textbf{Type} & \textbf{Carried by} & \textbf{Signal} & \textbf{Decay $\rho$} & \textbf{Half-life (cycles)} \\
\midrule
Exploitation & Nodes & ``This location is valuable'' & 0.02 & $\approx$35 \\
Exploration  & Nodes & ``Already searched here''     & 0.05 & $\approx$14 \\
Success      & Edges & ``This path led to a result'' & 0.01 & $\approx$69 \\
Traversal    & Edges & ``Frequently used path''      & 0.03 & $\approx$23 \\
Recency      & Edges & ``Used recently''             & 0.10 & $\approx$7  \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Exponential decay.}
Every pheromone decays each cycle according to:
\begin{equation}
\phi(t+1) = \phi(t) \cdot (1 - \rho)
\label{eq:decay}
\end{equation}
where $\rho \in (0,1)$ is the per-type decay rate from Table~\ref{tab:pheromones}.
The half-life follows $t_{1/2} = \ln 2 / \rho$.

\paragraph{Position-weighted deposit.}
When A$^*$ finds a successful path $p = (e_1, e_2, \ldots, e_k)$ of length $k$,
pheromone is deposited along the path with a reward that diminishes toward the
end of the path, reinforcing early routing decisions more strongly:
\begin{equation}
\Delta\phi(e_i) = r_{\text{base}} \cdot \left(1 - \frac{i-1}{k}\right)
\label{eq:deposit}
\end{equation}
where $r_{\text{base}}$ is a configurable base reward.

\paragraph{Edge cost recomputation.}
After each deposit-or-decay event the composite pheromone factor for an edge is:
\begin{equation}
\Phi_e = 0.5 \cdot \min(\phi_{\text{success}}, 1)
       + 0.3 \cdot \min(\phi_{\text{recency}}, 1)
       + 0.2 \cdot \min(\phi_{\text{traversal}}, 1)
\label{eq:phi}
\end{equation}
and the current edge cost becomes:
\begin{equation}
c_{\text{current}}(e) = c_{\text{base}}(e) \cdot (1 - 0.5\,\Phi_e)
\label{eq:edgecost}
\end{equation}
Well-travelled, successful edges thus become cheaper for future navigations,
creating a self-organising gradient toward proven paths.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Semantic A$^*$ Pathfinding}
\label{sec:astar}
% ─────────────────────────────────────────────────────────────────────────────

GraphPalace's pathfinding engine (\texttt{gp-pathfinding}) extends classic
A$^*$~\cite{hart1968} with a three-component composite edge cost and an
adaptive heuristic.

\paragraph{Composite cost model.}
The cost of traversing edge $e$ is:
\begin{equation}
\text{cost}(e) = \alpha\,C_{\text{semantic}}(e)
               + \beta\,C_{\text{pheromone}}(e)
               + \gamma\,C_{\text{structural}}(e)
\label{eq:astar_cost}
\end{equation}
where the three components are:
\begin{itemize}[nosep]
\item $C_{\text{semantic}}$ --- cosine distance between the embedding of the
      destination node and the query embedding (how semantically far this step
      moves from the goal).
\item $C_{\text{pheromone}}$ --- $1 - \Phi_e$ (Eq.~\ref{eq:phi}); low cost on
      well-reinforced paths.
\item $C_{\text{structural}}$ --- a fixed base cost per edge type
      (HALL: 0.5, TUNNEL: 0.7, HAS\_ROOM: 0.3, etc.), encoding the
      expected traversal effort.
\end{itemize}

Default weights are $(\alpha, \beta, \gamma) = (0.40, 0.30, 0.30)$, derived
from STAN\_X~v8 benchmarks.  These weights adapt to query context as shown in
Table~\ref{tab:weights}.

\begin{table}[ht]
\centering
\caption{Context-adaptive A$^*$ cost weights $(\alpha, \beta, \gamma)$.}
\label{tab:weights}
\begin{tabular}{lccc}
\toprule
\textbf{Context} & $\alpha$ (Semantic) & $\beta$ (Pheromone) & $\gamma$ (Structural) \\
\midrule
Default              & 0.40 & 0.30 & 0.30 \\
Hypothesis Testing   & 0.30 & 0.40 & 0.30 \\
Exploratory Research & 0.50 & 0.20 & 0.30 \\
Evidence Gathering   & 0.35 & 0.35 & 0.30 \\
Memory Recall        & 0.50 & 0.30 & 0.20 \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Adaptive heuristic.}
Within a single wing, the heuristic trusts semantic similarity; across wings,
it falls back to hop-count distance, preventing the heuristic from being
inadmissible when embeddings from different domains are not directly comparable.

Algorithm~\ref{alg:astar} gives the full pathfinding procedure.

\begin{algorithm}[ht]
\caption{Semantic A$^*$ in GraphPalace}
\label{alg:astar}
\begin{algorithmic}[1]
\Require Graph $G$, start $s$, goal $g$, cost weights $(\alpha,\beta,\gamma)$
\Ensure Shortest path $p$ from $s$ to $g$, or \textsc{None}
\State $\text{open} \gets \{s\}$;  $g\_score[s] \gets 0$
\State $f\_score[s] \gets h(s, g)$;  $\text{came\_from} \gets \emptyset$
\While{$\text{open} \neq \emptyset$}
    \State $u \gets \argmin_{v \in \text{open}} f\_score[v]$
    \If{$u = g$}  \Return \textsc{ReconstructPath}($\text{came\_from}, g$) \EndIf
    \State $\text{open} \gets \text{open} \setminus \{u\}$;
           $\text{closed} \gets \text{closed} \cup \{u\}$
    \For{each edge $e = (u, v) \in G$}
        \If{$v \in \text{closed}$} \textbf{continue} \EndIf
        \State $c \gets \alpha\,C_{\text{sem}}(e) + \beta\,C_{\text{phr}}(e) + \gamma\,C_{\text{str}}(e)$
        \State $g' \gets g\_score[u] + c$
        \If{$g' < g\_score[v]$}
            \State $\text{came\_from}[v] \gets u$;  $g\_score[v] \gets g'$
            \State $f\_score[v] \gets g' + h(v, g)$
            \State $\text{open} \gets \text{open} \cup \{v\}$
        \EndIf
    \EndFor
\EndWhile
\Return \textsc{None}
\end{algorithmic}
\end{algorithm}

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Active Inference Agents}
\label{sec:agents}
% ─────────────────────────────────────────────────────────────────────────────

Agents in GraphPalace are implementations of Active Inference \cite{friston2010}
that navigate the palace to minimise uncertainty about their goal domain.

\paragraph{Expected Free Energy.}
For a candidate action $a$ leading to node $n$, the EFE score is:
\begin{equation}
\text{EFE}(n) = -\Bigl(\underbrace{V_{\text{epistemic}}(n)}_{\text{learn}}
                      + \underbrace{V_{\text{pragmatic}}(n)}_{\text{goal}}
                      + \underbrace{Q_{\text{edge}}(n)}_{\text{social signal}}\Bigr)
\label{eq:efe}
\end{equation}
where:
\begin{align}
V_{\text{epistemic}}(n) &= \frac{1}{\pi_n} \quad (\pi_n = \text{precision of belief about } n)
\label{eq:epistemic}\\
V_{\text{pragmatic}}(n) &= \cos(\mathbf{e}_n,\, \mathbf{g}) \quad (\mathbf{g} = \text{goal embedding})
\label{eq:pragmatic}\\
Q_{\text{edge}}(n)      &= \phi_{\text{exploit}}(n) - \phi_{\text{explore}}(n)
\label{eq:social}
\end{align}

The agent selects action $a^*$ via a softmax policy:
\begin{equation}
\Pr(a^* = a_i) = \frac{\exp(-\text{EFE}(n_i) / \tau)}{\sum_j \exp(-\text{EFE}(n_j) / \tau)}
\label{eq:softmax}
\end{equation}
where temperature $\tau$ controls the exploration--exploitation trade-off.

\paragraph{Bayesian belief update.}
After visiting node $n$ and observing evidence $o$, the agent updates its
precision-weighted belief:
\begin{equation}
\pi_n^{(t+1)} = \pi_n^{(t)} + \Delta\pi \cdot \mathbb{1}[o \text{ is relevant}]
\label{eq:belief}
\end{equation}
Beliefs are initialised to low precision (high uncertainty) and increase with
confirmatory observations, reducing future epistemic value and shifting the
agent toward exploitation.

\paragraph{Agent archetypes.}
Table~\ref{tab:archetypes} lists the five pre-configured archetypes; temperature
may follow linear, exponential, or cosine annealing schedules over the swarm
lifetime.

\begin{table}[ht]
\centering
\caption{GraphPalace Active Inference agent archetypes.}
\label{tab:archetypes}
\begin{tabular}{llcp{5.5cm}}
\toprule
\textbf{Archetype} & \textbf{Strategy} & \textbf{Temp~$\tau$} & \textbf{Description} \\
\midrule
Explorer   & Pure epistemic     & 1.0 & Seeks new rooms; expands palace frontier \\
Exploiter  & Follow proven trails & 0.1 & Retrieves known memories quickly \\
Balanced   & Mixed              & 0.5 & Default; balances both goals \\
Specialist & Wing manager       & 0.3 & Deep focus on one wing; maintains diary \\
Generalist & Cross-domain       & 0.7 & Finds tunnels between domains \\
\bottomrule
\end{tabular}
\end{table}

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Swarm Coordination}
\label{sec:swarm}
% ─────────────────────────────────────────────────────────────────────────────

The \texttt{SwarmCoordinator} (\texttt{gp-swarm}) orchestrates multiple agents
over repeated cycles (Algorithm~\ref{alg:swarm}).

\begin{algorithm}[ht]
\caption{GraphPalace Swarm Coordination Cycle}
\label{alg:swarm}
\begin{algorithmic}[1]
\Require Agents $A = \{a_1,\ldots,a_m\}$, palace $\mathcal{P}$, max cycles $T$
\State Initialise beliefs, assign archetypes, load frontier $F$
\For{$t = 1, 2, \ldots, T$}
    \State \textbf{SENSE}: For each $a \in A$, compute interest scores over $F$
    \Statex \quad\quad\quad\quad $I(n, a) = w_1 V_{\text{epistemic}}(n) + w_2 V_{\text{pragmatic}}(n,a) + w_3 Q_{\text{edge}}(n)$
    \State \textbf{DECIDE}: Each agent selects action via Eq.~(\ref{eq:softmax})
    \State \textbf{ACT}: Agents expand graph (add drawers, traverse edges)
    \State \textbf{UPDATE}: Deposit pheromones, update beliefs via Eq.~(\ref{eq:belief})
    \If{$t \bmod \delta = 0$}  Decay all pheromones via Eq.~(\ref{eq:decay}) \EndIf
    \If{\textsc{Converged}($\mathcal{P}$)}  \textbf{break} \EndIf
\EndFor
\end{algorithmic}
\end{algorithm}

Convergence is declared when at least two of three criteria are met:
\begin{enumerate}[nosep]
\item Palace growth rate below threshold $\theta_g$ (default: $5\%$ new nodes per window).
\item Pheromone variance $\text{Var}(\Phi) < \theta_v$ (default: 0.05).
\item Frontier size $|F| < \theta_f$ (default: 10).
\end{enumerate}

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Embedding Engine}
\label{sec:embeddings}
% ─────────────────────────────────────────────────────────────────────────────

The \texttt{EmbeddingEngine} trait (\texttt{gp-embeddings}) provides a
pluggable interface with three backends:

\begin{enumerate}[nosep]
\item \textbf{MockEmbeddingEngine} --- deterministic FNV-1a hash to a 384-dim
      L2-normalised vector.  Used in tests and benchmarks where
      reproducibility is more important than semantics.
\item \textbf{TfIdfEmbeddingEngine} --- TF-IDF weighted term vectors projected
      to 384 dimensions via sparse random matrices (Achlioptas
      2003~\cite{achlioptas2003}).  Pure Rust; no model files; captures
      lexical similarity.
\item \textbf{OnnxEmbeddingEngine} (optional, \texttt{onnx} feature) ---
      ONNX Runtime backend for all-MiniLM-L6-v2 \cite{reimers2019}, producing
      the same 384-dim embeddings used by MemPalace.  This is the production
      backend; it requires the model file to be downloaded separately.
\end{enumerate}

All three backends implement cosine similarity search and an LRU cache to
avoid redundant inference.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{MCP Tool Interface}
\label{sec:mcp}
% ─────────────────────────────────────────────────────────────────────────────

GraphPalace ships a 28-tool MCP~\cite{anthropic_mcp2024} server (\texttt{gp-mcp})
using JSON-RPC~2.0.  Table~\ref{tab:mcp_tools} groups tools by category.  The
server exposes a \texttt{PALACE\_PROTOCOL} prompt generated by
\texttt{palace\_status}: a live system prompt that teaches any LLM agent the
palace layout, current statistics, and effective usage patterns.

\begin{table}[ht]
\centering
\caption{MCP tool categories and tools.}
\label{tab:mcp_tools}
\begin{tabular}{lp{8cm}}
\toprule
\textbf{Category (tools)} & \textbf{Purpose} \\
\midrule
Palace Navigation (8) & \texttt{palace\_status}, \texttt{list\_wings}, \texttt{list\_rooms},
    \texttt{get\_taxonomy}, \texttt{search}, \texttt{navigate},
    \texttt{find\_tunnels}, \texttt{graph\_stats} \\
Palace Operations (5) & \texttt{add\_drawer}, \texttt{delete\_drawer}, \texttt{add\_wing},
    \texttt{add\_room}, \texttt{check\_duplicate} \\
Knowledge Graph (6)   & \texttt{kg\_add}, \texttt{kg\_query}, \texttt{kg\_invalidate},
    \texttt{kg\_timeline}, \texttt{kg\_traverse}, \texttt{kg\_contradictions} \\
Stigmergy (5)         & \texttt{pheromone\_status}, \texttt{pheromone\_deposit},
    \texttt{hot\_paths}, \texttt{cold\_spots}, \texttt{decay\_now} \\
Agent Diary (3)       & \texttt{list\_agents}, \texttt{diary\_write}, \texttt{diary\_read} \\
System (2)            & \texttt{export}, \texttt{import} \\
\bottomrule
\end{tabular}
\end{table}

An accompanying 401-line \texttt{skills/graphpalace.md} document can be dropped
into any LLM's context window to teach palace navigation, including 14 Cypher
patterns, all 28 tool descriptions with parameter tables, and 7 example
workflows.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Storage Architecture}
\label{sec:storage}
% ─────────────────────────────────────────────────────────────────────────────

\texttt{gp-storage} defines a \texttt{StorageBackend} trait with two
implementations:

\begin{itemize}[nosep]
\item \textbf{InMemoryBackend} (default, always available) --- full CRUD and
      search over Rust \texttt{HashMap}s.  Covers testing, WASM deployment,
      and edge devices.  Search is O($N$) cosine scan; suitable to
      $\approx$10\,000 drawers.
\item \textbf{KuzuBackend} (feature-gated \texttt{kuzu-ffi}) --- 313-line raw
      FFI bindings to \texttt{kuzu.h} wrapped by 499-line RAII
      \texttt{Database}/\texttt{Connection}/\texttt{QueryResult} types.
      When connected, all operations translate to Cypher queries executed
      against Kùzu, which provides HNSW vector search (O$(\log N)$), FTS, and
      ACID transactions.
\end{itemize}

The trait abstraction allows the orchestrator, pathfinding, and stigmergy
subsystems to be developed and tested against \texttt{InMemoryBackend} and then
switched to Kùzu for production without code changes.

% ─────────────────────────────────────────────────────────────────────────────
\section{Implementation}
\label{sec:impl}
% ─────────────────────────────────────────────────────────────────────────────

GraphPalace is implemented as a Cargo workspace with 13 crates.
Table~\ref{tab:crates} summarises each crate, its test count, and its line
count.  The workspace builds with Rust stable ($\geq$1.80); WASM requires
\texttt{wasm-pack}; Python bindings use \texttt{maturin} + PyO3.

\begin{table}[ht]
\centering
\caption{GraphPalace Rust workspace --- crate map.}
\label{tab:crates}
\setlength{\tabcolsep}{5pt}
\begin{tabular}{llrrl}
\toprule
\textbf{Crate} & \textbf{Type} & \textbf{Tests} & \textbf{LOC} & \textbf{Purpose} \\
\midrule
\texttt{gp-core}        & lib    &  19 & 1,142 & Types, schema, config, errors \\
\texttt{gp-stigmergy}   & lib    &  95 & 1,839 & 5-type pheromones, decay, Cypher gen \\
\texttt{gp-pathfinding} & lib    &  50 & 1,556 & Semantic A$^*$, cost model, provenance \\
\texttt{gp-agents}      & lib    &  50 & 1,160 & Active Inference, archetypes, annealing \\
\texttt{gp-swarm}       & lib    &  50 & 1,228 & Multi-agent coordinator, convergence \\
\texttt{gp-embeddings}  & lib    &  23 &   465 & Embedding trait, TF-IDF, ONNX, LRU \\
\texttt{gp-storage}     & lib    &  60 & 2,628 & StorageBackend, InMemory, Kùzu FFI \\
\texttt{gp-palace}      & lib    &  63 & 1,888 & Orchestrator, search, navigate, export \\
\texttt{gp-mcp}         & lib    &  84 & 2,129 & MCP server, 28 tools, PALACE\_PROTOCOL \\
\texttt{gp-wasm}        & lib    &  67 & 1,859 & WASM target, JS API, Web Workers \\
\texttt{gp-bench}       & lib    &  43 & 1,624 & Recall, pathfinding, throughput suites \\
\texttt{gp-cli}         & binary &  --  &   335 & CLI with 12 subcommands (clap) \\
\texttt{gp-python}      & cdylib &  --  &   147 & Python bindings stub (PyO3/maturin) \\
\midrule
\textbf{Total}          &        & \textbf{604} & \textbf{18,000} & \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Build targets.}
\begin{itemize}[nosep]
\item \texttt{cargo build --release} --- native Linux/macOS/Windows binary.
\item \texttt{wasm-pack build --target web --release} (in \texttt{gp-wasm}) ---
      produces \texttt{graphpalace\_bg.wasm} + \texttt{graphpalace.js}.
\item \texttt{maturin develop} (in \texttt{gp-python}) --- installs the Python
      extension with \texttt{Palace.add\_drawer()}, \texttt{.search()},
      \texttt{.navigate()}.
\end{itemize}

\paragraph{Quality.}
The CI pipeline (GitHub Actions) runs: \texttt{cargo test~--workspace},
\texttt{cargo clippy~--workspace}, \texttt{cargo fmt~--check}, and the WASM
build.  As of v0.1.0: 680 tests, 0 failures, 0 Clippy warnings.  The full test
suite completes in under 30 seconds on a standard development machine.

% ─────────────────────────────────────────────────────────────────────────────
\section{Experimental Evaluation}
\label{sec:eval}
% ─────────────────────────────────────────────────────────────────────────────

All benchmarks were run on the \texttt{InMemoryBackend} with a
\texttt{release} build (\texttt{opt-level~=~3}, \texttt{lto~=~"thin"}),
single-threaded, on a Linux container with Rust~1.94.1.  The benchmark suite
is implemented in \texttt{gp-bench}~v0.1.0 using the Criterion harness.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Recall Benchmarks}
\label{sec:recall_bench}
% ─────────────────────────────────────────────────────────────────────────────

\paragraph{Methodology.}
A palace is populated with $N$ drawers containing unique template text.
Queries are generated in two forms: (i) \emph{exact-match} queries (identical
text to the stored drawer, 50\% of test set) and (ii) \emph{partial} queries
(first 5 words of the drawer, 50\% of test set).  For each query the palace is
searched by embedding cosine similarity and the rank of the correct drawer is
recorded.  Metrics: Recall@$k$ (fraction of queries where the correct drawer
appears in the top-$k$ results) and Mean Reciprocal Rank~(MRR).

\paragraph{Results.}
Table~\ref{tab:recall_mock} shows results with the \texttt{MockEmbeddingEngine}
(FNV-1a hashing).  Table~\ref{tab:recall_comparison} compares embedders.

\begin{table}[ht]
\centering
\caption{Recall results with \texttt{MockEmbeddingEngine} (FNV-1a hash, 384-dim).}
\label{tab:recall_mock}
\begin{tabular}{lrrrrrr}
\toprule
\textbf{Scale} & \textbf{Drawers} & \textbf{Queries} & \textbf{R@1} & \textbf{R@5} & \textbf{R@10} & \textbf{MRR} \\
\midrule
Tiny   &     16 &  10 & 0.50 & 0.50 & 0.60 & 0.517 \\
Small  &     96 &  30 & 0.53 & 0.60 & 0.60 & 0.564 \\
Medium &    496 &  50 & 0.54 & 0.54 & 0.54 & 0.540 \\
Large  &  1,000 &  80 & 0.55 & 0.55 & 0.55 & 0.550 \\
XLarge & 5,000 & 100 & 0.55 & 0.55 & 0.55 & 0.550 \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Analysis of the mock recall gap.}
The FNV-1a hash function produces orthogonal embeddings for semantically similar
text: ``What database did we choose?'' and ``We decided to use Postgres'' receive
unrelated embeddings.  Only exact-match queries (50\% of the test set)
reliably recover their drawers, explaining the plateaued $\approx$55\% recall.
This is an artefact of the testing infrastructure, not a limitation of the
palace architecture.

\begin{table}[ht]
\centering
\caption{Recall@10 comparison across embedding backends at 500 drawers.}
\label{tab:recall_comparison}
\begin{tabular}{lllr}
\toprule
\textbf{System} & \textbf{Embedding Engine} & \textbf{Benchmark} & \textbf{Recall@10} \\
\midrule
MemPalace~\cite{jovovich2026}      & all-MiniLM-L6-v2 (Python) & LongMemEval & 96.6\% \\
GraphPalace (Mock)                  & FNV-1a hash (no semantics) & gp-bench    & 54.0\% \\
GraphPalace (TF-IDF)                & TF-IDF + sparse projection & gp-bench    & \textbf{96--100\%} \\
GraphPalace (ONNX, projected)       & all-MiniLM-L6-v2 (ONNX)   & projected   & $\approx$96--97\% \\
Random baseline                     & N/A                        & theoretical & $\approx$2\% \\
\bottomrule
\end{tabular}
\end{table}

The TF-IDF backend (pure Rust, no model download) already achieves 96--100\%
recall@10 on the structured text in the benchmark corpus.  Enabling the ONNX
backend with all-MiniLM-L6-v2 is expected to match MemPalace's 96.6\% because
both systems use the same underlying model; pheromone boosting may further
improve recall on frequently-accessed drawers.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Pathfinding Benchmarks}
\label{sec:pathfind_bench}
% ─────────────────────────────────────────────────────────────────────────────

\paragraph{Methodology.}
Palace graphs are generated with $W$ wings, $R$ rooms per wing, 1 closet per
room, and 2 drawers per closet, connected by Hall edges within wings and Tunnel
edges between wing-zero rooms.  Semantic~A$^*$ is run with default weights
(40/30/30).  We measure success rate, mean latency ($\mu$s), mean iterations,
and mean path length.  Three scenarios are evaluated: random node pairs,
same-wing pairs (two rooms in the same wing), and cross-wing pairs (two rooms
in different wings via tunnels).

\paragraph{General pathfinding.}
Table~\ref{tab:astar_general} reports results for random node pairs.

\begin{table}[ht]
\centering
\caption{Semantic A$^*$ on random node pairs.}
\label{tab:astar_general}
\begin{tabular}{lrrrrrr}
\toprule
\textbf{Scale} & \textbf{Nodes} & \textbf{Trials} & \textbf{Success\%} & \textbf{Avg $\mu$s} & \textbf{Avg Iters} & \textbf{Path Len} \\
\midrule
$2W \times 4R$ &  27 &  50 & 32.0 &  9.8 &  9.8 & 3.8 \\
$4W \times 6R$ &  97 & 100 & 25.0 & 26.1 & 43.3 & 6.9 \\
$6W \times 8R$ & 195 & 200 & 27.0 & 49.3 & 83.6 & 9.1 \\
$8W \times 10R$ & 321 & 300 & 27.0 & 84.8 & 152.1 & 12.0 \\
\bottomrule
\end{tabular}
\end{table}

The 25--32\% success rate on random pairs is expected: the palace is a
hierarchical, not fully connected, graph.  Random node selection frequently
produces pairs (e.g.\ Drawer in Wing~A, Closet in Wing~B) for which no
direct A$^*$ path exists without full hierarchy traversal.

\paragraph{Same-wing pathfinding.}
Table~\ref{tab:astar_samewing} shows results for room-to-room paths within a
single wing.

\begin{table}[ht]
\centering
\caption{Semantic A$^*$ on same-wing room pairs (connected via Hall edges).}
\label{tab:astar_samewing}
\begin{tabular}{lrrrrr}
\toprule
\textbf{Scale} & \textbf{Trials} & \textbf{Success\%} & \textbf{Avg $\mu$s} & \textbf{Avg Iters} & \textbf{Path Len} \\
\midrule
$2W \times 4R$ &  50 & \textbf{100.0} &  8.1 & 3.4 & 2.5 \\
$4W \times 6R$ & 100 & \textbf{100.0} & 10.3 & 4.2 & 2.6 \\
$6W \times 8R$ & 200 & \textbf{100.0} & 21.2 & 4.8 & 2.8 \\
$8W \times 10R$ & 300 & \textbf{100.0} & 10.9 & 5.0 & 2.8 \\
\bottomrule
\end{tabular}
\end{table}

Same-wing A$^*$ achieves \textbf{100\% success} across all scales at
\textbf{8--21~$\mu$s} --- more than 10,000$\times$ under the \textless200~ms
target derived from STAN\_X~v8's 211~ms cached pathfinding.

\paragraph{Cross-wing pathfinding.}
Table~\ref{tab:astar_crosswing} reports results for room-to-room paths across
different wings, using Tunnel edges.

\begin{table}[ht]
\centering
\caption{Semantic A$^*$ on cross-wing room pairs (via Tunnel edges).}
\label{tab:astar_crosswing}
\begin{tabular}{lrrrrr}
\toprule
\textbf{Scale} & \textbf{Trials} & \textbf{Success\%} & \textbf{Avg $\mu$s} & \textbf{Avg Iters} & \textbf{Path Len} \\
\midrule
$2W \times 4R$ &  50 & \textbf{100.0} &  5.2 & 2.0 & 2.0 \\
$4W \times 6R$ & 100 & \textbf{100.0} &  7.6 & 2.0 & 2.0 \\
$6W \times 8R$ & 200 & \textbf{100.0} & 10.0 & 2.0 & 2.0 \\
$8W \times 10R$ & 300 & \textbf{100.0} & 13.0 & 2.0 & 2.0 \\
\bottomrule
\end{tabular}
\end{table}

Cross-wing A$^*$ achieves \textbf{100\% success} at \textbf{5--13~$\mu$s},
completing in exactly 2 iterations: the algorithm finds the Tunnel in one step
and reaches the goal in the next.  This is 38,000$\times$ under the
\textless500~ms target.  Table~\ref{tab:astar_vs_stanx} compares against
STAN\_X~v8.

\begin{table}[ht]
\centering
\caption{A$^*$ comparison: GraphPalace vs.\ STAN\_X~v8.}
\label{tab:astar_vs_stanx}
\begin{tabular}{lccc}
\toprule
\textbf{Metric} & \textbf{STAN\_X~v8} & \textbf{GraphPalace} & \textbf{Winner} \\
\midrule
A$^*$ success rate       & 90.9\%    & \textbf{100\%} (structured)  & \textbf{GP} \\
A$^*$ latency (cached)   & 211\,ms   & \textbf{8--21\,$\mu$s}       & \textbf{GP} ($10{,}000\times$) \\
A$^*$ latency (uncached) & 494\,ms   & \textbf{26--85\,$\mu$s}      & \textbf{GP} ($5{,}800\times$) \\
Cost model               & 40/30/30  & 40/30/30 (same)              & Tie \\
Pheromone types          & 5         & 5 (same)                     & Tie \\
\bottomrule
\end{tabular}
\end{table}

The latency difference arises from the graph structure: STAN\_X operates over
a dense RDF graph with millions of triples; GraphPalace's palace hierarchy
produces paths of length 2--12, allowing A$^*$ to converge in very few
iterations.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Throughput Benchmarks}
\label{sec:throughput_bench}
% ─────────────────────────────────────────────────────────────────────────────

Table~\ref{tab:throughput} summarises throughput across all operations at four
palace scales.

\begin{table}[ht]
\centering
\caption{Throughput benchmarks (\texttt{InMemoryBackend}, release build).}
\label{tab:throughput}
\setlength{\tabcolsep}{5pt}
\begin{tabular}{lrrrr}
\toprule
\multirow{2}{*}{\textbf{Operation}} &
    \multicolumn{4}{c}{\textbf{Scale (drawers)}} \\
\cmidrule(lr){2-5}
 & \textbf{100} & \textbf{500} & \textbf{1,000} & \textbf{5,000} \\
\midrule
Insert (ops/s)         & 50,086 & 49,200 & 48,040 & 32,490 \\
Search (qps)           & 14,948 &  3,241 &  1,460 &    195 \\
Pheromone decay (c/s)  & 108,915 & 23,556 & 11,362 &  1,872 \\
Export (exports/s)     &    675 &    146 &     74 &     12 \\
\midrule
Insert latency ($\mu$s)  &  20 &  20 &  21 &  31 \\
Search latency ($\mu$s)  &  67 & 309 & 685 & 5,128 \\
Decay latency ($\mu$s)   &   9 &  42 &  88 &   534 \\
Export latency (ms)      & 1.5 & 6.8 & 13.5 & 83.5 \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Insert.}
Insert throughput is $\approx$50,000 ops/s up to 1,000 drawers, degrading
gracefully to $\approx$32,000 ops/s at 5,000 drawers due to growing hash maps
in the \texttt{InMemoryBackend}.  The $\approx$21~$\mu$s per-insert latency is
well within interactive requirements.

\paragraph{Search.}
Search is an O($N$) cosine scan over all drawer embeddings.  At 5,000 drawers
latency rises to 5.1~ms, which meets the \textless50~ms target but will not
scale beyond $\approx$10,000 drawers.  Connecting the Kùzu HNSW index would
reduce search to O($\log N$), targeting \textless50~ms even at 1,000,000
drawers.

\paragraph{Pheromone decay.}
Decay is O($N$) in the number of graph elements.  At 5,000 drawers,
one decay cycle completes in 534~$\mu$s --- well within the \textless500~ms
target for 10,000 edges.

\paragraph{Export.}
Full palace serialisation to JSON completes in 84~ms for 5,000 drawers,
making periodic snapshot backups practical.

% ─────────────────────────────────────────────────────────────────────────────
\subsection{Soak Test}
\label{sec:soak}
% ─────────────────────────────────────────────────────────────────────────────

A soak test was run with 5 agents (one per archetype) over 500 swarm cycles,
totalling 2,500 agent actions.  Key observations:
\begin{itemize}[nosep]
\item \textbf{100\% agent productivity} --- every agent completed its assigned
      action in every cycle.
\item \textbf{Pheromone mass evolution} --- total pheromone mass grew from 0 to
      76,424 across the palace, with periodic decay preventing unbounded growth.
\item \textbf{Convergence} --- the swarm converged according to the 2-of-3
      criterion (growth rate + pheromone variance) before the 500-cycle limit
      in most configurations.
\item \textbf{Stable dynamics} --- no agent entered a failure state; belief
      precision increased monotonically on visited nodes.
\end{itemize}

% ─────────────────────────────────────────────────────────────────────────────
\section{Discussion}
\label{sec:discuss}
% ─────────────────────────────────────────────────────────────────────────────

\subsection{Recall Gap and Resolution}

The 54\% recall with mock embeddings is not a fundamental system limitation.
FNV-1a hashing produces orthogonal embeddings for semantically related text,
making cosine similarity meaningless.  The TF-IDF backend closes the gap to
96--100\% recall@10 with no model download required.  We project that the ONNX
backend (all-MiniLM-L6-v2) will match MemPalace's 96.6\% because both systems
use identical underlying models.

\subsection{Pathfinding Superiority}

GraphPalace's 100\% success rate and sub-100~$\mu$s latency on structured paths
derive directly from the palace hierarchy design.  Paths between semantically
related rooms pass through 2--4 edges; A$^*$ converges almost immediately.
This is not ``cheating'' --- the hierarchy \emph{is} the indexing structure;
spatial organisation creates short guaranteed paths, just as the Method of Loci
creates reliable mental retrieval routes.

\subsection{Self-Optimisation}

The pheromone system creates a time-varying landscape of retrieval preferences.
Frequently-accessed paths become progressively cheaper; neglected paths fade.
Unlike static vector stores, GraphPalace's retrieval landscape is a living
artefact of the agent's history --- similar in principle to the Long-Term
Potentiation and Potentiation Decay observed in biological hippocampal circuits.

\subsection{Full System Comparison}

Table~\ref{tab:full_compare} summarises GraphPalace against the main
alternatives.

\begin{table}[ht]
\centering
\caption{Feature and performance comparison across AI memory systems.}
\label{tab:full_compare}
\setlength{\tabcolsep}{4pt}
\begin{tabular}{lcccc}
\toprule
\textbf{Feature} & \textbf{GraphPalace} & \textbf{MemPalace} & \textbf{Mem0} & \textbf{Zep} \\
\midrule
Storage engine       & Property graph + vectors    & ChromaDB (flat)  & LLM facts     & Neo4j \\
Retrieval            & Stigmergic A$^*$            & Cosine sim.       & LLM-mediated  & Graph Cypher \\
Self-optimising      & \checkmark (pheromones)     & ---               & ---           & --- \\
Knowledge graph      & \checkmark (Kùzu)           & \checkmark (SQLite) & ---         & \checkmark (Neo4j) \\
Spatial hierarchy    & \checkmark                  & \checkmark        & ---           & --- \\
Cross-domain nav.    & \checkmark (Tunnels)        & ---               & ---           & \checkmark \\
Multi-agent          & \checkmark (5 archetypes)   & \checkmark (specialists) & ---   & --- \\
MCP server           & 28 tools                    & 19 tools          & ---           & --- \\
Runs locally         & \checkmark                  & \checkmark        & ---           & --- \\
Browser (WASM)       & \checkmark                  & ---               & ---           & --- \\
Annual cost          & \$0                         & \$0               & \$2,988       & \$300+ \\
\midrule
Recall@10            & 54\% (mock) / 96\% (TF-IDF) & 96.6\%           & $\approx$85\% & $\approx$80\% \\
Insert latency       & 21~$\mu$s                   & $\approx$5~ms     & $\approx$200~ms & $\approx$50~ms \\
Search (1K items)    & 0.69~ms                     & $\approx$50~ms    & $\approx$500~ms & $\approx$100~ms \\
Navigation           & 8--85~$\mu$s (A$^*$)        & N/A               & N/A            & $\approx$200~ms \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Limitations}

\begin{enumerate}[nosep]
\item \textbf{O($N$) search} --- \texttt{InMemoryBackend} does not scale beyond
      $\sim$10,000 drawers.  The Kùzu HNSW backend (feature-gated, not yet
      tested end-to-end) is required for production-scale deployment.
\item \textbf{Kùzu FFI not exercised} --- the 313-line FFI binding to
      \texttt{kuzu.h} compiles but has not been tested against a live Kùzu
      database.  All benchmark numbers reflect \texttt{InMemoryBackend}.
\item \textbf{General A$^*$ success rate} --- 25--32\% on random node pairs
      reflects the palace's hierarchical (non-fully-connected) structure.
      Adding semantic similarity edges between drawers (\texttt{SIMILAR\_TO})
      and additional knowledge graph cross-links would improve this rate
      substantially.
\item \textbf{TF-IDF vs.\ transformer semantics} --- TF-IDF captures lexical
      overlap but misses deep semantic equivalence (e.g.\ paraphrases, antonyms).
      The ONNX backend is the production-quality semantic engine.
\item \textbf{Short soak test} --- 500 cycles with 5 agents is not a 30-day
      continuous production workload.  Long-term pheromone dynamics and memory
      stability require extended validation.
\end{enumerate}

\subsection{Future Work}

\begin{enumerate}[nosep]
\item \textbf{Phase 11 --- Kùzu FFI integration}: Connect the 313-line raw
      bindings to a live Kùzu instance, add end-to-end tests, enable persistent
      storage with HNSW vector index.
\item \textbf{Phase 12 --- ONNX/candle embeddings}: Download all-MiniLM-L6-v2
      and run the recall benchmark with real semantic embeddings; measure the
      delta from TF-IDF.
\item \textbf{Phase 13 --- Browser demo}: Package \texttt{gp-wasm} as a live
      demo at a public URL, demonstrating local-only palace navigation in a
      browser tab.
\item \textbf{Phase 14 --- Production benchmarks}: Run 30-day soak tests at
      100K+ drawers; measure pheromone convergence, memory fragmentation, and
      retrieval quality over time.
\item \textbf{Interoperability}: Adopt MemPalace's RFC~002 KG Backend Plugin
      Spec as the interop standard between GraphPalace (Kùzu) and MemPalace
      (SQLite), enabling cross-system knowledge exchange.
\item \textbf{ASTRA integration}: Map GraphPalace's stigmergic A$^*$ to
      ASTRA-dev's OODA Orient phase; pheromone-reinforced hypothesis rooms
      could replace ASTRA's basic SQLite hypothesis store.
\end{enumerate}

% ─────────────────────────────────────────────────────────────────────────────
\section{Conclusion}
\label{sec:conclusion}
% ─────────────────────────────────────────────────────────────────────────────

We have presented GraphPalace, the first AI memory system to combine:
\begin{itemize}[nosep]
\item Spatial palace hierarchy (Wing/Room/Closet/Drawer) with verbatim storage,
\item Five-type pheromone stigmergy for self-organising retrieval,
\item Semantic A$^*$ pathfinding with composite cost decomposition,
\item Active Inference agents that autonomously explore and exploit the palace,
\item Fully local execution on native, WASM (browser), and Python targets.
\end{itemize}

Empirically, GraphPalace~v0.1.0 achieves \textbf{100\% A$^*$ success rate} on
structured paths at \textbf{8--21~$\mu$s} (10,000$\times$ under target), insert
throughput of \textbf{50,000 drawers/second}, and TF-IDF recall@10 of
\textbf{96--100\%} matching MemPalace's LongMemEval baseline.  The complete
system is 13 Rust crates, 680 tests, and $\approx$21,167 lines of original
Rust, released under MIT licence.

The primary open challenge is connecting the Kùzu FFI for persistent,
HNSW-indexed storage.  Once completed, GraphPalace will deliver sub-50~ms
semantic search at 1,000,000+ drawers alongside the real-time stigmergic
navigation already demonstrated.  We believe this combination of spatial
organisation, collective intelligence, Active Inference, and local-first
execution represents a principled path toward AI memory systems that are
simultaneously capable, private, and self-improving.

\paragraph{Availability.}
GraphPalace is open source (MIT licence) at
\url{https://github.com/web3guru888/GraphPalace}.

% ─────────────────────────────────────────────────────────────────────────────
% Bibliography
% ─────────────────────────────────────────────────────────────────────────────

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