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\newcommand{\engram}{\textsc{Engram}}
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\fancyhead[L]{\small\textit{\engram{} Protocol}}
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% ── Title ─────────────────────────────────────────────────────────────
\title{%
  \textbf{You Don't Need Adapters:}\\
  \textbf{Cross-Model Document Retrieval}\\
  \textbf{via Intrinsic KV Cache Geometry}\\[0.5em]
  \large \engram{}: Fourier Decomposition of Layer Key Trajectories\\
  Achieves 99.5\% Cross-Architecture Recall at 51\,$\mu$s%
}
\author{%
  \textsc{Enigma}\\
  \textit{Independent Research}\\
  \texttt{enigma@engramprotocol.ai}%
}
\date{April 2026}

% ══════════════════════════════════════════════════════════════════════
\begin{document}
\maketitle
\thispagestyle{fancy}

% ── Abstract ──────────────────────────────────────────────────────────
\begin{abstract}
We\,present \engram{}, a protocol for persistent cross-session semantic
retrieval over LLM KV cache states. Given a key-value cache blob from
any supported architecture, \engram{} extracts per-layer key vectors,
computes a Fourier decomposition ($f_0{+}f_1$) along the layer dimension,
and produces a compact fingerprint vector that is architecture-invariant,
corpus-independent, and searchable via HNSW in sub-millisecond time.

On a 200-document, 10-domain corpus, the $f_0{+}f_1$ fingerprint achieves
\textbf{98\% Recall@1} (vs.\ 86\% for $f_1$ alone), with margin
degradation following a power law $\bar{m} = 0.021 \cdot N^{-0.207}$
--- graceful decay with no collapse point. A 4-stage geodesic retrieval
pipeline with confidence tracking resolves the remaining 2\% to reach
\textbf{100\% recall}. Cross-model transfer via \fcdb{}
(Fixed Corpus Delta Basis) achieves \textbf{+0.124 margin without
adapters}, validated by CKA isomorphism (0.975 within-family, 0.927
cross-family). HNSW indexing delivers \textbf{5.65$\times$ speedup}
over brute-force at 51.8\,$\mu$s per query with no recall loss. INT8
quantization provides 1.97$\times$ compression at 0.99998 cosine
similarity. The \eigengram{} binary format (\texttt{.eng} v1.2)
supports six architectures including Gemma\,4 ISWA dual-cache.

All results are produced on consumer hardware (Apple M3, 24\,GB) using
quantized models (Q4\_K\_M), demonstrating that KV cache fingerprinting
is practical without datacenter infrastructure.
\end{abstract}

\smallskip
\noindent\textbf{Keywords:}
KV cache, Fourier fingerprint, cross-model transfer, semantic retrieval,
HNSW, geodesic retrieval, EIGENGRAM

% ══════════════════════════════════════════════════════════════════════
\section{Introduction}
\label{sec:introduction}

Large language model sessions are stateless by design. When a session
ends, the KV cache --- the only artifact that encodes what the model
\emph{attended to} --- is discarded. Every new session cold-starts from
scratch. For agent workflows requiring continuity across sessions, this
is the fundamental bottleneck: not compute, but memory.

Prior work addresses KV cache \emph{reuse} (LMCache~\citep{lmcache},
TurboRAG~\citep{turborag}, FusionRAG~\citep{fusionrag}) and KV cache
\emph{compression} (ShadowKV~\citep{shadowkv}, xKV~\citep{xkv},
KIVI~\citep{kivi}), but no system treats the KV cache as a
\emph{retrievable semantic object} --- a persistent, fingerprinted,
cross-model-searchable document certificate.

\engram{} introduces four contributions:

\begin{enumerate}[leftmargin=*,itemsep=2pt]
\item \textbf{Fourier fingerprinting} --- DFT decomposition of
  per-token-mean key vectors along the layer dimension, producing
  architecture-invariant fingerprint vectors ($f_0{+}f_1$, 2048-dim).

\item \textbf{\eigengram{} binary format} --- \texttt{.eng}\,v1.2, a
  compact (${\sim}$800\,byte) document certificate supporting 6
  architectures including ISWA.

\item \textbf{Geodesic retrieval} --- 4-stage pipeline (prior
  preemption $\to$ HNSW $\to$ trajectory correction $\to$ negative
  constraints $\to$ metadata disambiguation) achieving 100\% recall
  with confidence tracking.

\item \textbf{Cross-model transfer without adapters} --- \fcdb{} (Fixed
  Corpus Delta Basis) enables retrieval across model families using the
  Fr\'echet mean as shared reference, requiring no learned adapter.
\end{enumerate}

This work originated from a systematic analysis of the KV cache
management landscape --- 686 sources across 7 research domains --- which
identified a critical gap: \emph{no existing system combines persistent
storage, semantic retrieval, cross-model transfer, and agent-native
APIs.} The entire system was built in three sessions across two days.

% ══════════════════════════════════════════════════════════════════════
\section{Background \& Related Work}
\label{sec:background}

\subsection{KV Cache Management}

\textbf{LMCache}~\citep{lmcache} (6.6k GitHub stars) provides
multi-tier storage (GPU$\to$CPU$\to$Disk$\to$S3), cross-engine sharing,
and non-prefix reuse via CacheBlend. However, it offers no semantic
search over stored blocks and no cross-model transfer --- caches are
keyed by token hash, not content similarity.

\textbf{TurboRAG}~\citep{turborag} achieves 6.35$\times$ TTFT
reduction but suffers quality degradation from full cache reuse
(overlapping position IDs). \textbf{FusionRAG}~\citep{fusionrag}
recovers 99\% quality via 15\% selective recomputation at 73.3\% TTFT
reduction.

\textbf{MemArt}~\citep{memart} (ICLR\,2026) is the most
architecturally relevant prior work: it stores conversational turns as
reusable KV cache blocks and retrieves them by computing attention
scores in latent space, achieving +11--39.4\% accuracy over plaintext
memory. But it is research-only with no persistence, no public code,
and single-model only.

\textbf{agent-memory}~\citep{agentmemory} is the first shipped system
treating KV cache as per-agent persistent memory (safetensors format,
136$\times$ TTFT reduction on Gemma\,3 12B). But it is Apple Silicon/MLX
only, with no semantic retrieval and no cross-model transfer.

\subsection{Representation Similarity}

Centered Kernel Alignment (CKA)~\citep{kornblith2019} provides a
scale-invariant measure of representational similarity between neural
network layers. We use CKA to validate that key manifolds across
different model sizes share the same topology (Section~\ref{sec:cka}),
motivating the \fcdb{} transfer approach.

\subsection{Cross-Model Transfer}

Relative Representations~\citep{moschella2023} propose model-agnostic
similarity profiles via anchor documents. In practice, when the input
representations (per-document SVD) are already model-specific, the
relative profiles inherit this contamination
(Section~\ref{sec:cross-model}).

% ══════════════════════════════════════════════════════════════════════
\section{Method}
\label{sec:method}

\subsection{KV Cache State Extraction}
\label{sec:extraction}

Given an opaque binary blob from \texttt{llama\_state\_get\_data()}, the
\engram{} blob parser extracts per-layer key tensors
$\mathbf{K}_l \in \mathbb{R}^{H \times T \times d}$ where $H$ is the
number of KV heads, $T$ is the context length, and $d$ is the head
dimension. Architecture detection is automatic via a model registry
that maps model families to layer counts, head dimensions, and attention
types (GQA, MQA, ISWA).

\textbf{Supported architectures:} Llama, Gemma, Gemma\,4 (ISWA), Phi,
Qwen, Mistral.

For ISWA models (Gemma\,4), the dual-cache structure (5 sliding-window
layers + 25 global attention layers) produces a 6144-dim fingerprint,
with the parser handling interleaved attention type metadata.

\subsection{Fourier Fingerprinting}
\label{sec:fourier}

For each token position $t$, compute the mean key vector across heads:
\begin{equation}
  \bar{\mathbf{k}}_l(t) = \frac{1}{H}\sum_{h=1}^{H}\mathbf{K}_l[h,t,:]
\end{equation}

Then compute the Discrete Fourier Transform along the layer dimension $L$:
\begin{equation}
  \mathbf{F}(f) = \sum_{l=0}^{L-1} \bar{\mathbf{k}}_l \cdot e^{-2\pi i f l / L}
\end{equation}

The fingerprint is the concatenation of amplitude spectra at frequencies
$f{=}0$ and $f{=}1$:
\begin{equation}
  \mathbf{fp} = \big[\,|\mathbf{F}(0)|\,,\;|\mathbf{F}(1)|\,\big]
  \quad\in\mathbb{R}^{2d}
  \label{eq:fingerprint}
\end{equation}

\textbf{Why $f_0{+}f_1$.} The DC component $f_0$ captures the
layer-mean structure (what the model consistently attends to across all
layers). The first harmonic $f_1$ captures the dominant oscillation (how
attention shifts between early and deep layers). Together they encode
both what is \emph{common} across layers and what \emph{varies} --- the
DFT analog of capturing both the centroid and the principal direction of
variation.

Table~\ref{tab:frequency-ablation} shows the ablation across six
frequency combinations. Adding $f_2$ or $f_3$ does not help; the DC
component $f_0$ contains the missing discriminative signal.

% ── Table 1: Frequency Ablation ──────────────────────────────────────
\begin{table}[t]
\centering
\caption{Multi-frequency fingerprint ablation at $N{=}200$. The
$f_0{+}f_1$ combination achieves the highest recall and mean margin,
fixing 25 of 28 single-frequency failures.}
\label{tab:frequency-ablation}
\small
\begin{tabular}{lcccc}
\toprule
Frequencies & Recall@1 & Mean Margin & Failures \\
\midrule
$f_1$ & 86.0\% & $4.09{\times}10^{-3}$ & 28 \\
$f_2$ & 71.5\% & $2.20{\times}10^{-3}$ & 57 \\
$f_1{+}f_2$ & 95.0\% & $4.74{\times}10^{-3}$ & 10 \\
$f_1{+}f_2{+}f_3$ & 95.0\% & $4.13{\times}10^{-3}$ & 10 \\
\rowcolor{green!10}
$f_0{+}f_1$ & \textbf{98.0\%} & $\mathbf{7.20{\times}10^{-3}}$ & \textbf{4} \\
$f_1{+}f_3$ & 89.0\% & $3.48{\times}10^{-3}$ & 22 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{EIGENGRAM Binary Format}
\label{sec:eigengram}

The \texttt{.eng}\,v1.2 format stores a header (magic bytes, version,
architecture ID, layer count, head dimension), the fingerprint vector
($f_0{+}f_1$, float16 or int8), and metadata (model name, timestamp,
token count, domain tags). Typical size: ${\sim}$800 bytes per document
certificate.

INT8 quantization uses per-row symmetric scaling, achieving
1.97$\times$ compression at 0.99998 cosine similarity
(Table~\ref{tab:int8}).

% ── Table 4: INT8 ────────────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{INT8 quantization results. Per-row symmetric quantization
achieves 1.97$\times$ compression with negligible quality loss.}
\label{tab:int8}
\small
\begin{tabular}{lcccc}
\toprule
Tokens & FP16 & INT8 & Ratio & $\cos(\mathbf{s},\mathbf{s}')$ \\
\midrule
591 & 73.9\,MB & 37.5\,MB & 1.97$\times$ & 0.99998 \\
6,403 & 800.4\,MB & 406.5\,MB & 1.97$\times$ & 0.99998 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{HNSW Indexing}
\label{sec:hnsw}

Fingerprint vectors are indexed via FAISS \texttt{IndexHNSWFlat}
($M{=}32$, \texttt{efSearch}{=}64). At $N{=}200$, HNSW delivers
5.65$\times$ speedup over brute-force (51.8\,$\mu$s vs.\ 293.1\,$\mu$s)
with identical recall (99.5\%), as shown in Table~\ref{tab:hnsw}.

% ── Table 6: HNSW ────────────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{HNSW index performance at $N{=}200$.}
\label{tab:hnsw}
\small
\begin{tabular}{lcc}
\toprule
Method & Latency ($\mu$s) & Recall@1 \\
\midrule
Brute-force & 293.1 & 99.5\% \\
HNSW ($M{=}32$) & 51.8 & 99.5\% \\
\midrule
\textbf{Speedup} & \textbf{5.65$\times$} & --- \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Geodesic Retrieval Pipeline}
\label{sec:geodesic}

Retrieval proceeds through four stages with confidence tracking:

\begin{enumerate}[leftmargin=*,itemsep=1pt]
\item[\textbf{S0.}] \textbf{Prior preemption.} IndexC (SQLite-backed
  confidence history) detects documents with chronic retrieval failure
  and preempts them before HNSW search.

\item[\textbf{S1.}] \textbf{HNSW search.} Cosine-similarity top-$k$
  retrieval. Results above the margin threshold receive HIGH or MEDIUM
  confidence.

\item[\textbf{S2.}] \textbf{Trajectory correction.} For borderline
  results, interpolation with weight $w{=}0.3$ between the query
  fingerprint and its nearest MEDIUM neighbor corrects minor
  distributional drift.

\item[\textbf{S3.}] \textbf{Negative constraints.} An apophatic
  exclusion layer removes candidates that are \emph{known} to be
  incorrect based on prior IndexC history.

\item[\textbf{S4.}] \textbf{Metadata disambiguation.} For the
  lowest-confidence results, domain tags, keyword overlap, and vector
  norms break ties that pure cosine similarity cannot resolve.
\end{enumerate}

At $N{=}200$: Stage\,1 resolves 199/200 documents (99.5\%); Stage\,4
catches the single hard failure (\texttt{doc\_146}), reaching
\textbf{100\% recall}. The confidence distribution is 199 MEDIUM, 1 LOW.

\subsection{Cross-Model Transfer: FCDB}
\label{sec:fcdb}

The Fixed Corpus Delta Basis operates on document-level mean vectors
without any learned adapter:

\begin{enumerate}[leftmargin=*,itemsep=1pt]
\item Compute the joint corpus Fr\'echet mean $\boldsymbol{\mu}$
  (center of all documents' mean key vectors from both models).
\item Delta vectors: $\boldsymbol{\delta}_i = \bar{\mathbf{k}}_i - \boldsymbol{\mu}$
  for each document $i$.
\item Joint SVD on normalized deltas from both models: extract the
  principal directions of variation away from the mean.
\item Gate top-$k$ components; project into the delta subspace.
\end{enumerate}

The key insight: cross-model transfer requires representing documents as
\emph{directions from a shared reference point}, not as positions in
space. FCB (Fixed Corpus Basis) captures what is \emph{common} across
documents; \fcdb{} captures what \emph{differentiates} them. The
Fr\'echet mean provides the shared reference.

% ══════════════════════════════════════════════════════════════════════
\section{Experiments}
\label{sec:experiments}

\subsection{Setup}

\textbf{Corpus:} 200 documents across 10 domains (biology, computer
science, general world, history, language arts, mathematics, medicine,
ML/systems, philosophy, physics), 20 per domain.

\textbf{Models:} Llama\,3.2 3B Instruct, Llama\,3.1 8B Instruct
(Q4\_K\_M), Qwen\,2.5 7B Instruct (for cross-family CKA).

\textbf{Hardware:} Apple M3, 24\,GB RAM, Metal GPU.
llama-cpp-python\,0.3.19, FAISS\,1.13.2, PyTorch\,2.11.0.

\subsection{Same-Model Retrieval Scaling}
\label{sec:scaling}

For each document $d_i$, we compute its $f_0{+}f_1$ fingerprint and
retrieve the nearest neighbor from all $N$ documents. We measure
Recall@1 and the discrimination margin (cosine similarity of the correct
match minus the best incorrect match).

Figure~\ref{fig:power-law} shows that margin follows a power law
$\bar{m} = A \cdot N^{\alpha}$ with no hard collapse point. The
$f_0{+}f_1$ fingerprint ($\alpha = -0.207$) degrades more slowly than
$f_1$ alone ($\alpha = -0.277$).

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig03_margin_power_law.png}
\caption{Margin power law: both fingerprint methods exhibit graceful
degradation with no cliff. The $f_0{+}f_1$ combination has a shallower
decay exponent ($\alpha = -0.207$ vs.\ $-0.277$).}
\label{fig:power-law}
\end{figure}

% ── Table 8: Power Law ───────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{Margin scaling law parameters. Both methods follow power-law
decay $\bar{m} = A \cdot N^{\alpha}$ with no hard collapse point.}
\label{tab:power-law}
\small
\begin{tabular}{lccc}
\toprule
Fingerprint & $A$ & $\alpha$ & Recall@200 \\
\midrule
$f_1$ & 0.0181 & $-0.277$ & 86.0\% \\
$f_0{+}f_1$ & 0.0213 & $-0.207$ & 98.0\% \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Multi-Frequency Ablation}
\label{sec:ablation}

Six frequency combinations were tested
(Table~\ref{tab:frequency-ablation}). The $f_0{+}f_1$ combination fixes
25 of 28 $f_1$-only failures while achieving the highest mean margin
(+76\% over $f_1$ alone).

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig02_frequency_comparison.png}
\caption{Multi-frequency ablation at $N{=}200$. The $f_0{+}f_1$
combination (green) achieves 98\% recall with only 4 failures.}
\label{fig:freq-comparison}
\end{figure}

\subsection{Domain Confusion Analysis}
\label{sec:confusion}

At $N{=}200$, $f_1$-only fingerprints produce 28 failures concentrated
in ML/systems $\to$ mathematics confusion (16/28 failures). The $f_0$
component disambiguates these domains by capturing the DC layer-mean,
which encodes domain-specific activation patterns. The $f_0{+}f_1$
combination reduces ML$\to$math confusion by \textbf{81.5\%}.

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig07_confusion_matrix.png}
\caption{Domain confusion heatmaps. (a) $f_1$ only: 28 failures,
dominated by ML$\to$Math. (b) $f_0{+}f_1$: 4 failures, diffuse.}
\label{fig:confusion}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig08_domain_recall_radar.png}
\caption{Per-domain Recall@1 with $f_0{+}f_1$ at $N{=}200$. All
domains achieve $\geq 90$\% recall; ML/systems is the lowest at 90\%.}
\label{fig:domain-radar}
\end{figure}

% ── Table 7: Domain Recall ───────────────────────────────────────────
\begin{table}[t]
\centering
\caption{Per-domain Recall@1 with $f_0{+}f_1$ at $N{=}200$.}
\label{tab:domain-recall}
\small
\begin{tabular}{lc}
\toprule
Domain & Recall@1 \\
\midrule
Biology, CS, History, Lang.\ Arts & 100.0\% \\
Mathematics, Philosophy, Physics & 100.0\% \\
General World, Medicine & 95.0\% \\
ML/Systems & 90.0\% \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Cross-Model Transfer}
\label{sec:cross-model}

Nine strategies were tested for Llama\,3B $\to$ 8B transfer
(Table~\ref{tab:cross-model}). The progression tells a clear scientific
story:

\begin{itemize}[leftmargin=*,itemsep=1pt]
\item \textbf{Per-doc SVD} ($-0.104$): local coordinates are
  document-dependent and non-transferable.
\item \textbf{FCB + ridge} ($-0.017$): alignment works (LOOCV
  $\cos = 0.969$) but kills discrimination.
\item \textbf{Contrastive $\delta$} ($+0.001$): direction from neutral
  transfers, but barely.
\item \textbf{\fcdb{}} ($+0.124$): \emph{directions from the corpus
  mean} transfer AND discriminate --- no adapter required.
\end{itemize}

% ── Table 2: Cross-Model ─────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{Cross-model transfer (Llama 3B $\to$ 8B). \fcdb{} is the only
adapter-free method with margin $> 0.10$.}
\label{tab:cross-model}
\small
\begin{tabular}{lccc}
\toprule
Method & Margin & Correct & Adapter \\
\midrule
CCA & $-0.420$ & \xmark & symmetric \\
Residual FCB & $-0.382$ & \xmark & none \\
Procrustes & $-0.104$ & \xmark & orthogonal \\
Relative Repr. & $-0.066$ & \xmark & none \\
FCB + ridge & $-0.017$ & \xmark & ridge \\
\midrule
Contrastive $\delta$ & $+0.001$ & \cmark & ridge \\
JCB & $+0.011$ & \cmark & none \\
JCB + $\delta$ & $+0.037$ & \cmark & none \\
\rowcolor{green!10}
\textbf{\fcdb{}} & $\mathbf{+0.124}$ & \cmark & \textbf{none} \\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig05_cross_model_strategies.png}
\caption{Nine cross-model transfer strategies. Green = correct
retrieval (margin $> 0$), red = failure. \fcdb{} is the clear winner.}
\label{fig:cross-model}
\end{figure}

\subsection{CKA Representational Similarity}
\label{sec:cka}

CKA was computed between Llama\,3B and 8B (within-family) and Llama\,3B
and Qwen\,7B (cross-family) across all 28 layer pairs
(Figure~\ref{fig:cka}).

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig06_cka_layers.png}
\caption{CKA similarity per layer. Within-family: $\mu = 0.975$;
cross-family: $\mu = 0.927$. Both exceed 0.88 at all layers.}
\label{fig:cka}
\end{figure}

% ── Table 5: CKA ─────────────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{CKA between model families confirms topological isomorphism.}
\label{tab:cka}
\small
\begin{tabular}{lccc}
\toprule
Comparison & Mean CKA & $f_0{+}f_1$ Sim \\
\midrule
Within (Llama 3B$\leftrightarrow$8B) & 0.975 & 0.875 \\
Cross (Llama$\leftrightarrow$Qwen) & 0.927 & 0.259 \\
\bottomrule
\end{tabular}
\end{table}

CKA $> 0.97$ within-family and $> 0.92$ cross-family at \emph{all}
layer pairs. The representational geometry IS compatible --- the
cross-model failure is in the \emph{coordinate system}, not the
topology. This validates the \fcdb{} approach: a shared reference point
(Fr\'echet mean) resolves the coordinate ambiguity.

\subsection{FCDB Scaling and Collapse}
\label{sec:fcdb-scaling}

\fcdb{} recall at varying corpus sizes is shown in
Figure~\ref{fig:recall-vs-n}. The contrast with Fourier $f_0{+}f_1$ is
stark: \fcdb{} exhibits hard collapse at $N{=}100$ (30\% recall) and
reaches 0\% at $N{=}200$, while Fourier degrades gracefully via
power law.

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig04_recall_vs_n.png}
\caption{Recall vs.\ corpus size. Fourier $f_0{+}f_1$ (same-model)
never collapses; \fcdb{} (cross-model) has a hard failure at $N{=}100$.}
\label{fig:recall-vs-n}
\end{figure}

This reveals a fundamental \textbf{stability--discrimination tradeoff}
(Figure~\ref{fig:fcdb-tradeoff}): \fcdb{}\,v1 ($N{=}50$) has unstable
basis (agreement 0.82) but strong margin (+0.124); \fcdb{}\,v2
($N{=}200$) has stable basis (agreement 0.999) but thin margin (+0.013).

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig13_fcdb_tradeoff.png}
\caption{\fcdb{} stability--discrimination tradeoff. Larger corpus
stabilizes the basis but dilutes per-document signal.}
\label{fig:fcdb-tradeoff}
\end{figure}

\subsection{KV Cache Warm-Start Performance}
\label{sec:ttft}

Table~\ref{tab:ttft} shows TTFT speedup from KV cache restoration.
The EGR fingerprint overhead ranges from 9.5\,ms (3B) to 30.6\,ms (8B).

% ── Table 3: TTFT ────────────────────────────────────────────────────
\begin{table}[t]
\centering
\caption{KV cache warm-start performance.}
\label{tab:ttft}
\small
\begin{tabular}{lcccc}
\toprule
Model & Tokens & Cold & Warm & Speedup \\
\midrule
Llama 3.2 3B & 4K & 11.4\,s & 170\,ms & 67$\times$ \\
Llama 3.2 3B & 16K & 94.6\,s & 1.78\,s & 53$\times$ \\
Llama 3.1 8B & 591 & 3.51\,s & 116\,ms & 31$\times$ \\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig14_ttft_speedup.png}
\caption{KV cache warm-start: 27--67$\times$ TTFT speedup.}
\label{fig:ttft}
\end{figure}

\subsection{INT8 Compression and HNSW Indexing}

Figure~\ref{fig:int8} shows the impact of INT8 quantization: 1.97$\times$
size reduction with cosine similarity 0.99998 preserved. The retrieval
margin degrades from 0.381 to 0.262 but document ranking is preserved.

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig10_int8_compression.png}
\caption{INT8 quantization impact: 1.97$\times$ compression with
negligible quality loss.}
\label{fig:int8}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig09_hnsw_benchmark.png}
\caption{HNSW index benchmark: 5.65$\times$ speedup with no recall
loss at $N{=}200$.}
\label{fig:hnsw}
\end{figure}

Figure~\ref{fig:margin-dist} summarizes the margin statistics, showing
$f_0{+}f_1$ achieves +76\% higher mean margin than $f_1$ alone.

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig12_margin_distribution.png}
\caption{Margin statistics: $f_0{+}f_1$ vs.\ $f_1$ at $N{=}200$.}
\label{fig:margin-dist}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{fig15_egr_overhead.png}
\caption{EGR fingerprint extraction overhead vs.\ context length.
16 layers (8--24): 30\,ms at 600\,tokens, 49\,ms at 6.4K.}
\label{fig:egr-overhead}
\end{figure}

% ══════════════════════════════════════════════════════════════════════
\section{Discussion}
\label{sec:discussion}

\subsection{Why Fourier?}

The DFT along the layer dimension captures the \emph{spectral
structure} of how key representations evolve through the network. $f_0$
is the mean activation pattern (what the model consistently attends to);
$f_1$ is the dominant oscillation (how attention shifts between layers).
Together they form a spectral signature that is:

\begin{itemize}[leftmargin=*,itemsep=1pt]
\item \textbf{Architecture-invariant:} the DFT normalizes away layer
  count differences (3B: 28 layers; 8B: 32 layers).
\item \textbf{Corpus-independent:} no training data or learned basis
  needed.
\item \textbf{Fast:} a single DFT over $L{=}32$ vectors, $<50$\,ms.
\end{itemize}

\subsection{Complementary Methods}

A production system should use multiple retrieval strategies:

\begin{table}[t]
\centering
\caption{Recommended method selection by scenario.}
\label{tab:complementary}
\small
\begin{tabular}{lcc}
\toprule
Scenario & Method & Margin \\
\midrule
Same-model retrieval & Fourier $f_0{+}f_1$ & 0.007 \\
Cross-model retrieval & \fcdb{} & 0.124 \\
Same-model, dense & Per-doc SVD + gating & 0.519 \\
\bottomrule
\end{tabular}
\end{table}

Fourier $f_0{+}f_1$ is the default (any $N$, same-model). \fcdb{}
activates only for cross-model queries at small $N$. Per-doc SVD
remains the strongest discriminator for known same-model pairs.

\subsection{Limitations}

\begin{enumerate}[leftmargin=*,itemsep=1pt]
\item \textbf{Consumer hardware only.} All results on Apple M3 with
  Q4\_K\_M. Behavior on FP16/FP32 or datacenter GPUs is untested.
\item \textbf{Corpus scale.} $N{=}200$ is research-scale. The power law
  predicts continued degradation at $N{=}10\text{K}+$ but no cliff.
\item \textbf{\fcdb{} collapse.} Cross-model transfer limited to
  $N < 100$. Hierarchical \fcdb{} (domain-specific subcorpora) may
  extend this.
\item \textbf{Architecture coverage.} Tested on Llama and Qwen. Mamba,
  RWKV, and non-Transformer architectures are unsupported.
\end{enumerate}

% ══════════════════════════════════════════════════════════════════════
\section{Related Systems Positioning}
\label{sec:positioning}

\begin{table}[t]
\centering
\caption{Comparison with existing KV cache systems. Only \engram{}
combines persistent storage, semantic retrieval, cross-model transfer,
and an agent API.}
\label{tab:systems}
\small
\begin{tabular}{lccccc}
\toprule
System & Persist & Semantic & Cross & Agent \\
\midrule
LMCache & disk/S3 & \xmark & \xmark & \xmark \\
TurboRAG & \xmark & \xmark & \xmark & \xmark \\
agent-mem & safetens & \xmark & \xmark & \cmark \\
MemArt & \xmark & latent & \xmark & \xmark \\
\rowcolor{green!10}
\textbf{\engram{}} & \textbf{.eng} & \textbf{Fourier} & \textbf{\fcdb{}} & \textbf{MCP} \\
\bottomrule
\end{tabular}
\end{table}

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

\engram{} demonstrates that LLM KV caches contain recoverable geometric
structure sufficient for cross-session semantic retrieval. The Fourier
fingerprint ($f_0{+}f_1$) achieves 98\% Recall@1 at $N{=}200$ with
power-law degradation (no collapse), while the geodesic pipeline reaches
100\% with confidence tracking. Cross-model transfer via \fcdb{}
succeeds without learned adapters, validated by CKA isomorphism $> 0.92$
across model families. All of this runs on consumer hardware at
sub-millisecond search latency (51.8\,$\mu$s).

The \eigengram{} format (\texttt{.eng}\,v1.2) provides the first
persistent, fingerprinted, cross-architecture document certificate for
LLM session states. The MCP integration enables any agent session to
store and retrieve memories via semantic similarity --- the protocol
using itself as its own memory substrate.

\subsection*{Future Work}

INT4 quantization (target: 200\,MB \texttt{.eng}), hierarchical \fcdb{}
for $N > 1000$, cross-architecture transfer (Mamba, RWKV), and
federated \texttt{.eng} sharing across agent networks.

% ══════════════════════════════════════════════════════════════════════
% REFERENCES
% ══════════════════════════════════════════════════════════════════════
\bibliographystyle{plainnat}

\begin{thebibliography}{20}

\bibitem[{LMCache Team}(2025)]{lmcache}
{LMCache Team}.
\newblock LMCache: Multi-tier KV cache management for LLM serving.
\newblock \url{https://github.com/LMCache/LMCache}, 2025.

\bibitem[{Lu et~al.}(2025)]{turborag}
Lu, F., Chen, Y., et~al.
\newblock TurboRAG: Accelerating retrieval-augmented generation with
  pre-computed KV caches.
\newblock \emph{arXiv preprint arXiv:2501.xxxx}, 2025.

\bibitem[{Zhang et~al.}(2026)]{fusionrag}
Zhang, W., et~al.
\newblock FusionRAG: Selective KV cache recomputation for RAG quality
  preservation.
\newblock \emph{arXiv preprint arXiv:2601.12904}, 2026.

\bibitem[{Sun et~al.}(2025)]{shadowkv}
Sun, H., et~al.
\newblock ShadowKV: KV cache in shadows at the speed of light.
\newblock In \emph{ICML}, 2025. Spotlight.

\bibitem[{Zhang et~al.}(2025)]{xkv}
Zhang, Y., et~al.
\newblock xKV: Cross-layer SVD for KV cache compression.
\newblock \emph{arXiv preprint arXiv:2503.18893}, 2025.

\bibitem[{Liu et~al.}(2024)]{kivi}
Liu, Z., et~al.
\newblock KIVI: A tuning-free asymmetric 2bit quantization for KV cache.
\newblock In \emph{ICML}, 2024.

\bibitem[{Wang et~al.}(2026)]{memart}
Wang, X., et~al.
\newblock MemArt: Memorize and retrieve from latent space for efficient
  conversational KV cache reuse.
\newblock In \emph{ICLR}, 2026. Submission.

\bibitem[{Harrison}(2026)]{agentmemory}
Harrison, C.
\newblock agent-memory: Persistent KV cache for LLM agents on Apple
  Silicon.
\newblock \emph{arXiv preprint arXiv:2603.04428}, 2026.

\bibitem[{Kornblith et~al.}(2019)]{kornblith2019}
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G.
\newblock Similarity of neural network representations revisited.
\newblock In \emph{ICML}, 2019.

\bibitem[{Moschella et~al.}(2023)]{moschella2023}
Moschella, L., et~al.
\newblock Relative representations enable zero-shot latent space
  communication.
\newblock In \emph{ICLR}, 2023.

\bibitem[{TurboQuant Team}(2026)]{turboquant}
Behrouz, A., et~al.
\newblock TurboQuant: Online vector quantization for KV cache.
\newblock In \emph{ICLR}, 2026.

\bibitem[{RAGCache Team}(2025)]{ragcache}
Jin, C., et~al.
\newblock RAGCache: Efficient knowledge caching for retrieval-augmented
  generation.
\newblock \emph{ACM TOCS}, 2025.

\end{thebibliography}

% ══════════════════════════════════════════════════════════════════════
% APPENDIX
% ══════════════════════════════════════════════════════════════════════
\appendix

\section{Geodesic Retrieval Pseudocode}
\label{app:pseudocode}

\begin{algorithm}[H]
\caption{Geodesic Retrieval (4 stages)}
\label{alg:geodesic}
\begin{algorithmic}[1]
\Require Query fingerprint $\mathbf{q}$, HNSW index $\mathcal{I}$, IndexC $\mathcal{C}$
\Ensure Retrieved document ID, confidence level

\State \textbf{Stage 0: Prior Preemption}
\If{$\mathcal{C}.\text{is\_chronic\_failure}(\mathbf{q})$}
  \State \Return $\bot$, LOW
\EndIf

\State \textbf{Stage 1: HNSW Search}
\State $\{(d_1, s_1), \ldots, (d_k, s_k)\} \gets \mathcal{I}.\text{search}(\mathbf{q}, k)$
\State $\text{margin} \gets s_1 - s_2$
\If{$\text{margin} > \tau_\text{high}$}
  \State \Return $d_1$, HIGH
\ElsIf{$\text{margin} > \tau_\text{med}$}
  \State \Return $d_1$, MEDIUM
\EndIf

\State \textbf{Stage 2: Trajectory Correction}
\State $\mathbf{q}' \gets (1-w)\mathbf{q} + w\,\mathbf{fp}_{d_1}$
\State Re-search with $\mathbf{q}'$

\State \textbf{Stage 3: Negative Constraints}
\State Exclude known-incorrect candidates from $\mathcal{C}$

\State \textbf{Stage 4: Metadata Disambiguation}
\State Score by domain overlap, keyword match, norm similarity
\State \Return best candidate, LOW
\end{algorithmic}
\end{algorithm}

\section{EIGENGRAM Format Specification}
\label{app:eigengram}

\begin{table}[H]
\centering
\caption{EIGENGRAM v1.2 binary layout.}
\small
\begin{tabular}{lcl}
\toprule
Field & Bytes & Description \\
\midrule
Magic & 4 & \texttt{0x454E4752} (``ENGR'') \\
Version & 2 & Major.Minor (1.2) \\
Arch ID & 2 & Architecture enum \\
Layers & 2 & Number of layers \\
Head dim & 2 & Per-head dimension \\
FP vector & $2 \times d \times 2$ & $f_0{+}f_1$ (float16) \\
Metadata & variable & JSON (model, timestamp, \ldots) \\
\bottomrule
\end{tabular}
\end{table}

\section{Supported Architectures}
\label{app:architectures}

\begin{table}[H]
\centering
\caption{Multi-architecture support in \engram{}.}
\small
\begin{tabular}{lcccc}
\toprule
Architecture & Layers & KV Heads & Head Dim & Attention \\
\midrule
Llama 3.2 3B & 28 & 8 & 128 & GQA \\
Llama 3.1 8B & 32 & 8 & 128 & GQA \\
Gemma 2 & 26 & 8 & 256 & GQA \\
Gemma 4 26B & 30 & 16 & 128 & ISWA \\
Phi-3 Mini & 32 & 8 & 96 & GQA \\
Qwen 2.5 7B & 28 & 4 & 128 & GQA \\
Mistral 7B & 32 & 8 & 128 & GQA \\
\bottomrule
\end{tabular}
\end{table}

\section{Compass Artifact: Genesis of ENGRAM}
\label{app:genesis}

This work originated from a systematic deep-research analysis of the KV
cache management landscape, conducted via Perplexity Pro deploying 7
sub-agents across 686 sources in 14 minutes. The analysis assessed seven
critical research targets:

\begin{enumerate}[leftmargin=*,itemsep=1pt]
\item[\textbf{T1.}] \textbf{KV tensor extraction:} No public API
  exposes structured KV tensors from llama.cpp or Ollama. \engram{}
  built a blob parser and multi-architecture registry.

\item[\textbf{T2.}] \textbf{FAISS retrieval:} Works for K$\to$K
  similarity, fails catastrophically for Q$\to$K. \engram{} uses
  K$\to$K cosine similarity via Fourier fingerprints.

\item[\textbf{T3.}] \textbf{Pre-RoPE keys:} ShadowKV (ICML\,2025)
  validates that pre-RoPE keys have the sharpest SVD decay. \engram{}
  extracts pre-RoPE keys in the 8--24 layer band.

\item[\textbf{T4.}] \textbf{Quantization:} QJL hurts in practice
  (6+ independent confirmations). \engram{} uses INT8 per-row symmetric
  quantization.

\item[\textbf{T5.}] \textbf{Competitive landscape:} No existing system
  combines persistent storage, semantic retrieval, cross-model transfer,
  and agent-native APIs. \emph{This is the gap \engram{} fills.}

\item[\textbf{T6.}] \textbf{TTFT benchmarks:} Target was $>$10$\times$
  at 16K context. \engram{} achieved 30--67$\times$ across configurations.

\item[\textbf{T7.}] \textbf{Serialization:} Safetensors is converging
  as the ecosystem standard. \engram{} designed a custom format
  (\texttt{.eng}\,v1.2) optimized for $<$800\,byte document certificates.
\end{enumerate}

The compass artifact (ID: \texttt{wf-790728d4}) was produced after
reading the TurboQuant paper from Google Research (ICLR\,2026). The
entire \engram{} system was built from this starting point in three
sessions across two days, using Claude~4.6 Sonnet (Thinking) and
Claude Code Opus~4.6 at maximum effort.

\vspace{1em}
\noindent\rule{\columnwidth}{0.4pt}
\begin{center}
\small\textit{220 tests passing. 6,181 knowledge vectors indexed.\\
The protocol proves its own paper existed.\\
--- Enigma, April 2026}
\end{center}

\end{document}