% DinoVision paper-section freeze candidate, 2026-08-01 America/Los_Angeles.
% Host evidence and corrected Quest numerical agreement are complete. Keep
% the deployment-gate paragraph until the timing protocols have passed.

\subsection{End-to-end case study: DinoVision}
\label{sec:dinovision}

We evaluate Meganeura beyond isolated operators with DinoVision, an Android XR
application that reconstructs passthrough imagery from intermediate
DINOv3~\cite{simeoni2025dinov3} features. The application center-crops and
resizes an RGB frame to
$224\times224$, executes the first three layers of a frozen DINOv3 ViT-S/16
encoder, rearranges its $14\times14\times384$ patch features, and decodes them
to RGB. The encoder and decoder form one batch-one inference graph on the
device. In the live application, Meganeura sessions share Blade's Vulkan
context and queue with OpenXR rendering.

This case study exercises patch projection, learned prefix tokens, axial RoPE,
multi-head attention, LayerNorm, GELU MLPs, LayerScale, residual connections,
convolution, group normalization, SiLU, upsampling, autodiff, Adam, parameter
serialization, and Android cross-compilation. It is evidence about the
breadth and integration of the Meganeura stack, rather than a comparison with
PyTorch or a claim of capture-to-photon latency.

\paragraph{Training and evaluation.}
The frozen encoder is used to cache features on an NVIDIA host, after which
Meganeura trains a 2,012,547-parameter decoder-only batch graph. Deployment
uses the same decoder construction and learned parameters in a batch-one
joined graph. Thus the two paths share IR operations, compiler, and runtime,
but are not literally the same complete graph.

We preserve Imagenette's upstream train/validation split. Each of three runs
uses the same class-interleaved 2,500-image training subset (250 images per
class), 12,000 batch-eight Adam updates, and a distinct seed controlling both
initialization and data order. We evaluate every run on all 3,925 validation
images. Seed zero was selected for deployment before validation; seed one is
the strongest replicate, ruling out best-seed selection of the deployed
artifact. Table~\ref{tab:dinovision-quality} reports held-out quality. The
aggregate covers 11,775 reconstruction executions, with the same validation
set reused across seeds.

\begin{table}[t]
  \centering
  \caption{Held-out DinoVision reconstruction quality. The last row is the
  mean $\pm$ sample standard deviation across independently initialized
  decoders.}
  \label{tab:dinovision-quality}
  \begin{tabular}{rcccc}
    \hline
    Seed & Global PSNR & Median PSNR & Median SSIM & Median MAE \\
         & (dB) & (dB) & & \\
    \hline
    0 (deployed) & 21.7688 & 22.3603 & 0.6338 & 0.04729 \\
    1            & 21.9830 & 22.5714 & 0.6491 & 0.04598 \\
    2            & 21.8170 & 22.3911 & 0.6388 & 0.04700 \\
    mean $\pm$ SD & $21.86\pm0.11$ & $22.44\pm0.11$ &
                    $0.6406\pm0.0078$ & $0.0468\pm0.0007$ \\
    \hline
  \end{tabular}
\end{table}

\paragraph{Independent correctness gate.}
The audit found a silent layout error in the initial implementation:
Meganeura's NCHW-flat per-channel multiplication was applied directly to a
$[\mathrm{tokens},\mathrm{hidden}]$ LayerScale branch. Only the first token
received the intended residual update, yet downstream images remained
plausible. We invalidated all weights and measurements produced by that
graph, corrected the layout, and retrained all three replicates.

For the corrected implementation, an independent Torch/Transformers reference
and Meganeura consume the same normalized floating-point tensor and checkpoint
parameters. The predeclared acceptance thresholds are relative $L_2\leq
0.01$, CLS cosine $>0.999$, and every patch-token cosine $>0.999$.
Table~\ref{tab:dinovision-correctness} shows that the deployed three-layer
graph and the complete twelve-layer control pass. Exact-shape CPU tests also
cover the attention and LayerScale operators.

\begin{table}[t]
  \centering
  \caption{Meganeura encoder agreement with the independent reference.}
  \label{tab:dinovision-correctness}
  \begin{tabular}{rccc}
    \hline
    Encoder depth & Relative $L_2$ & CLS cosine & Worst patch cosine \\
    \hline
     1 & 0.000781 & 1.000000 & 0.999990 \\
     3 & 0.001403 & 1.000000 & 0.999995 \\
    12 & 0.002253 & 0.999997 & 0.999996 \\
    \hline
  \end{tabular}
\end{table}

The failure suggests a reusable Meganeura hardening change: graph construction
should validate the rank, flattened element count, channel/spatial product,
and gate length of per-channel multiplication. The compiler revision used
here derives dispatch length from the first shape coordinate, so the invalid
layout otherwise fails silently.

\paragraph{Deployment gate.}
The corrected graph cross-compiles for Android API 34/aarch64, including the
native evaluator, benchmark, and XR APK. A fixed-frame comparison on Quest 3S
passes before timing: patches are bit-identical; encoder output has relative
$L_2=0.001059$, cosine 0.99999944, and minimum token cosine 0.999973; spatial
decoder input has relative $L_2=0.001063$; and reconstruction has relative
$L_2=0.000450$ and cosine 0.99999994. We do not report historical headset
numbers: they were generated by the invalid graph and a minimum-of-samples
timing protocol. Device performance is admissible only after (1) three fresh
native processes each retain at least 20 synchronized samples after warmup;
and (2) a live-worn XR sweep records all predeclared submission-chunk settings
with power, thermal, display, and runtime state. Until those timing gates pass,
DinoVision supports Android numerical-correctness but not device-performance
claims.

The public artifact at
\texttt{https://huggingface.co/mad-bot/dinovision} contains the selected and
replicate decoder weights, per-image held-out records, immutable manifests,
independent-reference tensors, and exact source snapshots. It deliberately
excludes the gated DINOv3 checkpoint, dataset photographs, and private headset
captures.
