Cortex Hierarchy

examples/cortex_hierarchy.py builds two linked SILVA cortex equilibrium points. The first point contains a ten-layer internal state network and uses Picard iteration with \(\alpha=0.5\). The second point uses a different transition network, Anderson acceleration, and \(\alpha=0.2\).

python examples/cortex_hierarchy.py

The model computes

\[ u_0=R_\phi(x), \qquad z_1^\star=F_{\theta_1}(z_1^\star,u_0), \qquad u_1=\tanh(z_1^\star), \qquad z_2^\star=F_{\theta_2}(z_2^\star,u_1), \qquad \hat y=R_\psi(z_2^\star). \]

The solver steps are damped independently:

\[ z_{\ell,k+1} = (1-\alpha_\ell)z_{\ell,k} +\alpha_\ell F_{\theta_\ell}(z_{\ell,k},u_{\ell-1}). \]

The output prints the selected device, logits shape, state shapes, solver names, alphas, and a small training loss.

model = SILVACortexNetwork(
    [
        SILVACortexLayer(
            input_dim=5,
            state_dim=14,
            state_network=deep_state_network(14, depth=10),
            self_terms=torch.nn.Linear(14, 14, bias=False),
            config=SolverConfig(solver="picard", max_iter=5, alpha=0.5),
        ),
        SILVACortexLayer(
            input_encoder=torch.nn.Linear(14, 10),
            state_dim=10,
            state_network=torch.nn.Sequential(
                torch.nn.Linear(10, 20),
                torch.nn.GELU(),
                torch.nn.Linear(20, 10),
            ),
            config=SolverConfig(solver="anderson", max_iter=5, alpha=0.2, history=3),
            normalize=False,
        ),
    ],
    links="tanh",
    head=torch.nn.Linear(10, 2),
)

Use Cortex Hierarchies for the derivation and the image-cortex preset.

For each point, inspect its own state shape, residual trajectory, convergence flag, and parameter gradients. The five-iteration settings make this a compact architecture validation; they are not evidence that both points meet a strict equilibrium tolerance. Architecture sources are listed in Point Architecture Sources.

Complete Worked Study

The short construction above identifies the main API. A complete study must also distinguish the state equation, task objective, numerical residual, gradient path, and scale transfer. In this example, the equilibrium state is one image tensor per resolution or linked SILVA point, the condition is image features and per-scale source injections, and the repeated map is shape-preserving convolutional, U-Net, attention, or multiscale fusion blocks.

Derivation From Transition to Reported Result

The forward solve is defined by

\[ z^\star = T_\theta(z^\star,x). \]

The task output and task objective are separate from convergence:

\[ \widehat y = R_\phi(z^\star), \qquad \mathcal L_{\mathrm{task}}=\ell(\widehat y,y). \]

For a computed state \(z_K\), the normalized fixed-point residual is

\[ r_K = \frac{\lVert T_\theta(z_K,x)-z_K\rVert_2} {\lVert z_K\rVert_2+\varepsilon}. \]

A small task loss does not imply a small \(r_K\), and a small \(r_K\) does not establish task quality. Both belong in the result. For implicit training, the parameter sensitivity follows

\[ \frac{\mathrm d z^\star}{\mathrm d\theta} = \left(I-\partial_z T_\theta(z^\star,x)\right)^{-1} \partial_\theta T_\theta(z^\star,x). \]

This is why the example checks gradients in addition to forward convergence. The reader-facing evidence for this route is per-point state shapes, solver choices, logits, loss, and gradients. The invariants that must remain true are channel/spatial shape at every scale and deterministic fusion.

Run the Complete Example

python examples/cortex_hierarchy.py

Measured Compact Output

The following output was produced by the executable program in the current repository. Floating-point values may vary slightly across devices and library builds, while shapes, finite values, invariants, and declared tolerances must remain stable.

device cpu
logits_shape (6, 2)
state_shapes [(6, 14), (6, 10)]
solvers ['picard', 'anderson']
alphas [0.5, 0.2]
final_loss 0.54819655418396

Interpret the Output

Evidence	What it answers	What would require investigation
Tensor shapes	Did every source, state, branch, and readout preserve its declared contract?	A changed entity, channel, token, or spatial dimension
Task metric	Did the compact task execute and produce finite evidence?	Non-finite loss, a missing mask, or a metric computed on the wrong split
Fixed-point residual	Did the returned state satisfy the repeated transition to the requested tolerance?	A residual plateau, rising trajectory, or convergence flag inconsistent with the value
Iteration or trajectory data	How much numerical work was required?	Solver effort that grows sharply under a small input or resolution change
Gradient evidence	Can the loss reach every trainable component through the selected backward mode?	Missing, non-finite, or implausibly large gradients
Domain invariant	Did the method retain positivity, feasibility, boundary values, permutation behavior, or another structural requirement?	A task metric that looks acceptable while the structural contract fails

The compact output is a mechanism check, not a paper-scale benchmark claim. It shows that data enter the intended construction, the transition executes, the solver returns diagnostics, and differentiation reaches trainable parameters.

Add a Solver and Scale Sweep

The next run should hold model parameters and data fixed while changing one numerical control at a time. A complete experiment record can use this schema:

experiment:
  example: cortex-hierarchy
  state: one image tensor per resolution or linked SILVA point
  condition: image features and per-scale source injections
  repeated_transition: shape-preserving convolutional, U-Net, attention, or multiscale fusion blocks
  invariant_checks: channel/spatial shape at every scale and deterministic fusion
  compact_evidence: per-point state shapes, solver choices, logits, loss, and gradients
  scale_axes: image resolution, channels, scales, internal depth, and batch size
solver_sweep:
  methods: [picard, anderson, broyden]
  tolerances: [1.0e-4, 1.0e-6, 1.0e-8]
  maximum_iterations: [25, 50, 100]
report:
  - task_metric
  - fixed_point_residual
  - backward_linear_residual
  - iterations
  - wall_time
  - peak_memory
  - gradient_norm

At full scale, move toward a heterogeneous linked-point architecture on the target dataset. Increase only one of image resolution, channels, scales, internal depth, and batch size at a time. Retain this compact run as a regression test, preserve the source split and preprocessing receipt, archive the resolved configuration and checkpoint, and report convergence failures rather than discarding them.

Where to Go Next

Question	Page
How is the hierarchy derived?	Cortex Hierarchies
How are points placed across devices?	Stacking and Devices
Which architecture containers are public?	Architectures API