silva_consistency_deq Reproduction Dossier

trajectory-distilled equilibrium accelerator. This dossier connects the source mechanism to its SILVA implementation, compact evidence, replaceable components, and source-scale route. Existing tests and notebooks remain the executable authority.

Evidence boundary

The mechanism is compact-verified in the package suite. The final source-scale stage remains planned until the cited data, complete optimization budget, checkpoints, and evaluation protocol have actually run.

Identity and Sources

Field	Value
Domain	vision and generation
Task contract	condition plus fixed teacher transition -> one- or few-step equilibrium estimate
Source relation	paper-adaptation
References	[59]
Repositories	https://github.com/landrarwolf/CDEQ
Editable scale plan	experiments/reproduction/configs/silva_consistency_deq.json

Governing Equation

The domain-level state contract is

\[ z^\star=T_\theta(z^\star;\mathcal E(x),c),\qquad \widehat y=\mathcal D_\psi(z^\star). \]

The implementation registry specializes it operationally as

g_phi(z_t,t,x)=c_skip(t)z_t+c_out(t)P_phi(z_<=t,t,x)

Define the root residual

\[ R_\theta(z;x)=z-T_\theta(z;x). \]

At a regular equilibrium, differentiating \(R_\theta(z^\star;x)=0\) gives

\[ \frac{\partial z^\star}{\partial x} = \left(I-\frac{\partial T_\theta}{\partial z}\right)^{-1} \frac{\partial T_\theta}{\partial x}. \]

This identity explains why the forward residual, the conditioning derivative, and the adjoint linear solve must be diagnosed separately from the task metric.

What Is Preserved

• fixed initial state and solver-induced teacher trajectory
• terminally anchored consistency parameterization
• two-state Anderson-structured refinement and local/global consistency losses

What Can Be Replaced

Each item below is an explicit control rather than an undocumented modification:

• replace teacher transition, refiner, time schedule, Anderson structure, or readout
• attach task losses and an exponential-moving-average target
• teacher solver trajectory cache
• history window
• student width
• inference steps

Constructor and Shape Contract

silva_consistency_deq(state_dim: 'int', condition_dim: 'int', *, teacher_transition: 'nn.Module', initializer: 'Callable[[Tensor], Tensor] | None' = None, refiner: 'nn.Module | None' = None, readout: 'nn.Module | None' = None, epsilon: 'float' = 0.002, terminal_time: 'float' = 1.0, gamma: 'float' = 2.0, rho: 'float' = 0.1, anderson_beta: 'float' = 0.9, teacher_config: 'SolverConfig | None' = None)

The transition must preserve the declared equilibrium-state shape even when the encoder, branch operators, constraints, solver, and readout are replaced. Test the transition by itself before testing the complete root solve.

Progressive Experiment Ladder

1. Equation and tensor contract

Objective: Make the state, conditioning variables, operator, and readout explicit.

Procedure:

• Write and evaluate the family equation: g_phi(z_t,t,x)=c_skip(t)z_t+c_out(t)P_phi(z_<=t,t,x)
• Declare every tensor axis, boundary, mask, graph, or physical unit.
• Check the transition output has exactly the same shape as the equilibrium state.

Acceptance checks:

• finite transition values
• shape-preserving state update
• all conditioning variables affect the intended branch

Evidence target: contract-verified.

2. Primitive mechanism reconstruction

Objective: Build the retained source mechanism from replaceable modules.

Procedure:

• fixed initial state and solver-induced teacher trajectory
• terminally anchored consistency parameterization
• two-state Anderson-structured refinement and local/global consistency losses

Acceptance checks:

• primitive modules expose trainable parameters and gradients
• mechanism-specific invariance or constraint check passes
• direct transition evaluation is deterministic under a fixed seed

Evidence target: compact-verified.

3. Public abstraction equivalence

Objective: Verify that the assembled family evaluates the same transition as its primitives.

Procedure:

• Copy the primitive module parameters into the public family constructor.
• Evaluate one transition and one complete equilibrium with identical inputs.
• Compare outputs, residuals, and parameter gradients with declared tolerances.

Acceptance checks:

• transition outputs agree
• equilibrium residual is finite and decreases
• primitive and assembled gradients agree on the compact case

Evidence target: compact-verified.

4. Compact real or analytic task

Objective: Exercise training, evaluation, diagnostics, and serialization end to end.

Procedure:

• make_consistency_teacher_dataset gives exact contractive teacher equilibria and solver trajectories.

Acceptance checks:

• record task metric
• record one/few-step equilibrium error
• record local/global consistency
• record teacher evaluations
• checkpoint reload reproduces the recorded prediction
• result record contains data and configuration fingerprints

Evidence target: compact-verified.

5. Official-data subset

Objective: Validate the complete source data path before spending the full budget.

Procedure:

• Acquire one official task and reproduce its teacher preprocessing and evaluation first.
• Freeze preprocessing, split logic, metric code, and checkpoint format.
• Run a deterministic subset large enough to expose batching and memory failures.

Acceptance checks:

• dataset receipt and checksum are stored
• resume and evaluation paths reproduce the same subset metric
• memory and runtime are measured rather than estimated

Evidence target: subset-verified.

6. Source-scale reproduction or declared extension

Objective: Run the cited protocol, or change it explicitly as a SILVA extension.

Procedure:

• Acquire one official task and reproduce its teacher preprocessing and evaluation first.
• Load the teacher checkpoint into the matching SILVA transition and cache deterministic solver trajectories.
• Train the refiner with global/local consistency and an EMA target, then sweep one, two, and few-step inference against teacher quality and latency.
• pretrained teacher checkpoint and exact solver settings
• cached trajectories, time mapping, augmentation, optimizer, EMA, and task protocol
• WikiText-103, ImageNet, or OGB preprocessing and published evaluation budget

Acceptance checks:

• all required artifacts are archived
• reported metrics use the cited evaluation protocol
• every architectural or training deviation is listed
• claims match the achieved evidence status

Evidence target: planned.

Data, Access, and Storage

Candidate datasets:

• WikiText-103
• ImageNet
• ogbn-arxiv
• ogbn-products
• analytic contractive teacher trajectories

Authoritative routes:

• https://github.com/landrarwolf/CDEQ
• https://www.salesforce.com/blog/the-wikitext-long-term-dependency-language-modeling-dataset/
• https://ogb.stanford.edu/docs/nodeprop/
• https://www.image-net.org/

Access obligations:

• WikiText-103 and OGB provide public acquisition routes under their stated terms.
• ImageNet requires registration and acceptance of its access terms.
• Record the teacher and consistency-checkpoint revisions separately from the dataset checksum.

Storage planning:

• Teacher cache bytes = samples * stored solver states * state elements * bytes per element.
• For example, 1,000,000 vector samples with 8 stored 512-float32 states require about 15.3 GiB before labels, indices, and checkpoints.

Preprocessing record:

• record dataset version, split, normalization, shape convention, and seed
• preserve masks, graph indices, boundaries, or physical units required by the domain

Metrics and Current Evidence

Required metrics:

• task metric
• one/few-step equilibrium error
• local/global consistency
• teacher evaluations
• latency

This family is verified through its listed mechanism tests and executed notebook. It is not included in a same-task comparison when another family does not share its input, state, output, and loss contract. The absence of a comparison row is therefore a scope decision, not missing implementation evidence.

Executed notebook paths:

• notebooks/package_api/28_silva_consistency_deq.ipynb

Mechanism tests:

• tests/test_emerging_equilibria.py

Compact Defaults

Option	Value
tier	'smoke'
teacher_config	SolverConfig(solver='anderson', max_iter=12, tol=1e-05, alpha=1.0, history=3, ridge=0.0001, beta=1.0, stop_mode='relative', relative_eps=1e-08, anderson_batch_dims=1, track_residuals=True, reengage=True, backward_mode='implicit', backward_solver='gmres', backward_max_iter=20, backward_tol=1e-05, backward_stop_mode='relative', backward_relative_eps=1e-08, phantom_steps=1, phantom_tau=1.0, neumann_terms=5, shine_refine_steps=0, indexing=(), return_best=True)

Full Defaults

Option	Value
tier	'full'
teacher_config	SolverConfig(solver='anderson', max_iter=60, tol=1e-05, alpha=1.0, history=6, ridge=0.0001, beta=1.0, stop_mode='relative', relative_eps=1e-08, anderson_batch_dims=1, track_residuals=True, reengage=True, backward_mode='implicit', backward_solver='gmres', backward_max_iter=80, backward_tol=1e-05, backward_stop_mode='relative', backward_relative_eps=1e-08, phantom_steps=1, phantom_tau=1.0, neumann_terms=5, shine_refine_steps=0, indexing=(), return_best=True)

Defaults establish a starting budget; the cited source protocol takes precedence whenever reproduction is the claim.

Source-Scale Checklist

• Acquire one official task and reproduce its teacher preprocessing and evaluation first.
• Load the teacher checkpoint into the matching SILVA transition and cache deterministic solver trajectories.
• Train the refiner with global/local consistency and an EMA target, then sweep one, two, and few-step inference against teacher quality and latency.

Benchmark-specific requirements:

• pretrained teacher checkpoint and exact solver settings
• cached trajectories, time mapping, augmentation, optimizer, EMA, and task protocol
• WikiText-103, ImageNet, or OGB preprocessing and published evaluation budget

Required archived artifacts:

• machine-readable model and solver configuration
• dataset receipt with source revision, split, license, and checksum
• preprocessing and normalization record
• seeded training and evaluation log
• checkpoint and optimizer-resume state for trained experiments
• task metrics and equilibrium diagnostics in a machine-readable result
• runtime, peak-memory, device, precision, and dependency record
• declared deviations from the cited protocol

Reporting Rule

Report the achieved evidence status, not the intended one. A compact or subset run may validate the implementation and data path, but only a completed cited protocol supports a source-scale reproduction statement. Modified operators are valuable SILVA extensions when every deviation is named and measured.

Where to Go Next

Question	Page
Where are all family dossiers?	Family Dossier Index
How is a custom family assembled?	Advanced Extension Handbook
How are experiment stages represented in the API?	Research-Depth API
Which lab inspects every dossier?	Family Dossier Lab