protocol: require BP-free hardware-local debiasing

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+# BP-free and hardware-locality contract
+
+This contract applies to every result in the active two-state structured-bias
+program. A run cannot be labelled SDIL or hardware-local merely because the
+mathematical rule has a local interpretation. The executable learning path
+must satisfy the checks below.
+
+## Information available to the debiaser
+
+For one cell, population or physical edge, the debiaser may use only:
+
+- its local neutral state or fixed local basis features `phi(z_neutral)`;
+- its locally observed instruction-off teaching channel `a_neutral`;
+- its locally observed task-period channel `a`;
+- its own predictor coefficients and locally stored traces;
+- the native backbone eligibility already available at that synapse or edge;
+- a local phase/neutral gate.
+
+The frozen learning rule is
+
+```text
+prediction = P phi(z)
+r = a - prediction
+Delta w = eta_w r eligibility
+Delta P = eta_P (a_neutral - P phi(z_neutral)) phi(z_neutral)^T
+```
+
+`P` may be an affine or fixed-basis adaptive filter. A multilayer predictor
+trained by reverse-mode differentiation is outside the method. Fixed nonlinear
+basis functions are allowed only when every adaptive coefficient still has the
+local LMS update above.
+
+## Forbidden training information
+
+No SDIL training update may read or be derived from:
+
+- a task-loss gradient or hidden-state BP gradient;
+- a copied clean, centered, oracle or BP update;
+- a downstream weight, its transpose, or a reverse-mode computation graph;
+- a task-period target for the neutral predictor;
+- a global loss difference used to supervise the predictor;
+- an opposite-sign phase introduced only to create a debiasing target;
+- a backbone-specific learned controller trained with BP.
+
+BP, centered/oracle states and clean updates may be computed only in a clearly
+separate diagnostic process after the learning update has already been fixed.
+
+## Executable audit
+
+Every backbone adapter must pass all of the following before a task endpoint:
+
+1. **No-grad training:** the SDIL update executes with autograd disabled and
+   no learned tensor requiring gradients.
+2. **Local replay:** recording the local tuple `(z_neutral, a_neutral, z, a,
+   eligibility)` is sufficient to reproduce the update bitwise without the
+   model, label, task loss or downstream layers.
+3. **Downstream independence:** after the tuple is recorded, randomizing all
+   downstream parameters, labels and loss code leaves the update bitwise
+   unchanged.
+4. **Instruction isolation:** changing the task instruction while holding a
+   stored neutral tuple fixed cannot change the predictor update.
+5. **Manual/autograd equivalence:** if an author simulator uses autograd to
+   evaluate a mathematically local energy derivative, a test-only comparison
+   must show that the hand-written local formula agrees. Paper-facing training
+   uses the manual path.
+6. **Common-mode cancellation:** the same offset in both states cancels before
+   SDIL and produces no claimed gain.
+7. **Phase and storage audit:** every neutral observation, state copy,
+   predictor coefficient, multiplication and update is counted.
+
+Passing these checks establishes algorithmic BP freedom and an executable
+local information path. It does not by itself establish that a particular
+analog circuit has sufficient precision or energy advantage.
+
+## Hardware primitives
+
+The rule requires only the following primitive operations at each adaptive
+site:
+
+| operation | possible local realization |
+|---|---|
+| store `P` or a trace | capacitor, memristive state, SRAM/register |
+| form `a - P phi(z)` | differential amplifier or local subtractor |
+| multiply local factors | analog multiplier, coincidence circuit, local MAC |
+| integrate `Delta P` and `Delta w` | capacitor current or local accumulator |
+| select neutral/task observation | local phase gate or switch |
+
+Dillavou et al.'s coupled-learning hardware already measures local voltage
+drops, forms products with AD633 analog multiplier circuitry, and integrates
+updates on capacitors. Those are the needed operation classes. SDIL still adds
+predictor state, a prediction path, subtraction and a neutral schedule; the
+published hardware does not already implement that complete circuit. Until it
+is built or circuit-simulated, the correct claim is **implementable from local
+analog primitives**, not **demonstrated on hardware**.
+
+For EP, coupled-learning DCHNs and Dual Propagation, relaxation produces the
+native local teaching/eligibility variables. The SDIL adapter operates on
+those local variables and cannot ask autograd to reconstruct them. An author
+implementation that calls `.backward()` for software convenience is acceptable
+as a native accuracy reference, but not as the paper-facing SDIL training path
+without the manual-equivalence audit.
+
+## Claim language
+
+Allowed after the executable checks:
+
+> SDIL is algorithmically backpropagation-free: its adaptive predictor and
+> synaptic updates use local state, instruction-off measurements and native
+> local eligibilities, with no task-loss reverse pass or weight transport.
+
+Allowed before a physical circuit demonstration:
+
+> The update decomposes into storage, subtraction, multiplication, integration
+> and phase-gating primitives already common in analog local-learning systems.
+
+Not allowed without new evidence:
+
+- “SDIL has been demonstrated on the physical resistor hardware.”
+- “SDIL requires no extra observation, state or circuit.”
+- “Using autograd internally is irrelevant to the hardware claim.”
+- “Every two-state backbone is BP-free because its equations can be written
+  locally.”
+