# Albert-MoE-13: Learning Verification Report

## 1. Minimal Learning Experiment Design (Ternary-Constraint)
*   Target Task: Learn y = Wt * x where Wt is in {-1, 0, 1}.
*   Loss Function: MSE + Ternary Penalty (|W| - 1).
*   Optimizer: SGD with STE (Straight-Through Estimator).
*   Experiment:
    - Initialize W in R (FP32).
    - Forward pass: y = sign(W) * x.
    - Backward: dL/dW approx dL/dy * x (STE).
    - Update: W <- W - eta * dL/dW.

## 2. Verification Requirements
1. Loss Decrease: Delta Loss < 0 over 50 steps.
2. Weight Evolution: W_final != W_initial.
3. Gradient Flow: Grad L != 0.
4. Convergence: Deterministic loss trajectory.

## 3. Failure Detection Framework
*   Static Weight Check: Log W_0 vs W_50; trigger alert if norm(W_50 - W_0) < epsilon.
*   Gradient Auditing: Monitor norm of Grad W during backward pass.
*   Loss Validation: Compare computed loss vs expected theoretical minimum.

## 4. Implementation Boundary Test (Candle/Burn)
*   Goal: Ensure parameter variables in the backend are mutable and registered in the autograd DAG.
*   Indicator: Successful execution of optimizer.step() and subsequent change in param.tensor values.

## 5. Final Decision Framework
*   REAL LEARNING SUBSTRATE CONFIRMED: If gradient flow, weight updates, and loss convergence are sustained.
*   PARTIAL/INCOMPLETE AUTOGRAD: If gradients compute but weights do not update, or optimizer is unlinked.
*   NON-FUNCTIONAL COMPUTE LAYER: If the backend fails to allocate or mutate tensors.
*   SIMULATION ONLY: If the system bypasses compute kernels and manually updates tensors without a functional autograd graph.

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## Architectural Warning
Without a confirmed REAL LEARNING SUBSTRATE, any Transformer implementation is guaranteed to remain a symbolic rule-set. This verification is the final gate before scaling to LLM-scale model topologies.
