# Albert-MoE-13: Ground Truth Learning Substrate Execution Test Report

> **HISTORICAL ARTIFACT — 2026-05-03.** This report confirmed that the candle tensor backend could perform gradient-based learning — the first proof that the TIS stack could host a real neural model. That milestone is long past; albert. now runs 17L ternary MoE training at Global Epoch 1189+ on Modal T4 GPU. Preserved as a development record.

## 1. Single Parameter Learning Experiment
*   Target: $y = w \cdot x$
*   Dataset: $x=2.0, y^*=10.0$
*   Optimizer: SGD, $\eta=0.1$

## 2. Test Execution Log
*   Initial w: 0.0000
*   Step 0: w=4.0000, loss=100.0000
*   Step 10: w=5.0000, loss=0.0000
*   Final w: 5.0000

## 3. Results Verification
*   Loss Decrease: Confirmed (100.0 -> 0.0)
*   Weight Change: Confirmed (0.0 -> 5.0)
*   Gradient Flow: Verified (successful SGD step)
*   Optimizer Effect: Verified

## 4. Final Verdict
*   **REAL LEARNING SUBSTRATE CONFIRMED**

The system successfully demonstrates gradient-based learning using a tensor compute backend (`candle`). The TIS stack is fundamentally capable of neural parameter updates, validating the transition path to a Ternary Transformer LLM.
