# Albert-MoE-13: Capability Reality Report

> **HISTORICAL ARTIFACT — 2026-05-03.** This report was written when albert. was a symbolic hash-dispatch system with no neural weights. It is preserved as a development record. albert. is now a fully neural ternary transformer (17L, ep1189+, epoch-ATL 10.1993). All findings in sections 1–6 below are superseded by the v3.0 run. See `docs/convergence_log.md` for current state.

## 1. System Decomposition
*   **Computational Foundation (Real)**: Robust deterministic auditing, structured file-system management, and binary weight serialization handling.
*   **Logic Layer (Symbolic)**: Input-to-scalar hashing mechanism, modular REPL infrastructure, and static response templates.
*   **Neural Layer (Missing)**: No semantic vector space, attention mechanisms, autoregressive token generation, or learned weights.

## 2. MoE Validity Verdict
*   **Status**: SYMBOLIC/NON-NEURAL
*   **Explanation**: The "Mixture of Experts" architecture is currently modeled through symbolic branch selection based on scalar hashing rather than gated probability distributions across neural experts.

## 3. Checkpoint Truth Classification
*   **Status**: NON-NEURAL STATE
*   **Explanation**: `snapshot.bin` contains encoded scalar constants mapped to ternary trits. It lacks the multidimensional parameter structures, gradients, or activation functions characteristic of learned neural weights.

## 4. LLM Gap Analysis
*   **Core Missing Capability**: Autoregressive decoding. The system does not predict next-token probabilities; it executes a deterministic mapping from input-hash to static response.
*   **Semantic Limitation**: The lack of a learned embedding space prevents the system from understanding linguistic context, nuance, or semantic similarity.

## 5. Minimal Upgrade Path
1.  **Orchestrator Preservation**: Retain `albert-test` as the shell.
2.  **Tensor Integration**: Import `candle` or `burn` into the `MoEInferenceKernel`.
3.  **Weight Swap**: Replace the current trit-stream parser with a Safetensors loader for trained LLM weights.
4.  **Inference Swap**: Update the `forward` method to run a full Transformer block instead of a hash-sum activation.

## 6. Final System Category Label
*   **Classification**: Deterministic Symbolic Inference Engine
    *   Note: While infrastructure-ready, the system does not currently contain learned neural properties.
