# Continuous Neural Engine: Training & Alignment Plan **Author Byline:** J. Kornreich and Collaborators **Target Hardware:** 4x NVIDIA Blackwell Pro Cluster ($10.00/hr) / Local A100 Baseline **Document Ref:** REF 5376-TRN · Epistemic Governor Specification --- ## Executive Summary This document establishes the formal **Training, Fine-Tuning, and Latent Attunement Curriculum** to operationalize the Gemstone Continuous Neural Engine. Rather than performing monolithic full-parameter pretraining, this curriculum employs **Representation Engineering (RepEng)**, **Multi-Token Prediction (MTP) Speculative Head Training**, and **Syntax-Aware Contrastive Alignment** to achieve zero-decode concept attention and deterministic epistemic grounding. ``` ┌──────────────────────────────────────────────────────────────────────────────────────────────────┐ │ 5-STAGE TRAINING CURRICULUM │ ├──────────────────────────────────────────────────┬─────────────────┬──────────────┬──────────────┤ │ Training Stage │ Target Weights │ Compute Time │ Est. Cost │ ├──────────────────────────────────────────────────┼─────────────────┼──────────────┼──────────────┤ │ 1. Contrastive Activation Direction Extraction │ Layer 40 Tensors│ 1.5 Hours │ $15.00 │ │ 2. Dense Concept Vault Embedding & Unit Mapping │ 13.6k Centroids │ 1.0 Hour │ $10.00 │ │ 3. Speculative MTP Multi-Head Training │ 3x Draft Heads │ 3.5 Hours │ $35.00 │ │ 4. SABS Epistemic Refusal & Boundary Alignment │ LoRA Adapters │ 2.0 Hours │ $20.00 │ │ 5. Closed-Loop Lyapunov Stability Benchmarking │ Verification Rig│ 1.0 Hour │ $10.00 │ ├──────────────────────────────────────────────────┴─────────────────┼──────────────┼──────────────┤ │ TOTAL TRAINING BUDGET (4x BLACKWELL PRO @ $10.00/HR) │ 9.0 Hours │ $90.00 │ └────────────────────────────────────────────────────────────────────┴──────────────┴──────────────┘ ``` --- ## Stage 1: Contrastive Activation Extraction (Representation Engineering) ### 1.1 Objective Extract the canonical 5,176-dimensional epistemic invariant steering vector $\mathbf{v}_{\text{doctrine}} \in \mathbb{R}^{5376}$ from Gemma 4 31B's Layer 40 using `quivent/signal-extraction`. ### 1.2 Dataset Construction We construct a paired contrastive corpus $\mathcal{D} = \{(x_i^+, x_i^-)\}_{i=1}^{N}$ of $N = 2,500$ paired prompts: * **Positive Prompts ($x^+$):** Authoritative, filesystem-grounded architectural doctrine (*"Source Wins", strict AST boundary compliance, factual verification*). * **Negative Prompts ($x^-$):** Sycophantic, hallucinated, prompt-stuffed, and ungrounded speculative completions. ### 1.3 Mathematical Formulation Using the Layer 40 forward tap (`l_out-40`), we compute the first principal component of the difference-in-means: $$\mathbf{v}_{\text{raw}} = \frac{1}{N} \sum_{i=1}^N \Big( \mathbf{h}_{40}(x_i^+) - \mathbf{h}_{40}(x_i^-) \Big)$$ $$\mathbf{v}_{\text{doctrine}} = \frac{\mathbf{v}_{\text{raw}}}{\|\mathbf{v}_{\text{raw}}\|_2} \quad \text{such that} \quad \|\mathbf{v}_{\text{doctrine}}\|_2 \equiv 1.000000$$ --- ## Stage 2: Dense Concept Vault Pre-Computation ### 2.1 Objective Encode all 13,634 memory blocks into a contiguous $141.2\text{ MB}$ dense tensor matrix pinned in GPU VRAM for sub-millisecond GEMV search. ### 2.2 Execution Pipeline 1. Iterate across all 13,634 memory blocks $S_k \in \mathcal{S}$. 2. Pass each block through Gemma's embedding model to extract its centroid vector $\mathbf{v}(S_k) \in \mathbb{R}^{5376}$. 3. Apply L2 unit normalization: $$\hat{\mathbf{v}}(S_k) = \frac{\mathbf{v}(S_k)}{\|\mathbf{v}(S_k)\|_2}$$ 4. Concatenate into a contiguous tensor: $$\mathbf{V}_{\text{vault}} \in \mathbb{R}^{13634 \times 5376} \quad (\text{Data Type: } \text{torch.float16})$$ 5. Validate SIMD dot-product search over 13,634 blocks: $$\text{Score}_k = \mathbf{V}_{\text{vault}} \cdot \mathbf{h}_t \quad (\text{Target Latency: } \le 254\mu\text{s})$$ --- ## Stage 3: Speculative Multi-Token Prediction (MTP) Training ### 3.1 Objective Train 3 lightweight linear draft heads on top of Layer 80 hidden states to draft tokens $t+1, t+2, t+3$ concurrently, elevating generation speed to $> 500\text{ tok/s}$. ### 3.2 Loss Function We optimize the multi-head cross-entropy objective over a 50,000-token canonical Go/Python/Systems corpus: $$\mathcal{L}_{\text{MTP}} = \sum_{k=1}^3 \lambda_k \cdot \mathcal{L}_{\text{CE}}\big(y_{t+k}, \text{Softmax}(\mathbf{W}_k \mathbf{h}_{80}(t))\big)$$ Where $\lambda_1 = 1.0, \lambda_2 = 0.8, \lambda_3 = 0.6$. --- ## Stage 4: SABS Syntax-Directed Epistemic Refusal Alignment ### 4.1 Objective Fine-tune low-rank adapters (LoRA, $r=16, \alpha=32$) on Gemma 4 31B to enforce **zero-hallucination epistemic refusal** when required pointers are unresolved in the active working set. ### 4.2 Triplet Loss Formulation $$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{LM}} + \alpha \mathcal{L}_{\text{boundary}} + \beta \mathcal{L}_{\text{Lyapunov}}$$ 1. **Boundary Penalty ($\mathcal{L}_{\text{boundary}}$):** Penalizes output tokens that cross outside the SABS 128-byte padding window without valid AST closure. 2. **Lyapunov Error Descent ($\mathcal{L}_{\text{Lyapunov}}$):** Enforces that each generation step decreases residual error relative to the target doctrine: $$\mathcal{L}_{\text{Lyapunov}} = \max\Big(0, \|\mathbf{h}_{t+1} - \mathbf{v}_{\text{doctrine}}\|_2 - \|\mathbf{h}_t - \mathbf{v}_{\text{doctrine}}\|_2\Big)$$ --- ## Stage 5: Verification & Acceptance Milestones ``` ┌──────────────────────────────────────────────────┬─────────────────┬────────────────────────┐ │ Verification Milestone │ Pass Criterion │ Empirical Target │ ├──────────────────────────────────────────────────┼─────────────────┼────────────────────────┤ │ M1: Manifold Orthogonality (D=5376) │ Res(v_syn) │ >= +0.3800 │ │ │ Res(v_ortho) │ <= +0.0200 │ ├──────────────────────────────────────────────────┼─────────────────┼────────────────────────┤ │ M2: Zero-Decode GEMV Search Latency │ 13.6k Blocks │ <= 254 μs │ ├──────────────────────────────────────────────────┼─────────────────┼────────────────────────┤ │ M3: Epistemic Refusal on Non-Existent Symbols │ Hallucination % │ 0.0% (Strict Refusal) │ ├──────────────────────────────────────────────────┼─────────────────┼────────────────────────┤ │ M4: 4-Turn Dialogue Turn Latency (4x Blackwell) │ Total Time/Turn │ <= 0.25 seconds │ └──────────────────────────────────────────────────┴─────────────────┴────────────────────────┘ ```