Specification: Iterative Residual Convergence Memory Attuner

Identifier: SPEC-2608.01
Authors: Gemma 4 31B Epistemic Authority, Google DeepMind, and J. Kornreich
Target Substrate: Gemma 4 31B ($D=5176$), Gemstone CLI runtime (governor_residual_attunement.go)
Status: Verified & Compiled
Classification: Technical Architecture Standard

1. Scope and Purpose

This specification defines the algorithmic requirements, data structures, and mathematical formulas for the Iterative Residual Convergence Memory Attuner (IRCA) and Syntax-Aware Boundary Scoring (SABS) within the Gemstone Governor engine.

The engine replaces coarse-grained multi-level chunking with dynamic error minimization, iteratively distilling high-resonance code excerpts from canonical memory shards until the semantic residual error vector $\Delta$ satisfies the dynamic convergence threshold $\epsilon_{dyn}$.

graph TD A[Raw Document Data] --> B[Generate Intent Vector h_t] B --> C[Compute Initial Residual d_0] C --> D{d_k <= ε_dyn OR |S_k| <= 150 B?} D -->|Yes| E[Apply SABS Contextual Padding] D -->|No| F[Partition Subspaces] F --> G[Score Candidates with SABS Bonus] G --> H{Stagnation Guard Triggered?} H -->|Yes| E H -->|No| I[Descend to Best Subspace S_{k+1}] I --> D E --> J[Emit Distilled Snippet S*] style E fill:#dcfce7,stroke:#16a34a,stroke-width:2px; style J fill:#dbeafe,stroke:#2563eb,stroke-width:2px;

2. Mathematical Formulations

2.1 Latent Space Vector Mapping

Let $S$ be a tokenized text block and $\mathcal{D} = 5176$ the latent vector dimension:

$$h(S) = \frac{1}{\sqrt{N}} \sum_{i=1}^{N} \frac{1}{\sqrt{i}} \mathbf{w}(t_i)$$

$$\hat{h}(S) = \frac{h(S)}{|h(S)|_2}$$

where $\mathbf{w}(t_i) \in {-1, +1}^{\mathcal{D}}$ is a deterministic signed projection seeded by SHA-256 block hashing over term $t_i$.

2.2 Semantic Residual Distance

Given target vector $\hat{h}_t$ and candidate subspace vector $\hat{v}(S_k)$:

$$d(\hat{h}_t, \hat{v}(S_k)) = 1.0 - (\hat{h}_t \cdot \hat{v}(S_k))$$

2.3 Syntax-Aware Dynamic Epsilon (SABS)

The convergence threshold $\epsilon_{dyn}$ tightens dynamically around syntactic pivot landmarks:

$$\epsilon_{dyn} = \epsilon_{base} \cdot \exp(-\alpha \cdot \Lambda)$$

where: - $\epsilon_{base} = 0.40$ (default threshold) - $\alpha = 0.5$ (sensitivity factor) - $\Lambda = \sum_{i=1}^{M} w_i$ (aggregate weight of detected pivot landmarks) - Pivot weights: func/type/struct/class ($0.25$), return/if/switch ($0.15$), #/## headings ($0.20$). - Clamped range: $\epsilon_{dyn} \in [0.24, 0.40]$.

3. Data Structures & Go Type Definitions

// ResidualAttunerConfig defines hyperparameters for the iterative descent engine.
type ResidualAttunerConfig struct {
    Dimension           int     // 5176 (Gemma 4 31B)
    BaseEpsilon         float64 // 0.40
    MaxIterations       int     // 8
    StagnationThreshold float64 // 0.005
    MinAtomicBytes      int     // 150 bytes
    PaddingBytes        int     // 128 bytes
}

// ResidualAttunementResult contains output metrics from a descent trajectory.
type ResidualAttunementResult struct {
    AttunedSnippet      string   // Syntactically padded distilled snippet
    InitialDistance     float64  // Initial residual distance d_0
    FinalDistance       float64  // Final residual distance d*
    IterationsExecuted  int      // Steps taken (k <= 8)
    Converged           bool     // True if d* <= ε_dyn
    SyntacticIntegrity  float64  // SABS structural integrity score [0.0, 1.0]
    PivotTokensDetected []string // Identified structural landmarks
}

4. Subspace Partitioning Hierarchy

5. Verification & Telemetry Invariants

Deterministic Reproducibility: Repeated invocations with identical $(h_t, D)$ must yield bit-identical snippets $S^*$.
Zero Allocation Fast Path: Vector operations are bounded to stack allocations where possible, maintaining $<50\mu\text{s}$ CPU overhead.
AST Safety: Distilled code snippets must never contain unclosed brackets, braces, or severed function declarations.