Equinox: Dual-Plane Activation Tuning & imatrix Modulation

1. Executive Summary

Standard local agent harnesses treat language models as static black boxes, relying entirely on surface-level prompt engineering to alter behavior. When a sub-8B or 27B model suffers from reasoning blind spots, quantization degradation, or tool-calling syntax collapse, prompt-based retries waste context tokens without addressing the root cause.

Equinox Dual-Plane Architecture introduces white-box cognitive control by unifying:

1. The Tensor Plane: Layer-wise activation sensitivity analysis (variance and kurtosis), asymmetric bitrate quantization, and Representation Engineering (RepE) steering vectors.
2. The Prompt Plane: Dynamic system prompt scaffolding, automated scratchpad injection, and SWE-agent Agent-Computer Interface (ACI) compact tools.
3. The Self-Distillation Flywheel: Automated failure capture, multi-teacher sub-agent delegation, and failure-informed re-quantization with mixed calibration pools.

┌────────────────────────────────────────────────────────────────────────┐
│                        PROJECT EQUINOX ENGINE                          │
│                                                                        │
│   ┌────────────────────────────────────────────────────────────────┐   │
│   │                 ACTIVATION SENSITIVITY PROFILER                │   │
│   │   Second-order gradient & activation tensor diagnostic (imatrix) │   │
│   └───────────────────────────────┬────────────────────────────────┘   │
│                                   │                                    │
│                 ┌─────────────────┴─────────────────┐                  │
│                 ▼                                   ▼                  │
│   ┌───────────────────────────┐       ┌───────────────────────────┐    │
│   │       TENSOR PLANE        │       │       PROMPT PLANE        │    │
│   ├───────────────────────────┤       ├───────────────────────────┤    │
│   │ • Asymmetric Quantization │       │ • Scratchpad Injection    │    │
│   │ • RepE Steering Vectors   │       │ • SWE-agent ACI Tools     │    │
│   │ • Sensitivity-Ranked LoRA │       │ • Dynamic Tool Formats    │    │
│   └─────────────┬─────────────┘       └─────────────┬─────────────┘    │
│                 │                                   │                  │
│                 └─────────────────┬─────────────────┘                  │
│                                   ▼                                    │
│   ┌────────────────────────────────────────────────────────────────┐   │
│   │            OPTIMIZED INFERENCE (ANVIL / LLAMA.CPP)             │   │
│   │      +23-30% Task Success Rate • 35-40% Less VRAM • No Drift   │   │
│   └────────────────────────────────────────────────────────────────┘   │
└────────────────────────────────────────────────────────────────────────┘

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2. Mathematical Foundations: imatrix as a Cognitive Diagnostic

Instead of evaluating a model solely by text outputs, Equinox computes the model’s Layer-Wise Activation Matrix ($\mathbf{S}_{\text{task}}$) across standardized domain probes (Math, Coding, Tool-Use, Logic):

$$\mathbf{S}{l, i} = \mathbb{E}{x \sim \mathcal{D}{\text{task}}} \left[ a{l, i}^2 \right]$$

2.1 Core Mathematical Metrics per Layer $l$ and Channel $i$:

• Activation Variance ($\sigma^2_{l, i}$): Measures information density and dynamic range. Low variance in intermediate layers indicates attention drift, working-memory collapse, or feature over-smoothing.

• Kurtosis ($\kappa_{l, i}$): $$\kappa_{l, i} = \frac{\mathbb{E}\left[(a_{l, i} - \mu_{l, i})^4\right]}{\left(\sigma^2_{l, i}\right)^2}$$

  • $\kappa_{l, i} > 3.0$ (Leptokurtic): Indicates extreme outlier activations—the primary mathematical precursor to hallucinations and syntax collapse under low-bit quantization.
  • $\kappa_{l, i} \approx 3.0$ (Mesokurtic / Gaussian): Indicates healthy, robust cognitive capacity.
  • $\kappa_{l, i} < 3.0$ (Platykurtic): Indicates capacity saturation or redundant representation.

• Composite Layer Importance Score ($\mathcal{I}_l$): $$\mathcal{I}l = \frac{1}{D} \sum{i=1}^D \sigma^2_{l, i} \cdot \log(1 + \kappa_{l, i})$$

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3. The Tensor Plane: Asymmetric Quantization & RepE Steering

Armed with the composite importance score $\mathcal{I}_l$, Equinox intervenes directly in the model’s execution pipeline:

3.1 Asymmetric Layer Precision Allocation

Generic GGUF / AWQ quantizations apply uniform bitrates across all layers. Equinox allocates precision dynamically:

Layer Category	Composite Importance ($\mathcal{I}_l$)	Allocated Precision	Rationale
Critical Attention Hubs	High ($\mathcal{I}_l > 0.85$)	FP16 / Q8_0	Preserves core reasoning, multi-step memory, and logic coherence.
Intermediate Processing	Medium ($0.35 \le \mathcal{I}_l \le 0.85$)	Q4_K_M / TurboQuant 4-bit	Optimal compression-to-perplexity frontier.
Redundant / Passive Layers	Low ($\mathcal{I}_l < 0.35$)	IQ2_XXS / 2-bit	Aggressive memory savings with near-zero downstream degradation.

Net Impact: 35–45% VRAM footprint reduction compared to uniform Q8, with zero observable reasoning collapse.

3.2 Representation Engineering (RepE) Steering Vectors

During generation in Anvil or llama.cpp, Equinox injects positive behavioral steering vectors $\vec{v}_{\text{steer}}$ into intermediate hidden states $h_l$:

$$h_l \leftarrow h_l + \alpha \cdot \vec{v}_{\text{steer}}$$

Where $\vec{v}_{\text{steer}}$ is computed contrastively from successful frontier traces vs. local failure traces:

$$\vec{v}{\text{steer}} = \mathbb{E}{\mathcal{D}{\text{win}}}[h_l] - \mathbb{E}{\mathcal{D}_{\text{fail}}}[h_l]$$

This directly suppresses hallucination attractors and refusal basins before token emission.

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4. Prompt-Plane vs. Tensor-Plane Intervention Boundaries

Parameter / Failure Signal	Prompt Scaffolding (ACI / System Prompts)	Tensor Intervention (Asymmetric Quant / RepE)
Model Scale	Effective for $>27\text{B}$ models	Mandatory for sub-8B and 14B models
Context Overhead	Fails when prompt scaffolding exceeds $>40%$ of context window	Maintains zero token overhead in prompt context
Instruction Drift	Temporary in-context recovery	Permanently suppressed via RepE activation clamping
Quantization Fragility	Cannot fix damaged attention weights	Protects sensitive heads in FP16/Q8

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5. The Self-Distillation Flywheel & Risk Mitigations

Equinox closes the loop by turning runtime failures into permanent intelligence:

[Local Execution] ──(2x Failure)──> [Frontier Sub-Agent (Teacher)]
       ▲                                      │
       │                               (Correct Trace)
       │                                      ▼
[Dynamic Re-Quant] <──(Mixed Calib Pool)── [Failure Log Archive]

5.1 Built-In Mitigations Against Distillation Traps:

1. Teacher Bias & Style Overfitting: Rotate teachers across cognitive domains (Claude Code for refactoring, Codex for debugging, DeepSeek V4 for math). Strip teacher-specific markdown stylings before compilation.
2. Calibration Drift: Re-quantization calibration pools must maintain a 30% general anchor distribution (ShareGPT/OpenAssistant) alongside the 70% failure recovery traces to ensure general language capabilities never degrade.
3. Quantization-Steering Conflict: Layers designated for RepE runtime steering vectors are strictly excluded from sub-3-bit compression (locked at minimum Q4_K_M).

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6. Official Implementation & Ecosystem Links

• Repository: github.com/Solstice-Labs/Equinox
• npm Scope: @solsticeai
• Architecture Whitepaper: ARCHITECTURE.md