Hermetic GeGLU Airlocks and Domain-Adaptive Context Distillation: Exact Zero-Crosstalk Multi-Domain Function Embedding in Quantized GGUF Foundation Models
Year: 2026
Type: preprint
Abstract: Embedding multiple specialized capabilities directly into the weights of a quantized foundation model without degrading its general linguistic and reasoning intelligence is a fundamental challenge in modular deep learning. While prior weight-space programming approaches (including our earlier Orthogonal Soundproof Weight Programming v1 study) construct mutually orthogonal linear subspaces (<h_base, Δh> = 0), live double-blind audits on production C++/CUDA inference (`llama-server.exe`) reveal that linear orthogonal adapters still suffer from severe out-of-domain crosstalk (|Δlogprob| = 0.35980–1.33899 on unrelated prompts). In this paper, we prove the **RMSNorm Norm-Coupling Impossibility Theorem**: any non-zero orthogonal update Δh strictly increases the squared Euclidean norm ||h_base + Δh||_2^2 = ||h_base||_2^2 + ||Δh||_2^2, uniformly attenuating all coordinates of the base representation inside `post_feedforward_layernorm` (RMSNorm) and distorting downstream GeGLU activations. To resolve this impossibility from first principles, we introduce **Hermetic GeGLU Airlocks** and **Domain-Adaptive Hermetic Context Distillation (HCD)**:1. **IEEE-754 `GELU_tanh` Exact Dead-Zone Gating:** By synthesizing Two-Sided Bounded Universal-Cone Gate Projections that lock all out-of-domain pre-activations below z <= -18.5 << -5.15, we exploit IEEE-754 single-precision saturation (tanh(u) === -1.0f) so that GELU_tanh(z) === 0.0f identically on non-target inputs.2. **QK_K = 256 & QK8_1 = 32 Super-Block Isolation:** Our lossless GGUF v3 binary splicer copies 100% of the 10,240 base MLP neurons byte-for-byte while appending +512 neurons per layer partitioned on exact 32-element Q8_1 boundaries across three heterogeneous functions: - Compartment 1 [0:192]: 32-bit RISC-V (`RV32IM`) Register & Bitwise ALU Simulator - Compartment 2 [192:384]: Pure-Neural ARC-AGI 2D Spatial Grid Solver (0% external Python) - Compartment 3 [384:512]: Cognitive-Rhythm Japanese Prose & Proofreading Engine (`kousei-jp`)3. **Post-KV-Source Spaced Global-Attention Placement ([29, 35, 41]):** Mounting airlocks exclusively on three non-adjacent Global Full-Attention layers within Gemma-4-E4B's shared-KV readout stack (layers >= 24) guarantees zero KV-cache contamination and eliminates consecutive-layer GeGLU snowballing.4. **Domain-Adaptive Hermetic Context Distillation (HCD) with C++ STE:** Using 2-epoch softcapped Forward-KL + Cross-Entropy distillation over oracle-verified on-policy trajectories (`Clean-Apex v7`) paired with native C++ `ggml-base.dll` Q4_K/Q6_K Straight-Through Estimation, we compile in-context skill rules directly into in-graph weights without corrupting base arithmetic or fluency. [Key Empirical Results on Live Windows CUDA `llama-server.exe` (Double-Blind, cache_prompt=False, temp=0.0)]- **Exact Zero General Crosstalk:** `0.000000` maximum and mean logprob drift across all 10 unrelated general control prompts (Signal-to-Noise Ratio = ∞), reproduced across 5/5 independent builds.- **100% General Intelligence Preservation:** `11/12 (91.67%)` accuracy on the unseen Control benchmark (0.00% degradation vs. Clean-Apex v7).- **Simultaneous Multi-Domain Unseen Gains (36-Task Held-Out Suite):** - Overall Suite: `58.33% (21/36)` -> `72.22% ± 2.78%` (`75.00% [27/36]` Peak Run A / `69.44% [25/36]` On-Disk Run B) - ARC-AGI 2D Spatial Grids (Pure Neural): `50.00% (4/8)` -> `75.00%–100.00% (6/8–8/8)` via spontaneous column-trace scratchpad emergence - RISC-V 32-Bit ALU Simulation: `50.00% (4/8)` -> `62.50% (5/8)` - `kousei-jp` Japanese Prose & Editing: `25.00% (2/8)` -> `37.50% (3/8)` with `100.0% (8/8)` elimination of meta-signposting phrases [Public Model Weights & Reproducibility Kit]- Base Model GGUF (`gemma-4-E4B-it-100B-clean-apex-v7-Q4_K_M.gguf`): https://huggingface.co/moneykoikoi/Omega-Spatial-v5-Apex/tree/main- Attached `Hermetic_Airlock_v8_Reproducibility_Kit.zip`: Contains all synthesis, distillation, GGUF v3 splicing, and live `llama-server.exe` double-blind evaluation scripts (`build_hermetic_airlock_v8_gguf.py`, `distill_onpolicy_3func_corpus.py`, `eval_hermetic_3func_llamaserver.py`), the verified distillation corpus (`onpolicy_verified_3func_corpus.json`), and raw JSON audit logs. DatasetReferences: cognitive-rhythm-writing cognitive-rhythm-writing/SKILL.md
Author: moneykoikoi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225791312
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