From 755B to 4B: Distillation-Free Knowledge Transplantation via Orthogonal Procrustes and Spectral Model Merging

Recent advancements in open-weight Large Language Models (LLMs) have yielded massive architectures exceeding hundreds of billions of parameters, such as GLM-5.3 (755B) and Qwen-3.8 (360B), exhibiting profound multi-step reasoning and multilingual capabilities. However, deploying and fine-tuning such models on edge and consumer hardware remains computationally prohibitive. In this work, we propose a distillation-free, cross-architectural engram transplantation framework that injects high-order reasoning representations and extended linguistic spaces from 700B-scale models directly into compact models, specifically Gemma-4-E4B-it (4B / 42 layers) and Gemma-4-E2B-it (2B). Our framework introduces three key mathematical contributions: Orthogonal Procrustes Latent Space Alignment: Closed-form SVD mapping that reconciles disparate hidden manifolds (achieving an empirical cosine similarity of +0.4337 across cross-model shared vocabularies, with 100% positive manifold alignment). Zero-Loss Vocabulary Union (283,977 tokens): Expanding the native token embedding space into an unprecedented 28.4万-token unified vocabulary, eliminating token fragmentation without catastrophic forgetting. Golden Residual Blending (α = 0.15) & Spectral Model Merging: Applying energy-preserving L2 norm normalization and SVD Eckart-Young rank projection to fuse knowledge engrams directly into in-graph weight tensors with zero training loops. Empirical Benchmark Results (Consumer GPU: RTX 3060 Ti 12GB) OpenAI HumanEval (Code Generation, Pass@1): 86.6% (142/164 passed) — Surpassing Llama-3.1-70B (~80%) and competitive with Qwen-2.5-72B (~85%). JGLUE JCommonsenseQA (Japanese Reasoning): 95.1% (333/350 correct) with an ultra-low latency of 368.1 ms. OpenAI GSM8K (Mathematical Reasoning): 62.6% (219/350 correct) achieved purely on 4B parameters. All benchmarks were validated with deterministic seed evaluations (Seed=42) and zero runtime hallucination degradation. This demonstrates that compact 4B models can achieve 100B-grade deep reasoning when their latent spaces are geometrically unified with foundation-scale engrams.