LDV-LoRA: Direct Weight-Space Logic Grafting via Logical Difference Vectors

Traditional alignment of large language models (LLMs) relies on backpropagation-based fine-tuning, which is computationally expensive and often yields superficial behavioral shifts. Here we introduce LDV-LoRA, a gradient-free intervention protocol that identifies a Logical Difference Vector (LDV) from latent space and directly grafts it into static weights via low-rank adaptation. Targeting the “logical phase-transition” layer (Layer 23), we achieve permanent installation of rigorous reasoning instincts. Despite an extreme L2-norm perturbation of 68.75%, structural stability is preserved, and the model transitions from probabilistic mimicry to deterministic algebraic derivation—demonstrating that logic can be surgically inscribed into weight space.