Refusal behavior in aligned LLMs is often viewed as model-specific, yet we hypothesize it stems from a universal, low-dimensional semantic circuit shared across models. To test this, we introduce Trajectory Replay via Concept-Basis Reconstruction, a framework that transfers refusal interventions from donor to target models, spanning diverse architectures (e.g., Dense to MoE) and training regimes, without using target-side refusal supervision. By aligning layers via concept fingerprints and reconstructing refusal directions using a shared ``recipe'' of concept atoms, we map the donor's ablation trajectory into the target's semantic space. To preserve capabilities, we introduce a weight-SVD stability guard that projects interventions away from high-variance weight subspaces to prevent collateral damage. Our evaluation across 8 model pairs (including GPT-OSS-20B and GLM-4) confirms that these transferred recipes consistently attenuate refusal while maintaining performance, providing strong evidence for the semantic universality of safety alignment.

翻译：对齐后大型语言模型中的拒绝行为通常被视为模型特定的，然而我们假设其源于跨模型共享的通用低维语义电路。为验证此假设，我们提出了基于概念基重构的轨迹回放框架，该框架可将拒绝干预从供体模型迁移至目标模型，涵盖不同架构（如稠密模型到混合专家模型）和训练机制，且无需目标端的拒绝监督。通过概念指纹对齐网络层，并利用概念原子的共享“配方”重构拒绝方向，我们将供体模型的消融轨迹映射至目标模型的语义空间。为保持模型能力，我们引入了权重奇异值分解稳定性保护机制，通过将干预投影至高方差权重子空间的正交补空间来防止连带损伤。我们在8组模型对（包括GPT-OSS-20B和GLM-4）上的评估证实，这些迁移的干预配方能持续削弱拒绝行为同时保持模型性能，为安全对齐的语义普适性提供了有力证据。