Large language models (LLMs) encode vast amounts of knowledge in their parameters, but these facts often require precise post-training rectification as they become outdated or incorrect over time. Traditional batch editing methods typically apply a uniform update strength across all instances. However, this uniform approach ignores the inherent variation in editing difficulty, leading to a fundamental trade-off between correction success and model stability. To overcome this limitation, we introduce AdaPE (Adaptive Pressure Editing), a parameter-free adaptive editing framework that dynamically adjusts the update behavior for each specific instance. Specifically, we derive a closed-form adaptive update rate based on the ratio of residual energy to spatial sensitivity, which strictly quantifies the intrinsic injection difficulty. This formulation adaptively limits the update strength for difficult facts to preserve model locality, while ensuring sufficient knowledge injection for easier ones. Extensive experiments on three benchmarks and two prominent LLMs demonstrate the superiority of our approach in consistently circumventing the fixed-strength bottleneck, achieving a new state-of-the-art balance between editing efficacy and model integrity. Our code is available at https://github.com/skyandkibo/adape.