As large language models (LLMs) increasingly process instructions from multiple sources with different privilege ranks, preserving the priority of higher-rank directives over conflicting lower-rank ones is critical for safe and predictable deployment. However, existing evaluations mostly focus on limited conflict settings, leaving it unclear how models handle instruction hierarchy (IH) when multiple conflicts arise. To address this gap, we present the first systematic study of IH under multi-conflict settings and introduce MIH-Bench, a benchmark that scales the number of simultaneous conflicts across six pairwise hierarchy scenarios. Experiments on 21 LLMs show that IH compliance deteriorates sharply as conflicts accumulate, while neither model scale nor generation reliably predicts robustness. We further find that system-involving conflicts are the most vulnerable, a particularly concerning result given the safety-critical role of system prompts. Moreover, reasoning can improve conflict resolution, although the gains vary across models and are bounded by underlying model capability. These results show that current LLMs remain fragile in pairwise multi-conflict settings, highlighting the need for stronger hierarchy-aware alignment. Code and data are available at https://anonymous.4open.science/r/MIH-release-A5B8/

Supervised fine-tuning (SFT) is the de facto standard for adapting large language models (LLMs) to target domains, but it often degrades the model’s general capabilities, a phenomenon known as catastrophic forgetting. Existing approaches typically modify the SFT loss to mitigate forgetting, but they inevitably operate along a domain-generality trade-off. In this work, we step outside this trade-off by decoupling the two capabilities at the model level: we keep the original base model for general capability, and selectively invoke the SFT expert only when domain-specific knowledge is required. Specifically, we propose CPR (Critical-Point Routing), a token-level routing framework between a base model and its expert derivative, based on critical tokens where the base model fails but the expert succeeds. We train a lightweight hierarchical router that estimates the expert-call probability per token, and pair it with a tailored inference procedure that combines momentum smoothing and threshold gating. Across diverse model-domain configurations, CPR achieves state-of-the-art across all settings, surpassing SFT expert by 1.4-5.5% in domain performance while recovering its general-capability drop from 3.4-14.5% to at most 0.5%, with minimal overhead from invoking the expert on only one-third of tokens.