Beyond Equal Negotiation: Authority-Aware Multi-Agent Collaboration and Adaptive Distillation for Cultural Alignment

Multi-agent negotiation has emerged as a promising approach to cultural alignment. However, existing methods generally rely on equal-weight negotiation and overlook the asymmetric distribution of cultural expertise, which may lead to culturally uninformed majorities overriding informed minority judgments. Moreover, the high inference cost of multi-agent systems limits their practical deployment. To address these challenges, we propose **A**uthority-Aware **M**ulti-**A**gent **C**ollaboration and Adaptive **D**istillation (**AMACD**) for cultural alignment. First, AMACD constructs an authority-aware multi-agent teacher via **H**ome-**F**ield **C**ulture-**A**uthority **C**ollaboration (HFCAC). The teacher dynamically activates a target-culture Guardian agent and resolves disagreements through authority-aware arbitration, with a cultural affinity-based fallback when the Guardian fails. Second, its distillation component, **H**ybrid **A**daptive **D**istillation (HAD), transfers the teacher's heterogeneous cultural expertise into a single model through role-specific SFT and RL branches. The Judge's final answers provide outcome-level supervision, while the Guardian's cultural guidance provides preference-level direction, with both signals jointly optimized using mastery-adaptive weighting. Experiments on three cultural benchmarks demonstrate that AMACD achieves stronger cultural alignment than equal-weight negotiation approaches while substantially reducing inference cost compared with multi-agent systems.