ChurnMAS: Adaptive Role-Model Routing for LLM-based Multi-Agent Systems with Evolving Model Pools

LLM-based dynamic **m**ulti-**a**gent **s**ystems (MAS) increasingly adopt heterogeneous backbones to exploit complementary model capabilities across agent roles. However, most existing approaches focus on dynamically organizing collaboration structures while assuming a *fixed model pool*, overlooking continual model releases and retirements during deployment. Such model churn can invalidate existing role-model assignments, requiring routing mechanisms capable of adapting to changing model pools. To tackle this problem, we propose **ChurnMAS**, a framework that formulates role-model routing as a context-aware adaptation problem over dynamic model pools. ChurnMAS introduces *role-conditioned model profiles*, which unify capability priors with continuously accumulated behavioral evidence to characterize how different LLMs fit specific agent roles. Based on these evolving profiles, ChurnMAS dynamically reconfigures role-model assignments according to task requirements and changing model availability, enabling continual adaptation without *updating routing parameters* during deployment. Experiments on MATH, MMLU, MedQA, and MBPP demonstrate that ChurnMAS outperforms both single-model baselines and dynamic multi-agent methods. Further analyses show that role-conditioned model profiles effectively capture model specialization and facilitate adaptation to newly introduced LLM backbones under model churn. The code will be open-sourced.