Aligning large language models (LLMs) with multiple behavioral objectives from a certain corpus remains challenging, as joint optimization of attributes often leads to interference and trade-offs. While recent representation steering methods enable lightweight control over individual behaviors through hidden-state interventions, their composability for multi-attribute alignment remains underexplored. We present POLAR, Poly-attribute Alignment via Modular Representation Steering, a modular framework that composes independently trained alignment interventions within a frozen LLM. To mitigate conflicts between steering modules, we introduce calibrated and compatibility-aware intervention composition in hidden-state space. Experiments across heterogeneous alignment attributes show that POLAR achieves the best overall trade-off, with a 37.4% improvement in average rank over the strongest multi-attribute baseline while preserving response quality and controllability. The code and data will be publicly available.