The authors introduce ORBIT, a training-free method for simultaneously controlling multiple behavioral attributes in large language models. Existing activation steering techniques struggle with multi-attribute control due to norm imbalance and directional cancellation when using naive vector summation. ORBIT addresses this by constructing a joint subspace from per-attribute steering planes via singular value decomposition. It then applies a single norm-preserving rotation within that subspace toward a combined target direction. The method incorporates adaptive per-token gating to identify necessary corrections at each position and an optional additive boost for weak projections. To evaluate the approach, the authors present TraitFactory, a benchmark focusing on behavioral tendencies rather than surface style. Experiments across Llama-3.2-3B, Qwen-2.5-7B, and Llama-3.1-8B models demonstrate that ORBIT achieves stronger and more balanced steering than baselines while preserving output coherence.