The paper introduces TIPS (Transformer with Inductive Prior Synthesis), a knowledge distillation framework designed to integrate multiple inductive biases—causality, locality, and periodicity—into a single Transformer model for financial time series forecasting. The authors first demonstrate that different market regimes favor distinct inductive biases, and that naively combining multiple biases in one model leads to a 'merging penalty' where performance degrades. To address this, TIPS trains a set of bias-specialized Transformer teachers using attention masking and positional biases, then distills their collective knowledge into a compact student model using aggressive regularization techniques including low-temperature distillation, label smoothing, and stochastic weight averaging. This allows the student to exhibit regime-dependent alignment with different biases while maintaining the inference efficiency of a single model. Extensive experiments on four major equity markets (CSI300, CSI500, NI225, SP500) show that TIPS achieves state-of-the-art performance, outperforming strong ensemble baselines by up to 55% in annual return and 9% in Sharpe ratio, while requiring only 38% of the inference-time computation. The work highlights the importance of adaptive inductive bias utilization for robust generalization in non-stationary financial time series.