This paper proposes an innovative Multi-Modal Transformer framework (MMF-Trans) designed to significantly improve the prediction accuracy of the Chinese stock market by integrating multi-source heterogeneous information including macroeconomy, micro-market, financial text, and event knowledge. The framework consists of four core modules: (1) A four-channel parallel encoder that processes technical indicators, financial text, macro data, and event knowledge graph respectively for independent feature extraction of multi-modal data; (2) A dynamic gated cross-modal fusion mechanism that adaptively learns the importance of different modalities through differentiable weight allocation for effective information integration; (3) A time-aligned mixed-frequency processing layer that uses an innovative position encoding method to effectively fuse data of different time frequencies and solves the time alignment problem of heterogeneous data; (4) A graph attention-based event impact quantification module that captures the dynamic impact of events on the market through event knowledge graph and quantifies the event impact coefficient. We introduce a hybrid-frequency Transformer and Event2Vec algorithm to effectively fuse data of different frequencies and quantify the event impact. Experimental results show that in the prediction task of CSI 300 constituent stocks, the root mean square error (RMSE) of the MMF-Trans framework is reduced by 23.7% compared to the baseline model, the event response prediction accuracy is improved by 41.2%, and the Sharpe ratio is improved by 32.6%.

Addressing the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios, this paper proposes a Transformer$^{-1}$ architecture based on the principle of deep adaptivity. This architecture achieves dynamic matching between input features and computational resources by establishing a joint optimization model for complexity and computation. Our core contributions include: (1) designing a two-layer control mechanism, composed of a complexity predictor and a reinforcement learning policy network, enabling end-to-end optimization of computation paths; (2) deriving a lower bound theory for dynamic computation, proving the system's theoretical reach to optimal efficiency; and (3) proposing a layer folding technique and a CUDA Graph pre-compilation scheme, overcoming the engineering bottlenecks of dynamic architectures. In the ImageNet-1K benchmark test, our method reduces FLOPs by 42.7\% and peak memory usage by 34.1\% compared to the standard Transformer, while maintaining comparable accuracy ($\pm$0.3\%). Furthermore, we conducted practical deployment on the Jetson AGX Xavier platform, verifying the effectiveness and practical value of this method in resource-constrained environments. To further validate the generality of the method, we also conducted experiments on several natural language processing tasks and achieved significant improvements in resource efficiency.