## 1 Introduction
This demands analytical models that can not only handle long-range dependencies, but also possess deep semantic understanding capabilities to discern subtle tonal differences (such as hawkish vs. dovish leanings), comprehend implicit policy signals, and accurately grasp the overall stance of the text. Simple keyword counting or shallow models are inadequate for this task. Second, model efficiency and real-time requirements constitute another constraint. Even if long texts can be technically processed, if the model training and inference processes are too time-consuming, their value in a \"nowcasting\" scenario diminishes significantly. Financial markets react extremely rapidly to information, and the core utility of nowcasting lies in its immediacy. Therefore, not only must the model architecture itself be capable of efficiently processing long sequences, but model adaptation and deployment stages must also minimize computational overhead and inference latency as much as possible. Third, model adaptation and generalization emerge as critical bottlenecks. Current state-of-the-art NLP models are typically large-scale models with billions or more parameters, pretrained on massive general-purpose corpora [9]. Making these general models accurately understand and analyze the highly specialized domain language of central banking necessitates downstream task fine-tuning. However, full parameter fine-tuning is computationally resource-intensive [10] and prone to overfitting, especially when domain-specific labeled data (such as central bank texts precisely labeled with subsequent interest rate changes) is relatively scarce. Parameter-efficient fine-tuning (PEFT) techniques [11; 12] alleviate computational and storage pressures, but common methods like LoRA [10] (low-rank adaptation) might still lag behind full fine-tuning in performance [13; 14]. Deciphering how to maximize model performance while maintaining high efficiency remains an urgent problem. Finally, cross-domain robustness is a challenge that practical application must address. Different central banks exhibit variations in communication styles, policy frameworks, and areas of focus [15]. Even within the same central bank, communication strategies and linguistic patterns evolve across different historical periods [8]. A model trained in one scenario may suffer significant performance degradation when directly applied to another due to this domain drift. Consequently, building a model that can robustly adapt to different institutions and time periods requires strong domain adaptation capabilities. Despite prior research advancements in long sequence modeling [16], parameter-efficient fine-tuning [10; 14], and domain adaptation [17; 18], a comprehensive framework that systematically integrates these cutting-edge technologies to synergistically address the core challenges of ultra-long text processing, high-performance economical fine-tuning, and strong cross-domain generalization for the complex task of central bank interest rate nowcasting, has been lacking. To address this research gap, this paper proposes and implements a novel, integrated deep learning framework designed to overcome the limitations of existing methods by strategically combining complementary state-of-the-art techniques. The framework selects Hyena Hierarchy [19] as its foundational architecture, utilizing its gated long convolutional mechanism with sub-quadratic complexity (\(O(n\log n)\)) [20] to efficiently process complete central bank documents exceeding ten thousand tokens, thereby tackling the core challenge of long text processing. For high-performance and economical model adaptation, we employ Delta-LoRA [13] for parameter-efficient fine-tuning. This technique leverages the change in low-rank updates to drive adjustments in the pretrained weights, aiming to close the performance gap with full fine-tuning while maintaining an extremely low parameter count (<1%) and zero additional inference latency [13].