Contextual Reasoning Orchestration for Enhancing Black-Box Large Language Models in Specialized Decision Support

Recent advances in language models have greatly improved their ability to understand and generate natural language. Yet, when applied to specialized fields such as financial decision support or complex system diagnosis, they often struggle with limited domain expertise, weak logical reasoning, and unreliable performance under uncertainty. Fine-tuning these large models is typically constrained by cost, privacy, and proprietary limitations. To overcome these issues, this study introduces CRONUS: Contextual Reasoning Orchestration for Navigating Uncertain Scenarios, a framework designed to enhance general-purpose models in domain-specific and decision-intensive tasks. CRONUS employs a lightweight, trainable agent named CARA (Context-Aware Reasoning Agent) to guide the reasoning process of black-box models through structured contextual instructions. CARA is developed via a three-stage training strategy that builds domain understanding, refines reasoning path generation, and optimizes dynamic decision prompts. Experiments in financial analysis tasks show that CRONUS markedly improves reasoning depth, consistency, and robustness compared with direct model use, retrieval-augmented methods, and specialized domain models, demonstrating its effectiveness for high-stakes decision-making in complex environments.