Domain Adaptation of Financial Decision Code Generation Framework Based on Contrastive Learning and Dual-Agent Collaboration

This paper discussed the limitations of traditional financial analysis proceeds through a AI-assisted framework integrating contrastive learning and dual-agent collaboration. Proposed framework enhances accuracy and semantic alignment in financial decision-making tasks, achieving accuracy of 89% and decision quality of 0.83, with a latency of 0.76 seconds response. Experimental results show that significant improvements in financial educational effectiveness, with the experimental group demonstrating quicker task completion and better knowledge transfer rate. The proposed framework's dynamic knowledge graph engine combines real-time updates and complex query optimization, which is a practice approach for economic relevant educational case study.