Mergers and Acquisitions Pricing Multi-agent Cross-Questioning: Paradigm Evolution and Framework

Abstract

This paper systematically reviews the evolution direction of financial artificial intelligence (AI) from traditional statistical fitting to dynamic confrontation. With the help of functional isolation, evidence-based game theory and parameter correction, this paper analyzes the internal law of the multi-agent cross-questioning mechanism, and summarizes a set of structured analysis frameworks. Relevant studies have shown that compared with a single model, the core purpose of building a specific questioning mechanism using confrontation topology is to get through the logic of transforming unstructured semantic risk into structured valuation parameters, to clearly depict the cross-modal mapping path between the two. This model has significant theoretical feasibility in improving the internal consistency of pricing logic and restraining unreasonable premiums. This paper believes that the dynamic inquiry paradigm is conducive to optimizing and reshaping the existing asset valuation benchmark in a complex trading environment. To truly land the man-machine collaborative pricing system, the core is to focus on how to resolve the technical barriers of reasoning interpretability, privacy protection, collaborative deduction and penetration algorithm audit. Only by achieving substantial breakthroughs in these bottlenecks can the follow-up system construction have a solid foundation.