AI 中文总结
本文针对临床阶段生物技术估值的现金流假设失效问题，提出多智能体AI框架，其含估值层、跨市场协调层与冲突融合机制，基于作者过往实践确立智能体投资系统设计原则。
AI 中文摘要
一类新型软件系统正在改变投资分析，由分析师、研究人员和风险管理者组成协作团队结构的大语言模型智能体，正越来越多地应用于金融市场。然而，当前多智能体框架存在一个关键局限：它们依赖于“可通过传统现金流对公司估值”的基础假设，这一范式在临床阶段生物技术领域失效，该领域的企业价值完全取决于二元的科学与监管里程碑。为填补这一空白，本文提出一种专门的多智能体框架：其估值层将定性的科学判断转化为对无收入资产的可辩护估值；其跨市场协调层可同时协调国际市场的定价；其冲突融合机制以领域特定的方式系统地在看涨的科学信念与谨慎的监管约束之间进行仲裁。至关重要的是，该架构并非投机性设计：它编码了作者首次作为中国首个专注跨境生物技术基金的唯一投资组合经理手动执行的方法，该人类实践在16个月内实现了127.17%的回报率，而基准回报率为50.67%。该记录是底层方法的证据，而非任何AI系统的证据；本文未对任何实现进行评估。本文在架构层面呈现该框架，为将智能体投资系统扩展至当前服务不佳的复杂、事件驱动型资产类别确立了基础设计原则。
英文摘要
A new class of software systems is transforming investment analysis. Large language model agents assembled into collaborative team structures including analysts, researchers, and risk managers are increasingly deployed across financial markets. Yet current multi-agent frameworks share a critical limitation: they rely on the foundational assumption that companies can be valued through traditional cash flows. This paradigm fails in clinical-stage biotechnology, where enterprise value depends entirely on binary scientific and regulatory milestones. To bridge this gap, this paper introduces a specialized multi-agent framework. Its valuation layer translates qualitative scientific judgment into defensible valuations for pre-revenue assets; its cross-market coordination layer reconciles pricing across international venues simultaneously; and its conflict-fusion mechanism systematically arbitrates between bullish scientific conviction and cautious regulatory constraints in a domain-specific manner. Crucially, the architecture is not a speculative design: it encodes a method the author first executed by hand as sole portfolio manager of China's first dedicated cross-border biotechnology fund, a human practice that returned 127.17% against a 50.67% benchmark within sixteen months. That record is evidence for the underlying method rather than for any AI system; no implementation is evaluated here. This paper presents the framework at the architectural level, establishing foundational design principles for extending agentic investment systems into complex, event-driven asset classes they currently serve poorly.
