Yuhan Fang's multi-agent AI framework values pre-revenue biotech beyond cash flows

Yuhan Fang's new arXiv paper, "Beyond Cash Flows: A Multi-Agent AI Framework for Valuing Clinical-Stage, Cross-Border Biotechnology," tackles a critical blind spot in agentic finance: existing LLM-based multi-agent systems assume companies can be valued using cash-flow projections. That assumption breaks down for pre-revenue biotech firms, where value hinges entirely on binary scientific and regulatory milestones. Fang proposes a specialized architecture that translates qualitative scientific judgment into defensible valuations for assets with no revenue. It also includes a cross-market coordination layer to reconcile pricing across international venues simultaneously, and a conflict-fusion mechanism that systematically arbitrates between bullish scientific conviction and cautious regulatory constraints in a domain-specific way.

The framework is presented as an architectural blueprint rather than a tested AI implementation—Fang explicitly notes no AI system is evaluated in the paper. However, the method behind it has real-world validation: the author executed it by hand as sole portfolio manager of China's first dedicated cross-border biotech fund, returning 127.17% against a 50.67% benchmark within sixteen months. That evidence supports the underlying approach, not the AI. By encoding this human practice into a multi-agent system, the framework could extend agentic investment tools into event-driven asset classes where they currently perform poorly, offering a path for AI to manage the complex interplay of science, regulation, and international markets.