Can AI shape viral evolution for better therapies?
OPPONENT SHAPING FOR ANTIBODY DEVELOPMENT
September 18, 2024
https://arxiv.org/pdf/2409.10588This paper explores using opponent shaping, a multi-agent reinforcement learning technique, to design more effective antibodies. By simulating the virus's adaptation to different antibodies within a game-like framework, the researchers developed 'shaper' antibodies that guide viral evolution towards more treatable strains. These shaper antibodies demonstrated superior long-term efficacy compared to traditional 'myopic' antibodies.
While not directly using LLMs, the core principles are relevant to LLM-based multi-agent systems:
- Modeling adaptation: The paper highlights the importance of considering how agents (viruses) adapt to interventions (antibodies) in a multi-agent system. LLM agents will similarly adapt to each other's actions in a shared environment.
- Long-term optimization: The success of 'shaper' antibodies emphasizes the need to optimize for long-term goals in multi-agent systems. LLMs should be designed to consider the long-term consequences of their actions, anticipating and influencing the behavior of other agents.
- Explainability: Analyzing the 'shaper' antibodies' behavior provided insights into their success. Similarly, understanding the decision-making process of LLMs in multi-agent systems is crucial for ensuring desired outcomes and building trust.