How can imperfect LLM agent actions be optimized?
Optimal Modified Feedback Strategies in LQ Games under Control Imperfections
This paper examines how imperfections in real-world control systems (like delays and errors) affect the performance of agents in a multi-agent setting, specifically within a game-theoretic framework where agents are trying to minimize individual costs. It proposes a modified control strategy for an agent to compensate for another agent's imperfections, allowing the first agent to maintain optimal or near-optimal performance.
The key point relevant to LLM-based multi-agent systems is the idea of developing robust control strategies that can account for deviations from ideal behavior. This is directly applicable to situations where LLMs, acting as agents, might not always produce perfectly predictable outputs or follow pre-defined strategies precisely. The concept of compensating for deviations could improve the reliability and robustness of multi-agent LLM applications.