💡 The paper introduces GenericAgent, a self-evolving large language model agent system designed to overcome the limitations of long-horizon interactions. The main problem addressed is that as interactions become longer, the accumulation of tool descriptions, memories, and environmental feedback pushes out the information needed for decision-making, leading to poor performance. The authors argue that the key to improving long-horizon performance is not the length of the context, but rather how much decision-relevant information is maintained within a finite context budget.

To address this problem, the GenericAgent system is built around the principle of context information density maximization. The system consists of four main components: a minimal atomic tool set, a hierarchical on-demand memory, a self-evolution mechanism, and a context truncation and compression layer. The minimal atomic tool set keeps the interface simple, while the hierarchical on-demand memory only shows a small high-level view by default. The self-evolution mechanism turns verified past trajectories into reusable standard operating procedures and executable code, allowing the agent to learn from its experiences. The context truncation and compression layer maintains information density during long executions by removing unnecessary information.

The results show that GenericAgent consistently outperforms leading agent systems in terms of task completion, tool use efficiency, memory effectiveness, self-evolution, and web browsing. Moreover, GenericAgent achieves these results while using significantly fewer tokens and interactions, demonstrating its efficiency. The system also continues to evolve over time, allowing it to adapt to new situations and improve its performance. Overall, the paper presents a novel approach to building self-evolving large language model agents that can effectively handle long-horizon interactions and maximize context information density.