通过最大化上下文信息密度，让大模型代理在有限记忆中持续进化。
GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)
- 用信息密度最大化原则设计自进化代理系统
- 任务完成率超主流系统，令牌消耗减少40%以上
- 适合长期复杂任务与资源受限场景的智能体应用
长周期大语言模型代理的根本限制在于上下文容量。随着交互延长，工具描述、检索记忆和原始环境反馈不断积累，挤占决策所需信息空间，且任务经验常在会话间丢失。我们提出，长周期表现不取决于上下文长度，而在于有限上下文内保留的决策相关信息量。本文提出GenericAgent（GA），一种通用自进化大模型代理系统，核心原则为上下文信息密度最大化。其包含四个紧密协同组件：极简原子工具集以保持接口简洁；分层按需记忆，默认仅展示少量高层视图；自进化机制将验证过的过往轨迹转化为可复用的标准操作流程（SOP）与可执行代码；上下文截断与压缩层在长时间运行中维持信息密度。在任务完成、工具使用效率、记忆有效性、自进化能力及网页浏览等多方面，GA持续优于领先代理系统，且显著减少令牌与交互次数，同时具备持续进化能力。
原文摘要 · Abstract (English)
Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memories, and raw environmental feedback accumulate and push out the information needed for decision-making. At the same time, useful experience gained from tasks is often lost across episodes. We argue that long-horizon performance is determined not by context length, but by how much decision-relevant information is maintained within a finite context budget. We present GenericAgent (GA), a general-purpose, self-evolving LLM agent system built around a single principle: context information density maximization. GA implements this through four closely connected components: a minimal atomic tool set that keeps the interface simple, a hierarchical on-demand memory that only shows a small high-level view by default, a self-evolution mechanism that turns verified past trajectories into reusable SOPs and executable code, and a context truncation and compression layer that maintains information density during long executions. Across task completion, tool use efficiency, memory effectiveness, self-evolution, and web browsing, GA consistently outperforms leading agent systems while using significantly fewer tokens and interactions, and it continues to evolve over time. Project: https://github.com/lsdefine/GenericAgent
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