**主题：提升智能体效能的提示演进策略**

摘要

大型语言模型（LLM）智能体正日益部署于产生海量动态上下文的环境中。然而，一个关键瓶颈依然存在：尽管智能体能够访问这些上下文，但其静态提示缺乏有效管理机制，导致纠正与增强型故障反复发生。为弥补这一能力缺口，我们提出SCOPE（基于提示自我演进的上下文优化框架）。该框架将上下文管理构建为在线优化问题，通过综合执行轨迹中的指导原则，实现智能体提示的自主演进。我们设计了一种双流机制，在战术特异性（解决即时错误）与战略通用性（演进长期原则）之间实现平衡。此外，引入视角驱动探索机制以最大化策略覆盖范围，提升智能体针对任意任务具备正确策略的概率。在HLE基准测试中，SCOPE将任务成功率从14.23%提升至38.64%且无需人工干预。代码已开源：https://github.com/JarvisPei/SCOPE。

English

Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have access to this context, their static prompts lack the mechanisms to manage it effectively, leading to recurring Corrective and Enhancement failures. To address this capability gap, we introduce SCOPE (Self-evolving Context Optimization via Prompt Evolution). SCOPE frames context management as an online optimization problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles). Furthermore, we introduce Perspective-Driven Exploration to maximize strategy coverage, increasing the likelihood that the agent has the correct strategy for any given task. Experiments on the HLE benchmark show that SCOPE improves task success rates from 14.23\% to 38.64\% without human intervention. We make our code publicly available at https://github.com/JarvisPei/SCOPE.

主题：提升智能体效能的提示演进策略

SCOPE: Prompt Evolution for Enhancing Agent Effectiveness

摘要

Support