SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
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.
69436b43542d62d58a7bf665
Zehua Pei
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2025-12-19T09:07:18.794Z
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JarvisPei
JarvisPei
user
JarvisPei
69436b43542d62d58a7bf666
Hui-Ling Zhen
69436b43542d62d58a7bf667
Shixiong Kai
69436b43542d62d58a7bf668
Sinno Jialin Pan
69436b43542d62d58a7bf669
Yunhe Wang
69436b43542d62d58a7bf66a
Mingxuan Yuan
69436b43542d62d58a7bf66b
Bei Yu
SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
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.
SCOPE enhances LLM agents' context management through prompt evolution, improving task success rates in dynamic environments without human intervention.
Large Language Model (LLM) context management online optimization execution traces prompt evolution Dual-Stream mechanism tactical specificity strategic generality Perspective-Driven Exploration HLE benchmark