工具使用智能体的智能体控制遗忘：实践中的可逆上下文管理
Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice
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中文总结 AI 辅助
本研究提出智能体控制遗忘方法，通过在可恢复归档保留工具结果原始内容、替换为简短备注实现可逆上下文管理，在调试等任务中可减少50%输入令牌与API成本，但存在耗时增加等局限，其效果依赖工作负载。
中文摘要 AI 辅助
工具使用智能体反复接收观测结果，其中有用内容远少于原始有效载荷。我们研究智能体控制遗忘：行动模型选择先前观测到的工具结果，在其原始位置替换为简短备注，同时将完整原始内容保留在可恢复归档中。Python工具链提供批量归档和显式恢复功能，无需针对特定任务进行模型训练，且可保护用户指令和助手消息免受这些操作影响。在一项探索性OpenTelemetry调试案例及后续无关实现任务中，该方法最终产生231951个提供商报告的提示令牌，而保留完整历史的情况下为912492个，累计输入令牌减少50%，估计API成本为1.28至1.44美元，而保留历史的情况下约为4.38美元。两组均通过了两案例主要行为预言机测试，但均未完全满足后续评估要求。该方法发出更多请求，耗时增加17%。另一组对比应用开发对未产生上下文或成本节省，而早期延续案例尽管上下文减少，但人工评估质量更低。这些观察结果表明，在嘈杂的工具使用轨迹中可实现显著资源节省，并确定工作负载依赖性是可逆上下文管理的核心考虑因素。
英文摘要
Tool-using agents repeatedly carry observations whose useful content can be much smaller than their original payload. We study agent-controlled forgetting: the acting model selects previously observed tool results, replaces each with a short note at its original position, and retains the exact original in a recoverable archive. A Python harness exposes batch archival and explicit recovery without task-specific model training, while protecting user instructions and assistant messages from these operations. In an exploratory OpenTelemetry debugging case followed by an unrelated implementation task, the method ended with 231,951 provider-reported prompt tokens versus 912,492 under retained history, used 50% fewer cumulative input tokens, and had an estimated API cost of USD 1.28-1.44 versus approximately USD 4.38. Both arms passed the two-case primary behavioral oracle; neither fully satisfied the follow-up evaluation. The method made more requests and took 17% longer. A contrasting application-development pair produced no context or cost saving, and an earlier continuation exhibited lower manually assessed quality despite reduced context. These observations demonstrate substantial resource savings in noisy tool-use trajectories and identify workload dependence as a central consideration for reversible context management.
