用历史工作流记忆帮AI推荐下一步，避免幻觉和数据依赖。
Episodic Memory in Agentic Frameworks: Suggesting Next Tasks
- 构建记忆库存储过往工作流，匹配当前任务
- 基于历史模式推荐合理下一步操作
- 适合科研协作中需要稳定建议的场景
由大语言模型驱动的智能体框架在科学工作流中可促进人机协同。关键挑战在于不依赖大模型自身生成下一阶段任务，以避免幻觉并减少对稀缺专有数据的微调需求。本文提出一种情景记忆架构，通过存储和检索过往工作流来指导智能体推荐合理的下一步任务。通过将当前工作流与历史序列进行匹配，智能体能够基于已有模式给出可信的建议。
原文摘要 · Abstract (English)
Agentic frameworks powered by Large Language Models (LLMs) can be useful tools in scientific workflows by enabling human-AI co-creation. A key challenge is recommending the next steps during workflow creation without relying solely on LLMs, which risk hallucination and require fine-tuning with scarce proprietary data. We propose an episodic memory architecture that stores and retrieves past workflows to guide agents in suggesting plausible next tasks. By matching current workflows with historical sequences, agents can recommend steps based on prior patterns.
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