arXiv · cs.AI · Jinbiao Chen, Shuang Jin, Guoyun Zhang, Junyu Zhang, Guanyi Wang, Hanzhang Qin · 10 小时前模型评分39
AutoREM：用记忆增强大语言模型自动重构鲁棒优化模型
Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models
导读
研究者提出免调优的记忆增强框架 AutoREM，通过反思过往失败轨迹构建结构化文本经验记忆，自动完成鲁棒优化（RO，Robust Optimization）模型的可解重构，无需专家知识或参数更新。
分类置信度
中 · 待人工复核
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来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “To address the reformulation challenge, we propose Automated Reformulation with Experience Memory (AutoREM), a tuning-free memory-augmented framework that autonomously builds a structured textual experience memory by reflecting on past failed trajectories through a tailored offline adaptation procedure.”
- “AutoREM requires neither domain-specific expert knowledge nor parameter updates, and the resulting memory readily transfers across different base LLMs.”
来源：arXiv · cs.AI · arxiv.org