提出可跨对话自由检索的记忆模块，解决大模型记忆难共享问题。
Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval
- 设计类魔方记忆结构，实现非线性索引与动态检索。
- 在八组实验中验证跨对话记忆检索稳定性，指标表现优异。
- 适合关注大模型记忆优化与多轮对话系统的研究者。
针对当前大语言模型在跨对话场景下难以共享记忆的问题，本文提出一种虫洞记忆模块（WMM），其记忆结构如魔方般可任意跨对话检索。通过构建基于Python的实验框架，设置记忆屏障模拟现有模型间记忆难以互通的现状，引入CoQA开发数据集进行测试。实验验证了WMM在非线性索引与动态检索方面支持跨对话记忆检索的可行性，并与Titans及MemGPT的记忆模块能力进行对比分析。结果表明，WMM在八组实验中均展现出跨对话记忆检索能力，且各项定量指标稳定。该研究为大模型记忆管理优化提供了新方法，也为未来实际应用积累经验。
原文摘要 · Abstract (English)
In view of the gap in the current large language model in sharing memory across dialogues, this research proposes a wormhole memory module (WMM) to realize memory as a Rubik's cube that can be arbitrarily retrieved between different dialogues. Through simulation experiments, the researcher built an experimental framework based on the Python environment and used setting memory barriers to simulate the current situation where memories between LLMs dialogues are difficult to share. The CoQA development data set was imported into the experiment, and the feasibility of its cross-dialogue memory retrieval function was verified for WMM's nonlinear indexing and dynamic retrieval, and a comparative analysis was conducted with the capabilities of Titans and MemGPT memory modules. Experimental results show that WMM demonstrated the ability to retrieve memory across dialogues and the stability of quantitative indicators in eight experiments. It contributes new technical approaches to the optimization of memory management of LLMs and provides experience for the practical application in the future.
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