Computer Science > Machine Learning
arXiv:2610.21375
(cs)
[Submitted on 15 Oct 2026 (
v1
), last revised 3 Nov 2026 (this version, v3)]
Title:
Memorilla: Latent Semantic Memory for LLMs
Authors:
Adibvafa Fallahpour
,
Parsa Idehpour
,
Vignesh Kothapalli
,
Nikita Mounier
,
Shirley Wu
,
Shayan Pardis
,
Jure Leskovec
View a PDF of the paper titled Memorilla: Latent Semantic Memory for LLMs, by Adibvafa Fallahpour and 6 other authors
View PDF
HTML (experimental)
Abstract:
Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier actions. For a frozen LLM, access to this accumulated knowledge is limited to what a memory system places in its context window. However, gathering and combining relevan (EN)

---
**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2610.21375)获取最准确的信息。

---
🔗 **原文链接**: [Memorilla: Latent Semantic Memory for LLMs](https://arxiv.org/abs/2610.21375)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 75票 · 👤 handfuloflight

---
🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

---
⏱️ 2026-11-07 08:01