<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>HippoRAG on SoloSoft</title><link>https://www.solosoft.dev/tags/hipporag/</link><description>Recent content in HippoRAG on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/hipporag/index.xml" rel="self" type="application/rss+xml"/><item><title>HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs (NeurIPS 2024)</title><link>https://www.solosoft.dev/post/hipporag-memory-rag-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/hipporag-memory-rag-2026/</guid><description>&lt;p&gt;Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLM outputs in external knowledge. But standard RAG has a fundamental limitation: it treats each query independently, with no memory of past retrievals or ability to connect information across documents. &lt;strong&gt;HippoRAG&lt;/strong&gt; takes inspiration from the human brain&amp;rsquo;s hippocampus to overcome this, creating a long-term memory system that dramatically improves multi-hop question answering.&lt;/p&gt;
&lt;p&gt;Published at NeurIPS 2024 and available at &lt;a href="https://github.com/OSU-NLP-Group/HippoRAG"&gt;github.com/OSU-NLP-Group/HippoRAG&lt;/a&gt;, HippoRAG combines LLMs with knowledge graphs in a framework modeled on the hippocampal indexing theory of human memory. The result is a RAG system that builds a persistent knowledge structure from documents, enabling it to answer complex questions that require connecting information across multiple sources &amp;ndash; achieving approximately 20% improvement over standard RAG on multi-hop QA benchmarks.&lt;/p&gt;</description></item></channel></rss>