RAG

This post summarizes the Microsoft Research paper From Local to Global: A GraphRAG Approach to Query-Focused Summarization, also drawing on the video [Paper Review] GraphRAG by Seoul National University’s DSBA Lab. RAG works by building a trusted document collection ahead of time, then, when a question comes in, retrieving the relevant documents and handing them to an LLM as grounding for its answer. The basic pieces are indexing (chunking documents into a searchable form), retrieval (finding documents relevant to the question), and generation (producing an answer from the retrieved documents and the question). ...

This post is a review of SimpleMem: Efficient Lifelong Memory for LLM Agents. Some parts of the paper have been updated after this post was written (2026-01-24), so there may be differences from the version discussed here. Background LLMs are stateless. As a result, previous inference outputs do not directly affect later outputs. Because of this property, a plain LLM can fail to maintain continuity in long conversations. In other words, it may look like short-term memory loss, where it cannot remember what was just discussed. ...