Graph AI
There is an interesting graph RAG paper with the provocative title “You Don’t Need Pre-built Graphs for RAG”
TLDR: graph structures should match the reasoning task, not the other way around.
Here’s what it actually ponders.
Before you can retrieve anything, you transform your entire document corpus into a knowledge graph. That process is expensive (token cost, time, infrastructure), static (stale the moment your corpus changes), and one-size-fits-all. The same graph topology whether your user asks a simple factual question or a deep multi-hop reasoning chain.
𝗧𝗵𝗲 paper’s 𝗟𝗼𝗴𝗶𝗰𝗥𝗔𝗚 𝗶𝗱𝗲𝗮 is to skip the pre-built graph entirely. Instead, at query time:
- Decompose the question into subproblems
- Build a DAG (directed acyclic graph) of the logical dependencies between them
- Use topological sort to determine retrieval order
- Prune redundant nodes and irrelevant context
The graph is volatile, built for the query, discarded after. Structure follows the question, not the corpus. It doesn’t mean persistent knowledge graphs are obsolete. They shine when you need cross-document entity traversal, long-term organizational memory, or auditability back to source nodes. What LogicRAG challenges is the reflex to always build the graph upfront.
On a more contemplative level, it says what we all know but don’t like: the world is a complex place and every challenge requires a balanced plan of attack, taking into account all sorts of criteria. A good solution is seldom brute force or picking up some general (shall I say, popular) framework.