Multi-hop RAG with a graph planned for each query
A post published on August 12, 2025 describes a method that plans dependencies among subproblems for each query and reports outperforming GraphRAG systems on three multi-hop question-answering datasets.
Source: LogicRAG cria um grafo de raciocínio durante a consulta (x.com). Text prepared with AI from this source.
What happened and what to do
The post, published on August 12, 2025, describes LogicRAG: for each query, the method plans a directed acyclic graph of dependent subproblems, retrieves evidence sequentially, and maintains an incremental summary. The post reports that it outperformed GraphRAG systems on three multi-hop question-answering datasets, without building an offline graph. The approach is presented as an alternative to prebuilt knowledge graphs for multi-hop retrieval.
A company could test this strategy on questions that require combining information from multiple sources, comparing it with its current search on a representative query set. An implementation could record plans, retrieved evidence, answers, and latency, while enabling human review and quality monitoring. This lets the team assess whether the method improves retrieval without taking on the cost of maintaining a precomputed graph.
How the consultancy can help
Wendelmaques can assess relevant queries and sources, define a comparative evaluation, and propose a scoped implementation of the retrieval workflow, with monitoring and operation suited to the company’s environment.
Next step
Send a short description of the queries, sources, and systems involved to receive a proposal scoped for diagnosis and implementation.
Let's assess your project
I reply with scope and a proposal.