Multi-Hop-QA

TL;DR for operators Most retrieval systems treat the external knowledge layer as fixed infrastructure: build an index or graph, then improve the model, retriever, or reranker around it. KBevo tests a different decision. It lets downstream answer quality influence how the knowledge store itself is constructed. That changes what can be optimized. After reinforcement learning, average exact match rises from 36.2 to 41.3 for the 1.7B model and from 36.8 to 46.6 for the 4B model across four QA benchmarks. The resulting structured store can also be edited and reused without retraining the language model. ...

Ask a normal enterprise RAG system a simple factual question, and it behaves politely enough. Retrieve a few passages. Hand them to the model. Generate an answer. Fine. Ask it a question that requires two or three steps, and the machine starts developing expensive habits. It retrieves, reasons, retrieves again, expands the prompt, reasons again, rewrites a query, retrieves more evidence, and then asks the LLM to stitch the mess together. The architecture looks intellectually serious. The invoice looks even more serious. ...

TL;DR for operators A standard RAG system often retrieves the most individually relevant chunks. That is useful until the question needs several different pieces of evidence that must work together. Then the system may return five near-duplicates of the most obvious fact and miss the less obvious fact that actually completes the answer. Excellent. We have reinvented the meeting where everyone brings the same slide. ...