Train the Store, Audit the Facts: What KBevo Changes About Retrieval
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. ...