Knowledge-Base

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. ...

Write-Back to the Future: When Your RAG Starts Learning A RAG system usually fails in a very ordinary way. The retriever finds something relevant, but not quite enough. The generator receives five passages, three of which are useful, one of which is decorative furniture, and one of which looks relevant only because it shares the right vocabulary. The answer is then expected to emerge from this little committee of half-helpful paragraphs. Sometimes it does. Sometimes it does what committees do. ...