Multi-Hop Reasoning

Search the Graph, Not the Model: RSF-GLLM Separates Traversal from Generation

TL;DR for operators An enterprise assistant may need to reach an answer through an internal identifier or intermediary record that shares almost no wording with the user’s question. Semantic similarity can recognize a plausible final answer while suppressing the unremarkable bridge entity needed to reach it. Larger retrieved neighborhoods do not solve this automatically; they can expose more valid paths while adding more convincing distractions. ...

Replace, Don’t Expand: When RAG Learns to Throw Things Away

The inbox problem hiding inside RAG Inbox. That is the easiest way to understand what goes wrong in many retrieval-augmented generation systems. A query arrives. The system retrieves a few documents. The answer is not obvious. So the system retrieves more. Then more. Then perhaps a web search result. Then a rewritten query. Then another bundle of passages. ...

From Snippets to Synthesis: INRAExplorer and the Rise of Agentic RAG

TL;DR for operators Most enterprise RAG systems still behave like diligent interns with a search box: they retrieve a handful of plausible snippets, hand them to a language model, and hope the synthesis does not quietly forget half the question. That works for narrow Q&A. It fails when the user asks for a relationship chain, a complete list, or a decision-ready map of who did what, funded by whom, connected to which topic. ...