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Dobrik Georgiev

Dobrik Georgiev

Research Scientist

Posts

  • in Articles
  • 8 min read

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

Knowledge graphs are an efficient and easily verifiable repository of factual information and using knowledge graph queries as a tool for LLMs to improve the factuality of their output is a promising direction. But have you ever wondered how to make query execution work for knowledge graph RAG? "No!"/"Boring!" Let us guess — queries were flawed, knowledge graphs incomplete, results were simply suboptimal. What if we tell you that we have discovered a secret... recipe.

September Papers: The L in ML Stands for LLMs

For September, the research team reviewed a whopping 22 papers! Needless to say, competition was fierce, and only four made the final cut for this month’s edition, which is LLM-themed:

  • FlowRL uses GFlowNets to train LLMs on full reward distributions, promoting diverse reasoning paths instead of just reward maximization.
  • Soft Tokens, Hard Truths proposes using continuous “soft” tokens with injected noise to enable reinforcement learning fine-tuning of LLM reasoning.
  • Set Block Decoding accelerates LLM inference by generating multiple tokens in parallel using non-causal attention and iterative entropy-based sampling.
  • Metacognitive Reuse enables LLMs to extract and reuse concise reasoning “behaviors” to improve efficiency and reduce repeated computation.