Retrieval-Augmented Generation

When Hidden Variables Become Hidden Costs In causal inference, confounders are the uninvited guests at your data party — variables that influence both treatment and outcome, quietly skewing results. In healthcare, failing to adjust for them can turn life-saving insights into misleading noise. Traditionally, finding these culprits has been the realm of domain experts, a slow and costly process that doesn’t scale well. The paper from National Sun Yat-Sen University proposes a radical alternative: put Large Language Model (LLM)-based agents into the causal inference loop. These agents don’t just crunch numbers — they reason, retrieve domain knowledge, and iteratively refine estimates, effectively acting as tireless, always-available junior experts. ...

When it comes to retrieval-augmented generation (RAG), size matters—but not in the way you might think. Most high-performing GraphRAG systems extract structured triples (subject, predicate, object) from texts using large language models (LLMs), then link them to form reasoning chains. But this method doesn’t scale: if your corpus contains millions of documents, pre-processing every one with an LLM becomes prohibitively expensive. That’s the bottleneck the authors of “Millions of GeAR-s” set out to solve. And their solution is elegant: skip the LLM-heavy preprocessing entirely, and use existing knowledge graphs (like Wikidata) as a reasoning scaffold. ...