Researchers introduced Mosaic, a training-free framework that formulates GraphRAG retrieval as a per-query control problem, with an LLM analyzer converting query-specific evidence requirements into a …
Researchers propose FCPRAG, a fusion-controlled parametric retrieval-augmented generation framework that adds a lightweight controller for sample-level adapter fusion, improving multi-passage LoRA inj…
A new arXiv paper argues for ingest-time semantic compilation over query-time interpretation in RAG, reporting 85.2% correctness on compiled claims versus 72.5% for chunk-based RAG. Another study intr…
Researchers propose IterCOMP, a training-free prompt compression framework that iteratively integrates evidence for multi-hop question answering, achieving substantial improvements in Exact Match and …
A 2026 study by Daeyoung Roh and Donghee Han of 12,000 paired agent trajectories across HotpotQA, 2WikiMultiHopQA, and MuSiQue found that agentic RAG failures often stem from procedural errors: on MuS…
Researchers introduced FLARE (Few-shot Learning-based Adaptive Reflective Engine), a framework that uses reflective mechanisms and few-shot reference examples to optimize prompts for large language mo…
Graph-based retrieval outperforms vector RAG on multi-hop reasoning and global summarization, with Microsoft's GraphRAG beating vector RAG on answer comprehensiveness 72 to 83 percent of the time and …
Researchers have identified a new failure mode in multi-step agentic retrieval-augmented generation (RAG) systems called cascading hallucination, where errors from early pipeline stages propagate and …