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 …
A reasoning-aware compression framework that selectively restores the most quantization-sensitive circuits to FP16 achieves Pareto-optimal energy-accuracy points unreachable by uniform quantization, a…
A new AI memory system achieved #1 on the BABILong benchmark at 10M tokens, with a gaming audit showing 24/24 accuracy at 1M tokens and 16/16 at 2M, 5M, and 10M tokens on RULER variable tracking. The …
Researchers introduced TRIAD, a three-stage automated dataset generation approach for evaluating retrieval-augmented generation (RAG) systems on domain-specific knowledge bases, according to a paper o…
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…
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…
Hubmesh, a new open-source project, introduces a local-first multi-hop retrieval system that combines Chroma vector search with an entity graph and Personalized PageRank, achieving 75.2% supporting-fa…
A new open-source library, hubmesh, adds a graph layer on top of Qdrant to answer multi-hop questions that stymie vector search. The library builds an entity-document graph at index time and uses Pers…
A new arXiv preprint (2608.00585v1) shows that per-chunk verification fails on multi-hop retrieval-augmented generation, with entailment scoring reaching only 0.643, 0.523, and 0.560 AUC on HotpotQA, …
Researchers at arXiv propose Telegraph English, a readable symbolic format for context compression that rewrites retrieved passages into structured entity-relation statements. In experiments on multi-…
Yanjia Sun, Sifan Liu, and Jie Shao introduced CQC-RAG, a framework that improves Retrieval-Augmented Generation robustness by rewriting input questions into diverse queries and selecting answers base…
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 …