Researchers at KAIST and Microsoft published "Follow the Entities: A Corpus Map for Agentic Search" (arXiv:2609.37226), quantifying how much compute LLM search agents waste navigating raw document cor…
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…
Hubmesh, a new Python library, improves multi-hop RAG retrieval by adding a centrality-aware planner over existing vector databases, eliminating LLM calls in the query path. The library uses multi-com…
Researchers introduced AgentMemBench, a benchmark evaluating five long-term memory strategies for conversational AI agents across three datasets and 491 annotated question turns, finding that external…
Researchers introduce MemHop, a multi-hop memory benchmark of 1,000 questions at hop depths 1-5 across 10 social-network scenarios, and ProGraph, a two-layer memory architecture combining profile expa…
AWS announced HippoRAG, a neurobiologically inspired retrieval augmented generation framework using Amazon Bedrock, Amazon Neptune, and Personalized PageRank to improve multi-hop reasoning in large la…
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 …