Tag
SchemaRouter introduces a lightweight schema-graph routing layer for heterogeneous agentic RAG systems, minimizing retrieval overhead by selecting only necessary tools and fields while maintaining answer accuracy and provenance attribution.
This post explains how integrating MCP (Model Context Protocol) into Agentic RAG systems allows each data domain to manage its own MCP servers, enabling standardized data access, security, and decoupled evolution of retrieval systems.
Introduces LayerRAG-Bench, a cross-layer reliability benchmark for agentic retrieval-augmented generation systems, covering 9 fault scenarios and 38,880 records across nine models, with findings that schema normalization fixes schema drift but not stale, unauthorized, or wrong-session evidence.
Explains the difference between traditional RAG and Agentic RAG, highlighting that Agentic RAG adds a decision layer for iterative retrieval and multi-source reasoning, and mentions an upcoming bootcamp.
Introduces GRASP, a reinforcement learning framework that trains agents to adaptively coordinate semantic search, keyword search, and paragraph reading during multi-step reasoning, improving retrieval recall and question answering performance on multi-hop benchmarks.
This article introduces how to upgrade single-turn RAG to Agentic RAG, by allowing the LLM to autonomously decide on multiple retrievals and tool calls to solve multi-step reasoning for complex problems. It provides code examples and implementation ideas based on Qwen3.7-Max.
A clear explanation of Standard RAG, Graph RAG, and Agentic RAG, covering their differences, use cases, and how they handle single-hop vs. multi-hop queries.
This paper presents a Bayesian uncertainty propagation framework for Agentic RAG systems, evaluating it on multi-hop QA benchmarks with GPT models, showing promise for monitoring reliability in industrial decision support.
Agentic RAG uses AI agents to drive the retrieval process in a loop, enabling multi-step reasoning, automatic data source selection, and query optimization, overcoming the limitations of standard RAG in handling multi-hop questions, ambiguous queries, and multiple data sources.
ACIE, an agentic RAG system for clinical information extraction, achieves 96.5% acceptance rate in nuclear-medicine physicians' judgments across 7,326 instances, addressing challenges of heterogeneous patient contexts and missing metadata.
Introduces ARVRE, a two-stage generative framework combining agentic retrieval and reinforcement learning to generate complex and novel physics word problems with guaranteed mathematical correctness.
Google Research introduces an agentic RAG framework hosted on the Gemini Enterprise Agent Platform that uses multi-agent workflows to break down complex enterprise queries and iteratively search for sufficient context, achieving up to 34% increased accuracy over standard RAG.
This paper introduces FinAgent-RAG, an agentic framework for financial document question answering that combines iterative retrieval, Program-of-Thought reasoning, and adaptive resource allocation to improve accuracy and reduce costs.