arXiv (All)
open access
ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-step relationships but suffers from severe semantic drift and h
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG#984862
- AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models#173219
- Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval#974360
- A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models#419793
- Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation#1123914
Record · ID 819957
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive