arXiv (All)
open access
RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation
Retrieval-Augmented Generation (RAG) is typically evaluated under the implicit assumption that the underlying knowledge base (KB) is clean, leaving the behaviour of RAG systems under realistic KB degradation poorly characterised. We introduce RAGStress, a controlled evaluation benchmark for stress-t
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
- Collapse, Not Complexity: Failure-Conditioned Decomposition Repair for End-to-End Document Parsing#1049331
- Inductive Graph-based Knowledge Tracing#464508
- MARS - a Message Archiving and Retrieval Service#208899
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#1123430
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#919413
Record · ID 1200342
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