Retrieval-augmented generation
Astute RAG
Superseded baseline#40 of 1,179 most-superseded
Superseded — cited as a baseline and beaten by newer methods
2 papers critique it · 3 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites Astute RAG as a baseline.
it is not robust against simple adversarial attacks such as prompt injection.
“While prior work processes and filters retrieved documents collectively wang2024astuteragovercomingimperfect, weller-etal-2024-defending, our approach assigns each document to an independent agent”
Beaten on benchmarks
Head-to-head results where a newer method reports beating Astute RAG. Values are copied from the source paper's tables — verify against the cited paper.
MADAM beats Astute RAG
63.00 vs 15.00
AmbigDocs · [GPT-4o-mini]
Retrieval-Augmented Generation with Conflicting EvidenceRbFT beats Astute RAG
33.8 vs 19.6
EM · [Llama, Hard (τ=1.0) - Counterfactual]
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval DefectsBRIDGE_GRPO beats Astute RAG
75.66 vs 58.60
Accuracy · [GPT-3.5-turbo / TRD Real]
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- May 26, 2026
- May 19, 2026
- May 1, 2026
- Beyond Factual GroundingBeyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented GenerationApr 13, 2026
- RAGShieldRAGShield: Provenance-Verified Defense-in-Depth Against Knowledge Base Poisoning in Government Retrieval-Augmented Generation SystemsApr 1, 2026
- Mar 24, 2026
- Jan 13, 2026
- Oct 10, 2025
- RADARRADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized CollaborationSep 28, 2025
- RAGOriginWho Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented GenerationSep 17, 2025