Method Drift›Retrieval-augmented generation
Astute RAG
Retrieval-augmented generation
superseded — cited as a baseline and beaten by newer methods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.”
— ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search“While prior work processes and filters retrieved documents collectively wang2024astuteragovercomingimperfect, weller-etal-2024-defending, our approach assigns each document to an independent agent”
— Retrieval-Augmented Generation with Conflicting Evidence
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.
- After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
BRIDGE_GRPO beats Astute RAG · Accuracy [GPT-3.5-turbo / TRD Real]
75.66 vs 58.60
- After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
BRIDGE_GRPO beats Astute RAG · Accuracy [Qwen 72B / TRD Simu]
85.48 vs 66.29
- RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
RbFT beats Astute RAG · EM [Llama, Clean (τ=0)]
48.4 vs 37.6
- RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
RbFT beats Astute RAG · EM [Llama, Normal (τ=0.4) - Noisy]
44.5 vs 34.4
- RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
RbFT beats Astute RAG · EM [Llama, Hard (τ=1.0) - Counterfactual]
33.8 vs 19.6
- RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
RbFT beats Astute RAG · EM [Qwen, Hard (τ=1.0) - Counterfactual]
25.1 vs 15.3
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · FaithEval [Llama3.3-70B-Inst]
43.10 vs 37.10
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · AmbigDocs [Llama3.3-70B-Inst]
58.20 vs 46.80
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · MADAM_dataset [Llama3.3-70B-Inst]
34.40 vs 31.80
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · FaithEval [Qwen2.5-72B-Inst]
57.70 vs 44.60
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · AmbigDocs [Qwen2.5-72B-Inst]
52.70 vs 39.80
- Retrieval-Augmented Generation with Conflicting Evidence
MADAM beats Astute RAG · MADAM_dataset [Qwen2.5-72B-Inst]
26.40 vs 20.80
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- 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