Is Astute RAG superseded?

Astute RAG (Retrieval-augmented generation): superseded — cited as a baseline and beaten by newer methods. 2 paper(s) critique it, 3 beat it on benchmarks — #40 of 1179 most-superseded. Sub-problem: cluster led by RobustRAG. Newer alternatives in the same sub-problem include CORDON-MAS, BiRD, CleanBase, Beyond Factual Grounding, RAGShield.

Method Drift›Retrieval-augmented generation

Superseded baseline#40 of 1,179 most-superseded

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superseded — cited as a baseline and beaten by newer methods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.

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
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG
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
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
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
Retrieval-Augmented Generation with Conflicting Evidence

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.