Agentic AutoRAG: LLM Agents Diagnose Retrieval vs Generation Failures to Tune RAG Pipelines
_reachsumit · x · 2026-10-07
A new arXiv paper introduces Agentic AutoRAG, an LLM-agent optimizer for multi-objective RAG hyperparameter optimization, addressing the expensive search space of chunking, embeddings, reranking and generation choices.
Key design:
- Unlike greedy or Bayesian optimizers that reduce trials to aggregate scores, a Diagnoser attributes each failed question to retrieval or generation, and a Proposer grounded in model-ranking and pricing knowledge selects the next configuration, balancing accuracy and cost along a Pareto frontier
- Configurations are scored on a frozen exam built from the corpus
Results: higher LLM-judge accuracy than all baselines on three multi-hop QA benchmarks; matches or beats 30-trial baselines within its first 10 trials. In cost-aware mode on a real healthcare corpus it reaches 77% median exam accuracy, above the strongest baseline.
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