Paper detail

Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains

In sensitive domains, Retrieval-Augmented Generation (RAG) must be interpretable and robust because errors do not just mislead, they invite lawsuits, undermine scholarly credibility, and breach compliance. Stakeholders require traceable evidence, clear rationales for why specific evidence is selected, and safeguards against poisoned or misleading content. Yet current RAG pipelines rely on similarity-based retrieval with arbitrary top-k cutoffs, provide no explanation for selections, and remain vulnerable to poisoning attacks. We propose METEORA, which replaces these drawbacks with rationale-driven selection, using explicit reasoning to guide evidence choice, explain decisions, and improve robustness to RAG poisoning. METEORA operates in three stages: (1) a general-purpose LLM is preference-tuned to generate query-conditioned rationales using direct preference optimization; (2) these rationales drive an Evidence Chunk Selection Engine that pairs rationales with retrieved evidence for query-specific relevance and applies elbow detection to choose an adaptive cutoff (optionally expanding context with neighboring chunks); and (3) a Verifier LLM uses the rationales to detect and filter poisoned or misleading evidence before generation. Across six datasets, METEORA achieves 13.41% higher recall and, without expansion, 21.05% higher precision than the strongest baseline. It reduces the evidence needed for comparable recall by 80%, improving downstream answer accuracy by 33.34%, and strengthens adversarial defense by increasing F1 from 0.10 to 0.44. Code is available at: https://anonymous.4open.science/r/METEORA-DC46/README.md