FVA-RAG: Falsification-Verification Alignment for Mitigating Sycophantic Hallucinations

• URL: http://arxiv.org/abs/2512.07015v1
• Date: Sun, 07 Dec 2025 21:28:42 GMT
• Title: FVA-RAG: Falsification-Verification Alignment for Mitigating Sycophantic Hallucinations
• Authors: Mayank Ravishankara,
• Abstract summary: Falsification-Verification Alignment RAG (FVA-RAG) is a framework that shifts the retrieval paradigm from Inductive Verification (seeking support) to Deductive Falsification (seeking disproof)<n>We introduce a dual-verification mechanism that explicitly weighs the draft answer against this "Anti-Context"<n>Preliminary experiments on a dataset of common misconceptions demonstrate that FVA-RAG significantly improves robustness against sycophantic hallucinations compared to standard RAG baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems have significantly reduced hallucinations in Large Language Models (LLMs) by grounding responses in external context. However, standard RAG architectures suffer from a critical vulnerability: Retrieval Sycophancy. When presented with a query based on a false premise or a common misconception, vector-based retrievers tend to fetch documents that align with the user's bias rather than objective truth, leading the model to "hallucinate with citations." In this work, we introduce Falsification-Verification Alignment RAG (FVA-RAG), a framework that shifts the retrieval paradigm from Inductive Verification (seeking support) to Deductive Falsification (seeking disproof). Unlike existing "Self-Correction" methods that rely on internal consistency, FVA-RAG deploys a distinct Adversarial Retrieval Policy that actively generates "Kill Queries"-targeted search terms designed to surface contradictory evidence. We introduce a dual-verification mechanism that explicitly weighs the draft answer against this "Anti-Context." Preliminary experiments on a dataset of common misconceptions demonstrate that FVA-RAG significantly improves robustness against sycophantic hallucinations compared to standard RAG baselines, effectively acting as an inference-time "Red Team" for factual generation.

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