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    "headline": "Researchers Propose VerTox Framework to Poison Neural Ranking Models",
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    "prose": "The authors propose VerTox, the first framework to formulate corpus poisoning as a verifiable reward-guided reinforcement learning problem for neural ranking models. [^1]\n\nFine-tuned BioBART with NER achieves the best overall performance, highlighting entity-aware extraction as the primary driver of improved patient-friendly summaries. [^2]\n\nResults show that NER consistently improves readability and overall quality, while RAG alone offers no benefit and can introduce hallucinations from irrelevant retrieved terms. [^3]\n\nExperiments demonstrate that the VerTox method achieves near-perfect attack success rates, producing adversarial documents that frequently rank higher than target documents across major neural ranking architectures. [^4]\n\nThe article states that the difficult part of Retrieval-Augmented Generation (RAG) does not end once the retriever returns the correct text chunks. [^5]\n\nThe author asserts that retrieved chunks must still be merged with the user's query to be effectively used. [^6]\n\nThe text implies that without merging retrieved context with the query, the RAG process is incomplete. [^7]",
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