The authors propose VerTox, the first framework to formulate corpus poisoning as a verifiable reward-guided reinforcement learning problem for neural ranking models. ^[1] Fine-tuned BioBART with NER achieves the best overall performance, highlighting entity-aware extraction as the primary driver of improved patient-friendly summaries. ^[2] Results show that NER consistently improves readability and overall quality, while RAG alone offers no benefit and can introduce hallucinations from irrelevant retrieved terms. ^[3] Experiments 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] The article states that the difficult part of Retrieval-Augmented Generation (RAG) does not end once the retriever returns the correct text chunks. ^[5] The author asserts that retrieved chunks must still be merged with the user's query to be effectively used. ^[6] The text implies that without merging retrieved context with the query, the RAG process is incomplete. ^[7]