The authors propose VerTox, the first framework to formulate corpus poisoning as a verifiable reward-guided reinforcement learning problem for neural ranking models. ^[1] By explicitly encouraging factual corruption, the adversarial documents generated by VerTox significantly degrade the performance of a downstream retrieval-augmented generation (RAG) application. ^[2] 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. ^[3] The VerTox framework explicitly couples ranking distortion with factual corruption through specialized reward shaping to fine-tune compact large language models into adversarial generators. ^[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]