Document Visual Question Answering (DocVQA) is a practical yet challenging task, which is to ask questions based on documents while referring to multiple pages and different modalities of information, e.g., images and tables.To handle multi-modality, recent methods follow a similar Retrieval Augmented Generation (RAG) pipeline, but utilize Visual Language Models (VLMs) based embedding model to embed and retrieve relevant pages as images, and generate answers with VLMs that can accept an image as input.In this paper, we introduce SimpleDoc, a lightweight yet powerful retrieval-augmented framework for DocVQA.It boosts evidence page gathering by first retrieving candidates through embedding similarity and then filtering and re-ranking these candidates based on page summaries.A single VLM-based reasoner agent repeatedly invokes this dual-cue retriever, iteratively pulling fresh pages into a working memory until the question is confidently answered.SimpleDoc outperforms previous baselines by 3.2% on average on 4 DocVQA datasets with much fewer pages retrieved.

Large Language Models (LLMs) have demonstrated strong performance in information retrieval tasks like passage ranking.Our research examines how instruction-following capabilities in LLMs interact with multi-document comparison tasks, identifying what we term the "Ranking Blind Spot"-a characteristic of LLM decision processes during comparative evaluation.We analyze how this ranking blind spot affects LLM evaluation systems through two approaches: Decision Objective Hijacking, which alters the evaluation goal in pairwise ranking systems, and Decision Criteria Hijacking, which modifies relevance standards across ranking schemes.These approaches demonstrate how content providers could potentially influence LLM-based ranking systems to affect document positioning.These attacks aim to force the LLM ranker to prefer a specific passage and rank it at the top.Malicious content providers can exploit this weakness, which helps them gain additional exposure by attacking the ranker.In our experiment, We empirically show that the proposed attacks are effective in various LLMs and can be generalized to multiple ranking schemes.We apply these attack to realistic examples to show their effectiveness.We also found stronger LLMs are more vulnerable to these attacks.