In document AI, reliable analytics require converting long, noisy (often multi-document) corpora into heterogeneous structured data—e.g., tables for numerical fields, graphs for relations, and trees for hierarchies. Yet, one-pass LLM extraction often yields incomplete or inconsistent structures because it lacks explicit verification and opportunities to revise earlier choices. We present CycleIE, an iterative information extraction (IE) framework that closes the loop between reasoning and acting by coupling ReAct with Monte Carlo Tree Search (MCTS). CycleIE employs a multi-agent workflow to iteratively retrieve, structure, extract, and refine content under verification guidance. By treating extraction as a feedback-driven search process, CycleIE systematically corrects omissions and inconsistencies that defeat one-pass methods, while remaining orthogonal to retrieval-augmented generation (RAG). Experiments on challenging document-based QA benchmarks demonstrate that CycleIE delivers >10% relative improvements over strong baselines, particularly in lengthy or multi-document contexts.

A multimodal large language model MLLMs may struggle with answering visual-based (personal) entity questions (VEQA), such as ”who is A?” or ”who is A that B is talking to?” for various reasons, e.g., the absence of the name of A in the caption or the inability of MLLMs to recognize A, particularly for less common entities. Furthermore, even if the MLLMs can identify A, it may refrain from answering due to privacy concerns. In this paper, we introduce a novel method called Matching-Augmented Reasoning (MAR) to enhance VEQA. Given a collection of visual objects with captions, MAR preprocesses each object individually, identifying faces, names, and their alignments within the object. It encodes this information and stores their vector representations in vector databases. When handling VEQA, MAR retrieves matching faces and names and organizes these entities into a matching graph. MAR then derives the answer to the query by reasoning over this matching graph. Extensive experiments show that MAR significantly improves VEQA compared with the state-of-the-art methods using MLLMs.