Mind2Report: A Cognitive Deep Research Agent for Expert-Level Commercial Report Synthesis
Mingyue Cheng, Daoyu Wang, Qi Liu, Shuo Yu, Xiaoyu Tao, Yuqian Wang, Chengzhong Chu, Yu Duan, Mingkang Long, Enhong Chen

TL;DR
Mind2Report is a novel cognitive research agent that emulates expert analysts to synthesize high-quality, reliable commercial reports from web sources, outperforming existing models through a dynamic, memory-augmented workflow.
Contribution
It introduces a training-free, memory-augmented agentic framework for expert-level report synthesis, advancing the capabilities of large language models in complex research tasks.
Findings
Outperforms leading deep research agents like OpenAI and Gemini.
Constructed QRC-Eval with 200 real-world commercial tasks for evaluation.
Demonstrates improved report quality, reliability, and coverage.
Abstract
Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although current deep research agents achieve notable progress, their reports still remain limited in terms of quality, reliability, and coverage. In this work, we propose Mind2Report, a cognitive deep research agent that emulates the commercial analyst to synthesize expert-level reports. Specifically, it first probes fine-grained intent, then searches web sources and records distilled information on the fly, and subsequently iteratively synthesizes the report. We design Mind2Report as a training-free agentic workflow that augments general large language models (LLMs) with dynamic memory to support these long-form cognitive processes. To rigorously evaluate Mind2Report, we further construct QRC-Eval comprising 200 real-world commercial tasks and establish a…
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Taxonomy
TopicsExpert finding and Q&A systems · Topic Modeling · Computational and Text Analysis Methods
