Mind2Report: A Cognitive Deep Research Agent for Expert-Level Commercial Report Synthesis
🤖 AI Summary
This work addresses the challenge of generating high-quality, comprehensive, and reliable business reports from vast amounts of noisy web data to support high-stakes decision-making. The authors propose a training-free agentic workflow that emulates the cognitive process of professional business analysts. Their approach integrates fine-grained intent parsing, dynamic web retrieval, real-time information distillation, and iterative report generation, augmented by a dynamic memory mechanism to enhance the long-horizon reasoning capabilities of large language models. Evaluated on QRC-Eval—a newly curated benchmark comprising 200 real-world business tasks—the proposed method significantly outperforms state-of-the-art deep research agents from OpenAI and Gemini, achieving expert-level performance in automated business report synthesis.
Application Category
📝 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 holistic evaluation strategy to assess report quality, reliability, and coverage. Experiments demonstrate that Mind2Report outperforms leading baselines, including OpenAI and Gemini deep research agents. Although this is a preliminary study, we expect it to serve as a foundation for advancing the future design of commercial deep research agents. Our code and data are available at https://github.com/Melmaphother/Mind2Report.
Problem
Research questions and friction points this paper is trying to address.
commercial report synthesis
deep research agent
information reliability
report quality
web source integration
Innovation
Methods, ideas, or system contributions that make the work stand out.
cognitive agent
dynamic memory
training-free agentic workflow
expert-level report synthesis
deep research
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