CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation

TL;DR

CogGen is a cognitively inspired recursive framework that improves deep research report generation by enabling flexible planning, global restructuring, and multimodal content integration, achieving state-of-the-art results.

Contribution

Introducing CogGen, a recursive architecture with Abstract Visual Representation and CLEF benchmark, advancing the quality and flexibility of research report generation.

Findings

CogGen achieves state-of-the-art results among open-source systems.

Reports generated by CogGen are comparable to professional analysts' outputs.

CogGen surpasses Gemini Deep Research in quality.

Abstract

The autonomous synthesis of deep research reports represents a critical frontier for Large Language Models (LLMs), demanding sophisticated information orchestration and non-linear narrative logic. Current approaches rely on rigid predefined linear workflows, which cause error accumulation, preclude global restructuring from subsequent insights, and ultimately limit in-depth multimodal fusion and report quality. We propose CogGen, a Cognitively inspired recursive framework for deep research report Generation. Leveraging a Hierarchical Recursive Architecture to simulate cognitive writing, CogGen enables flexible planning and global restructuring. To extend this recursivity to multimodal content, we introduce Abstract Visual Representation (AVR): a concise intent-driven language that iteratively refines visual-text layouts without pixel-level regeneration overhead. We further present CLEF, a Cognitive Load Evaluation Framework, and curate a new benchmark from Our World in Data (OWID). Extensive experiments show CogGen achieves state-of-the-art results among open-source systems, generating reports comparable to professional analysts' outputs and surpassing Gemini Deep Research. Our code and dataset are available at https://github.com/NJUNLP/CogGen.