Year: 2026
Type: conference-paper
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 opensource 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.
Authors: Kuo Tian, Pengfei Sun, Zhen Wu, Junran Ding, Xinyu Dai
Institution: Nanjing University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166871960