Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation

ACL ARR 2026 January Submission3457 Authors

Keywords: Large Language Models, Chain-of-Thought Reasoning, Agentic Systems, Deep Research, Financial Report Generation

Abstract: Financial report generation is a complex task that requires gathering and reasoning over multi-source information. Recent advances in Large Language Models (LLMs) have made them a promising solution for automating this process. However, the reasoning paths in traditional Chain-of-Thought paradigms are inherently constrained by predefined, static computational topologies, rendering them ill-equipped to handle the dynamic uncertainties of real-world financial environments. To tackle this challenge, we propose Cogito, a cognitively grounded agentic framework for professional financial report generation. At its core, Cogito is driven by Dynamic Graph of Thoughts, a novel reasoning mechanism that models the agent’s reasoning process as an evolving topology for adaptive exploration. We further introduce a Social Collaboration Mechanism to facilitate coordinated agent interaction. Finally, Cogito is instantiated as a multi-agent system, where four specialized agents collaboratively execute the end-to-end report generation task. Extensive experiments on enterprise- and industry-level financial report generation benchmarks demonstrate the superiority of Cogito in data quality, analytical validity, and presentation quality.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: planning in agents, chain-of-thought, LLM agents, financial/business NLP, multi-agent systems, agent coordination and negotiation

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English, Chinese

Submission Number: 3457