AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agents

Open-ended deep research agents have emerged as a promising paradigm for autonomously performing comprehensive information gathering and synthesis. However, existing approaches typically integrate information exploration and exploitation into a single unified module—such as an outline generator or a report generator—thereby limiting their flexibility and optimization potential. In this paper, we introduce AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial optimization problem between information exploration and exploitation. Specifically, a critic agent is optimized to evaluate and critique the generated outlines (serving as information exploitation states) and subsequently refine the search queries (serving as information exploration states), while a generator agent is optimized to retrieve updated search results based on the refined search queries (serving as information exploration states) and accordingly update the generated outlines (serving as information exploitation states). The resulting outline, progressively refined through iterative adversarial optimization, is subsequently delivered to a downstream report writer module. This module leverages the structured outline alongside the accumulated search results to synthesize a comprehensive, coherent, and well-grounded research report.