Multi-Agent Approach for Dynamic Research Insight Path Generation

In recent years, researchers have witnessed rapid advancements in conducting paper surveys using generative AI, enhancing survey efficiency to some extent. However, today’s generative AI lacks deep research training to analyze logical threads woven across multiple papers. A concise visualization method is also expected to present logical connections among various papers. These logical threads are often implicit in the issue ontology authors commonly employ when writing papers. Building on this issue ontology, our method utilizes Dynamic Programming with multiple agents to generate an insight path. The key feature of this approach is the collaboration of multiple agents to adapt to a complex environment and make optimized decisions on issue ontology selection. This path aims to succinctly express longitudinal logical connections among multiple papers, including commonality, difference, and inheritance.