Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution

2026-08-11

AI Summary

This work addresses the limitations of existing approaches in modeling the evolution of research problems and solutions within academic literature, particularly their neglect of interactions across distinct research trajectories. To overcome this, the authors propose the Tree-of-Ideas framework, which reconstructs branching scholarly trajectories via EvoTrace and introduces EvoAgent—the first cross-trajectory evolutionary reasoning mechanism—to identify common challenges and synthesize complementary solutions, thereby generating well-grounded novel ideas. Integrating citation network analysis, trajectory reconstruction algorithms, and large language model–based reasoning, the method transcends the constraints of traditional isolated citation-chain modeling. Automatic evaluation across six AI research topics demonstrates that the generated ideas achieve near-human performance in novelty (6.36), grounding (7.00), and overall score (6.27), closely matching human-level results (6.29).

Abstract

Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature. Existing methods either treat papers as unstructured context or model scholarly evolution as isolated citation chains, overlooking interactions among research trajectories. We propose Tree-of-Ideas (ToI), a two-stage framework. EvoTrace reconstructs branching scholarly trajectories from citations, tracking evolving methods, resolved problems, and gaps. EvoAgent then reasons across trajectories to identify convergent problems and complementary solutions, generating grounded research ideas. Across six AI research topics, ToI achieves the highest score among automatic methods (6.27 vs. 5.36 for the strongest baseline on a 10-point scale), with strong Novelty (6.36) and Groundedness (7.00). Also, its score approaches that of human-paper references (6.29), demonstrating the value of cross-path evolutionary reasoning.

Problem

• research ideation
• scholarly evolution
• citation trajectories
• cross-trajectory reasoning
• research gaps

Innovation

• cross-trajectory reasoning
• scholarly evolution
• research ideation
• citation network analysis
• automated idea generation