Diversifying Personalized Research Ideation against AI-Induced Homogenization

2026-07-30

AI Summary

This study addresses the tendency of existing AI-assisted research topic recommendation methods to produce generic suggestions lacking individual specificity, thereby fostering homogenization across research communities. To counter this, the authors propose DivAlign, a four-stage pipeline that integrates fine-grained researcher profiling, conditional generation of personalized research directions, three-dimensional alignment scoring (assessing feasibility, interpretability, and growth potential), and community-level redundancy suppression. This framework uniquely incorporates community-wide diversity into the design of personalized topic recommendation systems, balancing individual suitability with the health of the collective innovation ecosystem. Evaluated on a benchmark of 95 AI researchers, DivAlign reduced average pairwise topic similarity from 0.331 to 0.294 and nearest-neighbor similarity from 0.704 to 0.608, while preserving 99.9% of individual alignment scores.

Abstract

AI-assisted research ideation has emerged as a promising paradigm for accelerating scientific discovery, with systems now capable of generating research directions conditioned on papers, topics, or lightweight researcher contexts. Yet current systems largely optimize individual suggestions in isolation. This leaves two blind spots. First, coarse researcher representations may elicit mainstream directions that appear broadly feasible, but lack sufficient researcher-specific grounding. Second, independent recommendations can concentrate a community's portfolio around recurring high-probability themes. To address these blind spots, we propose DivAlign, a four-stage pipeline for alignment-preserving de-homogenization. DivAlign extracts fine-grained researcher profiles, generates profile-conditioned candidate directions, scores them along three alignment dimensions (Executability, Comprehensibility, and Growth Potential), and surfaces researcher-local directions while reducing redundancy across the community portfolio. On a benchmark we construct from 95 AI researchers across five subfields, DivAlign reduces community-level redundancy while preserving researcher-direction fit. Compared with coarse single-shot ideation, it lowers average pairwise similarity from 0.331 to 0.294 and nearest-neighbor similarity from 0.704 to 0.608. Compared with the independent top-choice variant, DivAlign reduces nearest-neighbor similarity from 0.663 to 0.608 while retaining 99.9% of the researcher-direction fit score. Code and data are available at https://github.com/Ruixxxx/DivAlign.

Problem

• AI-assisted research ideation
• homogenization
• personalization
• research direction diversity
• researcher representation

Innovation

• de-homogenization
• fine-grained researcher profiling
• alignment-preserving diversification
• AI-assisted ideation
• research direction generation