MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation

2026-10-08

AI Summary

This study addresses the limitations of static workflows, the trade-off between novelty and feasibility, and uncontrollable evaluation in scientific idea generation by proposing an explicitly controllable graph-structured flow-of-thought framework. This method models ideation as a directed graph, incorporating modular cognitive operators and a probabilistic supernetwork. A controller dynamically samples high-quality reasoning paths via tournament-based relative ranking optimization, while a comprehensive evaluation protocol is established to balance problem discovery with resolution. Multi-topic experiments demonstrate the superiority of this framework, achieving explicit generation, controllable optimization, and high-quality innovation of scientific research ideas.

Abstract

Research idea innovation is a fundamental engine of scientific progress, yet it remains difficult to generate and evaluate in a scalable and controllable way. This challenge lies in its inherently open-ended and multi-objective nature, where ideas should balance novelty, plausibility and feasibility. While recent LLM-based approaches have made progress through carefully designed prompts or agent pipelines, they are constrained by predefined, static ideation workflows. To address this limitation, we propose MindFlow, a framework that explicitly formulates ideation as a graph-structured Flow in Mind, which is composed of modular thinking operators and modeled by a probabilistic mind supernet. Given a research topic, a controller dynamically samples thinking flows to generate candidate ideas. This open-ended problem is optimized using a tournament-based relative ranking, enabling the controller to progressively favor higher-quality thinking flows. We further introduce an evaluation protocol that jointly assesses problem finding and problem solving, going beyond title- or abstractonly judgments. Across diverse topics, MindFlow shows its superiority as an explicit, controllable and optimizable research idea innovator.

Problem

• research idea innovation
• open-ended generation
• multi-objective optimization
• ideation workflow
• idea evaluation

Innovation

• Thinking Flows
• Mind Supernet
• Graph-structured Ideation
• Tournament-based Optimization
• Research Idea Innovation