HybridQuestion: Human-AI Collaboration for Identifying High-Impact Research Questions

arXiv 2025 47.6 method

TLDR

Proposes a human-AI hybrid system with three phases to identify high-impact research questions, validated by an experiment across five disciplines.

Reasoning

Strengths include a clear three-phase methodology combining AI scalability with human judgment, and a concrete experiment. Weaknesses: abstract is cut off, limiting full assessment; the approach is hybrid, not fully autonomous, which may limit novelty claims.

Read-first score

Read-first score 47.6, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 37.

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=analysis,experiment,validation

Topical relevance 42%

30.8

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Field roles

FrontierBridgeMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 34.

Keyword Scores

AI for scientific research

AI scientist

automated scientific discovery

research automation

automated research

literature review agent

scientific discovery agent

autonomous research agent

survey generation

automated experimentation

experiment design agent

paper writing agent

Deep Analysis

Innovations

Human-AI hybrid framework for identifying high-impact research questions, integrating AI's scalable literature processing with human value judgment.
Three-phase methodology: AI-accelerated information gathering, candidate question proposing via ensemble of six LLMs with cross-model voting, and hybrid question selection with progressive human oversight.
Application of the system to both retrospective breakthrough recognition and prospective question forecasting across multiple disciplines.

Methodology

The system first uses AI to process vast literature into a hybrid information base. An ensemble of six diverse LLMs then proposes candidate questions, filtered by cross-model voting. A multi-stage filtering process with increasing human involvement refines the candidates. Validation involved identifying the Top 10 Scientific Breakthroughs of 2025 and Top 10 Scientific Questions for 2026 across five disciplines.

Key Results

AI agents aligned closely with human experts on established breakthroughs but diverged significantly on forecasting future questions, showing human judgment is essential for subjective, forward-looking challenges.

Limitations

AI agents exhibit greater divergence from human experts in forecasting prospective questions, indicating that human judgment remains crucial for evaluating subjective, forward-looking challenges.

Resources

Paper