Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

arXiv 2026 38.4 method

TLDR

Idea-Catalyst uses LLMs to generate interdisciplinary insights for creative brainstorming, avoiding premature solution anchoring.

Reasoning

The paper addresses an important gap in interdisciplinary creativity but lacks empirical validation or real-world experiments, relying on a conceptual framework. Its strength lies in augmenting reasoning rather than automating discovery, but the absence of concrete results limits impact.

Read-first score

Read-first score 38.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 29.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 25%

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

Reproducibility 25%

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

Topical relevance 42%

24.2

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 40.

Keyword Scores

AI for scientific research

literature review agent

automated scientific discovery

survey generation

scientific discovery agent

AI scientist

automated research

research automation

autonomous research agent

automated experimentation

experiment design agent

paper writing agent

Resources

Paper