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Spark: A System for Scientifically Creative Idea Generation

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arxiv 2504.20090 v2 pith:7N7EZVAK submitted 2025-04-25 cs.AI cs.IRcs.LG

classification cs.AIcs.IRcs.LG
keywords generationideallmssystemcreativeexplorefoundationalideas
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, large language models (LLMs) have shown promising abilities to generate novel research ideas in science, a direction which coincides with many foundational principles in computational creativity (CC). In light of these developments, we present an idea generation system named Spark that couples retrieval-augmented idea generation using LLMs with a reviewer model named Judge trained on 600K scientific reviews from OpenReview. Our work is both a system demonstration and intended to inspire other CC researchers to explore grounding the generation and evaluation of scientific ideas within foundational CC principles. To this end, we release the annotated dataset used to train Judge, inviting other researchers to explore the use of LLMs for idea generation and creative evaluations.

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Cited by 1 Pith paper

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  1. AI Scientists Fail Without Strong Implementation Capability

    cs.AI 2025-06 conditional novelty 4.0 of 10

    AI scientist systems can propose ideas but cannot reliably implement and verify experiments, making the implementation gap, not idea generation, the current bottleneck.

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