---
title: "Who cites The Ideation-Execution Gap: — 13 citing papers"
description: "13 papers of the Elamur Research library cite «The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human…"
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# Who cites «The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas»](https://md.research.elamur.ai/free/en/papers/si-2025-ideation-execution-gap-execution/)

1. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
2. [**AI for Auto-Research: Roadmap & User Guide arXiv:2605.18661**Lingdong Kong, Xian Sun, Wei Chow, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/kong-2026-ai-auto-research-roadmap-user/)
3. [**AlphaResearch: Accelerating New Algorithm Discovery with Language Models arXiv:2511.08522**Zhaojian Yu, Kaiyue Feng, Yilun Zhao, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-alpharesearch-accelerating-new-algorithm/)
4. [**SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones? arXiv:2605.30329**Sy-Tuyen Ho, Minghui Liu, Huy Nghiem, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/ho-2026-soundnessbench-ai-scientist-really/)
5. [**FARS: A Fully Automated Research System Deployed at Scale arXiv:2606.31651**Qiong Tang, Tianxiang Sun, Xiangkun Hu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tang-2026-fars-fully-automated-research/)
6. **[Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation](https://md.research.elamur.ai/free/en/papers/mule-2026-teaching-language-models-forecast-research/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Srujan P Mule, Aniketh Garikaparthi, Manasi Patwardhan*2026*

7 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/si-2025-ideation-execution-gap-execution/) — The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas