---
title: "SciImpact — paper by Zhu et al. (2026) — summary and ideas"
description: "Most work on forecasting research influence treats citation counts as the only target. 49 references."
authors: ["Hangxiao Zhu", "Yuyu Zhang", "Ping Nie", "Yu Zhang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1445/"
doi: "10.18653/v1/2026.findings-acl.1445"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhu-2026-sciimpact-multi-dimensional-multi-field/"
---
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# SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction

**Authors:** [Hangxiao Zhu](https://md.research.elamur.ai/free/en/authors/hangxiao-zhu/), [Yuyu Zhang](https://md.research.elamur.ai/free/en/authors/yuyu-zhang/), [Ping Nie](https://md.research.elamur.ai/free/en/authors/ping-nie/) and [Yu Zhang](https://md.research.elamur.ai/free/en/authors/yu-zhang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1445/) · [PDF](https://aclanthology.org/2026.findings-acl.1445.pdf) · [DOI 10.18653/v1/2026.findings-acl.1445](https://doi.org/10.18653/v1/2026.findings-acl.1445) · [OpenAlex](https://openalex.org/W7166827432)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most work on forecasting research influence treats citation counts as the only target. This leaves open whether models can judge other kinds of influence. SciImpact addresses this with a benchmark covering seven impact dimensions: Citation, Award, Patent, Media, Code (GitHub stars), Dataset and Model (Hugging Face downloads). It spans all 19 fields of the Microsoft Academic Graph.

The authors merged scattered existing resources and crawled the web for signals that were missing, including star totals for GitHub repositories and best-paper honors granted by MDPI. They used a three-stage pipeline: candidate retrieval, labeling with pair generation, and quality filtering. The task is pairwise: given two artifacts, a model decides which one has greater impact in a given dimension.

They tested 11 LLMs and found that accuracy varies widely by dimension and field. Multi-task instruction tuning of small models (Qwen3-4B, LLaMA-3.2-3B) produced predictors that beat a 30B model and o4-mini. This shows that impact prediction beyond citations is both learnable and challenging.

## Key points
- **Scale:** 215,928 contrastive pairs. They cover short-horizon signals (conference Best Paper Awards) and long-horizon ones (the Nobel Prize in physics, chemistry and medicine).
- **Pair construction:** For count-based dimensions, both items must pass a minimum count (10 or 5, depending on the dimension). The stronger item must have at least twice the count of the weaker one.
- **Models evaluated:** 11 LLMs, 3 closed-source and 8 open-source. SFT-Qwen3-4B achieved the best average score.
- **Difficulty by dimension:** Best Paper Award prediction is the easiest. Patent and Media are the hardest, likely because they depend on external factors such as market timing and societal relevance.
- **Input and time effects:** Inputs are text only and capped at 1,000 words. Citation accuracy stays roughly flat across publication periods from 2001 to 2020. The authors note a limitation: some instances may test recognition of already-famous papers rather than true forecasting.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) — University, Waterloo, Canada · 80 works · 15,030 citations
- [Texas A&M University](https://md.research.elamur.ai/free/en/universities/texas-a-m-university/) — University, College Station, United States · 29 works · 1,871 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works · [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 249 works · [🎯 Statistics, Probability and Uncertainty](https://md.research.elamur.ai/free/en/shelves/decision-sciences/statistics-probability-and-uncertainty/) · 65 works

🧭 Topics (1): [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,341 works

## Cite

Cite as: Hangxiao Zhu, Yuyu Zhang, Ping Nie and Yu Zhang. 2026. SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhu2026sciimpact,
  title = {{SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction}},
  author = {Hangxiao Zhu, Yuyu Zhang, Ping Nie and Yu Zhang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1445},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1445}
}
```

- [← previous SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration](https://md.research.elamur.ai/free/en/papers/tang-2026-sciexplore-evaluating-autonomous-agents-scientific/)
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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/48/)

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*Updated 2 November 2026*