---
title: "ErrorRadar — paper by Yan et al. (2026) — summary and ideas"
description: "The authors define this as a new task, multimodal error detection, and release ErrorRadar as a… 141 references, cited by 22 papers here."
authors: ["Yibo Yan", "Shen Wang", "Jiahao Huo", "Hang Li", "Boyan Li", "Jiamin Su", "Xiong Gao", "YiFan Zhang", "Tianlong Xu", "Zhendong Chu", "Aoxiao Zhong", "Kun Wang", "Hui Xiong", "Philip S. Yu", "Xuming Hu", "Qingsong Wen"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1217/"
doi: "10.18653/v1/2026.findings-acl.1217"
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/yan-2026-errorradar-benchmarking-complex-mathematical-reasoning/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yan-2026-errorradar-benchmarking-complex-mathematical-reasoning/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/) / ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

# ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

**Authors:** [Yibo Yan](https://md.research.elamur.ai/free/en/authors/yibo-yan/), [Shen Wang](https://md.research.elamur.ai/free/en/authors/shen-wang/), [Jiahao Huo](https://md.research.elamur.ai/free/en/authors/jiahao-huo/), [Hang Li](https://md.research.elamur.ai/free/en/authors/hang-li/), [Boyan Li](https://md.research.elamur.ai/free/en/authors/boyan-li/), [Jiamin Su](https://md.research.elamur.ai/free/en/authors/jiamin-su/), Xiong Gao, [YiFan Zhang](https://md.research.elamur.ai/free/en/authors/yifan-zhang/), Tianlong Xu, Zhendong Chu, Aoxiao Zhong, [Kun Wang](https://md.research.elamur.ai/free/en/authors/kun-wang/), [Hui Xiong](https://md.research.elamur.ai/free/en/authors/hui-xiong/), [Philip S. Yu](https://md.research.elamur.ai/free/en/authors/philip-s-yu/), [Xuming Hu](https://md.research.elamur.ai/free/en/authors/xuming-hu/) and [Qingsong Wen](https://md.research.elamur.ai/free/en/authors/qingsong-wen/) · **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.1217/) · [PDF](https://aclanthology.org/2026.findings-acl.1217.pdf) · [DOI 10.18653/v1/2026.findings-acl.1217](https://doi.org/10.18653/v1/2026.findings-acl.1217) · [OpenAlex](https://openalex.org/W4403963967)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [22](https://md.research.elamur.ai/free/en/papers/yan-2026-errorradar-benchmarking-complex-mathematical-reasoning/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Existing math benchmarks for multimodal large language models (MLLMs) mostly check whether a model can solve a problem. In education, a different skill matters: diagnosing where and why a student's solution went wrong. The authors define this as a new task, multimodal error detection, and release ErrorRadar as a benchmark for it. ErrorRadar contains 2,500 K-12 math problems that combine text and images. Each problem is paired with a real incorrect student answer collected from an educational organization. Experts annotated the steps and added metadata.

Models are tested on two subtasks:
- locating the first wrong step in a solution (STEP);
- assigning the mistake to an error category (CATE).

The authors evaluated more than 20 open-source and proprietary MLLMs against educators with graduate-level qualifications. GPT-4o performed best but still trailed the human experts by roughly 10%. Even the humans scored below 70% on both subtasks. The benchmark therefore shows that fine-grained, student-centred reasoning remains hard for current MLLMs.

## Key points
- **Dataset composition:** 2,500 real student-error instances.
  - Problem types: plane geometry, solid geometry, diagram, algebra, mathematical common sense.
  - Error classes: visual perception, calculation, reasoning, knowledge, problem misinterpretation.
- **Closed vs. open models:** proprietary models (GPT-4o, Gemini Pro 1.5, Claude 3.5) beat open-source ones such as InternVL2, LLaVA-NEXT and CogVLM2. They also spread their accuracy more evenly across error categories.
- **Effect of model size:** STEP accuracy grows with scale.
  - InternVL2 rises from 9.8% (2B) to 54.4% (76B).
  - LLaVA-NEXT rises from 30.3% to 51.8%.
  - CATE does not improve consistently and dips at the largest sizes.
- **Specialized models:** reasoning- and math-focused models trail GPT-4o, including QVQ and G-LLaVA, which scored lowest.
- **Subtask difficulty:** both models and humans find locating the erroneous step easier than categorizing the error.

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

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [➗ Mathematics](https://md.research.elamur.ai/free/en/shelves/mathematics/) · 177 works · [➗ Applied Mathematics](https://md.research.elamur.ai/free/en/shelves/mathematics/applied-mathematics/) · 46 works

🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works · [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 657 works

## Cite

Cite as: Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu and Qingsong Wen. 2026. ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection. 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{yan2026errorradar,
  title = {{ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection}},
  author = {Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu and Qingsong Wen},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1217},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1217}
}
```

- [← previous AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://md.research.elamur.ai/free/en/papers/orel-2026-aicd-bench-challenging-benchmark-ai/)
- [next → Qwen3-ASR Technical Report](https://md.research.elamur.ai/free/en/papers/shi-2026-qwen3-asr-technical-report/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/)

## References

141 references, 52 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Amine Abbad-Andaloussi, Andrea Burattin, Tijs Slaats, Ekkart Kindler, and Barbara Weber. 2023. Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load. Expert Systems with Applications, 233:120924. [find](https://scholar.google.com/scholar?q=Amine+Abbad%2DAndaloussi%2C+Andrea+Burattin%2C+Tijs+Slaats%2C+Ekkart+Kindler%2C+and+Barbara+Weber%2E+2023%2E+Complexity+in+declarative+process+models%3A+Metrics+and+multi%2Dmodal+assessment+of+cognitive+load%2E+Expert+Sy)
2. Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, and 1 others. 2024. [Phi-3 technical report: A highly capable language model locally on your phone](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/). arXiv preprint arXiv:2404.14219.
3. Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, and Luke Zettlemoyer. 2023. [Scaling laws for generative mixed-modal language models](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/). In International Conference on Machine Learning, pages 265–279. PMLR.
4. Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, and Wenpeng Yin. 2024. [Large language models for mathematical reasoning: Progresses and challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/). arXiv preprint arXiv:2402.00157.
5. Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019. [Mathqa: Towards interpretable math word problem solving with operation-based formalisms](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/). arXiv preprint arXiv:1905.13319.
6. Anthropic. 2024a. Claude 3. [find](https://scholar.google.com/scholar?q=Anthropic%2E+2024a%2E+Claude+3%2E)
7. Anthropic. 2024b. Claude 3.5. [find](https://scholar.google.com/scholar?q=Anthropic%2E+2024b%2E+Claude+3%2E5%2E)
8. Muhammad Haseeb Aslam, Marco Pedersoli, Alessandro Lameiras Koerich, and Eric Granger. 2024. Multi teacher privileged knowledge distillation for multimodal expression recognition. arXiv preprint arXiv:2408.09035. — [arXiv:2408.09035](https://arxiv.org/abs/2408.09035) [find](https://scholar.google.com/scholar?q=Muhammad+Haseeb+Aslam%2C+Marco+Pedersoli%2C+Alessandro+Lameiras+Koerich%2C+and+Eric+Granger%2E+2024%2E+Multi+teacher+privileged+knowledge+distillation+for+multimodal+expression+recognition%2E+arXiv+preprint+arXiv)
9. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. [Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/). arXiv preprint arXiv:2308.12966.
10. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
11. Jing Bi, Susan Liang, Xiaofei Zhou, Pinxin Liu, Junjia Guo, Yunlong Tang, Luchuan Song, Chao Huang, Guangyu Sun, Jinxi He, and 1 others. 2025. Why reasoning matters? a survey of advancements in multimodal reasoning (v1). arXiv preprint arXiv:2504.03151. — [arXiv:2504.03151](https://arxiv.org/abs/2504.03151) [find](https://scholar.google.com/scholar?q=Jing+Bi%2C+Susan+Liang%2C+Xiaofei+Zhou%2C+Pinxin+Liu%2C+Junjia+Guo%2C+Yunlong+Tang%2C+Luchuan+Song%2C+Chao+Huang%2C+Guangyu+Sun%2C+Jinxi+He%2C+and+1+others%2E+2025%2E+Why+reasoning+matters%3F+a+survey+of+advancements+in+multim)
12. Marcel Binz and Eric Schulz. 2023. Turning large language models into cognitive models. arXiv preprint arXiv:2306.03917. — [arXiv:2306.03917](https://arxiv.org/abs/2306.03917) [find](https://scholar.google.com/scholar?q=Marcel+Binz+and+Eric+Schulz%2E+2023%2E+Turning+large+language+models+into+cognitive+models%2E+arXiv+preprint+arXiv%3A2306%2E03917%2E+%E2%80%94+%5BarXiv%3A2306%2E03917%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2306%2E03917%29)
13. Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P Xing, and Liang Lin. 2021. [Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning](https://md.research.elamur.ai/free/en/papers/chen-2021-geoqa-geometric-question-answering-benchmark/). arXiv preprint arXiv:2105.14517.
14. Shijie Chen, Yu Zhang, and Qiang Yang. 2024. Multi-task learning in natural language processing: An overview. ACM Computing Surveys, 56(12):1–32. [find](https://scholar.google.com/scholar?q=Shijie+Chen%2C+Yu+Zhang%2C+and+Qiang+Yang%2E+2024%2E+Multi%2Dtask+learning+in+natural+language+processing%3A+An+overview%2E+ACM+Computing+Surveys%2C+56%2812%29%3A1%E2%80%9332%2E)
15. Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, and Tony Xia. 2023a. [Theoremqa: A theorem-driven question answering dataset](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7889–7901.
16. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. 2023b. [Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/). arXiv preprint arXiv:2312.14…
17. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and 1 others. 2021. [Training verifiers to solve math word problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/). arXiv preprint arXiv:2110.14168.
18. Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, and 1 others. 2024. A survey on multimodal large language models for autonomous driving. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages… [find](https://scholar.google.com/scholar?q=Can+Cui%2C+Yunsheng+Ma%2C+Xu+Cao%2C+Wenqian+Ye%2C+Yang+Zhou%2C+Kaizhao+Liang%2C+Jintai+Chen%2C+Juanwu+Lu%2C+Zichong+Yang%2C+Kuei%2DDa+Liao%2C+and+1+others%2E+2024%2E+A+survey+on+multimodal+large+language+models+for+autonomous+)
19. Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, Quanquan Gu, James Y Zou, Kai-Wei Chang, and Wei Wang. 2024. Enhancing large vision language models with self-training on image comprehension. Advances in Neural Information Processing Systems, 37:131369–131397. [find](https://scholar.google.com/scholar?q=Yihe+Deng%2C+Pan+Lu%2C+Fan+Yin%2C+Ziniu+Hu%2C+Sheng+Shen%2C+Quanquan+Gu%2C+James+Y+Zou%2C+Kai%2DWei+Chang%2C+and+Wei+Wang%2E+2024%2E+Enhancing+large+vision+language+models+with+self%2Dtraining+on+image+comprehension%2E+Advance)
20. Adrien Doerig, Tim C Kietzmann, Emily Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, and Ian Charest. 2022. Visual representations in the human brain are aligned with large language models. arXiv preprint arXiv:2209.11737. — [arXiv:2209.11737](https://arxiv.org/abs/2209.11737) [find](https://scholar.google.com/scholar?q=Adrien+Doerig%2C+Tim+C+Kietzmann%2C+Emily+Allen%2C+Yihan+Wu%2C+Thomas+Naselaris%2C+Kendrick+Kay%2C+and+Ian+Charest%2E+2022%2E+Visual+representations+in+the+human+brain+are+aligned+with+large+language+models%2E+arXiv+pr)
21. Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, and Xin Wang. 2024. Muffin or chihuahua? challenging multimodal large language models with multipanel vqa. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume… [find](https://scholar.google.com/scholar?q=Yue+Fan%2C+Jing+Gu%2C+Kaiwen+Zhou%2C+Qianqi+Yan%2C+Shan+Jiang%2C+Ching%2DChen+Kuo%2C+Yang+Zhao%2C+Xinze+Guan%2C+and+Xin+Wang%2E+2024%2E+Muffin+or+chihuahua%3F+challenging+multimodal+large+language+models+with+multipanel+vqa%2E)
22. Deqing Fu, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, and Willie Neiswanger. 2024. Isobench: Benchmarking multimodal foundation models on isomorphic representations. arXiv preprint arXiv:2404.01266. — [arXiv:2404.01266](https://arxiv.org/abs/2404.01266) [find](https://scholar.google.com/scholar?q=Deqing+Fu%2C+Ghazal+Khalighinejad%2C+Ollie+Liu%2C+Bhuwan+Dhingra%2C+Dani+Yogatama%2C+Robin+Jia%2C+and+Willie+Neiswanger%2E+2024%2E+Isobench%3A+Benchmarking+multimodal+foundation+models+on+isomorphic+representations%2E+ar)
23. Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, and 1 others. 2024. [Omni-math: A universal olympiad level mathematic benchmark for large language models](https://md.research.elamur.ai/free/en/papers/gao-2024-omni-math-universal-olympiad-level/). arXiv preprint arXiv:2410.07985.
24. Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, and 1 others. 2023. G-llava: Solving geometric problem with multi-modal large language model. arXiv preprint arXiv:2312.11370. [find](https://scholar.google.com/scholar?q=Jiahui+Gao%2C+Renjie+Pi%2C+Jipeng+Zhang%2C+Jiacheng+Ye%2C+Wanjun+Zhong%2C+Yufei+Wang%2C+Lanqing+Hong%2C+Jianhua+Han%2C+Hang+Xu%2C+Zhenguo+Li%2C+and+1+others%2E+2023%2E+G%2Dllava%3A+Solving+geometric+problem+with+multi%2Dmodal+larg)
25. Arindam Ghosh, Thomas Schaaf, and Matthew Gormley. 2022. Adafocal: Calibration-aware adaptive focal loss. Advances in Neural Information Processing Systems, 35:1583–1595. [find](https://scholar.google.com/scholar?q=Arindam+Ghosh%2C+Thomas+Schaaf%2C+and+Matthew+Gormley%2E+2022%2E+Adafocal%3A+Calibration%2Daware+adaptive+focal+loss%2E+Advances+in+Neural+Information+Processing+Systems%2C+35%3A1583%E2%80%931595%2E)
26. Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, and Nathalie Japkowicz. 2024. The class imbalance problem in deep learning. Machine Learning, 113(7):4845–4901. [find](https://scholar.google.com/scholar?q=Kushankur+Ghosh%2C+Colin+Bellinger%2C+Roberto+Corizzo%2C+Paula+Branco%2C+Bartosz+Krawczyk%2C+and+Nathalie+Japkowicz%2E+2024%2E+The+class+imbalance+problem+in+deep+learning%2E+Machine+Learning%2C+113%287%29%3A4845%E2%80%934901%2E)
27. Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, and 1 others. 2024. [Chatglm: A family of large language models from glm-130b to glm-4 all tools](https://md.research.elamur.ai/free/en/papers/glm-2024-chatglm-family-large-language-models/). arXiv preprint arXiv:2406.12793.
28. Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, and 1 others. 2024. Infimm-webmath-40b: Advancing multimodal pre-training for enhanced mathematical reasoning. arXiv preprint arXiv:2409.12568. [find](https://scholar.google.com/scholar?q=Xiaotian+Han%2C+Yiren+Jian%2C+Xuefeng+Hu%2C+Haogeng+Liu%2C+Yiqi+Wang%2C+Qihang+Fan%2C+Yuang+Ai%2C+Huaibo+Huang%2C+Ran+He%2C+Zhenheng+Yang%2C+and+1+others%2E+2024%2E+Infimm%2Dwebmath%2D40b%3A+Advancing+multimodal+pre%2Dtraining+for+e)
29. Xixuan Hao, Wei Chen, Yibo Yan, Siru Zhong, Kun Wang, Qingsong Wen, and Yuxuan Liang. 2024. Urbanvlp: A multi-granularity vision-language pre-trained foundation model for urban indicator prediction. arXiv preprint arXiv:2403.16831. — [arXiv:2403.16831](https://arxiv.org/abs/2403.16831) [find](https://scholar.google.com/scholar?q=Xixuan+Hao%2C+Wei+Chen%2C+Yibo+Yan%2C+Siru+Zhong%2C+Kun+Wang%2C+Qingsong+Wen%2C+and+Yuxuan+Liang%2E+2024%2E+Urbanvlp%3A+A+multi%2Dgranularity+vision%2Dlanguage+pre%2Dtrained+foundation+model+for+urban+indicator+prediction%2E+a)
30. Yunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li, Zhengyuan Yang, Lijuan Wang, and Yu Cheng. 2025. Can mllms reason in multimodality? emma: An enhanced multimodal reasoning benchmark. arXiv preprint arXiv:2501.05444. — [arXiv:2501.05444](https://arxiv.org/abs/2501.05444) [find](https://scholar.google.com/scholar?q=Yunzhuo+Hao%2C+Jiawei+Gu%2C+Huichen+Will+Wang%2C+Linjie+Li%2C+Zhengyuan+Yang%2C+Lijuan+Wang%2C+and+Yu+Cheng%2E+2025%2E+Can+mllms+reason+in+multimodality%3F+emma%3A+An+enhanced+multimodal+reasoning+benchmark%2E+arXiv+prepri)
31. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, and 1 others. 2024a. [Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/). In Proceedings of the 62nd Annual Meetin…
32. Jinlong He, Pengfei Li, Gang Liu, Zixu Zhao, and Shen-jun Zhong. 2024b. Pefomed: Parameter efficient fine-tuning on multimodal large language models for medical visual question answering. arXiv preprint arXiv:2401.02797. — [arXiv:2401.02797](https://arxiv.org/abs/2401.02797) [find](https://scholar.google.com/scholar?q=Jinlong+He%2C+Pengfei+Li%2C+Gang+Liu%2C+Zixu+Zhao%2C+and+Shen%2Djun+Zhong%2E+2024b%2E+Pefomed%3A+Parameter+efficient+fine%2Dtuning+on+multimodal+large+language+models+for+medical+visual+question+answering%2E+arXiv+prepri)
33. Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu, and Hua Huang. 2024c. Cmmu: A benchmark for chinese multimodal multi-type question understanding and reasoning. arXiv preprint arXiv:2401.14011. — [arXiv:2401.14011](https://arxiv.org/abs/2401.14011) [find](https://scholar.google.com/scholar?q=Zheqi+He%2C+Xinya+Wu%2C+Pengfei+Zhou%2C+Richeng+Xuan%2C+Guang+Liu%2C+Xi+Yang%2C+Qiannan+Zhu%2C+and+Hua+Huang%2E+2024c%2E+Cmmu%3A+A+benchmark+for+chinese+multimodal+multi%2Dtype+question+understanding+and+reasoning%2E+arXiv+p)
34. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). arXiv preprint arXiv:2103.03874.
35. Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. [Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/). arXiv preprint arXiv:2305.02301.
36. Haigen Hu, Xiaoyuan Wang, Yan Zhang, Qi Chen, and Qiu Guan. 2024a. A comprehensive survey on contrastive learning. Neurocomputing, page 128645. [find](https://scholar.google.com/scholar?q=Haigen+Hu%2C+Xiaoyuan+Wang%2C+Yan+Zhang%2C+Qi+Chen%2C+and+Qiu+Guan%2E+2024a%2E+A+comprehensive+survey+on+contrastive+learning%2E+Neurocomputing%2C+page+128645%2E)
37. Yushi Hu, Weijia Shi, Xingyu Fu, Dan Roth, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, and Ranjay Krishna. 2024b. Visual sketchpad: Sketching as a visual chain of thought for multimodal language models. arXiv preprint arXiv:2406.09403. [find](https://scholar.google.com/scholar?q=Yushi+Hu%2C+Weijia+Shi%2C+Xingyu+Fu%2C+Dan+Roth%2C+Mari+Ostendorf%2C+Luke+Zettlemoyer%2C+Noah+A+Smith%2C+and+Ranjay+Krishna%2E+2024b%2E+Visual+sketchpad%3A+Sketching+as+a+visual+chain+of+thought+for+multimodal+language+m)
38. Junquan Huang, Haotian Wu, Yubo Gao, Yibo Yan, Junyan Zhang, Yonghua Hei, Song Dai, Jie Zhang, Puay Siew Tan, and Xuming Hu. 2025. Effireason-bench: A unified benchmark for evaluating and advancing efficient reasoning in large language models. arXiv preprint arXiv:2511.10201. — [arXiv:2511.10201](https://arxiv.org/abs/2511.10201) [find](https://scholar.google.com/scholar?q=Junquan+Huang%2C+Haotian+Wu%2C+Yubo+Gao%2C+Yibo+Yan%2C+Junyan+Zhang%2C+Yonghua+Hei%2C+Song+Dai%2C+Jie+Zhang%2C+Puay+Siew+Tan%2C+and+Xuming+Hu%2E+2025%2E+Effireason%2Dbench%3A+A+unified+benchmark+for+evaluating+and+advancing+ef)
39. Xuhan Huang, Qingning Shen, Yan Hu, Anningzhe Gao, and Benyou Wang. 2024. Mamo: a mathematical modeling benchmark with solvers. arXiv preprint arXiv:2405.13144. [find](https://scholar.google.com/scholar?q=Xuhan+Huang%2C+Qingning+Shen%2C+Yan+Hu%2C+Anningzhe+Gao%2C+and+Benyou+Wang%2E+2024%2E+Mamo%3A+a+mathematical+modeling+benchmark+with+solvers%2E+arXiv+preprint+arXiv%3A2405%2E13144%2E)
40. Jiahao Huo, Yibo Yan, Boren Hu, Yutao Yue, and Xuming Hu. 2024. Mmneuron: Discovering neuron-level domain-specific interpretation in multimodal large language model. arXiv preprint arXiv:2406.11193. [find](https://scholar.google.com/scholar?q=Jiahao+Huo%2C+Yibo+Yan%2C+Boren+Hu%2C+Yutao+Yue%2C+and+Xuming+Hu%2E+2024%2E+Mmneuron%3A+Discovering+neuron%2Dlevel+domain%2Dspecific+interpretation+in+multimodal+large+language+model%2E+arXiv+preprint+arXiv%3A2406%2E11193%2E)
41. Farkhund Iqbal, Ahmed Abbasi, Abdul Rehman Javed, Ahmad Almadhor, Zunera Jalil, Sajid Anwar, and Imad Rida. 2024. Data augmentation-based novel deep learning method for deepfaked images detection. ACM Transactions on Multimedia Computing, Communications and Applications, 20(11):1–15. [find](https://scholar.google.com/scholar?q=Farkhund+Iqbal%2C+Ahmed+Abbasi%2C+Abdul+Rehman+Javed%2C+Ahmad+Almadhor%2C+Zunera+Jalil%2C+Sajid+Anwar%2C+and+Imad+Rida%2E+2024%2E+Data+augmentation%2Dbased+novel+deep+learning+method+for+deepfaked+images+detection%2E+ACM)
42. Mengzhao Jia, Zhihan Zhang, Wenhao Yu, Fangkai Jiao, and Meng Jiang. 2024a. Describe-then-reason: Improving multimodal mathematical reasoning through visual comprehension training. arXiv preprint arXiv:2404.14604. — [arXiv:2404.14604](https://arxiv.org/abs/2404.14604) [find](https://scholar.google.com/scholar?q=Mengzhao+Jia%2C+Zhihan+Zhang%2C+Wenhao+Yu%2C+Fangkai+Jiao%2C+and+Meng+Jiang%2E+2024a%2E+Describe%2Dthen%2Dreason%3A+Improving+multimodal+mathematical+reasoning+through+visual+comprehension+training%2E+arXiv+preprint+arXi)
43. Mengzhao Jia, Zhihan Zhang, Wenhao Yu, Fangkai Jiao, and Meng Jiang. 2024b. Describe-then-reason: Improving multimodal mathematical reasoning through visual comprehension training. arXiv preprint arXiv:2404.14604. — [arXiv:2404.14604](https://arxiv.org/abs/2404.14604) [find](https://scholar.google.com/scholar?q=Mengzhao+Jia%2C+Zhihan+Zhang%2C+Wenhao+Yu%2C+Fangkai+Jiao%2C+and+Meng+Jiang%2E+2024b%2E+Describe%2Dthen%2Dreason%3A+Improving+multimodal+mathematical+reasoning+through+visual+comprehension+training%2E+arXiv+preprint+arXi)
44. Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, and Xu Tang. 2021. New generation deep learning for video object detection: A survey. IEEE Transactions on Neural Networks and Learning Systems, 33(8):3195–3215. [find](https://scholar.google.com/scholar?q=Licheng+Jiao%2C+Ruohan+Zhang%2C+Fang+Liu%2C+Shuyuan+Yang%2C+Biao+Hou%2C+Lingling+Li%2C+and+Xu+Tang%2E+2021%2E+New+generation+deep+learning+for+video+object+detection%3A+A+survey%2E+IEEE+Transactions+on+Neural+Networks+an)
45. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. [Scaling laws for neural language models](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/). arXiv preprint arXiv:2001.08361.
46. Mehran Kazemi, Hamidreza Alvari, Ankit Anand, Jialin Wu, Xi Chen, and Radu Soricut. 2023. Geomverse: A systematic evaluation of large models for geometric reasoning. arXiv preprint arXiv:2312.12241. — [arXiv:2312.12241](https://arxiv.org/abs/2312.12241) [find](https://scholar.google.com/scholar?q=Mehran+Kazemi%2C+Hamidreza+Alvari%2C+Ankit+Anand%2C+Jialin+Wu%2C+Xi+Chen%2C+and+Radu+Soricut%2E+2023%2E+Geomverse%3A+A+systematic+evaluation+of+large+models+for+geometric+reasoning%2E+arXiv+preprint+arXiv%3A2312%2E12241%2E+%E2%80%94)
47. Michael J Kennedy and John Elwood Romig. 2024. Cognitive load theory: An applied reintroduction for special and general educators. TEACHING Exceptional Children, 56(6):440–451. [find](https://scholar.google.com/scholar?q=Michael+J+Kennedy+and+John+Elwood+Romig%2E+2024%2E+Cognitive+load+theory%3A+An+applied+reintroduction+for+special+and+general+educators%2E+TEACHING+Exceptional+Children%2C+56%286%29%3A440%E2%80%93451%2E)
48. Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. 2023. [Understanding the effects of rlhf on llm generalisation and diversity](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/). arXiv preprint arXiv:2310.06452.
49. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). Advances in neural information processing systems, 35:22199– 22213.
50. Eldar Kurtic, Amir Moeini, and Dan Alistarh. 2024. Mathador-lm: A dynamic benchmark for mathematical reasoning on large language models. arXiv preprint arXiv:2406.12572. [find](https://scholar.google.com/scholar?q=Eldar+Kurtic%2C+Amir+Moeini%2C+and+Dan+Alistarh%2E+2024%2E+Mathador%2Dlm%3A+A+dynamic+benchmark+for+mathematical+reasoning+on+large+language+models%2E+arXiv+preprint+arXiv%3A2406%2E12572%2E)
51. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, and 1 others. 2022. [Solving quantitative reasoning problems with language models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/). Advances in Neural Information Processing Systems, 35:3843–3857…
52. Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, and Chang Zhou. 2023. Query and response augmentation cannot help out-of-domain math reasoning generalization. [find](https://scholar.google.com/scholar?q=Chengpeng+Li%2C+Zheng+Yuan%2C+Hongyi+Yuan%2C+Guanting+Dong%2C+Keming+Lu%2C+Jiancan+Wu%2C+Chuanqi+Tan%2C+Xiang+Wang%2C+and+Chang+Zhou%2E+2023%2E+Query+and+response+augmentation+cannot+help+out%2Dof%2Ddomain+math+reasoning+gen)
53. Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang, Xing Fan, Haoyang Li, Jiliang Tang, and Qingsong Wen. 2024a. Bringing generative ai to adaptive learning in education. arXiv preprint arXiv:2402.14601. — [arXiv:2402.14601](https://arxiv.org/abs/2402.14601) [find](https://scholar.google.com/scholar?q=Hang+Li%2C+Tianlong+Xu%2C+Chaoli+Zhang%2C+Eason+Chen%2C+Jing+Liang%2C+Xing+Fan%2C+Haoyang+Li%2C+Jiliang+Tang%2C+and+Qingsong+Wen%2E+2024a%2E+Bringing+generative+ai+to+adaptive+learning+in+education%2E+arXiv+preprint+arXiv%3A)
54. Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, and Yansong Feng. 2024c. Are llms capable of data-based statistical and causal reasoning? benchmarking advanced quantitative reasoning with data. arXiv preprint arXiv:2402.17644. [find](https://scholar.google.com/scholar?q=Xiao+Liu%2C+Zirui+Wu%2C+Xueqing+Wu%2C+Pan+Lu%2C+Kai%2DWei+Chang%2C+and+Yansong+Feng%2E+2024c%2E+Are+llms+capable+of+data%2Dbased+statistical+and+causal+reasoning%3F+benchmarking+advanced+quantitative+reasoning+with+data%2E)
55. Qintong Li, Leyang Cui, Xueliang Zhao, Lingpeng Kong, and Wei Bi. 2024b. [Gsm-plus: A comprehensive benchmark for evaluating the robustness of llms as mathematical problem solvers](https://md.research.elamur.ai/free/en/papers/li-2024-gsm-plus-comprehensive-benchmark-evaluating/). arXiv preprint arXiv:2402.19255.
56. Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, and 1 others. 2024a. [Deepseek-vl: towards real-world vision-language understanding](https://md.research.elamur.ai/free/en/papers/lu-2024-deepseek-vl-real-world/). arXiv preprint arXiv:2403.05525.
57. Xiang Li, Chengqi Lv, Wenhai Wang, Gang Li, Lingfeng Yang, and Jian Yang. 2022. Generalized focal loss: Towards efficient representation learning for dense object detection. IEEE transactions on pattern analysis and machine intelligence, 45(3):3139–3153. [find](https://scholar.google.com/scholar?q=Xiang+Li%2C+Chengqi+Lv%2C+Wenhai+Wang%2C+Gang+Li%2C+Lingfeng+Yang%2C+and+Jian+Yang%2E+2022%2E+Generalized+focal+loss%3A+Towards+efficient+representation+learning+for+dense+object+detection%2E+IEEE+transactions+on+patte)
58. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. 2024b. [Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/). arXiv preprint arXiv:2310.02255.
59. Xiaoyuan Li, Wenjie Wang, Moxin Li, Junrong Guo, Yang Zhang, and Fuli Feng. 2024c. Evaluating mathematical reasoning of large language models: A focus on error identification and correction. arXiv preprint arXiv:2406.00755. [find](https://scholar.google.com/scholar?q=Xiaoyuan+Li%2C+Wenjie+Wang%2C+Moxin+Li%2C+Junrong+Guo%2C+Yang+Zhang%2C+and+Fuli+Feng%2E+2024c%2E+Evaluating+mathematical+reasoning+of+large+language+models%3A+A+focus+on+error+identification+and+correction%2E+arXiv+pre)
60. Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Fan-Hu Zeng, Jian Xu, Jia-Xin Zhang, and Cheng-Lin Liu. 2024d. Cmmath: A chinese multi-modal math skill evaluation benchmark for foundation models. arXiv preprint arXiv:2407.12023. [find](https://scholar.google.com/scholar?q=Zhong%2DZhi+Li%2C+Ming%2DLiang+Zhang%2C+Fei+Yin%2C+Zhi%2DLong+Ji%2C+Jin%2DFeng+Bai%2C+Zhen%2DRu+Pan%2C+Fan%2DHu+Zeng%2C+Jian+Xu%2C+Jia%2DXin+Zhang%2C+and+Cheng%2DLin+Liu%2E+2024d%2E+Cmmath%3A+A+chinese+multi%2Dmodal+math+skill+evaluation+benc)
61. Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, and Tianyi Zhou. 2024a. Module-wise adaptive distillation for multimodality foundation models. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Chen+Liang%2C+Jiahui+Yu%2C+Ming%2DHsuan+Yang%2C+Matthew+Brown%2C+Yin+Cui%2C+Tuo+Zhao%2C+Boqing+Gong%2C+and+Tianyi+Zhou%2E+2024a%2E+Module%2Dwise+adaptive+distillation+for+multimodality+foundation+models%2E+Advances+in+Neural)
62. Zhenwen Liang, Dian Yu, Wenhao Yu, Wenlin Yao, Zhihan Zhang, Xiangliang Zhang, and Dong Yu. 2024b. Mathchat: Benchmarking mathematical reasoning and instruction following in multi-turn interactions. arXiv preprint arXiv:2405.19444. [find](https://scholar.google.com/scholar?q=Zhenwen+Liang%2C+Dian+Yu%2C+Wenhao+Yu%2C+Wenlin+Yao%2C+Zhihan+Zhang%2C+Xiangliang+Zhang%2C+and+Dong+Yu%2E+2024b%2E+Mathchat%3A+Benchmarking+mathematical+reasoning+and+instruction+following+in+multi%2Dturn+interactions%2E+a)
63. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024a. Llava-next: Improved reasoning, ocr, and world knowledge. [find](https://scholar.google.com/scholar?q=Haotian+Liu%2C+Chunyuan+Li%2C+Yuheng+Li%2C+Bo+Li%2C+Yuanhan+Zhang%2C+Sheng+Shen%2C+and+Yong+Jae+Lee%2E+2024a%2E+Llava%2Dnext%3A+Improved+reasoning%2C+ocr%2C+and+world+knowledge%2E)
64. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. [Visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/). Advances in neural information processing systems, 36:34892– 34916.
65. Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, and Kai Chen. 2024b. Mathbench: Evaluating the theory and application proficiency of llms with a hierarchical mathematics benchmark. arXiv preprint arXiv:2405.12209. [find](https://scholar.google.com/scholar?q=Hongwei+Liu%2C+Zilong+Zheng%2C+Yuxuan+Qiao%2C+Haodong+Duan%2C+Zhiwei+Fei%2C+Fengzhe+Zhou%2C+Wenwei+Zhang%2C+Songyang+Zhang%2C+Dahua+Lin%2C+and+Kai+Chen%2E+2024b%2E+Mathbench%3A+Evaluating+the+theory+and+application+proficien)
66. Wentao Liu, Qianjun Pan, Yi Zhang, Zhuo Liu, Ji Wu, Jie Zhou, Aimin Zhou, Qin Chen, Bo Jiang, and Liang He. 2025. [Cmm-math: A chinese multimodal math dataset to evaluate and enhance the mathematics reasoning of large multimodal models](https://md.research.elamur.ai/free/en/papers/liu-2024-cmm-math-chinese-multimodal-math/). In Proceedings of the 33rd ACM International Conference on Multi…
67. Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2024c. [Chameleon: Plug-and-play compositional reasoning with large language models](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/). Advances in Neural Information Processing Systems, 36.
68. Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, and Kai-Wei Chang. 2022. A survey of deep learning for mathematical reasoning. arXiv preprint arXiv:2212.10535. [find](https://scholar.google.com/scholar?q=Pan+Lu%2C+Liang+Qiu%2C+Wenhao+Yu%2C+Sean+Welleck%2C+and+Kai%2DWei+Chang%2E+2022%2E+A+survey+of+deep+learning+for+mathematical+reasoning%2E+arXiv+preprint+arXiv%3A2212%2E10535%2E)
69. Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, and Bingzhe Wu. 2023a. [Fairness-guided few-shot prompting for large language models](https://md.research.elamur.ai/free/en/papers/ma-2023-fairness-guided-few-shot-prompting/). Advances in Neural Information Processing Systems, 36:43136–43155.
70. Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun. 2023b. [Large language model is not a good few-shot information extractor, but a good reranker for hard samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/) arXiv preprint arXiv:2303.08559.
71. Yujun Mao, Yoon Kim, and Yilun Zhou. 2024. Champ: A competition-level dataset for fine-grained analyses of llms’ mathematical reasoning capabilities. arXiv preprint arXiv:2401.06961. [find](https://scholar.google.com/scholar?q=Yujun+Mao%2C+Yoon+Kim%2C+and+Yilun+Zhou%2E+2024%2E+Champ%3A+A+competition%2Dlevel+dataset+for+fine%2Dgrained+analyses+of+llms%E2%80%99+mathematical+reasoning+capabilities%2E+arXiv+preprint+arXiv%3A2401%2E06961%2E)
72. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. 2024. [Large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). arXiv preprint arXiv:2402.06196.
73. Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and 1 others. 2022. [Lila: A unified benchmark for mathematical reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/). arXiv preprint arXiv:2210.17517.
74. Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel. 2024. Scaling data-constrained language models. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Niklas+Muennighoff%2C+Alexander+Rush%2C+Boaz+Barak%2C+Teven+Le+Scao%2C+Nouamane+Tazi%2C+Aleksandra+Piktus%2C+Sampo+Pyysalo%2C+Thomas+Wolf%2C+and+Colin+A+Raffel%2E+2024%2E+Scaling+data%2Dconstrained+language+models%2E+Advance)
75. OpenAI. 2023. [GPT-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
76. OpenAI. 2024a. Gpt-4o mini: advancing cost-efficient intelligence. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2024a%2E+Gpt%2D4o+mini%3A+advancing+cost%2Defficient+intelligence%2E)
77. OpenAI. 2024b. [GPT-4o system card](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/).
78. Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See-Kiong Ng, Lidong Bing, and Roy Ka-Wei Lee. 2024b. [Math-llava: Bootstrapping mathematical reasoning for multimodal large language models](https://md.research.elamur.ai/free/en/papers/shi-2024-math-llava-bootstrapping-mathematical-reasoning/). arXiv preprint arXiv:2406.17294.
79. Fred Paas, Alexander Renkl, and John Sweller. 2010. Cognitive load theory and instructional design: Recent developments. Educational Psychologist. [find](https://scholar.google.com/scholar?q=Fred+Paas%2C+Alexander+Renkl%2C+and+John+Sweller%2E+2010%2E+Cognitive+load+theory+and+instructional+design%3A+Recent+developments%2E+Educational+Psychologist%2E)
80. Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See-Kiong Ng, Lidong Bing, and Roy Ka-Wei Lee. 2024c. [Math-llava: Bootstrapping mathematical reasoning for multimodal large language models](https://md.research.elamur.ai/free/en/papers/shi-2024-math-llava-bootstrapping-mathematical-reasoning/). arXiv preprint arXiv:2406.17294.
81. Ankit Pal and Malaikannan Sankarasubbu. 2024. Gemini goes to med school: exploring the capabilities of multimodal large language models on medical challenge problems & hallucinations. arXiv preprint arXiv:2402.07023. [find](https://scholar.google.com/scholar?q=Ankit+Pal+and+Malaikannan+Sankarasubbu%2E+2024%2E+Gemini+goes+to+med+school%3A+exploring+the+capabilities+of+multimodal+large+language+models+on+medical+challenge+problems+%26+hallucinations%2E+arXiv+preprint+a)
82. Mingyang Song, Zhaochen Su, Xiaoye Qu, Jiawei Zhou, and Yu Cheng. 2025. Prmbench: A fine-grained and challenging benchmark for process-level reward models. arXiv preprint arXiv:2501.03124. [find](https://scholar.google.com/scholar?q=Mingyang+Song%2C+Zhaochen+Su%2C+Xiaoye+Qu%2C+Jiawei+Zhou%2C+and+Yu+Cheng%2E+2025%2E+Prmbench%3A+A+fine%2Dgrained+and+challenging+benchmark+for+process%2Dlevel+reward+models%2E+arXiv+preprint+arXiv%3A2501%2E03124%2E)
83. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024. [Unifying large language models and knowledge graphs: A roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/). IEEE Transactions on Knowledge and Data Engineering.
84. Shezheng Song, Xiaopeng Li, and Shasha Li. 2023. How to bridge the gap between modalities: A comprehensive survey on multimodal large language model. arXiv preprint arXiv:2311.07594. — [arXiv:2311.07594](https://arxiv.org/abs/2311.07594) [find](https://scholar.google.com/scholar?q=Shezheng+Song%2C+Xiaopeng+Li%2C+and+Shasha+Li%2E+2023%2E+How+to+bridge+the+gap+between+modalities%3A+A+comprehensive+survey+on+multimodal+large+language+model%2E+arXiv+preprint+arXiv%3A2311%2E07594%2E+%E2%80%94+%5BarXiv%3A2311%2E075)
85. Shuai Peng, Di Fu, Liangcai Gao, Xiuqin Zhong, Hongguang Fu, and Zhi Tang. 2024. Multimath: Bridging visual and mathematical reasoning for large language models. arXiv preprint arXiv:2409.00147. — [arXiv:2409.00147](https://arxiv.org/abs/2409.00147) [find](https://scholar.google.com/scholar?q=Shuai+Peng%2C+Di+Fu%2C+Liangcai+Gao%2C+Xiuqin+Zhong%2C+Hongguang+Fu%2C+and+Zhi+Tang%2E+2024%2E+Multimath%3A+Bridging+visual+and+mathematical+reasoning+for+large+language+models%2E+arXiv+preprint+arXiv%3A2409%2E00147%2E+%E2%80%94+%5Bar)
86. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023. Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph. arXiv preprint arXiv:2307.07697. [find](https://scholar.google.com/scholar?q=Jiashuo+Sun%2C+Chengjin+Xu%2C+Lumingyuan+Tang%2C+Saizhuo+Wang%2C+Chen+Lin%2C+Yeyun+Gong%2C+Heung%2DYeung+Shum%2C+and+Jian+Guo%2E+2023%2E+Think%2Don%2Dgraph%3A+Deep+and+responsible+reasoning+of+large+language+model+with+knowled)
87. Annabelle Rabillas, Osias Kit Kilag, Neil Cañete, Maria Trazona, Mery Lou Calope, and Jacqueline Kilag. 2023. Elementary math learning through piaget’s cognitive development stages. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(4):128–142. [find](https://scholar.google.com/scholar?q=Annabelle+Rabillas%2C+Osias+Kit+Kilag%2C+Neil+Ca%C3%B1ete%2C+Maria+Trazona%2C+Mery+Lou+Calope%2C+and+Jacqueline+Kilag%2E+2023%2E+Elementary+math+learning+through+piaget%E2%80%99s+cognitive+development+stages%2E+Excellencia%3A+Inter)
88. Liangtai Sun, Yang Han, Zihan Zhao, Da Ma, Zhennan Shen, Baocai Chen, Lu Chen, and Kai Yu. 2024. Scieval: A multi-level large language model evaluation benchmark for scientific research. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19053–19061. [find](https://scholar.google.com/scholar?q=Liangtai+Sun%2C+Yang+Han%2C+Zihan+Zhao%2C+Da+Ma%2C+Zhennan+Shen%2C+Baocai+Chen%2C+Lu+Chen%2C+and+Kai+Yu%2E+2024%2E+Scieval%3A+A+multi%2Dlevel+large+language+model+evaluation+benchmark+for+scientific+research%2E+In+Proceeding)
89. AM Rahman, Junyi Ye, Wei Yao, Sierra S Liu, Jesse Yu, Jonathan Yu, Wenpeng Yin, and Guiling Wang. 2024. [From blind solvers to logical thinkers: Benchmarking llms’ logical integrity on faulty mathematical problems](https://md.research.elamur.ai/free/en/papers/rahman-2024-blind-solvers-logical-thinkers-benchmarking/). arXiv preprint arXiv:2410.18921.
90. Qwen Team. 2024. Qvq: To see the world with wisdom. [find](https://scholar.google.com/scholar?q=Qwen+Team%2E+2024%2E+Qvq%3A+To+see+the+world+with+wisdom%2E)
91. Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, and 1 others. 2024. [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/). arXiv preprint arXi…
92. Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, and Armen Aghajanyan. 2022. [Memorization without overfitting: Analyzing the training dynamics of large language models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/). Advances in Neural Information Processing Systems, 35:38274–38290.
93. Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. 2024. [Solving olympiad geometry without human demonstrations](https://md.research.elamur.ai/free/en/papers/trinh-2024-solving-olympiad-geometry-without-human/). Nature, 625(7995):476–482.
94. Junhong Shen, Liam Li, Lucio M Dery, Corey Staten, Mikhail Khodak, Graham Neubig, and Ameet Talwalkar. 2023. Cross-modal fine-tuning: Align then refine. In International Conference on Machine Learning, pages 31030–31056. PMLR. [find](https://scholar.google.com/scholar?q=Junhong+Shen%2C+Liam+Li%2C+Lucio+M+Dery%2C+Corey+Staten%2C+Mikhail+Khodak%2C+Graham+Neubig%2C+and+Ameet+Talwalkar%2E+2023%2E+Cross%2Dmodal+fine%2Dtuning%3A+Align+then+refine%2E+In+International+Conference+on+Machine+Learning)
95. George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, and Swarat Chaudhuri. 2024. Putnambench: Evaluating neural theorem-provers on the putnam mathematical competition. arXiv preprint arXiv:2407.11214. [find](https://scholar.google.com/scholar?q=George+Tsoukalas%2C+Jasper+Lee%2C+John+Jennings%2C+Jimmy+Xin%2C+Michelle+Ding%2C+Michael+Jennings%2C+Amitayush+Thakur%2C+and+Swarat+Chaudhuri%2E+2024%2E+Putnambench%3A+Evaluating+neural+theorem%2Dprovers+on+the+putnam+math)
96. Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, and Hao Wang. 2024a. Continual learning of large language models: A comprehensive survey. arXiv preprint arXiv:2404.16789. [find](https://scholar.google.com/scholar?q=Haizhou+Shi%2C+Zihao+Xu%2C+Hengyi+Wang%2C+Weiyi+Qin%2C+Wenyuan+Wang%2C+Yibin+Wang%2C+Zifeng+Wang%2C+Sayna+Ebrahimi%2C+and+Hao+Wang%2E+2024a%2E+Continual+learning+of+large+language+models%3A+A+comprehensive+survey%2E+arXiv+pr)
97. Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn Rognvaldsson, and KC Santosh. 2024. Advances and challenges in meta-learning: A technical review. IEEE Transactions on Pattern Analysis and Machine Intelligence. [find](https://scholar.google.com/scholar?q=Anna+Vettoruzzo%2C+Mohamed%2DRafik+Bouguelia%2C+Joaquin+Vanschoren%2C+Thorsteinn+Rognvaldsson%2C+and+KC+Santosh%2E+2024%2E+Advances+and+challenges+in+meta%2Dlearning%3A+A+technical+review%2E+IEEE+Transactions+on+Pattern+)
98. Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2023. Detecting pretraining data from large language models. arXiv preprint arXiv:2310.16789. [find](https://scholar.google.com/scholar?q=Weijia+Shi%2C+Anirudh+Ajith%2C+Mengzhou+Xia%2C+Yangsibo+Huang%2C+Daogao+Liu%2C+Terra+Blevins%2C+Danqi+Chen%2C+and+Luke+Zettlemoyer%2E+2023%2E+Detecting+pretraining+data+from+large+language+models%2E+arXiv+preprint+arXiv%3A)
99. Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, and 1 others. 2024a. Secrets of rlhf in large language models part ii: Reward modeling. arXiv preprint arXiv:2401.06080. [find](https://scholar.google.com/scholar?q=Binghai+Wang%2C+Rui+Zheng%2C+Lu+Chen%2C+Yan+Liu%2C+Shihan+Dou%2C+Caishuang+Huang%2C+Wei+Shen%2C+Senjie+Jin%2C+Enyu+Zhou%2C+Chenyu+Shi%2C+and+1+others%2E+2024a%2E+Secrets+of+rlhf+in+large+language+models+part+ii%3A+Reward+model)
100. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. 2024b. [Measuring multimodal mathematical reasoning with math-vision dataset](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/). Advances in Neural Information Processing Systems, 37:95095–95169.
101. Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021. [Raise a child in large language model: Towards effective and generalizable fine-tuning](https://md.research.elamur.ai/free/en/papers/xu-2021-raise-child-large-language-model/). arXiv preprint arXiv:2109.05687.
102. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Mingjie Zhan, and Hongsheng Li. 2024c. [Measuring multimodal mathematical reasoning with math-vision dataset](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/). arXiv preprint arXiv:2402.14804.
103. Yibo Yan, Jiahao Huo, Guanbo Feng, Mingdong Ou, Yi Cao, Xin Zou, Shuliang Liu, Yuanhuiyi Lyu, Yu Huang, Jungang Li, and 1 others. 2026. Unlocking multimodal document intelligence: From current triumphs to future frontiers of visual document retrieval. arXiv preprint arXiv:2602.19961. — [arXiv:2602.19961](https://arxiv.org/abs/2602.19961) [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Jiahao+Huo%2C+Guanbo+Feng%2C+Mingdong+Ou%2C+Yi+Cao%2C+Xin+Zou%2C+Shuliang+Liu%2C+Yuanhuiyi+Lyu%2C+Yu+Huang%2C+Jungang+Li%2C+and+1+others%2E+2026%2E+Unlocking+multimodal+document+intelligence%3A+From+current+triumph)
104. Peijie Wang, Zhong-Zhi Li, Fei Yin, Dekang Ran, and Cheng-Lin Liu. 2025. Mv-math: Evaluating multimodal math reasoning in multi-visual contexts. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19541–19551. [find](https://scholar.google.com/scholar?q=Peijie+Wang%2C+Zhong%2DZhi+Li%2C+Fei+Yin%2C+Dekang+Ran%2C+and+Cheng%2DLin+Liu%2E+2025%2E+Mv%2Dmath%3A+Evaluating+multimodal+math+reasoning+in+multi%2Dvisual+contexts%2E+In+Proceedings+of+the+Computer+Vision+and+Pattern+Recog)
105. Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S Yu, and Qingsong Wen. 2024d. Large language models for education: A survey and outlook. arXiv preprint arXiv:2403.18105. [find](https://scholar.google.com/scholar?q=Shen+Wang%2C+Tianlong+Xu%2C+Hang+Li%2C+Chaoli+Zhang%2C+Joleen+Liang%2C+Jiliang+Tang%2C+Philip+S+Yu%2C+and+Qingsong+Wen%2E+2024d%2E+Large+language+models+for+education%3A+A+survey+and+outlook%2E+arXiv+preprint+arXiv%3A2403%2E18)
106. Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, and 1 others. 2023. [Cogvlm: Visual expert for pretrained language models](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/). arXiv preprint arXiv:2311.03079.
107. Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R Loomba, Shichang Zhang, Yizhou Sun, and Wei Wang. 2024e. [Scibench: Evaluating college-level scientific problem-solving abilities of large language models](https://md.research.elamur.ai/free/en/papers/wang-2023-scibench-evaluating-college-level-scientific/). arXiv preprint arXiv:2307.10635.
108. Felix A Wichmann and Robert Geirhos. 2023. Are deep neural networks adequate behavioral models of human visual perception? Annual Review of Vision Science, 9(1):501–524. [find](https://scholar.google.com/scholar?q=Felix+A+Wichmann+and+Robert+Geirhos%2E+2023%2E+Are+deep+neural+networks+adequate+behavioral+models+of+human+visual+perception%3F+Annual+Review+of+Vision+Science%2C+9%281%29%3A501%E2%80%93524%2E)
109. Yibo Yan and Joey Lee. 2024. Georeasoner: Reasoning on geospatially grounded context for natural language understanding. arXiv preprint arXiv:2408.11366. — [arXiv:2408.11366](https://arxiv.org/abs/2408.11366) [find](https://scholar.google.com/scholar?q=Yibo+Yan+and+Joey+Lee%2E+2024%2E+Georeasoner%3A+Reasoning+on+geospatially+grounded+context+for+natural+language+understanding%2E+arXiv+preprint+arXiv%3A2408%2E11366%2E+%E2%80%94+%5BarXiv%3A2408%2E11366%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F240)
110. Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, and Xuming Hu. 2024a. A survey of mathematical reasoning in the era of multimodal large language model: Benchmark, method & challenges. arXiv preprint arXiv:2412.11936. [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Jiamin+Su%2C+Jianxiang+He%2C+Fangteng+Fu%2C+Xu+Zheng%2C+Yuanhuiyi+Lyu%2C+Kun+Wang%2C+Shen+Wang%2C+Qingsong+Wen%2C+and+Xuming+Hu%2E+2024a%2E+A+survey+of+mathematical+reasoning+in+the+era+of+multimodal+large+lang)
111. Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S Yu, Carla Gomes, Bart Selman, and Qingsong Wen. 2025a. Position: Multimodal large language models can significantly advance scientific reasoning. arXiv preprint arXiv:2502.02871. [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Shen+Wang%2C+Jiahao+Huo%2C+Jingheng+Ye%2C+Zhendong+Chu%2C+Xuming+Hu%2C+Philip+S+Yu%2C+Carla+Gomes%2C+Bart+Selman%2C+and+Qingsong+Wen%2E+2025a%2E+Position%3A+Multimodal+large+language+models+can+significantly+adva)
112. Yibo Yan, Shen Wang, Jiahao Huo, Philip S Yu, Xuming Hu, and Qingsong Wen. 2025b. Mathagent: Leveraging a mixture-of-math-agent framework for real-world multimodal mathematical error detection. arXiv preprint arXiv:2503.18132. [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Shen+Wang%2C+Jiahao+Huo%2C+Philip+S+Yu%2C+Xuming+Hu%2C+and+Qingsong+Wen%2E+2025b%2E+Mathagent%3A+Leveraging+a+mixture%2Dof%2Dmath%2Dagent+framework+for+real%2Dworld+multimodal+mathematical+error+detection%2E+arXiv+)
113. Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu, Zhipeng Li, Xin Liu, and Xiaoxuan Huang. 2024. A comprehensive survey of large language models and multimodal large language models in medicine. arXiv preprint arXiv:2405.08603. [find](https://scholar.google.com/scholar?q=Hanguang+Xiao%2C+Feizhong+Zhou%2C+Xingyue+Liu%2C+Tianqi+Liu%2C+Zhipeng+Li%2C+Xin+Liu%2C+and+Xiaoxuan+Huang%2E+2024%2E+A+comprehensive+survey+of+large+language+models+and+multimodal+large+language+models+in+medicine%2E+)
114. Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. 2024b. Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web. In Proceedings of the ACM on Web Conference 2024, pages 4006–4017. [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Haomin+Wen%2C+Siru+Zhong%2C+Wei+Chen%2C+Haodong+Chen%2C+Qingsong+Wen%2C+Roger+Zimmermann%2C+and+Yuxuan+Liang%2E+2024b%2E+Urbanclip%3A+Learning+text%2Denhanced+urban+region+profiling+with+contrastive+language%2Dim)
115. Yi Xin, Junlong Du, Qiang Wang, Ke Yan, and Shouhong Ding. 2024. Mmap: Multi-modal alignment prompt for cross-domain multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 16076–16084. [find](https://scholar.google.com/scholar?q=Yi+Xin%2C+Junlong+Du%2C+Qiang+Wang%2C+Ke+Yan%2C+and+Shouhong+Ding%2E+2024%2E+Mmap%3A+Multi%2Dmodal+alignment+prompt+for+cross%2Ddomain+multi%2Dtask+learning%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligen)
116. Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng. 2024a. Exploring diverse in-context configurations for image captioning. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Xu+Yang%2C+Yongliang+Wu%2C+Mingzhuo+Yang%2C+Haokun+Chen%2C+and+Xin+Geng%2E+2024a%2E+Exploring+diverse+in%2Dcontext+configurations+for+image+captioning%2E+Advances+in+Neural+Information+Processing+Systems%2C+36%2E)
117. Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, and 1 others. 2025. Towards large reasoning models: A survey of reinforced reasoning with large language models. arXiv preprint arXiv:2501.09686. [find](https://scholar.google.com/scholar?q=Fengli+Xu%2C+Qianyue+Hao%2C+Zefang+Zong%2C+Jingwei+Wang%2C+Yunke+Zhang%2C+Jingyi+Wang%2C+Xiaochong+Lan%2C+Jiahui+Gong%2C+Tianjian+Ouyang%2C+Fanjin+Meng%2C+and+1+others%2E+2025%2E+Towards+large+reasoning+models%3A+A+survey+of+r)
118. Zhen Yang, Jinhao Chen, Zhengxiao Du, Wenmeng Yu, Weihan Wang, Wenyi Hong, Zhihuan Jiang, Bin Xu, and Jie Tang. 2024b. [Mathglm-vision: Solving mathematical problems with multi-modal large language model](https://md.research.elamur.ai/free/en/papers/yang-2024-mathglm-vision-solving-mathematical-problems/). arXiv preprint arXiv:2409.13729.
119. Liang Xu, Hang Xue, Lei Zhu, and Kangkang Zhao. 2024. [Superclue-math6: Graded multi-step math reasoning benchmark for llms in chinese](https://md.research.elamur.ai/free/en/papers/xu-2024-superclue-math6-graded-multi/). arXiv preprint arXiv:2401.11819.
120. Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, and 1 others. 2024. [Minicpm-v: A gpt-4v level mllm on your phone](https://md.research.elamur.ai/free/en/papers/yao-2024-minicpm-v-gpt-4v/). arXiv preprint arXiv:2408.01800.
121. Shuo Yin, Weihao You, Zhilong Ji, Guoqiang Zhong, and Jinfeng Bai. 2024. Mumath-code: Combining tool-use large language models with multi-perspective data augmentation for mathematical reasoning. arXiv preprint arXiv:2405.07551. [find](https://scholar.google.com/scholar?q=Shuo+Yin%2C+Weihao+You%2C+Zhilong+Ji%2C+Guoqiang+Zhong%2C+and+Jinfeng+Bai%2E+2024%2E+Mumath%2Dcode%3A+Combining+tool%2Duse+large+language+models+with+multi%2Dperspective+data+augmentation+for+mathematical+reasoning%2E+arXi)
122. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, and 1 others. 2023. [A survey of large language models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/). arXiv preprint arXiv:2303.18223.
123. Weihao You, Shuo Yin, Xudong Zhao, Zhilong Ji, Guoqiang Zhong, and Jinfeng Bai. 2024. Mumath: Multi-perspective data augmentation for mathematical reasoning in large language models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2932–2958. [find](https://scholar.google.com/scholar?q=Weihao+You%2C+Shuo+Yin%2C+Xudong+Zhao%2C+Zhilong+Ji%2C+Guoqiang+Zhong%2C+and+Jinfeng+Bai%2E+2024%2E+Mumath%3A+Multi%2Dperspective+data+augmentation+for+mathematical+reasoning+in+large+language+models%2E+In+Findings+of+th)
124. Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, and Xuming Hu. 2024. Reefknot: A comprehensive benchmark for relation hallucination evaluation, analysis and mitigation in multimodal large language models. arXiv preprint arXiv:2408.09429. [find](https://scholar.google.com/scholar?q=Kening+Zheng%2C+Junkai+Chen%2C+Yibo+Yan%2C+Xin+Zou%2C+and+Xuming+Hu%2E+2024%2E+Reefknot%3A+A+comprehensive+benchmark+for+relation+hallucination+evaluation%2C+analysis+and+mitigation+in+multimodal+large+language+model)
125. Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, and 1 others. 2024. [Yi: Open foundation models by 01](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/). ai. arXiv preprint arXiv:2403.04652.
126. Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2024. [Large language model as attributed training data generator: A tale of diversity and bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/). Advances in Neural Information Processing Systems, 36.
127. Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, and 1 others. 2024. Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark. arXiv preprint arXiv:2409.02813. [find](https://scholar.google.com/scholar?q=Xiang+Yue%2C+Tianyu+Zheng%2C+Yuansheng+Ni%2C+Yubo+Wang%2C+Kai+Zhang%2C+Shengbang+Tong%2C+Yuxuan+Sun%2C+Botao+Yu%2C+Ge+Zhang%2C+Huan+Sun%2C+and+1+others%2E+2024%2E+Mmmu%2Dpro%3A+A+more+robust+multi%2Ddiscipline+multimodal+understan)
128. Liang Zeng, Liangjun Zhong, Liang Zhao, Tianwen Wei, Liu Yang, Jujie He, Cheng Cheng, Rui Hu, Yang Liu, Shuicheng Yan, and 1 others. 2024a. [Skywork-math: Data scaling laws for mathematical reasoning in large language models–the story goes on](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/). arXiv preprint arXiv:2407.08348.
129. Zhongshen Zeng, Yinhong Liu, Yingjia Wan, Jingyao Li, Pengguang Chen, Jianbo Dai, Yuxuan Yao, Rongwu Xu, Zehan Qi, Wanru Zhao, and 1 others. 2024b. Mr-ben: A comprehensive meta-reasoning benchmark for large language models. arXiv preprint arXiv:2406.13975. — [arXiv:2406.13975](https://arxiv.org/abs/2406.13975) [find](https://scholar.google.com/scholar?q=Zhongshen+Zeng%2C+Yinhong+Liu%2C+Yingjia+Wan%2C+Jingyao+Li%2C+Pengguang+Chen%2C+Jianbo+Dai%2C+Yuxuan+Yao%2C+Rongwu+Xu%2C+Zehan+Qi%2C+Wanru+Zhao%2C+and+1+others%2E+2024b%2E+Mr%2Dben%3A+A+comprehensive+meta%2Dreasoning+benchmark+for)
130. Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang, Yangqiu Song, and Yuxuan Liang. 2024. Ur-bancross: Enhancing satellite image-text retrieval with cross-domain adaptation. arXiv preprint arXiv:2404.14241. — [arXiv:2404.14241](https://arxiv.org/abs/2404.14241) [find](https://scholar.google.com/scholar?q=Siru+Zhong%2C+Xixuan+Hao%2C+Yibo+Yan%2C+Ying+Zhang%2C+Yangqiu+Song%2C+and+Yuxuan+Liang%2E+2024%2E+Ur%2Dbancross%3A+Enhancing+satellite+image%2Dtext+retrieval+with+cross%2Ddomain+adaptation%2E+arXiv+preprint+arXiv%3A2404%2E14241%2E)
131. Minxuan Zhou, Hao Liang, Tianpeng Li, Zhiyu Wu, Mingan Lin, Linzhuang Sun, Yaqi Zhou, Yan Zhang, Xiaoqin Huang, Yicong Chen, and 1 others. 2024a. Mathscape: Evaluating mllms in multimodal math scenarios through a hierarchical benchmark. arXiv preprint arXiv:2408.07543. — [arXiv:2408.07543](https://arxiv.org/abs/2408.07543) [find](https://scholar.google.com/scholar?q=Minxuan+Zhou%2C+Hao+Liang%2C+Tianpeng+Li%2C+Zhiyu+Wu%2C+Mingan+Lin%2C+Linzhuang+Sun%2C+Yaqi+Zhou%2C+Yan+Zhang%2C+Xiaoqin+Huang%2C+Yicong+Chen%2C+and+1+others%2E+2024a%2E+Mathscape%3A+Evaluating+mllms+in+multimodal+math+scenari)
132. Zihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu, Jindong Wang, Derek F. Wong, Xiaowei Huang, Qiufeng Wang, and Kaizhu Huang. 2024b. Is your model really a good math reasoner? evaluating mathematical reasoning with checklist. [find](https://scholar.google.com/scholar?q=Zihao+Zhou%2C+Shudong+Liu%2C+Maizhen+Ning%2C+Wei+Liu%2C+Jindong+Wang%2C+Derek+F%2E+Wong%2C+Xiaowei+Huang%2C+Qiufeng+Wang%2C+and+Kaizhu+Huang%2E+2024b%2E+Is+your+model+really+a+good+math+reasoner%3F+evaluating+mathematical+re)
133. Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, and Ji-Rong Wen. 2023. [Large language models for information retrieval: A survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/). arXiv preprint arXiv:2308.07107.
134. Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng. 2024. Math-puma: Progressive upward multimodal alignment to enhance mathematical reasoning. arXiv preprint arXiv:2408.08640. — [arXiv:2408.08640](https://arxiv.org/abs/2408.08640) [find](https://scholar.google.com/scholar?q=Wenwen+Zhuang%2C+Xin+Huang%2C+Xiantao+Zhang%2C+and+Jin+Zeng%2E+2024%2E+Math%2Dpuma%3A+Progressive+upward+multimodal+alignment+to+enhance+mathematical+reasoning%2E+arXiv+preprint+arXiv%3A2408%2E08640%2E+%E2%80%94+%5BarXiv%3A2408%2E08640%5D)
135. Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng. 2025. Math-puma: Progressive upward multimodal alignment to enhance mathematical reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 26183–26191. [find](https://scholar.google.com/scholar?q=Wenwen+Zhuang%2C+Xin+Huang%2C+Xiantao+Zhang%2C+and+Jin+Zeng%2E+2025%2E+Math%2Dpuma%3A+Progressive+upward+multimodal+alignment+to+enhance+mathematical+reasoning%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+I)
136. Jiaxin Zhang, Zhongzhi Li, Mingliang Zhang, Fei Yin, Chenglin Liu, and Yashar Moshfeghi. 2024a. Geoeval: benchmark for evaluating llms and multi-modal models on geometry problem-solving. arXiv preprint arXiv:2402.10104. [find](https://scholar.google.com/scholar?q=Jiaxin+Zhang%2C+Zhongzhi+Li%2C+Mingliang+Zhang%2C+Fei+Yin%2C+Chenglin+Liu%2C+and+Yashar+Moshfeghi%2E+2024a%2E+Geoeval%3A+benchmark+for+evaluating+llms+and+multi%2Dmodal+models+on+geometry+problem%2Dsolving%2E+arXiv+preprin)
137. Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, and Huan Zhang. 2024. Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models. arXiv preprint arXiv:2411.00836. [find](https://scholar.google.com/scholar?q=Chengke+Zou%2C+Xingang+Guo%2C+Rui+Yang%2C+Junyu+Zhang%2C+Bin+Hu%2C+and+Huan+Zhang%2E+2024%2E+Dynamath%3A+A+dynamic+visual+benchmark+for+evaluating+mathematical+reasoning+robustness+of+vision+language+models%2E+arXiv+pr)
138. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, and 1 others. 2024b. [Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?](https://md.research.elamur.ai/free/en/papers/zhang-2024-mathverse-multi-modal-llm-truly/) arXiv preprint arXiv:2403.14624.
139. Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, and 1 others. 2025. Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook. Information Fusion, 113:102606. [find](https://scholar.google.com/scholar?q=Xingchen+Zou%2C+Yibo+Yan%2C+Xixuan+Hao%2C+Yuehong+Hu%2C+Haomin+Wen%2C+Erdong+Liu%2C+Junbo+Zhang%2C+Yong+Li%2C+Tianrui+Li%2C+Yu+Zheng%2C+and+1+others%2E+2025%2E+Deep+learning+for+cross%2Ddomain+data+fusion+in+urban+computing%3A+T)
140. Renrui Zhang, Xinyu Wei, Dongzhi Jiang, Yichi Zhang, Ziyu Guo, Chengzhuo Tong, Jiaming Liu, Aojun Zhou, Bin Wei, Shanghang Zhang, and 1 others. 2024c. Mavis: Mathematical visual instruction tuning. arXiv e-prints, pages arXiv–2407. [find](https://scholar.google.com/scholar?q=Renrui+Zhang%2C+Xinyu+Wei%2C+Dongzhi+Jiang%2C+Yichi+Zhang%2C+Ziyu+Guo%2C+Chengzhuo+Tong%2C+Jiaming+Liu%2C+Aojun+Zhou%2C+Bin+Wei%2C+Shanghang+Zhang%2C+and+1+others%2E+2024c%2E+Mavis%3A+Mathematical+visual+instruction+tuning%2E+ar)
141. Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. 2023. Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3):257–276. [find](https://scholar.google.com/scholar?q=Zhengxia+Zou%2C+Keyan+Chen%2C+Zhenwei+Shi%2C+Yuhong+Guo%2C+and+Jieping+Ye%2E+2023%2E+Object+detection+in+20+years%3A+A+survey%2E+Proceedings+of+the+IEEE%2C+111%283%29%3A257%E2%80%93276%2E)

*Updated 1 November 2026*