🧑‍🔬 Siwei Wu

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2016–2026 · 32 paperscurrent🎓Beijing Normal UniversityBeijing · China2005–2026 · 4 paperscurrent🎓University of ManchesterManchester · United Kingdom2024–2026 · 4 paperscurrent🎓Chongqing Technology and Business UniversityChongqing · China2005–2022 · 10 papers🎓Hengyang Normal UniversityHengyang · China2012–2021 · 8 papers🎓Shanghai UniversityShanghai · China2016–2021 · 8 papers🔬State Key Laboratory of Advanced Special SteelShanghai · China2019–2021 · 6 papers🎓Guangdong University of TechnologyGuangzhou · China2022–2025 · 5 papers🎓Nanjing University of Science and TechnologyNanjing · China2023–2025 · 5 papers🎓Northeastern UniversityBoston · United States2017–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 12 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. OmniBench: Towards The Future of Universal Omni-Language Models (2024 · 17 citations)
   Most multimodal LLMs are built and tested on pairs of modalities (image–text or audio–text), so it is unclear how well they handle an image, an audio clip and a text instruction together.
2. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026)
   Chinese alignment work lacks good preference data. Existing Chinese datasets are small, often drawn from a single source such as Zhihu, and seldom checked by training models on them. Human labelling is also too costly to scale. The authors build an annotation pipeline that uses LLMs only, with no human labelling.…
3. LoopCoder: Scaling Code Intelligence via Looped Language Models (2026)
   Code LLMs usually get better at algorithmic reasoning by adding depth and parameters, which ties reasoning capacity to memory cost.
4. MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026)
   Existing multimodal benchmarks mostly test facts within a single image. Earlier multi-image suites such as MuirBench and MIRB ask factual questions and do not probe the commonsense links between pictures or whether a model tracks their order. The authors introduce a multi-image relation association task and MMRA, a…

🤝 Co-authors here

12 of 25 co-authors with a page, the most papers together first.

• Xingwei Qu ×3
• Yiming Liang ×3
• Yizhi Li ×3
• Ge Zhang ×2
• Jiaheng Liu ×2
• Jian Yang ×2
• Shuyue Guo ×2
• Tianyu Zheng ×2
• Che Liu
• Hangyu Guo
• Haoning Wu
• Jiajun Wu

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×3: Findings of the Association for Computational Linguistics 2026 ×3
• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2024

🔗 Cited by

Papers of our library cite their works 16 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated 2 November 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/siwei-wu/