--- title: "Tong Mo · Peking University — author profile and papers" description: "Tong Mo, Peking University: 2 papers on Elamur Research (2026), h-index 10 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/tong-mo/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/tong-mo/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Tong Mo # Tong Mo Tong Mo has 2 papers in our library, in 2026, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Guanyu Wang](https://md.research.elamur.ai/free/en/authors/guanyu-wang/), [Weiping Li](https://md.research.elamur.ai/free/en/authors/weiping-li/), [Xu Chu](https://md.research.elamur.ai/free/en/authors/xu-chu/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5059356240) · [ORCID](https://orcid.org/0000-0002-3564-4610) · [**h-index 10**](https://openalex.org/authors/A5059356240) · [73 works](https://openalex.org/works?filter=author.id:A5059356240) · [335 citations](https://openalex.org/works?filter=author.id:A5059356240&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/tong-mo/#papers) · [⭐ Paper Stars ranking: #12259](https://md.research.elamur.ai/free/en/paper-stars/page/123/#star-12259) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Tong+Mo) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Tong+Mo) · [dblp](https://dblp.org/search?q=Tong+Mo) · [ACL Anthology](https://aclanthology.org/search/?q=Tong+Mo) ## Affiliations [🎓**Peking University** Beijing · China 2010–2026 · 54 papers*current*](https://md.research.elamur.ai/free/en/universities/peking-university/) · [🎓**Harbin Institute of Technology** Harbin · China 2007–2013 · 11 papers](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) · [🎓**University of Alberta** Edmonton · Canada 2020–2021 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-alberta/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 3 Research subjects and languages the work is about.[Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/tong-mo/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[MuSe: Multi-Stage Graph Reasoning via Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-muse-multi-stage-graph-reasoning/)** *(2026)* Graph reasoning is usually handled by GNNs or graph transformers, which need task-specific training. LLMs that read graphs serialized as text struggle to keep two-dimensional structure and multi-hop relations. Vision-language methods such as GITA draw the entire graph as one picture, but large graphs then become… 2. **[RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains](https://md.research.elamur.ai/free/en/papers/tan-2026-rado-reasoning-audit-driven-optimization/)** *(2026)* Reinforcement learning for LLM reasoning usually rewards only the final answer. A model can therefore reach correct conclusions through faulty intermediate logic, which is a serious problem in finance, law and biomedicine. RADO exploits an asymmetry: finding one local flaw in a derivation is easier than producing a… ## Co-authors here 4 co-authors with a page, the most papers together first. - [Guanyu Wang](https://md.research.elamur.ai/free/en/authors/guanyu-wang/) ×2 - [Weiping Li](https://md.research.elamur.ai/free/en/authors/weiping-li/) ×2 - [Xu Chu](https://md.research.elamur.ai/free/en/authors/xu-chu/) ×2 - [Zhijie Tan](https://md.research.elamur.ai/free/en/authors/zhijie-tan/) ×2 ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 Updated 1 November 2026