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Xuchen Song

Xuchen Song has 3 papers in our library, in 2025, most of them on Reasoning, Vision & language, Language models & pretraining; often with Yang Liu, Jiangbo Pei, Jianhao Zhang.

3 papers in our library · 2025

OpenAlex · h-index 8 · 29 works · 293 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 36 times in our library · ⭐ Paper Stars ranking: #9136

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢ByteDanceBeijing · China2020–2024 · 10 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 2 worksVision & language · 2 worksCode & programming languages · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20253 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning (2025 · 10 citations)
    "Slow-thinking" vision-language models tend to improve on math and science benchmarks, but they often lose accuracy on everyday perception tasks and hallucinate more about image content.
  2. Skywork-R1V3 Technical Report (2025 · 6 citations)
    Open-source vision-language models still trail proprietary systems on complex visual reasoning, partly because high-quality multimodal annotations are scarce.
  3. Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs (2025 · 3 citations)
    Training LLM agents to fix real GitHub issues is held back by scarce data. Every task needs an executable environment and validated unit tests, and building these by hand is slow, so most public SWE datasets have only a few thousand instances. The authors created an automated, incremental pipeline with three stages…

 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 36 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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