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Xiaodong Gu

Xiaodong Gu has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Question answering, Code & programming languages; often with Yuling Shi, Beijun Shen, Min Wang.

3 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 19 · 64 works · 2,435 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 41 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #8859

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging (2024 · 15 citations)
    LLMs often write code that is almost right but fails tests because of small mistakes. Existing LLM-based repair loops usually look at the whole program at once, which makes bugs hard to locate. The authors propose MGDebugger, which needs no extra training and works in a zero-shot setting. First, an LLM restructures…
  2. SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026 · 7 citations)
    Most code question-answering benchmarks, such as CoSQA and CodeQA, test models on short, isolated snippets. Real developer questions often require moving across files, following dependencies and understanding a project's architecture. SWE-QA is a benchmark built for this repository-level setting.
  3. GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026 · 1 citation)
    Large reasoning models produce long chains of thought, which makes inference slow and expensive. Collaborative inference addresses this by splitting work between a small and a large model. The hard part is deciding cheaply which model should handle each step. Existing step-level routers either generate a full step…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder ↗arXiv (Cornell University) · no summary yetXiaodong Gu et al.2018 · 132 citations
  2. From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical DebuggingarXiv:2410.01215Yuling Shi, Songsong Wang, Chengcheng Wan, et al.2024 · 15 citations
  3. Continuous Decomposition of Granularity for Neural Paraphrase Generation ↗Proceedings of the 29th International Conference on Computational Linguistics · no summary yetXiaodong Gu et al.2022 · 2 citations
  4. EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetHeng Zhang et al.2026

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 41 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

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