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Weiji Zhuang

Weiji Zhuang has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Dialogue systems, Speech & spoken language; often with Dong Zhang, Xin Zhang, Gang Wang.

2 papers in our library · 2025–2026

OpenAlex · h-index 4 · 19 works · 58 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8647

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

🏢XiaomiBeijing · China2021–2026 · 9 paperscurrent🏢China Electronics CorporationBeijing · China2026 · 1 papercurrent🏢China Energy Engineering CorporationBeijing · China2026 · 1 papercurrent🎓Nanjing Xiaozhuang UniversityNanjing · China2026 · 1 papercurrent

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workSpeech & spoken language · 1 workDialogue systems · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MiMo-Audio: Audio Language Models are Few-Shot Learners (2025 · 24 citations)
    Most current audio language models need separate fine-tuning for each speech task. This work asks whether the GPT-3 recipe, scaling next-token prediction pretraining, also gives audio models few-shot generalization. The Xiaomi team built MiMo-Audio around a tokenizer designed to keep paralinguistic detail. They used…
  2. ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026 · 3 citations)
    Synthesizing two-speaker spoken conversations is harder than single-speaker TTS. A model must switch voices at the right moments and learn text-to-speech alignment across different timbres. Existing dialogue systems are mostly autoregressive, which makes them slow and prone to skipped or repeated words. The authors…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. MiMo: Unlocking the Reasoning Potential of Language Model — From Pretraining to Posttraining ↗arXiv:2505.07608 · no summary yetLLM-Core Xiaomi et al.2025 · 37 citations
  2. MiMo-Audio: Audio Language Models are Few-Shot LearnersarXiv:2512.23808Xiaomi LLM-Core Team, Dong Zhang, Gang Wang, et al.2025 · 24 citations
  3. ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow MatchingFindings of the Association for Computational LinguisticsHan Zhu, Wei Kang, Liyong Guo, et al.2026 · 3 citations

 Co-authors here

7 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 93 times (of the 149,439 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated

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