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Boyi Deng

Boyi Deng has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Baosong Yang, Fuli Feng, Yu Wan.

4 papers in our library · 2025–2026

OpenAlex · h-index 2 · 14 works · 44 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 25 times in our library · ⭐ Paper Stars ranking: #8605

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

🎓University of Science and Technology of ChinaHefei · China2023–2026 · 9 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksEfficient models & compression · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2025 · 18 citations)
    The paper asks how large language models represent individual languages internally. Earlier work relied on single neurons, which suffer from superposition, or on logit-lens readouts, which are unreliable outside the final layers. The authors instead use pretrained Sparse Autoencoders (SAEs), which split…
  2. Controllable LLM Reasoning via Sparse Autoencoder-Based Steering (2026 · 3 citations)
    Large Reasoning Models (LRMs) choose their own reasoning moves, such as re-checking or backtracking, and these choices can lead to wasted effort or wrong answers.
  3. DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression (2026 · 3 citations)
    Long-running AI agents mostly consume input tokens. As a result, serving cost now depends less on attention compute and more on the size of key-value (KV) caches. These caches must fit in HBM, be kept on SSD or host memory for prefix reuse, and be moved over limited bandwidth. DeepSeek-AI presents…
  4. Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models (2026 · 3 citations)
    Sparse autoencoders (SAEs) break LLM hidden states into a small number of interpretable directions. In practice, though, they have mostly been used to inspect and label features after the fact, not to build or fix models. The Qwen Team addresses this with Qwen-Scope, an openly released collection of SAEs trained on…

 Co-authors here

12 of 20 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 25 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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