🧑‍🔬 Kai Han

🏛️ Affiliations

🎓Shanghai University of Finance and EconomicsShanghai · China2025–2026 · 3 paperscurrent

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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning (2024 · 43 citations)
   Prompting schemes such as Chain-of-Thought and Tree-of-Thought usually follow one reasoning attempt to its end.
2. Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition (2025 · 13 citations)
   Reasoning-focused LLMs such as DeepSeek-R1 are expensive to deploy because they are very large and generate long chains of thought, sometimes tens of thousands of tokens per problem.
3. PanGu-π: Enhancing Language Model Architectures via Nonlinearity Compensation (2023 · 10 citations)
   Progress in large language models has mostly come from adding parameters and data. The design of the Transformer block itself gets little attention, even though large models are costly to run. The authors study LLaMA-style decoders and identify feature collapse: a rank metric shows token representations growing more…
4. PanGu-π Pro:Rethinking Optimization and Architecture for Tiny Language Models (2024 · 8 citations)
   On-device deployment needs small language models that still perform well. Most tiny models, however, simply reuse recipes designed for much larger LLMs, and few studies test which of those choices actually suit a ~1B-parameter model. The authors run a systematic set of ablations on a 1B LLaMA-style baseline, trained…
5. Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity (2025 · 4 citations)
   Sparse Mixture-of-Experts LLMs are cheap per token. However, when experts are spread across accelerators, uneven routing overloads some devices, and the slowest device limits overall speed. The Pangu Team at Huawei proposes Mixture of Grouped Experts (MoGE). Experts are split into equal, non-overlapping groups…
6. MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026)
   Sparse-attention LLMs, such as models built on sliding windows, save compute and memory. They tend to fail, however, when an answer depends on recalling an exact detail far back in the input. MATCH tackles this by pairing a sparsified Transformer with a lightweight retrieval component that treats the prompt itself as…
7. Mitigating Coordinate Prediction Bias from Positional Encoding Failures (2026)
   Multimodal LLMs that output click coordinates as digit strings often drift on high-resolution screenshots, because visual positional encodings (VPEs) lose grounding strength in long visual contexts.

🤝 Co-authors here

12 of 29 co-authors with a page, the most papers together first.

• Yehui Tang ×4
• Fangcheng Liu ×3
• Hanting Chen ×3
• Yunhe Wang ×3
• Fei Mi ×2
• Jinpeng Li ×2
• Zhenni Bi ×2
• Zheyuan Bai ×2
• Bin Wang
• Dong Li
• Hang Zhou
• Haoyu Wang

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): ACL 2026
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 75 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated 1 November 2026

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