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Kun He

Kun He has 4 papers in our library, in 2019–2026, most of them on Language models & pretraining, Vision & language, Reasoning; often with Haoran Wang, Jing Chen, Nan Yan.

4 papers in our library · 2019–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 256 times in our library · ⭐ Paper Stars ranking: #2671

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

🎓Wuhan UniversityWuhan · 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).Computer Science · 4 worksArtificial Intelligence · 3 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Vision and Pattern Recognition · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 2 worksBias, fairness & safety · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20251 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (2019 · 884 citations)
    The paper tackles adversarial attacks on neural text classifiers. Such attacks are harder to build than image attacks because words are discrete, and edits must stay lexically, grammatically and semantically sound. The authors propose PWWS (Probability Weighted Word Saliency), a greedy black-box attack built on…
  2. VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models (2025 · 9 citations)
    Multimodal reasoning models (MLRMs), trained with reinforcement learning and CoT supervision, solve visual problems better.
  3. Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing (2026)
    Federated LoRA lets several clients jointly adapt an LLM without sharing raw data. Standard systems average the two low-rank factors B and A separately under FedAvg. This introduces a systematic aggregation error, because the product of averaged factors is not the average of the per-client products. Edge clients also…
  4. Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models (2026)
    Large Vision-Language Models (LVLMs) often describe objects or details that are not in the image. The authors trace this to attention sinks, tokens such as [BOS] that absorb a large share of attention, and frame it as a statistical problem. Softmax attention is only an optimal estimator if logit noise is i.i.d.…

 Co-authors here

8 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 256 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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