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Zhenhong Zhou

Zhenhong Zhou has 3 papers in our library, in 2026, most of them on Language models & pretraining, Speech & spoken language, Evaluation & benchmarks; often with Kun Wang, Li Sun, Liang Lin.

3 papers in our library · 2026

OpenAlex · h-index 1 · 7 works · 1 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 7 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10119

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

🎓Nanyang Technological UniversitySingapore · Singapore2025–2026 · 6 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.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksSocial Sciences · 1 workArtificial Intelligence · 3 worksComputer Networks and Communications · 1 workSociology and Political Science · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksSpeech & spoken language · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026 · 7 citations)
    The paper identifies a security gap in LLM-based multi-agent systems (LLM-MASs). Prior work mostly studied harmful content or attacks on single models and services. Here the authors target the cooperation between agents. They define a new threat class, Denial-of-Collaboration (DoC): the system keeps running but does…
  2. SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (2026 · 1 citation)
    Large Audio Language Models (LALMs) used in settings like car assistants or meeting tools often receive audio degraded by device and environmental noise.
  3. HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026)
    Audio Large Language Models (ALLMs) are trained on huge amounts of speech. The authors ask whether this training lets them work out private facts about a speaker from the voice alone, with no help from what is actually said. To test this, they built HearSay, a benchmark of more than 22,000 real recordings. Most clips…

All works

4 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. CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsFindings of the Association for Computational LinguisticsZhenhong Zhou, Zherui Li, Jie Zhang, et al.2026 · 7 citations
  2. SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanhe Zhang, Jiayu Tian, Yibo Zhang, et al.2026 · 1 citation
  3. HearSay Benchmark: Do Audio LLMs Leak What They Hear?Findings of the Association for Computational LinguisticsJin Wang, Kaiwen Luo, Liang Lin, et al.2026
  4. RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYibo Zhang et al.2026

 Co-authors here

10 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 7 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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