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Ziqian Zeng

Ziqian Zeng has 5 papers in our library, in 2025–2026, most of them on Reasoning, Bias, fairness & safety, Language models & pretraining; often with Haoran Li, Cen Chen, Hao Peng.

5 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 10 · 73 works · 464 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 20 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8451

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

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. PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025 · 13 citations)
    Cloud-hosted LLMs receive user queries that may contain sensitive details, such as diagnoses or legal facts.
  2. Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning (2025 · 7 citations)
    LLMs deployed as tool-using agents raise safety and privacy risks. Typical defenses detect sensitive patterns instead of reasoning about the situation, and they rarely reflect actual legal standards. The authors reframe safety and privacy as compliance questions grounded in Contextual Integrity (CI) theory. Under CI…
  3. MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning (2026 · 5 citations)
    Reasoning-oriented LLMs produce very long Chain-of-Thought outputs, so their KV cache consumes large amounts of GPU memory and bandwidth.
  4. SDD: Self-Degraded Defense against Malicious Fine-tuning (2025 · 4 citations)
    Open-weight LLMs that have been safety-aligned can be turned harmful again by fine-tuning them on a small amount of harmful data (malicious fine-tuning, MFT).
  5. Activation-Guided Local Editing for Jailbreaking Attacks (2026 · 1 citation)
    The paper addresses the weaknesses of current jailbreak techniques used to red-team aligned LLMs. Token-level attacks tend to yield gibberish suffixes that filters catch easily and that rarely transfer to other models. Prompt-level attacks depend on manual crafting or costly iterative search. The authors present…

All works

6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetHuiping Zhuang et al.2023 · 72 citations

 Co-authors here

12 of 15 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 20 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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