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researcher

Ninghao Liu

4 papers hereh-index 5222 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CR1
  • cs.CV1
  • cs.LG1
same name
  • Ninghao Liu — 53 papers, h 34
  • Ninghao Liu — 29 papers, h 13
  • Ninghao Liu — 6 papers, h 5
  • Ninghao Liu — 3 papers, h 4
  • Ninghao Liu — 2 papers
  • Ninghao Liu — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedAn LLM can Fool Itself: A Prompt-Based Adversarial Attack

13 citations · 13 across the 3 of their papers we have counts for

collaborators

4 papers

cs.AI2026

Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability

Xinyan Jiang, Ninghao Liu, Di Wang +1

Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality th…

cs.LG2025

EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identification

Lin Zhang, Wenshuo Dong, Zhuoran Zhang +5

Understanding the internal mechanisms of transformer-based language models remains challenging. Mechanistic interpretability based on circuit discovery aims to reverse engineer neu…

cs.CV2023

Improving Interpretation Faithfulness for Vision Transformers

Lijie Hu, Yixin Liu, Ninghao Liu +3

Vision Transformers (ViTs) have achieved state-of-the-art performance for various vision tasks. One reason behind the success lies in their ability to provide plausible innate expl…

cs.CR2023★ 13 cited

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Xilie Xu, Keyi Kong, Ning Liu +4

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This pa…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.