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researcher

Jun Sun

12 papers hereh-index 551 citations18 works total

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

author position
  • middle author7
  • last author4

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CR6
  • cs.AI4
  • cs.LG1
  • cs.SE1
same name
  • Jun Sun — 25 papers, h 18
  • Jun Sun — 22 papers, h 38
  • Jun Sun — 13 papers, h 3
  • Jun Sun — 12 papers, h 8
  • Jun Sun — 9 papers, h 5
  • Jun Sun — 9 papers, h 7

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 citedExploiting Machine Unlearning for Backdoor Attacks in Deep Learning System

3 citations · 5 across the 10 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Tripwire: Triggering Aligned Refusal via Statistically Certified Safety Neurons

Wei Zhao, Zhe Li, Peixin Zhang +1

Neuron- and path-level interventions offer the finest-grained route to defending large language models (LLMs) against jailbreak attacks, yet existing methods fall short of this pro…

cs.AI2026

Forecasting Side Effects of Activation Steering

Chong Yong Ong, Alson Wei Jie Sim, Peixin Zhang +1

Activation steering modifies a language model by adding a learned direction to its hidden activations, enabling targeted behavioral changes without retraining. While effective, ste…

cs.AI2026

NuHF Claw: A Risk Constrained Cognitive Agent Framework for Human Centered Procedure Support in Digital Nuclear Control Rooms

Xingyu Xiao, Jiejuan Tong, Jun Sun +4

The rapid digitization of nuclear power plant main control rooms has fundamentally reshaped operator interaction patterns, introducing complex soft-control behaviors and elevated c…

cs.AI2024

LLMScan: Causal Scan for LLM Misbehavior Detection

Mengdi Zhang, Kai Kiat Goh, Peixin Zhang +3

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful, biased and harmful responses poses significant risks, particularl…

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