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Jiakai Wang

21 papers hereh-index 231.8k citations72 works total

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

author position
  • first author1
  • middle author13
  • last author1

Across the 15 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • cs.AI4
  • cs.CR3
  • cs.LG3
  • cs.RO2
  • cs.CL1
same name
  • Jiakai Wang — 8 papers, h 7
  • Jiakai Wang — 1 paper, h 0
  • Jiakai Wang — 1 paper, h 2
  • Jiakai Wang — 1 paper, 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

works on
adversarial attacks 1autonomous driving 1multimodal learning 1robustness evaluation 1vision-language models 1

From the 1 of 21 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents

Haojie Hao, Longkun Hao, Yihang Lou +8

Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too…

cs.AI2026

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic

Tianyuan Zhang, Peng Yue, Zihao Peng +8

Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse safety threats, particularly i…

cs.AI2025

Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction

Jin Hu, Jiakai Wang, Linna Jing +6

Recently, semantically constrained adversarial examples (SemanticAE), which are directly generated from natural language instructions, have become a promising avenue for future res…

cs.AI2024

Compromising Embodied Agents with Contextual Backdoor Attacks

Aishan Liu, Yuguang Zhou, Xianglong Liu +9

Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal…

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