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researcher

Hang Su

23 papers hereh-index 12512 citations28 works total

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

author position
  • first author1
  • middle author18
  • last author4

Across the 23 of 23 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.CL5
  • cs.AI4
  • cs.CR3
  • cs.LG1
  • cs.MM1
same name
  • Hang Su — 86 papers, h 52
  • Hang Su — 21 papers, h 8
  • Hang Su — 18 papers, h 11
  • Hang Su — 14 papers, h 8
  • Hang Su — 10 papers, h 12
  • Hang Su — 9 papers, h 1

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 citedEvil Geniuses: Delving into the Safety of LLM-based Agents

6 citations · 9 across the 21 of their papers we have counts for

collaborators
Showing 2025 · cs.AIShow all

4 papers · 2 filters

cs.AI2025

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Ranjie Duan, Jiexi Liu, Xiaojun Jia +27

Large language models (LLMs) typically deploy safety mechanisms to prevent harmful content generation. Most current approaches focus narrowly on risks posed by malicious actors, of…

cs.AI2025

Towards Safe Reasoning in Large Reasoning Models via Corrective Intervention

Yichi Zhang, Yue Ding, Jingwen Yang +7

Although Large Reasoning Models (LRMs) have progressed in solving complex problems, their chain-of-thought (CoT) reasoning often contains harmful content that can persist even when…

cs.AI2025

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents

Hang Su, Jun Luo, Chang Liu +4

Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. Th…

cs.AI2025

MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments

Xiao Yang, Jiawei Chen, Jun Luo +4

The emergence of multimodal LLM-based agents (MLAs) has transformed interaction paradigms by seamlessly integrating vision, language, action and dynamic environments, enabling unpr…

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