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researcher

Feng Zhang

5 papers hereh-index 211 citations8 works total

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

author position
  • middle author1
  • last author4

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.AI3
  • cs.CR1
  • cs.HC1
same name
  • Feng Zhang — 9 papers, h 4
  • Feng Zhang — 9 papers, h 7
  • Feng Zhang — 7 papers, h 2
  • Feng Zhang — 7 papers, h 2
  • Feng Zhang — 6 papers, h 2
  • Feng Zhang — 6 papers, h 3

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

collaborators

5 papers

cs.AI2026

Toward Scalable Terminal Task Synthesis via Skill Graphs

Zhiyuan Fan, Tinghao Yu, Yuanjun Cai +8

Terminal agents have demonstrated strong potential for autonomous command-line execution, yet their training remains constrained by the scarcity of high-quality and diverse executi…

cs.CR2026

ClawLess: A Security Model of AI Agents

Hongyi Lu, Nian Liu, Shuai Wang +1

Autonomous AI agents powered by Large Language Models can reason, plan, and execute complex tasks, but their ability to autonomously retrieve information and run code introduces si…

cs.HC2026

Benchmarking LLM Tool-Use in the Wild

Peijie Yu, Wei Liu, Yifan Yang +4

Fulfilling user needs through Large Language Model multi-turn, multi-step tool-use is rarely a straightforward process. Real user interactions are inherently wild, being intricate,…

cs.AI2025

C3-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking

Peijie Yu, Yifan Yang, Jinjian Li +4

Agents based on large language models leverage tools to modify environments, revolutionizing how AI interacts with the physical world. Unlike traditional NLP tasks that rely solely…

cs.AI2025

Multi-Mission Tool Bench: Assessing the Robustness of LLM based Agents through Related and Dynamic Missions

Peijie Yu, Yifan Yang, Jinjian Li +4

Large language models (LLMs) demonstrate strong potential as agents for tool invocation due to their advanced comprehension and planning capabilities. Users increasingly rely on LL…

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