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researcher

Qiming Zhang

University of Wisconsin-Madison

4 papers hereh-index 331 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.CR2
  • cs.AI1
  • cs.SE1
affiliations
  • University of Wisconsin-Madison
ORCID 0009-0004-6400-4884
same name
  • Qiming Zhang — 25 papers, h 17
  • Qiming Zhang — 7 papers, h 4
  • Qiming Zhang — 5 papers, h 4
  • Qiming Zhang — 4 papers, h 5
  • Qiming Zhang — 2 papers, h 1
  • Qiming Zhang — 1 paper, h 4

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

4 papers

cs.AI2026

Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents

Weidi Luo, Qiming Zhang, Yihao Quan +5

Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risk…

cs.SE2025

Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operation Orchestration

Yanbin Zhang, Hanhui Ye, Yue Bai +4

Workflow automation promises substantial productivity gains in everyday document-related tasks. While prior agentic systems can execute isolated instructions, they struggle with au…

cs.CR2025

Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent

Weidi Luo, Qiming Zhang, Tianyu Lu +9

Command-line interface (CLI) agents powered by large language models (LLMs) can interpret natural-language requests, plan multi-step tasks, execute shell commands, and modify files…

cs.CR2025

Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models

Weidi Luo, Tianyu Lu, Qiming Zhang +8

Recent advances in multi-modal large reasoning models (MLRMs) have shown significant ability to interpret complex visual content. While these models enable impressive reasoning cap…

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