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Zhiming Ma

8 papers hereh-index 431 citations9 works total

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

author position
  • first author2
  • middle author4

Across the 6 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL6
  • cs.CV1
  • cs.SD1
same name
  • Zhiming Ma — 15 papers, h 10
  • Zhiming Ma — 10 papers, h 21
  • Zhiming Ma — 7 papers, h 3
  • Zhiming Ma — 4 papers, h 1
  • Zhiming Ma — 3 papers, h 2
  • Zhiming Ma — 1 paper, h 5

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
20242026
most citedLanguage Models as Continuous Self-Evolving Data Engineers

1 citations · 1 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

Proactive Physical AI through Continuous Observation and Timely Intervention

Zhenhua Yang, Jiafeng Liang, Xingyu Zhou +26

Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real-world settings, however, im…

cs.CL2026

Self-Evolving Embodied Agents via Skill-Harness Evolution

Peidong Wang, Zhiming Ma, Ying Chang +7

Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces,…

cs.CL2026

FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection

Y. H. Zhou, Z. M. Ma, Y. J. Zhou +12

SMS fraud is increasingly cross-channel: a message directs the user to a webpage, and the final risk depends on how the SMS claim aligns with the page content and requested user ac…

cs.SD2026

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning

Peidong Wang, Zhiming Ma, Xin Dai +8

Existing fraud detection methods predominantly rely on transcribed text, suffering from ASR errors and missing crucial acoustic cues like vocal tone and environmental context. This…

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