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Peidong Wang

4 papers hereh-index 469 citations11 works total

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

author position
  • first author1
  • middle author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.SD1
same name
  • Peidong Wang — 6 papers, h 11
  • Peidong Wang — 3 papers
  • Peidong Wang — 3 papers, h 3
  • Peidong Wang — 3 papers, h 2
  • Peidong Wang — 1 paper, h 6
  • Peidong 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

activity
20242026
most citedLanguage Models as Continuous Self-Evolving Data Engineers

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CL2026

NEAT: Neuron-Based Early Exit for Large Reasoning Models

Kang Liu, Yongkang Liu, Xiaocui Yang +5

Large Reasoning Models (LRMs) often suffer from \emph{overthinking}, a phenomenon in which redundant reasoning steps are generated after a correct solution has already been reached…

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…

cs.CL2025

AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation

Ming Wang, Peidong Wang, Lin Wu +6

Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configura…

cs.CL2024★ 1 cited

Language Models as Continuous Self-Evolving Data Engineers

Peidong Wang, Ming Wang, Zhiming Ma +5

Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addit…

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