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Hua Wu

12 papers hereh-index 5107 citations14 works total

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

author position
  • middle author5
  • last author6

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI3
  • cs.CV2
  • cs.IR1
  • cs.RO1
  • cs.SD1
same name
  • Hua Wu — 82 papers, h 59
  • Hua Wu — 25 papers, h 10
  • Hua Wu — 18 papers, h 24
  • Hua Wu — 15 papers, h 12
  • Hua Wu — 6 papers, h 6
  • Hua Wu — 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

activity
20242026
most citedERNIE 5.0 Technical Report

2 citations · 2 across the 12 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026★ 2 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…

cs.CL2026

ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning

Shuaiyi Nie, Siyu Ding, Wenyuan Zhang +7

Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redu…

cs.CL2025

LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking

Junhong Wu, Jinliang Lu, Zixuan Ren +4

Human cognition naturally engages with abstract and fluid concepts, whereas existing reasoning models often rely on generating discrete tokens, potentially constraining their expre…

cs.CL2024

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

Xiaoshuai Song, Muxi Diao, Guanting Dong +13

Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on b…

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