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Jialiang Wang

4 papers hereh-index 29 citations6 works total

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

author position
  • first author4

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

fields
  • cs.LG2
  • cs.AI1
  • cs.MA1
same name
  • Jialiang Wang — 12 papers, h 10
  • Jialiang Wang — 4 papers, h 3
  • Jialiang Wang — 3 papers, h 0
  • Jialiang Wang — 3 papers, h 0
  • Jialiang Wang — 2 papers, h 2
  • Jialiang Wang — 2 papers, h 9

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

most citedWhen AI reviews science: Can we trust the referee?

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

collaborators

4 papers

cs.LG2026

Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design

Jialiang Wang, Hanmo Liu, Shimin Di +4

Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural archit…

cs.AI2026★ 1 cited

When AI reviews science: Can we trust the referee?

Jialiang Wang, Yuchen Liu, Hang Xu +7

The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large langu…

cs.MA2026

Learning to Compose for Cross-domain Agentic Workflow Generation

Jialiang Wang, Shengxiang Xu, Hanmo Liu +5

Automatically generating agentic workflows -- executable operator graphs or codes that orchestrate reasoning, verification, and repair -- has become a practical way to solve comple…

cs.LG2026

Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models

Jialiang Wang, Hanmo Liu, Shimin Di +4

High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…

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