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Zonglin Yang

Nanyang Technological University

18 papers hereh-index 12619 citations22 works total

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

author position
  • first author7
  • middle author9
  • last author1

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.CV2
  • cs.LG2
affiliations
  • Nanyang Technological University
  • Cornell University
Homepage
same name
  • ZongLin Yang — 4 papers, h 2
  • Zonglin Yang — 3 papers, h 2
  • Zonglin Yang — 2 papers, h 1
  • Zonglin Yang — 1 paper, h 3
  • Zonglin Yang — 1 paper
  • Zonglin Yang — 1 paper, h 1

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
20202026
most citedLanguage Models as Inductive Reasoners

7 citations · 34 across the 16 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery

Hongran An, Zonglin Yang

Large language models (LLMs) show remarkable potential in scientific hypothesis discovery. However, existing approaches face two critical limitations: they treat divergent explorat…

cs.CV2026

PolyReal: A Benchmark for Real-World Polymer Science Workflows

Wanhao Liu, Weida Wang, Jiaqing Xie +12

Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning…

cs.CL2026

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

MiroMind Team, S. Bai, L. Bing +39

We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends…

cs.LG2026

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

Zonglin Yang, Lidong Bing

While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the gene…

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