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researcher

Jiang Bian

9 papers hereh-index 7319 citations14 works total

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

author position
  • middle author5
  • last author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL3
  • cs.AI1
  • cs.CE1
same name
  • Jiang Bian — 35 papers, h 36
  • Jiang Bian — 26 papers, h 21
  • Jiang Bian — 20 papers, h 14
  • Jiang Bian — 19 papers, h 8
  • Jiang Bian — 17 papers, h 10
  • Jiang Bian — 15 papers, h 6

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
20232025
most citedLarge Language Model as a Universal Clinical Multi-task Decoder

1 citations · 3 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

Deep Self-Evolving Reasoning

Zihan Liu, Shun Zheng, Xumeng Wen +3

Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprieta…

cs.AI2025

Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

Xumeng Wen, Zihan Liu, Shun Zheng +9

Recent advancements in long chain-of-thought (CoT) reasoning, particularly through the Group Relative Policy Optimization algorithm used by DeepSeek-R1, have led to significant int…

cs.LG2025

Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains

Jiawen Zhang, Zhenwei Zhang, Shun Zheng +3

Time-Series Foundation Models (TSFMs) are rapidly transitioning from research prototypes to core components of critical decision-making systems, driven by their impressive zero-sho…

cs.CL2025

Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models

Xumeng Wen, Shun Zheng, Zhen Xu +2

Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabiliti…

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