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Haotian Xu

9 papers hereh-index 6399 citations13 works total

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

author position
  • first author1
  • middle author5

Across the 6 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI3
  • cs.LG2
same name
  • Haotian Xu — 8 papers, h 3
  • Haotian Xu — 7 papers, h 2
  • Haotian Xu — 6 papers, h 4
  • Haotian Xu — 5 papers, h 5
  • Haotian Xu — 5 papers, h 3
  • Haotian Xu — 4 papers, h 11

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 citedFrom System 1 to System 2: A Survey of Reasoning Large Language Models

20 citations · 22 across the 8 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

LiteLong: Resource-Efficient Long-Context Data Synthesis for LLMs

Junlong Jia, Xing Wu, Chaochen Gao +8

High-quality long-context data is essential for training large language models (LLMs) capable of processing extensive documents, yet existing synthesis approaches using relevance-b…

cs.CL2025

MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph

Duzhen Zhang, Zixiao Wang, Zhong-Zhi Li +10

The rapid expansion of medical literature challenges the scalable structuring of domain knowledge. Knowledge Graphs (KGs) offer a solution, yet current construction methods lack ge…

cs.CL2025

TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

Zhong-Zhi Li, Xiao Liang, Zihao Tang +11

Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge…

cs.CL2025★ 1 cited

Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs

Yi Hu, Shijia Kang, Haotong Yang +2

Length generalization, the ability to solve problems longer than those seen during training, remains a critical challenge for large language models (LLMs). Previous work modifies p…

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