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Xin Cong

33 papers hereh-index 174.2k citations43 works total

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

author position
  • middle author29

Across the 29 of 33 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.AI11
  • cs.SE4
  • cs.CR1
  • cs.CY1
  • cs.LG1
same name
  • Xin Cong — 8 papers, h 11
  • Xin Cong — 7 papers, h 17
  • Xin Cong — 4 papers, h 7
  • Xin Cong — 2 papers, h 2
  • Xin Cong — 1 paper, h 2

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
20232026
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 211 across the 32 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

4 papers · 2 filters

cs.CL2025

MiniCPM4: Ultra-Efficient LLMs on End Devices

MiniCPM Team, Chaojun Xiao, Yuxuan Li +80

This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…

cs.CL2025

Synergizing Large Language Models and Theory for Human-like Causal Reasoning

Yanxi Zhang, Xin Cong, Zhong Zhang +3

Performing causal reasoning akin to humans is fundamental to achieving strong artificial intelligence. However, existing studies on causal reasoning and large language models (LLMs…

cs.CL2025★ 1 cited

Learning to Generate Structured Output with Schema Reinforcement Learning

Yaxi Lu, Haolun Li, Xin Cong +6

This study investigates the structured generation capabilities of large language models (LLMs), focusing on producing valid JSON outputs against a given schema. Despite the widespr…

cs.CL2025

AgentRM: Enhancing Agent Generalization with Reward Modeling

Yu Xia, Jingru Fan, Weize Chen +7

Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning t…

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