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20242026
most citedCan Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models

3 citations · 12 across the 14 of their papers we have counts for

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cs.CL2025★ 1 cited

The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

Xinbei Ma, Ruotian Ma, Xingyu Chen +14

LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates th…

cs.CL2025

DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning

Ziyin Zhang, Jiahao Xu, Zhiwei He +10

Theorem proving serves as a major testbed for evaluating complex reasoning abilities in large language models (LLMs). However, traditional automated theorem proving (ATP) approache…

cs.CL2025★ 1 cited

DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Zhiwei He, Tian Liang, Jiahao Xu +12

Reinforcement learning (RL) with large language models shows promise in complex reasoning. However, its progress is hindered by the lack of large-scale training data that is suffic…

cs.CL2025

Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique

Yansi Li, Jiahao Xu, Tian Liang +8

Enhancing the reasoning capabilities of large language models (LLMs), particularly for complex tasks requiring multi-step logical deductions, remains a significant challenge. Tradi…

cs.CL2025

RaSA: Rank-Sharing Low-Rank Adaptation

Zhiwei He, Zhaopeng Tu, Xing Wang +7

Low-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stem…

cs.CL2025

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Yue Wang, Qiuzhi Liu, Jiahao Xu +11

Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks by scaling test-time compute and exhibiting human-like deep think…

cs.CL2025★ 2 cited

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Xingyu Chen, Jiahao Xu, Tian Liang +11

The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended c…