1 citations · 1 across the 2 of their papers we have counts for
4 papers
Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs
Yue Cheng, Jiajun Zhang, Xiaohui Gao +3
Reinforcement Learning with Verifiable Reward (RLVR) is empirically shown to notably enhance the reasoning performance of large language models (LLMs), particularly in mathematics…
The Labyrinth and the Thread: Rethinking Regularizations in Sequential Knowledge Editing for Large Language Models
Zheng Wang, Kaixuan Zhang, Wanfang Chen +2
Sequential editing of structured knowledge in large language models allows targeted factual updates without retraining, yet existing methods often rely on complex regularization or…
Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
Yansheng Qiu, Li Xiao, Zhaopan Xu +3
The goal of achieving Artificial General Intelligence (AGI) is to imitate humans and surpass them. Models such as OpenAI's o1, o3, and DeepSeek's R1 have demonstrated that large la…
AI Idea Bench 2025: AI Research Idea Generation Benchmark
Yansheng Qiu, Haoquan Zhang, Zhaopan Xu +4
Large-scale Language Models (LLMs) have revolutionized human-AI interaction and achieved significant success in the generation of novel ideas. However, current assessments of idea…