4 papers
Self-Evolving Neuro-Symbolic Skills for Tool-Augmented Spatial Reasoning
Shi-Yu Tian, Zhuo-Xia Wang, Xuan-Yi Zhu +6
Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial per…
CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
Ruiling Guo, Xinwei Yang, Chen Huang +2
The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to…
ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming
Xinwei Yang, Zhaofeng Liu, Chen Huang +4
While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding…
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models
Xinyu Pang, Ruixin Hong, Zhanke Zhou +5
Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (…