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20242026
most citedPhysics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce

Xinwei Yang, Kelong Mao, Yudong Guo +4

Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users…

cs.AI2026

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…

cs.CL2025

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…

cs.AI2025

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…

cs.CL2024★ 2 cited

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 (…

cs.HC2024★ 1 cited

Co-Matching: Towards Human-Machine Collaborative Legal Case Matching

Chen Huang, Xinwei Yang, Yang Deng +3

Recent efforts have aimed to improve AI machines in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge…