13 citations · 16 across the 6 of their papers we have counts for
7 papers
RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
Xiyuan Zhou, Zhuoqi Li, Xinlei Wang +6
Data contamination undermines the reliable evaluation of large language models (LLMs) on mathematical problem solving. While rewriting-based evaluation mitigates memorization, exis…
Hugging Carbon: Quantifying the Training Carbon Emissions of AI Models at Scale
Xinlei Wang, Ruibo Ming, Jing Qiu +2
The scaling-law era has transformed artificial intelligence (AI) from research into a global industry, but its rapid growth also raises concerns over energy usage, carbon emissions…
EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
Xiyuan Zhou, Ruixi Zou, Xinlei Wang +4
Engineering problem solving is central to real-world decision-making, requiring mathematical formulations that not only represent complex problems but also produce feasible solutio…
Toward Reasoning-Centric Time-Series Analysis
Xinlei Wang, Mingtian Tan, Jing Qiu +2
Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift,…
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
Xiyuan Zhou, Xinlei Wang, Yirui He +9
Large language models (LLMs) have shown strong performance on mathematical reasoning under well-defined conditions. However, real-world engineering problems involve uncertainty, co…
From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
Xinlei Wang, Maike Feng, Jing Qiu +2
This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time s…
Near Real-time CO Emissions Based on Carbon Satellite and Artificial Intelligence
Zhengwen Zhang, Jinjin Gu, Junhua Zhao +2
To limit global warming to pre-industrial levels, global governments, industry and academia are taking aggressive efforts to reduce carbon emissions. The evaluation of anthropogeni…