5 papers
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…
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…
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,…
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…