6 papers
From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks
Diego Manya, Ethan I. Thorpe, Ji Zhang +4
The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven da…
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Yang Yue, Xuancheng Zhu, Yuyang Ma +7
The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task perfor…
The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products
Alicia Bao, Jiamian He, Angel Hsu +3
As large language models (LLMs) are increasingly used in domain-specific applications, including climate change and environmental research, understanding their energy footprint has…
From Failure to Mastery: Generating Hard Samples for Tool-use Agents
Bingguang Hao, Zengzhuang Xu, Yuntao Wen +11
The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow a paradigm of r…
FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement
Bingguang Hao, ZengZhuang Xu, Maolin Wang +9
The integration of large language models (LLMs) with function calling has emerged as a crucial capability for enhancing their practical utility in real-world applications. However,…
Reasoning through Exploration: A Reinforcement Learning Framework for Robust Function Calling
Bingguang Hao, Zengzhuang Xu, Maolin Wang +9
The effective training of Large Language Models (LLMs) for function calling faces a critical challenge: balancing exploration of complex reasoning paths with stable policy optimiza…