2 papers
cs.CL2025
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
Bowen Jin, TJ Collins, Donghan Yu +10
Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialize…
cs.CL2025
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Jiarui Lu, Thomas Holleis, Yizhe Zhang +9
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation o…