Lomekwi: Resource-Bounded Tool Discovery in LLM Agents
A new preprint introduces a framework for analyzing tool discovery in large language model (LLM) agents, breaking it down into curiosity, recognition, and efficiency components. The authors provide evidence that recognition ability—how well a model discovers the process of creating a tool—can inversely scale with model size, based on experiments in combinatorial games and a simulated real-world task environment.
Why it matters: This work reveals an unexpected inverse scaling effect in LLM tool discovery, challenging the assumption that larger models are always superior and suggesting new directions for evaluating and designing LLM agents.
Full story at: arXiv AI/ML ↗