Solving Tool Overload in AI Agents with Semantic Selection Modern AI agents are integrating with rapidly expanding tool catalogs, sometimes numbering in the hundreds or thousands. This growth, while promising, introduces a substantial challenge: how can agent... AI agents cost efficiency LLM performance open-source models scalability semantic selection tool routing
MIT-IBM Watson Lab: How AI Scaling Laws Are Transforming LLM Training Efficiency Training large language models (LLMs) is an expensive endeavor, driving the need for strategies that maximize performance while minimizing costs. Researchers at the MIT-IBM Watson AI Lab have develope... AI cost efficiency LLM training machine learning model development research scaling laws