This January 18, 2026 massive collaboration between University of Illinois Urbana-Champaign, Meta, Amazon, Google Deepmind, UCSD and Yale explores the evolution of agentic reasoning in large language models, moving beyond static text generation toward dynamic planning and external interaction. It details how models utilize tool integration and multi-agent systems to solve complex problems in fields like software engineering, scientific discovery, and robotics. The text categorizes specialized roles within these systems—such as leaders, executors, and critics—to facilitate sophisticated collaboration and feedback-driven behaviors. Furthermore, it examines various post-training methods and architectural frameworks designed to optimize how agents communicate and manage long-term memory. Finally, the sources provide a comprehensive overview of benchmarks used to evaluate these autonomous systems across diverse, real-world applications. Source: January 18, 2026 Agentic Reasoning for Large Language Models https://arxiv.org/pdf/2601.12538