LLM Efficiency

Opening — Why this matters now Agentic AI is finally escaping the demo phase and entering production. And like most things that grow up too fast, it’s discovering an uncomfortable truth: thinking is expensive. Every planning step, every tool call, every reflective pause inside an LLM agent adds latency, cost, and failure surface. When agents are deployed across customer support, internal ops, finance tooling, or web automation, these inefficiencies stop being academic. They show up directly on the cloud bill—and sometimes in the form of agents confidently doing the wrong thing. ...

The Chain-of-Thought (CoT) paradigm has become a cornerstone in improving the reasoning capabilities of large language models (LLMs). But as CoT matures, one question looms larger: Does every problem really need an elaborate chain? In this article, we dive into a new method called AdaR1, which rethinks the CoT strategy by asking not only how to reason—but how much. ...