When Agents Loop: Geometry, Drift, and the Hidden Physics of LLM Behavior
Opening — Why this matters now Agentic AI systems are everywhere—self-refining copilots, multi-step reasoning chains, autonomous research bots quietly talking to themselves. Yet beneath the productivity demos lurks an unanswered question: what actually happens when an LLM talks to itself repeatedly? Does meaning stabilize, or does it slowly dissolve into semantic noise? The paper “Dynamics of Agentic Loops in Large Language Models” offers an unusually rigorous answer. Instead of hand-waving about “drift” or “stability,” it treats agentic loops as discrete dynamical systems and analyzes them geometrically in embedding space. The result is less sci‑fi mysticism, more applied mathematics—and that’s a compliment fileciteturn0file0. ...