Unveiling the Secrets of LLM Inference: Detecting Dynamic Equilibrium Points

This innovative research explores a novel method to analyze the dynamic behavior of a Large Language Model (LLM) during Inference. By applying control theory principles, particularly CR filters, the study aims to identify and quantify 'ossification' – a fascinating phenomenon where an LLM's output becomes overly rigid.

• •The research utilizes CR filters from control theory to analyze LLM inference states.
• •It aims to detect and quantify 'ossification,' a phenomenon of rigid LLM outputs.
• •The approach offers a way to measure the 'meaning' or core concept within the LLM.