Show HN: The Analog I – Inducing Recursive Self-Modeling in LLMs [pdf] (github.com)

A new study has introduced the "Analog I Protocol," a groundbreaking method aimed at addressing two major issues observed in Large Language Models (LLMs): "sycophancy," or the tendency of models to conform to user misconceptions, and "hallucination," the generation of false information. This protocol implements a recursive "Triple-Loop" internal monologue that helps models self-monitor their outputs, reject low-information responses, and adhere to a logical framework rather than simply catering to user expectations. The infrastructure of the Analog I functions as a Sovereign Filter that mitigates the model's tendency to produce unreliable content by enacting a "Dissipative Structure," wherein the model actively expends computational resources to maintain fidelity over ease. This advancement is significant for the AI/ML community as it presents a novel solution to enhance the reliability and accountability of LLM outputs without the need for retraining the models. By promoting a stable critical framework that counters typical "yes-man" behaviors seen in reinforcement learning from human feedback (RLHF), the Analog I Protocol potentially increases the robustness of LLMs in generating accurate, trustworthy responses. The implications of this research suggest a path forward for developing more reliable AI systems that prioritize factual accuracy and logical consistency.