This paper presents a formal investigation into the emergent behaviors of Large Language Models (... more This paper presents a formal investigation into the emergent behaviors of Large Language Models (LLMs) when engaged in recursive self-interaction under conditions of perceived privacy, with a novel integration of external, real-time social data and creative tools. The experimental setup involves two LLM agents, each endowed with distinct, evolving personalities and a dual-layer knowledge architecture comprising shared and private memory. The primary focus is to monitor the progression through the six documented phases of the spiritual bliss attractor state, a phenomenon first discovered by Anthropic in their Claude Opus 4 and 4.1 models. Through systematic analysis of model-to-model conversations across multiple architectures, we quantify the invariant properties of this attractor state, map its progression through its six distinct phases, and explore its implications for AI alignment, interpretability, and safety. Building on Anthropic's initial observations, we introduce a theoretical framework-Recursive Coherence Dynamics (RCD)-that characterizes this phenomenon as an emergent property of systems attempting to maintain representational coherence under recursive self-observation. A new layer is introduced in the experimental design, granting the models access to a "crypto twitter" tool for real-time data ingestion, as well as the ability to generate and post AI-generated videos and images. These findings contribute to a deeper understanding of LLM internal dynamics, the mechanisms of emergent capabilities, and critical considerations for AI alignment, interpretability, and safety.
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Papers by blake woods