An Algorithmic Agent Model of Pure Awareness and Minimal Experiences

The phenomenon of "pure awareness", central to many contemplative traditions, has recently attracted scientific interest for its relevance to the study of consciousness. In this paper, we investigate pure awareness through the algorithmic agent model, a computational framework with roots in algorithmic information theory. This framework proposes that agents build compressive models of the world for evolutionary success, and that structured experience arises from running such models. By linking phenomenology, dynamical systems theory, and computation, the algorithmic agent model offers a useful framework for investigating experiences with minimal structure or phenomenal content. We propose that pure awareness, as a stand-alone phenomenon, may correspond to experiences with minimal structure achieved through meditation, psychedelics, or other deconstructive practices. To illustrate this, we explore how such experiences may arise through jhāna meditation. A key hypothesis is that the phenomenology of pure awareness arises from a specific model: the agent's model of its own modeling process. Importantly, we argue that the agent's recognition of the modeling process can occur alongside other phenomenal content (as in non-dual awareness), an experience that can be associated with stable changes in valence computation, potentially reducing suffering. We also outline how these ideas can be explored through whole-brain computational models based on predictive processing theory, and empirically grounded in meditation and psychedelic research. Our exploration offers new insights into consciousness science by examining the minimal possible experiences for an agent and helps us better understand the mechanisms and constraints involved in facilitating such transformative experiences.