Cassi Field Intelligence: Persistent Learning, Exact Evidence, and Transparent Nonverbal Deliberation. A new kind of ML architecture built on physics [R]
The Cassi team has proposed a new machine learning architecture based on physical theory, aiming to achieve continuous learning, uncertainty tracking, and transparent non-verbal deliberation through a single tensor. This architecture consists of two interconvertible fluid components (contraction and expansion) and their flow mechanisms, used to explain consciousness and solve mathematical problems. The Cassi framework defines four core concepts: consciousness as an embodied field experience, self-awareness based on state modeling, intelligence guided by internal possibilities, and causal continuity of identity. Although the prototype has been iterated, the related theory remains valuable, and the extended significance of this organizational law far exceeds the realm of machine learning.