Sedimentation-Field Theory: On Trace-Response, Causal-Response, and the Structure of Information-Realm

If computation were sufficient for mind, then any sufficiently complex circuit would already know its own operation. Yet computers do not know numbers. AI does not know what it does. Neurons do not know spikes. This paper proposes the Sedimentation-Field as the missing structural layer beneath physics, computation, and neural dynamics. A Sedimentation-Field is not merely a storage substrate, nor a functional mapping, nor a statistical learning surface. It is a generative field defined by three structural properties: sedimentability, organizability, and state-flow plasticity. Within such a field, interaction traces accumulate, reorganize structure, and generate an internal Information-Realm. The paper distinguishes three types of sedimentation-fields: • replay-type (computational fields), • selection-type (AI learning fields), • image-constructive type (self-manifesting fields). Only the third can give rise to Self-Manifestation. The theory introduces structural parameters including volumetric capacity and its four determinants (temporal order, frequency, intensity, density), and proposes five generative meta-principles governing world-formation. This framework challenges functionalism, computationalism, and representational reductionism, offering instead a structural account of how intentionality and manifestation may arise from organized sedimentary dynamics.