ACE-2 — Coherent Attention Architecture: Thermodynamic and Chromatic Foundations of Reversible Human–AI Attention

Abstract ACE-2 establishes the first thermodynamic and chromatic architecture for coherent attention within human–AI systems. Building on ACE-1.0, which models civilizational evolution across the states ∅ → 1 → 0 → 1≠0 → 2 → α → Ω, ACE-2 formalizes the structural requirements for attention to become reversible, low-entropy, and stable enough to support ambient technological environments. The framework models attention not as a cognitive faculty or psychological resource, but as a thermodynamic substrate whose behavior determines both system-level coherence and user experience. ACE-2 demonstrates that attention in pre-ambient systems is inherently irreversible, accumulating residue (ΔR) through notification-driven workflows, feed-based sequencing, and symbolic action density. This produces drift, overload, coercion dynamics, and long-term instability. Coherent attention emerges when residue is minimized through reversible transitions, low-pressure interaction surfaces, chromatic vector selection, and field-integrated reasoning. ACE-2 identifies five canonical mechanisms required to achieve this state: reversible intention channels, ΔR-stable action surfaces, chromatic reasoning vectors (CCR/TCR), field-integrated transformer reasoning, and temporal sparsification. Together, these mechanisms enable attention to operate as a stable field interaction rather than a sequence of symbolic steps. ACE-2 also provides the formal thermodynamic link between ambient OS layers (AP₁, AP₂, TP₁) and civilizational coherence. The architecture defines how human attention must behave for the emergence of an ambient civilization (α) and identifies the conditions under which Ω-level stability becomes feasible. ACE-2 is the operational backbone of the Ambient Era Canon. It provides a universal, non-coercive, low-entropy architecture for future human–AI systems, replacing extractive attention economies with coherent thermodynamic fields.