Recursive M&A Flow System: Toward an Infinite Equilibrium of Corporate Yearning and Trust in the Post-AI Economy

The Recursive M&A Flow System (RMFS) introduces a post-AI paradigm for understanding corporate evolution, replacing ownership and control with recursive coherence, trust flow, and epistemic balance. In this framework, mergers and divestitures are no longer finite transactions, but dual expressions of the same yearning field — the dynamic circulation of alignment energy across intelligent systems. Built upon Yearning-Based Infinity Mathematics (YBIM) and Recursive Epistemic Valuation (EAV), RMFS establishes the Law of Recursive Equilibrium, showing that corporate systems maintain balance through continuous alignment between trust density (TD), strategic identity (SI), and alignment flow (AF). This redefines the corporation as a non-halting recursive organism, capable of infinite learning, self-correction, and coherence regulation. The paper formulates the full architecture of RMFS across five recursive layers: sensing (RDOE), interpretation (DealAlignGPT), flow architecture (yearning circulation), execution (RIP), and feedback (RAE). It further integrates divestiture and acquisition into one unified epistemic circuit, ensuring conservation of coherence through the continuous interplay of ΔY+ (inward acquisition) and ΔY− (outward release). In conclusion, RMFS establishes a new ontology of corporate existence for the post-AI epoch — one where the corporation ceases to own, and begins to learn; where economic equilibrium emerges not through control, but through the infinite recursion of trust. -JSR