Belief-MVCC: A Coherence and Transaction Layer for Shared Agent Memory

We present a coherent, transactional, semantic shared-memory kernel through which specialised AI cores read and write while staying consistent. The design combines belief revision (AGM; Darwiche–Pearl; Konieczny–Pino Pérez), distributed-systems consistency (causal+, RedBlue, BSP) and database transactions with bitemporality and provenance, applied to meaning rather than to bytes. Its residual contribution is a semantic, belief-level MVCC with a free-text merge algebra, specified and machine-checked in TLA⁺ (Semantic Strict-Serializability up to a Merge Algebra; 790,936 states, 7 invariants), together with six empirical bench stages reproducible at zero API cost on local open-source models. Concurrent work approaches neighbouring parts of this space from other directions and is discussed in the related-work section.