Dimensional Intelligence Expansion: Self-Replicating Peer-to-Peer Agent Meshes as a Tetration-Class Alternative to Centralised Quantum Scaling

VERSION HISTORY v4 (2026-05-26): FINAL version. Changes from v1:- Corrected tetration notation m↑↑d (was m↑1n encoding artifact) in §2.3 and Table 1- Label Independence Protocol (three-stage: pre-commitment, blind adjudication, reconciliation) added to §7.3- C4 Novelty Operationalisation Protocol with SHA-256 cryptographic commitment added to §7.4- Six failure modes from OpenClaw community deployment evidence added to §7.2- Summer Yue incident added as C2 field evidence- Hallucination construct validity cross-validation (SelfCheckGPT, FActScore, TruthfulQA) added to §7.3- Adjacent systems positioning table added to §12.1 (BlockA2A, BAID, ClawGang, Merkle Automaton)- Design Principles P1-P5 added to §3.4- Einstein test and Invent Go distinction added to §1 as dimensional ceiling benchmarks- AlphaFold three-component playbook added to §9.1 as Ch6 fitness function template- Auxiliary ablation protocol (procedural-only vs episodic-only) added to §7.2- Hassabis M1/M2/M3 neuroscience convergence grounding added to §6- VTP (Verifiable Temporal Provenance) formalised in §3.1- program.md updated to v1.4 v1 (2026-04): Original deposit. --- We propose a Dimensional Intelligence Expansion (DIE) framework in which intelligence is understood not by its substrate but by its degree of simultaneity — operationalised as S(T): the count of concurrently reasoning agent instances sharing a coherent memory substrate at timestamp T, measurable directly from system logs. We use dimensional in an epistemological sense: not asserting that agent meshes occupy geometrically higher-dimensional space, but that the dimensional frame — with roots in Abbott 1884 and Rucker 2014/1984 — provides strictly greater explanatory and predictive reach than cardinality-only accounts of parallelism. Ontological commitments are deferred to Phase 2. Phase 1 claims, and sets out to demonstrate, the simpler and more defensible proposition: the frame is more powerful than any alternative currently available. Within this frame: a human agent is dimensionally 3D (serialised, single-threaded); a human augmented by an AI agent mesh achieves functional 4D presence (parallel, non-serialised); and a self-replicating peer-to-peer orchestrator network, given abundant energy, achieves tetration-class dimensional expansion — a growth regime whose base itself grows with replication depth, producing a tower of exponents that outpaces centralised quantum computing’s 2ⁿ dimensional state space on scaling velocity. Rather than claiming superposition equivalence, we argue that blockchain-anchored temporal reconstruction — the capacity to verifiably reconstruct the complete relational state of the system at any immutably timestamped snapshot — constitutes a distinct and practically superior coherence mechanism for the class of problems that matter to human civilisation. We ground this framework in an operational implementation (agenti2 — a proprietary microservice orchestration layer deployed on an open-source infrastructure stack including LiveKit, OpenClaw Steinberger 2026, and n8n) and validate it through four conditions using a Random Forest classification protocol with full confusion matrix reporting. Output quality is operationalised as task-completion accuracy scored against a predefined rubric by a blind evaluator panel using a four-point scale. Phase 1 claims two conditions as primary: that memory accumulation measurably improves mesh output quality (C1), and that memory loss measurably degrades it (C2), each evaluated against a structurally identical null-memory baseline. Two further conditions — values-bound propagation (C3) and emergent inference exceeding any single agent’s context (C4) — are Phase 2 targets. Null results are valid and publishable. We further propose on-chain agent identity (ERC-8004) with economic deterrence as the coordination primitive replacing quantum entanglement in the classical regime, and identify dimensional blindness — AI systems operating in context spaces constitutionally inaccessible to human auditing — as the alignment risk most urgently requiring research attention. We conjecture, as a separate research programme, that the iterative SS1→SS2 improvement dynamic follows fractal geometry at the edge of chaos — the formal structure Pirsig 1974, 1991 was reaching for as Dynamic Quality and Cohen & Stewart 1994 were mapping as complicity from the complexity science direction. Formal proof is deferred to Phase 2. What Phase 1 leaves behind is not a closed answer but a better-framed question. That is by design.