The Five-Tier Evolutionary Hierarchy: Foundational Boundary Constraints, Phase-Space Divergence, and Convergence Mechanisms in Artificial Intelligence

The Five-Tier Evolutionary Hierarchy: Foundational Boundary Constraints, Phase-Space Divergence, and Convergence Mechanisms in Artificial Intelligence Author: Peter Ye (Ye Qiang) Disciplinary Field: Complex Dynamical Systems / Foundations of Artificial Intelligence / Computational Physics Document Type: Formal Position Paper / Theoretical Framework Version Identifier: Formal Working Paper v1.4 Final Abstract: Contemporary paradigms in artificial intelligence predominantly rely on empirical scaling laws and autoregressive statistical sampling, operating under the unvalidated assumption that unconstrained parameter expansion and massive data ingestion will spontaneously induce self-consistent causal reasoning and formal physical logic. This paper formally establishes the Five-Tier Evolutionary Hierarchy, an axiomatic, cross-disciplinary framework reconciling natural philosophy, thermodynamic dissipation, and computational dynamical stability: L1 (Physics) — Constitutional Legislation: Enforces unbreakable physical invariants, including irreversible entropy production, the invariant speed-of-light causal limit, energy-momentum conservation, and Landauer erasure dissipation thresholds; L2 (Mathematics) — Metric Calibration: Quantifies formal phase-space manifolds, symplectic geometry, symmetry breaking, and maximal Lyapunov exponents (\lambda_{max}) to determine exact boundary singularities and divergence thresholds; L3 (Chemistry) — Material Scaffolding: Explores molecular configurations and reaction networks strictly within Gibbs free-energy landscapes and quantum orbital structures; L4 (Biology) — Grounded Negentropy: Governs localized negentropic self-replication, Darwinian evolutionary selection, and grounded embodied priors developed through low-power carbon-based biological computation; L5 (Intelligence & AI) — Symbolic Mapping: Executes parametric manifold approximation, abstract representation, and high-dimensional information compression. We demonstrate that higher-tier intelligence (L5) cannot be detached from its underlying evolutionary foundations. When ungrounded autoregressive language models operate without explicit isomorphism to L1 conservation laws and L2 symplectic invariants, they suffer inevitable topological breakdowns during formal reasoning within closed conservative systems—empirically corroborated by the deterministic onset of "The Algebraic Death Lock" under rigid conservation constraints. Consequently, brute-force scaling inevitably collides with fundamental thermodynamic horizons (The Computation Trilemma). The definitive trajectory toward robust, low-power general intelligence lies in projecting high-dimensional computational representations back onto L1–L2 invariant scaffolds while orchestrating an orthogonal geometric synergy with L4 biological evolutionary priors (YQ-HAET). Keywords: Five-Tier Evolutionary Hierarchy; Computation Trilemma; Phase-Space Divergence; Adversarial Failure Modes; Landauer Bound; Spatial-Logical Isomorphism; Sovereign Decision Pivot; Human-AI Synergy Theory.