元收敛认知引擎(MCCE) V8.1:三论并置与四模式闭环——跨学科通用因果动力学评估语言

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Abstract (Chinese)

English Abstract
 
This paper presents the Meta-Convergence Cognitive Engine (MCCE) V8.1, an interdisciplinary general causal dynamical assessment language grounded on falsifiability as the meta-axiom, difference as information substrate, κ=3 as the convergence skeleton, negative dynamics for decay tracking, and time irreversibility as the directional foundation.
 
The methodology rests on five permanently locked core axioms: M-0 (falsifiability priority), M-1 (difference equals information), M-2 (κ=3), M-3 (negative dynamics), and M-4 (time irreversibility). Its operational workflow covers Stages 0–6 plus a Stage 0.5 validity-domain check across 11 cognitive spaces. It integrates three forecasting modules: M-FORE (system dynamics sandbox), M-YI (Yin-Yang variation), and M-HUANGJI (long-period temporal framing), alongside a four-mode closed loop (forward induction / backward deduction / diagnostic deconstruction / constraint regulation) and an extensible parsing ecosystem with seven plug-in subtypes.
 
Empirical base (94 cases): 50 classical single-stability baselines (100% passed), 31 complex-system stress tests (100% synchronous activation of four failure-domain criteria), 12 dynamical boundary cases, 12 blank-domain supplements covering seven classes of boundary systems (non-equilibrium thermodynamics, active matter, continuous fields, multiscale nesting, time-varying constraints, stochastic intermediates, network delay), and 7 cross-era benchmarks. Aggregated statistics: pass rate 59.6%, grey-zone warnings 7.4%, failures 33.0%, zero false negatives.
 
Valid and failure domains are rigorously defined via 81 baseline cases, 12 blank supplements, and 3 cross-era benchmark experiments. The valid domain requires simultaneous satisfaction of deterministic gradient driving, a unique global attractor, and absence of stochasticity, oscillation, branching, or path dependence, with long-run convergence guaranteed. Ten failure classes plus two subclasses (statistical criticality and algorithmic irreducibility) are characterized by a unified four-signature failure fingerprint: κ_free≠3 / Δλ_free<0.08 / BiasRate>15% / significant constraint effect, which triggers synchronously in all failure cases. Seven grey-zone cases pass Branch-A while raising Branch-B alerts, successfully identifying boundary phenomena including pseudo-steady states, time-varying constraints, noise escape, and small-delay synchronization.
 
Three cross-era benchmark suites are established: (1) A1 meta-causal structural evolution, distinguishing ill-posed fixed graphs from time-evolving graphs and producing a classification protocol for invariant vs self-modifying structures; (2) A2 computational irreducibility typing, separating statistical criticality, algorithmic irreducibility, compressible order, and pure randomness to build a CA+MCCE labelled benchmark set; (3) A3 dynamical well-posedness hallucination detection targeting four hallucination types (convergence hallucination, inverse-problem hallucination, design hallucination, grey-zone insensitivity), filling a gap in existing XAI benchmarks that only evaluate temporal correctness rather than dynamical well-posedness. The framework anchors itself at the intersection of Pearl’s causal theory, Lyapunov stability theory, computational irreducibility, and AI causal reasoning.
 
Audit rating and execution model: An internal audit following the methodology’s native auditing rules yields a composite rating of AA (high confidence), with a target of AA+ after EXP-012, a 132-case batch stress test of human-AI negotiation and virtual grounding. All experiments and audits are conducted by a single operator with multi-AI assistance: the human carries out reasoning, coding, and decisions, while multiple AIs (ChatGPT, Claude, ERNIE, Tongyi Qianwen, etc.) serve as auxiliary tools for coding, statistical analysis, audit review, and report generation. The entire workflow is traceable, reproducible, and contestable. The methodology does not claim universal truth; it specifies clear falsification conditions and empirical support.
 
Keywords (English)
 
meta-methodology; cognitive convergence; κ=3; falsifiability; negative dynamics; time irreversibility; five-axiom system; four-mode closed loop; seven-plug-in ecosystem; cross-era benchmarking; meta-causality; computational irreducibility; hallucination detection; single-operator multi-AI collaboration; Meta-Convergence Cognitive Engine