ESCT-CSI v1.6: From Mathematical Closure to Phenomenological Engineering — A Predictive Framework for Semantic Traction via Finite-Audit Wavelet-Flux Baseline

ESCT-CSI v1.6 A Falsifiable Phenomenological Engineering Framework for Semantic Traction and Basin Stiffness Prediction)

AbstractWe present ESCT-CSI v1.6 (Entropy-Signaling Convergence Theory – Coherent Ignition), repositioned as a conditional, falsifiable phenomenological-engineering framework for predicting semantic traction collapse and hallucination onset in large language models. Beginning from a single normative axiom—Maximum-Entropy Stability—and six explicitly labeled modeling assumptions (W1–W6), we derive a finite-audit dyadic wavelet-flux baseline that conditionally yields the structural constants 5/2 and Q = 40. The basin-stiffness reference parameter β₀ = 40^(1/3) ≈ 3.420 is treated as an empirically calibrated reference fixed point (analogous to the fine-structure constant α in QED), not a universal constant. Under reference conditions, the framework predicts a calibrated baseline interval β₀ ∈ [3.420, 3.495] (relative width ~2.2%), supported by a theoretical safety net spanning [3.175, 3.495].We introduce the β-Running Constant Experiment (β-RCE) protocol to operationalize cross-model measurement of effective basin stiffness as a function of scale, audit personality, architecture, temperature, and task complexity. A normalized feasibility function Ψ discriminates stable traction (Ψ > 0) from predicted collapse (Ψ ≤ 0). The manuscript explicitly acknowledges the emergence gap between continuous wavelet-flux field structure and discrete combinatorial channel accounting, treating this mathematical non-closure as the standard phenomenological boundary encountered in complex-systems physics. The framework’s scientific value lies in its precise vulnerability to experiment: if empirical measurements under standardized reference conditions systematically fall outside the predicted interval, the core mismatch-cost structure is challenged. This work synthesizes axiomatic reconstruction, critical peer-review, and operational measurement protocols into a unified predictive structure designed to evolve via experimental feedback.

Keywords / Tags (for Zenodo)semantic traction · maximum entropy · basin stiffness · hallucination prediction · running constant · wavelet flux · EROC audit · falsifiability · phenomenological engineering · complex systems · large language models · beta-RCE