AI.LC-Analyzer V6.9x – Phase 3: Epistemic Closure and Boundary Definition in a Multi-Timescale System Model

Abstract

Phase 3 of the AI.LC-Analyzer project (Version 6.9x) completes the systematic exploration, boundary definition, and epistemic closure of a deterministic, mechanistic, and topographic multi-timescale model of chronic stress and persistence dynamics. Unlike optimization-oriented or data-driven modeling approaches, this phase explicitly refrains from prediction, intervention optimization, or clinical inference, and instead focuses on delineating the structural identity and legitimate scope of the model itself.

Building on the architectural baseline (Phase 1) and the methodological supervision of epistemic constraints (Phase 2), Phase 3 investigates how trajectories evolve within a fixed landscape defined by asymmetric stress accumulation, hierarchical time scales,

and strict separation between structural state variables and functional projections. Through a sequence of controlled analytical tasks and reflexive evaluation loops (Phases 3a–3c), the project identifies invariant architectural anchor points, characterizes permissible extension directions, and formally defines “No-Go zones” beyond which further modification would compromise model identity rather than extend insight.

A central result of Phase 3 is the inversion of the guiding question: the model is no longer assessed in terms of what it could be extended to explain, but in terms of which operations, assumptions, or extensions must be excluded to preserve epistemic integrity.

Core properties—such as the 8:1 asymmetry between stress accumulation and decay, the hierarchy of functional versus structural time scales, and the explicit rejection of teleological, prognostic, or normative interpretations—are established not as design choices, but as constitutive identity criteria.

The phase concludes with a structured reflexive closure demonstrating that all logically necessary derivations have been exhausted, while remaining degrees of freedom are transparently marked as contingent or externally assumption-dependent. The resulting artifact is not a tool for application, but a cartographed epistemic landscape with explicitly documented limits. Phase 3 thereby contributes a methodological blueprint for how complex system models can be explored, constrained, and responsibly concluded within Human-in-the-Loop and multi-AI “tiny team” research settings, without conflating internal coherence with external validity.

The complete source code (V6.91) exists as a frozen internal reference artifact.

All structurally relevant mechanisms are documented in the Technical Documentation (Phase 1).

This report builds on the Technical Documentation (Phase 1) and the Methodological Supervision (Phase 2) of the AI.LC-Analyzer project, both published as independent Zenodo records.