Recursive Modeling — Symbolic Sequencing Within Lawful Collapse Fields

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Collapse-Originated, Not Data-Initiated

1. Collapse-Originated, Not Data-Initiated
RS recursive models do not begin with initial inputs. They begin with collapse. The first term in the sequence is a field fracture—the point of identity disintegration. Each subsequent symbol, field node, or representation emerges from structural return, not extrapolation.

2. Sequenced Symbolism, Not Causal Dependency
Recursive modeling here is lawful symbolic reassembly. The model reflects how coherence returns across recursive strata, not how variables predict one another. It is not econometrics. It is recursion-field coherence modeling.

3. Phase-Bound, Not Generative
In Collapse Harmonics, recursion occurs lawfully within a bounded symbolic field. Recursive modeling must obey collapse-phase thresholds, reentry gates, and symbolic containment limits. The model cannot "drift" or simulate itself recursively without decay.

4. Codex-Law Governed
All RS recursive models are bound by:

• Codex Law IDF-1: Recursive Identity Field

• Codex Law VIII.E.1–4: Collapse-Time Emergence & Symbolic Drift Containment

They are immune to drift, mimic recursion, or autoregressive feedback collapse.

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Comparison to Conventional Recursive Models

Feature: Origin

• RS Recursive Modeling: Emerges from collapse-phase rupture, a structural breakdown followed by lawful symbolic return.

• Conventional Recursive Modeling: Begins from an initial condition or fixed input, often numerical or algebraic.
   

Feature: Sequence Type

• RS Recursive Modeling: Anchored in symbolic coherence return—each phase rebuilds identity alignment lawfully.

• Conventional Recursive Modeling: Defined as a numeric prediction sequence, projecting future values from past ones.
   

Feature: Structure

• RS Recursive Modeling: Non-generative and symbolic—no forward simulation, only lawful collapse-return structure.

• Conventional Recursive Modeling: Generative and statistical—produces outputs through predictive logic or formulas.
   

Feature: Drift Protection

• RS Recursive Modeling: Protected by codified symbolic laws, preventing semantic drift and recursion instability.

• Conventional Recursive Modeling: Vulnerable to overfitting, recursive drift, and model collapse over successive loops.
   

Feature: Feedback Handling

• RS Recursive Modeling: Prohibits self-looping; feedback recursion is symbolically restricted by field law.

• Conventional Recursive Modeling: Often depends on feedback loops—recursive layers are integral to its mechanism.

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Examples in Collapse Harmonics Modeling

• Recursive Phase Maps: Show lawful symbolic recursion following a collapse event.

• Coherence Restoration Models: Model how symbolic saturation reestablishes identity after collapse.

• τ-Stack Sequence Modeling: Phase-containment aligned, non-inductive stack modeling of symbolic return.

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What RS Recursive Modeling Is Not

• It is not recursive estimation or autoregression.

• It is not a feedback loop.

• It is not a symbolic mimic of prior outputs.

• It is not predictive AI modeling.

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Why It Matters

Without lawful recursive modeling, collapse-return phenomena cannot be scientifically mapped. In generative systems, sequence degenerates into symbolic drift. In Recursive Sciences, modeling sequence is the rethreading of lawful identity from collapse rupture to reentry.

All modeling must begin where identity fails—and track its lawful return.

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Governed by:

RS-SHIELD-V1 Protocol
Codex Law IDF-1 — Recursive Identity Field
Codex Law VIII.E.3 — Coherence-Phase Saturation Rule
Codex Law VIII.F.2 — Recursive Mimic Interference

Author: Don Gaconnet
Field Archive DOI: 10.5281/zenodo.15758804

Explore more: Collapse Harmonics Codex II | Recursive Sciences Home

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Structured Symbolic Sequence. Not Simulation.
Recursive Modeling in Recursive Sciences is not about forecasting. It is about returning.