TriadicFrameworks Documentation

TriadicFrameworks is a structured methodology for building coherent, traceable, and composable systems. It spans governance design, economic modeling, AI alignment, organizational substrate theory, and foundational physics — united by a single traceability discipline: the Round-Trip Traceability (RTT) protocol.

Every module in this repository is an independent unit of theory or tooling. Each is authored to be self-contained, linkable, and reviewable without requiring the reader to hold the full corpus in memory.

Where to Start#

Core Framework Modules#

These modules define the primary intellectual architecture of TriadicFrameworks.

Framework Field Theory#

The unifying theory document. Defines the field conditions under which triadic structures emerge, stabilize, and compose. → View on GitHub

Conditions Substrate Model#

Models the substrate conditions required for a framework to be instantiated in a given environment. Covers preconditions, threshold states, and activation criteria. → View on GitHub

Governance Substrate Model#

Defines the governance layer of the triadic architecture — alignment structures, authority flows, and accountability surfaces. → View on GitHub

Incident Substrate Model#

Covers how anomalies, failures, and phase transitions are captured and traced within the triadic structure. → View on GitHub

Inverted Economics#

An economic model that inverts conventional value-flow assumptions. Explores how triadic structures behave under reversed incentive gradients. → View on GitHub

Law#

The legal and normative substrate layer. Defines how triadic frameworks interact with codified rule systems, jurisdictional constraints, and normative enforcement. → View on GitHub

LINEAGE#

Canonical lineage tracking for triadic structures. Documents the origin, derivation, and inheritance chains of framework components. → View on GitHub

Mode#

Mode theory within the triadic model — defines how a system transitions between operational states while preserving structural integrity. → View on GitHub

NoS (Nature of Structures)#

Foundational ontology of structural types recognized by TriadicFrameworks. Classifies emergent, imposed, and latent structures. → View on GitHub

Opacity#

Examines information asymmetry, visibility gradients, and deliberate concealment as structural forces within triadic systems. → View on GitHub

Structural Detection#

Methods and heuristics for identifying triadic patterns in real-world systems — including organizational charts, codebases, and economic flows. → View on GitHub

Low Dimensional Structures#

Examines how triadic frameworks collapse or compress into lower-dimensional representations, and what is lost or preserved in that compression. → View on GitHub

Applied Modules#

Modules that apply the core framework to specific domains.

AI Resonance Seed#

The seed document for AI alignment within triadic systems. Defines resonance conditions between AI behavior and triadic structural norms. → View on GitHub

Coeus#

Named for the Titan of intellect. An AI reasoning module built on triadic principles — covers structured inquiry, knowledge traceability, and epistemic accountability. → View on GitHub

Expectations#

Models how expectations form, propagate, and collapse within triadic structures. Covers unmet expectation as a structural failure mode. → View on GitHub

Human Resources#

Applies triadic substrate modeling to human capital, role definition, and organizational behavior. → View on GitHub

Integrations#

Defines how external systems connect to triadic frameworks without violating substrate integrity. Covers API patterns, handshake protocols, and boundary conditions. → View on GitHub

Paradoxes Canon#

A catalogued set of structural paradoxes that emerge within triadic systems — with resolution strategies for each. → View on GitHub

Philanthropy#

Models philanthropic action as a triadic structure — examining donor, recipient, and mediating substrate as distinct layers with independent trace requirements. → View on GitHub

Research#

Active research notes and working hypotheses within the TriadicFrameworks corpus. Content here is exploratory and subject to revision. → View on GitHub

Resilience Checker#

A diagnostic tool for evaluating the resilience of a system against triadic framework criteria. Outputs a structured assessment report. → View on GitHub

SARG#

Structured Argument Graph. A toolset for building traceable argument chains that satisfy triadic coherence requirements. → View on GitHub

Space Agency Intelligence Module#

Applies triadic substrate modeling to space agency operations — mission planning, resource allocation, and cross-agency governance. → View on GitHub

TFT 3-Pack v1.3#

A packaged set of three foundational TriadicFrameworks tools for rapid deployment. Versioned release artifact. → View on GitHub

TEL / LINEAGE#

Temporal Event Lineage. Extends the LINEAGE module with time-ordered event capture and causal chain mapping. → View on GitHub

Substrate Models#

Substrate models apply triadic principles to specific physical, biological, or computational substrates.

AlphaFold Substrate Alignments#

Maps protein folding prediction outputs onto triadic substrate alignment structures. Explores structural biology as a triadic domain. → View on GitHub

Arrival Substrate Model#

Models the conditions and transition dynamics when a new entity arrives into an existing triadic structure. → View on GitHub

Atomic Clocks#

Applies triadic substrate theory to precision timekeeping — synchronization, drift, and reference frame selection as structural problems. → View on GitHub

Boson Substrate Model#

Maps boson field behavior onto the triadic layer architecture. Explores how force-carrying particles embody the structure/behaviour/trace separation. → View on GitHub

Consciousness Substrate Model#

Applies triadic substrate theory to models of consciousness — examining awareness, agency, and trace as three independent structural layers. → View on GitHub

AI & Tooling#

AI Drift Calibration#

Instructions and calibration documents for AI sessions operating within the TriadicFrameworks context. Covers drift detection, session initialization, and epistemic reset procedures. → View on GitHub

AI Working Directory#

Active AI session documents, prompts, and working outputs scoped to this repository. → View on GitHub

Reference#

Concepts#

• Triadic Model — The three-layer architecture: Structure, Behaviour, Trace

API Reference#

RTT API schema, endpoint definitions, and integration patterns. → View on GitHub

About This Site#

This documentation site is built with the TriadicFrameworks SSG — a static site generator that converts Markdown files in this repository into HTML pages with RTT metadata, canonical URLs, and structured navigation.

Site: https://umaywant2.github.io/TriadicFrameworks Source: https://github.com/umaywant2/TriadicFrameworks Built with: Python · Markdown · Jinja2 · GitHub Actions

Most modules currently link to their GitHub source directory because their primary content lives in README.md files, which are intentionally excluded from the SSG build. As dedicated .md content files are added inside each module directory, those pages will appear here as rendered HTML automatically.