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finkeissen/README.md

Maximally Truth-Seeking Pipeline for Reliable & Auditable AI

Central entry point to the Ekkehard Finkeissen Research Architecture

  • Coherent and constraint-based epistemic framework
  • Infrastructure for auditable knowledge and decision systems
  • The repositories together form one pipeline and must be interpreted together
  • Core Principle: Any statement not explicitly bound to a concrete problem context is treated as epistemically inadmissible → processing stops
  • Actively exploring pathways to stabilize AI systems (e.g. LLMs) against hallucinations and unbound generalizations via epistemic constraints Modern AI systems produce claims without explicit epistemic binding. This pipeline enforces context-bound admissibility as a hard constraint.

The following cyclical pipeline is a constraint architecture for epistemic governance in which admissibility precedes claims: The pipeline is cyclical: artifacts can regress to Legacy when admissibility fails. Examples: → see Matrix repository

What it is NOT:

  • Not a prompt framework
  • Not an absolute or unquestionable knowledge-base
  • Not an LLM wrapper

Research areas:

  • epistemic governance
  • epistemic admissibility
  • constraint-based reasoning
  • knowledge provenance
  • AI safety
  • hallucination mitigation
  • scientific workflows for AI
  • verification of claims
  • formal reasoning for LLM systems
  • auditable decision systems

Step 0 – Legacy

Active sink for inadmissible or unresolved artifacts. https://github.com/finkeissen/legacy

          ⬇️ interface ⬇️

Step 1 – Research-Program (start here!)

Epistemic admissibility rules, STOP conditions, and core grammar. https://github.com/finkeissen/research-program

          ↕️ interface ↕️

Step 2 – Matrix-Management-System (MMS)

Constraint execution layer enforcing epistemic governance across artifacts. https://github.com/finkeissen/mms

          ↕️ interface ↕️          if STOP ➡️ put in Legacy

Step 3 – Matrix

Matrix registers admissible knowledge artifacts with their originating context. https://github.com/finkeissen/matrix

          ↕️ interface ↕️

Step 4 – Hypotheses

Versioned, falsifiable claims derived from the epistemic core. https://github.com/finkeissen/hypotheses

          ↕️ interface ↕️          if STOP ➡️ put in Legacy

Step 5 – Predictions

Operationally verifiable projections derived from hypotheses and matrix. https://github.com/finkeissen/predictions


Step 6 - The LIFE Art Framework

Framework and art projects about life — grounded in the research-program, yet not formally bound. Contributions are welcome - if they operate under the architectural constraints of this repository. under the terms of the license. https://github.com/finkeissen/life


Status: actively maintained · sequentially evolving · deliberately minimal

→ External references: https://x.com/EFinkeissen
→ Books: https://www.amazon.de/s?k=finkeissen+ekkehard

Pinned Loading

  1. research-program research-program Public

    Theoretical foundation, epistemic assumptions, and audit principles for the Matrix Management System. Focuses on problem-centric reasoning, transparency, non-optimization, and traceable knowledge s…

    Python

  2. mms mms Public

    The Matrix Management System is a DBMS engine for knowledge. It defines roles, responsibilities, and governance for the knowledge matrix. Builds on Research-Program (theory) and provides the organi…

    Python

  3. matrix matrix Public

    The Matrix ist the knowledge structure connecting information about a large number of problems from 80 domains and respective solutions including their history. Based on research-program (theory, a…

  4. hypotheses hypotheses Public

    Falsifiable claims layer within the epistemic architecture.

  5. predictions predictions Public

    Verifiable projection artifacts.

  6. life life Public

    An architectural art project structurally grounded in the research program, yet formally independent.