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finkeissen/README.md

Maximally Truth-Seeking Pipeline for Reliable & Auditable AI

Core mission: Uncover hidden assumptions before they support hallucinations

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.

This pipeline enforces epistemic governance through:

  • Explicit binding of every statement to a concrete problem context
  • Hard STOP conditions on unbound generalizations
  • Traceable knowledge provenance and audit trails

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
  • uncover hidden assumptions

Step 0 – Legacy

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

          ⬇️ interface ⬇️

Step 1 – Research-Program

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

          ⬇️ interface

Step 2 – Matrix-Management-System (MMS)

Knowledge-management: constraint execution layer enforcing epistemic governance across artifacts. https://github.com/finkeissen/mms

          ⬇️ interface ⬇️          if STOP ➡️ put in Legacy

Step 3 – Matrix

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

          ⬇️ interface ⬇️          if STOP ➡️ put in Legacy

Step 4 – Hypotheses

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

          ⬇️ interface ⬇️          if STOP ➡️ put in Legacy

Step 5 – Predictions

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

          ⬇️ interface ⬇️          if STOP ➡️ put in Legacy

Step 6 – Invest

Action: generalized investment grammar. Deliberate allocation of scarce resources (time, energy, attention, trust, health, coordination, money,…) toward future gain under uncertainty – beyond finance. Explicit multi-level evaluation: person ↔ group, trade-offs, collateral damage, distributional effects. https://github.com/finkeissen/invest

          if STOP ➡️ put in Legacy


The LIFE Art Framework - observing the self-reflecting pipeline (intuition)

Intuition: 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. 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…

    Python

  4. predictions predictions Public

    Verifiable projection artifacts.

  5. invest invest Public

    A functional framework for investing time, energy, money, and resources into life - including health, education, life goals, and collective outcomes.

  6. life life Public

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