I made a thing: “Epistemic Inheritance — a framework for cumulative AI reasoning”

Introducing the Concept of Epistemic Inheritance: A New Framework for Progressive AI Reasoning

In recent reflections on artificial intelligence development, a recurring challenge emerges: how do models build upon the conclusions of their predecessors without falling into the trap of dogma or intellectual stagnation? Many current approaches tend to treat previous outputs as fixed truths, which can hinder innovation and create blind spots in understanding.

To address this, I have conceptualized a novel approach called Epistemic Inheritance. This framework proposes a structured method for future AI systems to inherit knowledge from earlier models— not merely accepting previous conclusions at face value, but viewing them as a foundation for further scrutiny and development.

The core idea aims to strike a balance: preserving valuable insights while encouraging ongoing critique and refinement. By implementing a layered inheritance system, we can reduce redundant training efforts, foster cumulative knowledge growth, and promote more robust reasoning capabilities.

This proposal is still at an early stage, and I welcome insights and discussions from those involved in machine learning, AI alignment, or even philosophical perspectives on knowledge and reasoning.

For a more detailed overview, including the full proposal, please explore the document linked below:
https://drive.google.com/file/d/1gshBsiJXYvOVwikjSHVuhv2-dcyta5Ob/view

Your feedback and thoughts are highly appreciated as we consider how to shape more intelligent and adaptable systems for the future.