Dialectics for Artificial Intelligence

TL;DR

This paper introduces a formal framework for understanding and discovering fluid human-like concepts in AI through an information-theoretic dialectical process that involves expansion, contraction, and alignment of concepts across agents.

Contribution

It proposes a novel algorithmic-information approach to define and manipulate concepts as structural, reversible information objects, enabling AI to discover and align fluid concepts without supervision.

Findings

Defines concepts as reversible information structures based on total experience.

Formulates dialectics as an optimization process for concept evolution.

Introduces a method for low-cost multi-agent concept transmission.

Abstract

Can artificial intelligence discover, from raw experience and without human supervision, concepts that humans have discovered? One challenge is that human concepts themselves are fluid: conceptual boundaries can shift, split, and merge as inquiry progresses (e.g., Pluto is no longer considered a planet). To make progress, we need a definition of "concept" that is not merely a dictionary label, but a structure that can be revised, compared, and aligned across agents. We propose an algorithmic-information viewpoint that treats a concept as an information object defined only through its structural relation to an agent's total experience. The core constraint is determination: a set of parts forms a reversible consistency relation if any missing part is recoverable from the others (up to the standard logarithmic slack in Kolmogorov-style identities). This reversibility prevents "concepts" from floating free of experience and turns concept existence into a checkable structural claim. To judge whether a decomposition is natural, we define excess information, measuring the redundancy overhead introduced by splitting experience into multiple separately described parts. On top of these definitions, we formulate dialectics as an optimization dynamics: as new patches of information appear (or become contested), competing concepts bid to explain them via shorter conditional descriptions, driving systematic expansion, contraction, splitting, and merging. Finally, we formalize low-cost concept transmission and multi-agent alignment using small grounds/seeds that allow another agent to reconstruct the same concept under a shared protocol, making communication a concrete compute-bits trade-off.