Dialectics for Artificial Intelligence
Zhengmian Hu
TL;DR
The paper advances an information-theoretic framework in which concepts are not fixed labels but reversible information objects tied to an agent’s total experience. It introduces determinations as algorithmic-parity structures that ensure mutual recoverability and defines excess information to measure the redundancy of splitting experience into parts. Dialectics is formulated as a compute-grounded optimization: competing concepts absorb new patches to minimize a description-length loss, enabling growth, contraction, splitting, and merging of concepts. The work further develops practical methods (grounded splits, seeds, and computable surrogates like LLM log-probabilities) and sketches an infrastructure (common ontology server) for cross-agent alignment and verification, aiming to operationalize concept discovery, communication, and coordination at scale. Collectively, the framework unifies clustering, segmentation, and representation learning under a single, testable principle: concepts emerge as low-complexity, reproducible explanations of experience that compete under a shared compression objective.
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.