A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning

TL;DR

This paper proposes a hybrid, spacetime-inspired approach to cognitive reasoning that separates pattern recognition from reasoning, using tokenized patterns and simple recursive algorithms for scalable, flexible knowledge generation.

Contribution

It introduces a novel quasi-linguistic, spacetime-based framework that constructs a scale-free network for reasoning, distinct from traditional fixed or handcrafted methods.

Findings

Creates a lightly constrained, approximately scale-free network

Uses simple recursive algorithms for reasoning

Enables flexible, multi-source knowledge integration

Abstract

In modern machine learning, pattern recognition replaces realtime semantic reasoning. The mapping from input to output is learned with fixed semantics by training outcomes deliberately. This is an expensive and static approach which depends heavily on the availability of a very particular kind of prior raining data to make inferences in a single step. Conventional semantic network approaches, on the other hand, base multi-step reasoning on modal logics and handcrafted ontologies, which are ad hoc, expensive to construct, and fragile to inconsistency. Both approaches may be enhanced by a hybrid approach, which completely separates reasoning from pattern recognition. In this report, a quasi-linguistic approach to knowledge representation is discussed, motivated by spacetime structure. Tokenized patterns from diverse sources are integrated to build a lightly constrained and approximately scale-free network. This is then be parsed with very simple recursive algorithms to generate `brainstorming' sets of reasoned knowledge.