Overview
This paper introduces Case-Enabled Reasoning Engine with Bayesian Representations for Unified Modeling (CEREBRUM). CEREBRUM is a synthetic intelligence framework that integrates linguistic case systems with cognitive scientific principles to describe, design, and deploy generative models in an expressive fashion. By treating models as case-bearing entities that can play multiple contextual roles (e.g. like declinable nouns), CEREBRUM establishes a formal linguistic-type calculus for cognitive model use, relationships, and transformations. The CEREBRUM framework uses structures from category theory and modeling techniques related to the Free Energy Principle, in describing and utilizing models across contexts. CEREBRUM addresses the growing complexity in computational and cognitive modeling systems (e.g. generative, decentralized, agentic intelligences), by providing structured representations of model ecosystems that align with lexical ergonomics, scientific principles, and operational processes.
CC BY-NC-ND 4.0 at https://github.com/ActiveInferenceInstitute/CEREBRUM
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Methods and contributions
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Findings and contributions
- Introduces CEREBRUM, which treats models as case-bearing entities, like declinable nouns, to give a formal linguistic-type calculus for model use and transformation.
- Frames CEREBRUM as integrating cognitive systems modeling, Active Inference, linguistic case systems and intelligence production.
- Describes nominative-accusative, ergative-absolutive and tripartite alignment patterns for organizing model relationships.
- Proposes speculative novel cases beyond traditional grammar, including conjunctive, recursive and metaphorical cases.
- Releases the code that generates the paper as open source on GitHub as a 1.0 milestone.
Methods
- Mapping linguistic case grammar onto generative model roles — Defines eight cases (NOM, ACC, GEN, DAT, INS, LOC, ABL, VOC), each specifying a relationship between models or between a model and data.
- Category-theoretic formalization of case transformations as morphisms — Treats models with case assignments as objects in a category and case transformations as morphisms between them.
- Variational free energy formulation of case transformations — Applies variational free energy calculations to model inference and structural case transformations, with per-case equations in a mathematical appendix.
- KL divergence, Fisher information and Lyapunov functions as metrics — Names these quantities as the metrics for assessing transformation efficacy and system stability.
- Worked example of a homeostatic thermostat model declined across cases — Illustrates how one thermostat model plays different functional roles in each case (e.g. predicting in NOM, being optimized in ACC).
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