Constrained Object Hierarchies as a Unified Theoretical Model for Intelligence and Intelligent Systems
Bibliographic record
Abstract
Achieving Artificial General Intelligence (AGI) requires a unified framework capable of modeling the full spectrum of intelligence—from logical reasoning and sensory perception to emotional regulation and collective behavior. This paper introduces Constrained Object Hierarchies (COH), a neuroscience-inspired theoretical model that represents intelligent systems as hierarchical compositions of objects governed by symbolic structure, neural adaptation, and constraint-based control. Each object is defined by a 9-tuple O = (C, A, M, N, E, I, T, G, D), encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity, Trigger, Goal, and Daemon constraints. We demonstrate COH’s expressiveness by formalizing 19 distinct intelligence types—including human-centric, artificial, and collective intelligences—each with detailed COH parameters and implementation blueprints. These formalizations span logical-mathematical, linguistic, spatial, emotional, social, computational, perceptual, motor, and embodied intelligences, among others. To bridge theory and practice, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL enables modular development and integration of intelligent agents, supporting a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms under a constraint-governed architecture, COH provides a scalable foundation for building general-purpose intelligent systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".