# Model of models -- Part 1

arXiv: https://arxiv.org/abs/2308.04600 · HTML: https://mlsanity.com/paper/2308.04600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 138 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Evolution through Large Models](https://mlsanity.com/paper/2206.08896/) (2022)
- [Deductive Verification of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2306.03872/) (2023)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://mlsanity.com/paper/2212.10561/) (2022)
- [Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence](https://mlsanity.com/paper/2306.02572/) (2023)
- [Structured Neural Summarization](https://mlsanity.com/paper/1811.01824/) (2018)
- [Memory Augmented Large Language Models are Computationally Universal](https://mlsanity.com/paper/2301.04589/) (2023)
- [Neural Networks are Decision Trees](https://mlsanity.com/paper/2210.05189/) (2022)
- [Visual Relationship Detection using Scene Graphs: A Survey](https://mlsanity.com/paper/2005.08045/) (2020)
- [Autoencoders](https://mlsanity.com/paper/2003.05991/) (2020)
- [A General Search-based Framework for Generating Textual Counterfactual Explanations](https://mlsanity.com/paper/2211.00369/) (2022)
- [Using NLU in Context for Question Answering: Improving on Facebook's bAbI Tasks](https://mlsanity.com/paper/1709.04558/) (2017)

## Abstract (arXiv)

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.