# On the Computation of Meaning, Language Models and Incomprehensible Horrors

arXiv: https://arxiv.org/abs/2304.12686 · HTML: https://mlsanity.com/paper/2304.12686/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [The Optimal Choice of Hypothesis Is the Weakest, Not the Shortest](https://mlsanity.com/paper/2301.12987/) (2023)
- [Symbol Emergence and The Solutions to Any Task](https://mlsanity.com/paper/2109.01281/) (2021)
- [Emergent Causality and the Foundation of Consciousness](https://mlsanity.com/paper/2302.03189/) (2023)
- [Philosophical Specification of Empathetic Ethical Artificial Intelligence](https://mlsanity.com/paper/2107.10715/) (2021)
- [Computational Dualism and Objective Superintelligence](https://mlsanity.com/paper/2302.00843/) (2023)
- [Compression, The Fermi Paradox and Artificial Super-Intelligence](https://mlsanity.com/paper/2110.01835/) (2021)
- [The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI](https://mlsanity.com/paper/2002.10221/) (2020)
- [Computable Artificial General Intelligence](https://mlsanity.com/paper/2205.10513/) (2022)
- [Extending Environments To Measure Self-Reflection In Reinforcement Learning](https://mlsanity.com/paper/2110.06890/) (2021)

## Built on by (most cited first, 5 of 5)

- [Is Complexity an Illusion?](https://mlsanity.com/paper/2404.07227/) (2024)
- [Time, Identity and Consciousness in Language Model Agents](https://mlsanity.com/paper/2603.09043/) (2026)
- [LLMs grasp morality in concept](https://mlsanity.com/paper/2311.02294/) (2023)
- [Why Is Anything Conscious?](https://mlsanity.com/paper/2409.14545/) (2024)
- [What the F*ck Is Artificial General Intelligence?](https://mlsanity.com/paper/2503.23923/) (2025)

## Abstract (arXiv)

We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.