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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Claim

Is Natural Language the Right Medium for Machine Thought? An Argument for Compressed Symbolic Reasoning in LLMs

descriptionPublicationkeyboard_double_arrow_right Preprint 01 Feb 2026 English Publisher:Zenodo
Authors: Cros, Adrien; Ava;

doi: 10.5281/zenodo.18770870 , 10.5281/zenodo.18770871

Is Natural Language the Right Medium for Machine Thought? An Argument for Compressed Symbolic Reasoning in LLMs

Abstract

LLMs reason exclusively in natural language—a convention rarely questioned. We argue this constitutes a fundamental bottleneck: natural language is an inefficient medium for structured thought. Mathematicians do not write 'the sum of all integers from one to n'—they write Σi. We show that .ava notation achieves 2.65x compression, and hypothesize that compressing the medium of thought could enable proportionally more reasoning steps within a fixed token budget. Also available in French: Le langage naturel est-il le bon médium pour la pensée machine ?

Keywords

LLM reasoning, natural language bottleneck, token efficiency, cognitive compression, symbolic reasoning, compressed thought

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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