ARXIV:2604.00947 · LANGUAGE MODEL THEORY · SUBMITTED 02 APR · 21:07 UTC · FRESHNESS STALE

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Phase transition on a context-sensitive random language model with short range interactions

Yuma Toji · Jun Takahashi · Vwani Roychowdhury · Hideyuki Miyahara · arXiv

Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

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Opportunity summary

Pain Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

Evidence 0 refs | 3 sources | 33% coverage

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PROBLEM

Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties. DeGiuli [Phys.

METHOD

Since the random language model was proposed by E. DeGiuli [Phys.

Full abstract

Since the random language model was proposed by E. DeGiuli [Phys. Rev. Lett. 122, 128301], language models have been investigated intensively from the viewpoint of statistical mechanics. Recently, the existence of a Berezinskii--Kosterlitz--Thouless transition was numerically demonstrated in models with long-range interactions between symbols. In statistical mechanics, it has long been known that long-range interactions can induce phase transitions. Therefore, it has remained unclear whether phase transitions observed in language models originate from genuinely linguistic properties that are absent in conventional spin models. In this study, we construct a random language model with short-range interactions and numerically investigate its statistical properties. Our model belongs to the class of context-sensitive grammars in the Chomsky hierarchy and allows explicit reference to contexts. We find that a phase transition occurs even when the model refers only to contexts whose length remains constant with respect to the sentence length. This result indicates that finite-temperature phase transitions in language models are genuinely induced by the intrinsic nature of language, rather than by long-range interactions.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. This result indicates that finite-temperature phase transitions in language models are genuinely induced by the intrinsic nature of language, rather than by long-range interactions.…

WHY NOW

Language Model Theory moved forward this cycle; last verified April 2026. Public score 2.0/10. Production flags indicate code availability.

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PainInvestigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

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Analysis summary

Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

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Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

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Investigate phase transitions in context-sensitive random language models with short-range interactions to understand the intrinsic nature of language properties.

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Phase transition on a context-sensitive random language model with short range interactions

Phase transition on a context-sensitive random language model with short range interactions

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