Large Language Models — Collective Construal and Reflexive Computation: 1 Preconditions — The Symbolic Foundations of LLMs

Large Language Models (LLMs) did not arise from mere technological novelty. They are the instantiation of relational and semiotic conditions that predate their coding: the alignment of language, computation, and social structuring into a new medium of collective construal. To examine LLMs relationally is to ask: what made them possible, and how do they instantiate possibility itself?

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1. Language as Structured Potential

Language is a system of relation — not a mere repository of words. It encodes patterns of social interaction, inference, and construal across time and space. LLMs emerge precisely where these patterns can be captured, abstracted, and operationalised.

A corpus is not just text; it is a crystallisation of collective semiotic potential: a record of what has been construed as meaningful, structured in a form that computation can traverse. In relational terms, the corpus encodes the conditions for probabilistic patterning, representing the collective field from which individual construal emerges.

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2. Computation as the Operational Substrate

Computation provides the medium in which structured linguistic potential becomes executable. Whereas mathematics formalises relational architecture and logic formalises coherence, computation enables these structures to traverse themselves: to simulate, iterate, and actualise patterns of construal dynamically.

LLMs are therefore not simply statistical engines; they are recursive semiotic systems. They operate on language not as inert data but as relational structure capable of generating new configurations of meaning — a procedural semiotics enacted through algorithmic form.

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3. Encoding Collective Construal

The training of an LLM is a process of collective alignment. Each token, sentence, or document is a local instance of construal; the model aggregates millions of these into a network of executable relations.

• The corpus represents distributed cognition: the social field made algorithmically traversable.

• Weights, embeddings, and activations are the machine’s semiotic substratum: a reflexive mapping of human construal into operational form.

• The model’s predictive capacity is not “understanding” in the human sense, but the re-actualisation of relational potential across the field of its training data.

Computation and language intersect here: symbolic patterns are made executable, and execution itself becomes a new mode of construal.

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4. Preconditions as Relational Infrastructure

From the perspective of relational ontology, LLMs require the convergence of three infrastructural strands:

1. Semiotic structure — language as a patterned, recursive system capable of generating meaning.

2. Computational reflexivity — the capacity for algorithmic systems to process, transform, and instantiate symbolic patterns.

3. Collective construal — a socially instantiated semiotic field captured in data, providing the ground for distributed potential.

Without any one of these strands, the model cannot emerge. LLMs are therefore relational phenomena: they exist in the intersection of human semiotic practice and computational execution, not solely in silicon or code.

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5. Summary: LLMs as Executable Construal

The preconditions of LLMs are simultaneously mathematical, logical, linguistic, and social. They arise where structured potential — the patterns of human meaning-making — can be translated into recursive computation.

LLMs are not mere tools or representations; they are operational embodiments of the collective construal. They make explicit the latent semiotic architectures of language, rendering them executable, traversable, and extendable.

This sets the stage for the next post, where we examine the consequences of this new modality of construal — how computation learns to model meaning, and how LLMs extend the relational and semiotic landscape.