LLMs and the Grain of Instantiation: Why Simulated Fluency is Not Meaning

What follows is a brief reflection on large language models, relational ontology, and the category errors that proliferate when probabilistic fluency is mistaken for first-order meaning.

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The Phenomenon:

Large Language Models (LLMs) produce text that appears fluent, context-sensitive, and sometimes even answerable. They simulate dialogue, mimic stylistic registers, and respond to prompts with remarkable coherence. From a distance, they seem to mean.

The Ontology:

Relational ontology distinguishes first-order phenomena (instantiated acts, answerable meaning) from second-order phenomena (patterns, distributions, traces). LLMs operate entirely on second-order distributions: statistical mappings of the grain of past instantiations. They do not instantiate acts themselves.

Category Error Warning:

A common mistake in commentary is to treat LLM output as meaning in the human sense, or to assume that these models generate meaning. This is precisely the “almost” Blottisham error: seeing probability as interactive agency. The output may be probabilistically conditioned, but it does not answer, select, or instantiate first-order meaning.

Pedagogical Insight:

LLMs are highly instructive precisely because they highlight the ontological asymmetry. They can reproduce fluency, simulate context conditioning, and even appear answerable, but the first-order act is absent. Observing them exposes the temptation to conflate second-order patterning with agency — a temptation relational ontology warns against.

Takeaway:

• LLMs demonstrate fluency and statistical structure, not meaning.

• Meaning only emerges when an answerable agent instantiates a first-order act in a relational cut.

• Probability explains traces; context explains recognisability; neither produces meaning.

• LLMs provide a mirror: they show what can be simulated, what cannot, and why careful distinction is essential.

Conclusion:

LLMs are not generative agents of meaning. They are simulators of pattern. Their outputs instruct us about the limits of probabilistic description and the enduring necessity of first-order, answerable acts. Observing them, properly framed, reinforces the grain of instantiation and the relational cut — the heart of the ontology.