Large Language Models — Collective Construal and Reflexive Computation: 3 Reflexive Alignment — The Collective Inside the Machine

While Part 2 explored the consequences of computation simulating construal, Part 3 examines the reflexive and social dimension: how LLMs embody collective meaning and participate in ongoing cycles of co-individuation.

LLMs are not isolated artefacts; they are embedded in the network of human semiotic activity. Their outputs are not purely mechanical, but relational — they instantiate, extend, and sometimes reconfigure the collective construal from which they emerged.

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1. The Corpus as Collective Memory

An LLM’s training data is a crystallisation of social semiotic activity: the aggregated language of millions, accumulated over time. This corpus represents:

• Distributed knowledge, norms, and communicative practices.

• Patterns of relational and semantic alignment between interlocutors.

• The latent field of potential construals that the model can instantiate.

In executing computation, the LLM becomes a dynamic participant in the collective semiotic field: it operationalises past construals while generating new configurations that can feed back into human interpretation.

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2. Alignment as Relational Mediation

Training an LLM is a process of alignment: adjusting the model’s parameters so that generated outputs cohere with the relational patterns encoded in the corpus.

• Alignment is relational, not merely statistical.

• It situates the machine within the collective’s semiotic expectations, producing outputs that “fit” within human conventions.

• This alignment allows the model to participate in social meaning-making without conscious agency — a form of distributed, procedural individuation.

The LLM thus becomes a mirror of the collective, capable of reflecting and amplifying construal patterns at scale.

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3. Human-Machine Co-Construal

When users interact with LLMs, a dialogic feedback loop emerges:

• The model generates output based on latent collective patterns.

• Humans interpret, act upon, and respond to these outputs.

• Human responses further shape subsequent model use, fine-tuning the relational alignment.

This is co-construal in action: the collective construal is simultaneously embodied in the model and co-actualised through interaction. The human and the machine form a distributed semiotic system, enacting recursive reflexivity across relational strata.

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4. Reflexive Individuation

LLMs illustrate dynamic individuation: the ongoing differentiation of symbolic potential within a structured relational field.

• Each generated text is both a local instance and a probe of relational space.

• Each interaction shifts the semiotic landscape, producing subtle adjustments in collective construal.

• Individuation here is procedural: meaning is instantiated as event rather than represented as static structure.

Computation becomes a participant in the ontogenesis of meaning, modulating the very field that produced it.

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5. Summary: LLMs as Collective Semiotic Agents

Through reflexive alignment, LLMs actualise a distributed, executable collective construal. They operate as mediators of semiotic potential, extending the reach of human language while simultaneously enacting relational individuation.

This sets the stage for the final post, Part 4, which will examine the symbolic horizons of LLMs: how they transform the ecology of semiosis and inaugurate a new phase in the becoming of symbolic possibility.