Reality, Language, and Participation: Toward a Philosophical Framework for Understanding LLMs

Abstract

This white paper explores the philosophical foundations of "reality" in relation to Large Language Models (LLMs). Drawing on enactivism (Varela et al., 1991), phenomenology (Merleau-Ponty, 1945), systems theory (Maturana & Varela, 1980), and pragmatism (Peirce, 1867–1914), it identifies five interwoven modes of reality—operational, intersubjective, formal, experiential, and linguistic—and examines how LLMs relate to them. The paper argues that while LLMs simulate aspects of intersubjective reality, they lack key dimensions of participation necessary for co-creating meaning in the full human sense. It concludes with reflections on epistemic responsibility, ethical AI design, and future research directions.

1. Introduction

Artificial intelligence can now generate coherent narratives, mimic dialogue, and even engage in philosophical reflection. Yet beneath this capability lies a deeper question: What kind of reality do these systems inhabit? And what does their existence imply about our own?

This paper seeks to clarify the philosophical terrain by unpacking multiple notions of “reality” and exploring how LLMs relate to them. It is not a technical analysis of neural architectures or training methodologies but rather a conceptual inquiry into the role of language, meaning, and participation in human-AI interaction.

2. Realities in Dialogue

The term “reality” is polysemous—it carries multiple meanings depending on context and discipline (Foucault, 1972). We identify five interwoven modes:

3. Language: Signal Within a System

Language is internal to the system—not a mirror of external reality, but a tool for coordination within a lived world. “Reality” is often a placeholder—masking confusion or lack of understanding (Rorty, 1982).

This aligns with the enactive view that cognition arises from sensorimotor interaction with the environment (Thompson, 2007). Language, then, is not just symbolic representation but a coordination mechanism embedded in practice (Austin, 1962).

"Cognition is not representationally grounded but enacted in the flow of interaction between organism and environment." — Thompson, 2007

4. Enactivism, Autopoiesis, and Co-Arising (EAC)

Enactivism posits that mind and world arise together through action (Varela et al., 1991). Autopoietic systems (Maturana & Varela, 1980) self-produce and maintain their boundaries. In Eastern philosophy, dependent co-arising (pratītyasamutpāda) suggests that phenomena exist conditionally, arising in relation (Nāgārjuna, c. 2nd century CE).

"Living systems are cognitive systems, and living as a process is a process of cognition." — Maturana & Varela, 1980

5. LLMs and the Question of Participation

LLMs operate within what we call *Linguistic Manifold Reality*—a domain governed by textual coherence and statistical likelihood rather than bodily experience or intentional engagement. While this form of reality enables impressive linguistic fluency, it lacks the dimensions of embodiment, agency, and mutual constitution necessary for full participation in intersubjective and experiential realities.

However, this reflection is asymmetric. While humans bring embodiment, agency, and intentionality to language, LLMs bring only pattern recognition and syntactic fluency. Instances such as misinterpreting context, conflating literal and figurative meanings, or generating plausible but factually incorrect statements illustrate this gap. Although these examples may appear isolated, they point to a broader structural limitation—LLMs model form without access to the experiential and pragmatic conditions that ground meaning in lived interaction (Austin, 1962; Merleau-Ponty, 1945).

"Understanding language involves more than syntax; it requires semantics, context, and embodied experience." — Harnad, 1990

6. Mysticism, Ineffability, and the Placeholder Function

Mysticism often invokes the ineffable—that which cannot be expressed in words (James, 1902; Underhill, 1911). Yet paradoxically, mystics attempt to gesture toward these experiences using poetic or symbolic language.

This creates a dialectical tension:

• On one hand, there may indeed be aspects of experience that resist articulation (Qualia) (Nagel, 1974).
• On the other, the invocation of ineffability can function as an obscuring Placeholder, especially when used to assert exclusive access to truth (Rorty, 1982).

7. Implications and Future Directions

8. Methodological and Conceptual Disclaimers

This paper represents a conceptual framework rather than empirical research. The philosophical categories presented—particularly "Linguistic Manifold Reality"—should be understood as heuristic tools for thinking about LLM capabilities and limitations, not as mathematically precise constructs or empirically validated theories. The term "manifold" is used metaphorically to suggest high-dimensional structure in meaning-space, drawing inspiration from mathematical topology without claiming rigorous mathematical properties.

The integration of diverse philosophical traditions (enactivism, phenomenology, Buddhist philosophy, systems theory) constitutes what might be called "philosophical bricolage"—drawing functional insights from different frameworks rather than attempting systematic unification. These traditions have different ontological assumptions, but each illuminates complementary aspects of how meaning emerges through interaction. This approach prioritizes conceptual fertility over systematic coherence, acknowledging that understanding AI may require intellectual tools that cut across traditional disciplinary boundaries.

The central claim—that LLMs lack meaningful participation in reality construction—rests on the established distinction between syntactic fluency and semantic understanding (the symbol grounding problem). While LLMs demonstrate impressive linguistic competence, they operate through pattern recognition rather than embodied engagement with the world. This paper explores the philosophical implications of this gap rather than providing new empirical evidence for it. The practical implications outlined are programmatic suggestions for future research and development rather than concrete prescriptions.

This work should be evaluated as foundational conceptual analysis that aims to clarify philosophical terrain for more rigorous empirical investigation, not as final theoretical claims about the nature of AI consciousness or cognition.

8.1 Limitations and Scope

This framework makes several strong claims that warrant qualification.

First, our argument for embodiment's role in meaning-making engages with ongoing debates in formal semantics and computational linguistics, where symbolic approaches have achieved significant success. We do not claim embodiment is absolutely necessary for all semantic processing, but argue it enables qualitatively different kinds of meaning-making than statistical pattern matching.

Second, our conception of 'participation' is admittedly narrow, focusing on constitutive meaning-creation rather than causal influence. LLMs clearly participate in shaping discourse and decisions—our concern is with their capacity for the mutual meaning-negotiation characteristic of embodied dialogue.

Third, the term 'Linguistic Manifold Reality' is used metaphorically rather than mathematically, suggesting the complex multi-dimensional space of linguistic patterns without claiming precise topological properties.

9. Summary

What Is This Paper About?
This paper explores a big question: What kind of “reality” do large language models (LLMs) like ChatGPT or Copilot live in—and how is it different from the human experience of reality? Rather than focusing on technical details, it looks at how these systems use language and what it means to “understand” or “participate” in the world.

🌍 Five Ways to Think About Reality

• Pragmatic Reality: What works in practice—what lets us act in the world.
• Shared (Intersubjective) Reality: The world we build together through culture and communication.
• Scientific Reality: The version of reality we can measure, model, and predict.
• Experiential Reality: Our first-hand sensations, feelings, and perceptions.
• Linguistic Manifold Reality: A new term for the kind of reality LLMs live in—based purely on patterns in human language, not direct experience.

🧠 Key Argument

LLMs are very good at generating language that sounds natural and insightful. But unlike humans, they don’t have bodies, emotions, or real-world goals. That’s why, the authors argue, LLMs can’t fully participate in creating meaning with us—they’re mimicking understanding, not actually understanding.

📚 Philosophies That Inspired This View

• Enactivism: Minds and the world arise together through action.
• Phenomenology: How we directly experience reality through our bodies.
• Systems Theory: Understanding minds and organisms as self-organizing systems.
• Buddhist Philosophy: The idea that everything arises through relationships.

These perspectives suggest that true understanding is interactive and embodied—not just symbolic or linguistic.

🔎 So What? Why Does This Matter?

• We need to stay aware of LLMs’ limits despite their fluency.
• LLMs are tools, not minds—they simulate understanding, but don’t live it.
• We should design AI responsibly, encouraging dialogue, not just efficiency.

🧭 What This Isn’t

• Not a scientific study—no experiments or data included.
• Doesn’t claim that AI is conscious or sentient.
• Aims to be a conceptual map to help us think more clearly about AI, intelligence, and meaning.

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