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The Simulation of Reason: Why Large Language Models Are Not Reasoning Engines
Copyright ©: Coherent Intelligence 2025 Authors: Coherent Intelligence Inc. Research Division
Date: June 10th 2025
Classification: Academic Research Paper | Critical Analysis
Framework: Universal Coherent Principle Applied Analysis | OM v2.0
Abstract
The contemporary discourse surrounding Large Language Models (LLMs) frequently describes them as "reasoning models" capable of logical deduction and inference. This paper presents a formal critique of this assertion, arguing from the first principles of the Theory of Domain-Coherent Systems (ToDCS) that such a claim is architecturally impossible. We posit that genuine reasoning is a computational process that can only occur within a Single Closed Ontologically Coherent Information Space (SCOCIS), a system defined and governed by a stable Domain Anchor (DA).
We argue that the underlying architecture of an LLM—a vast, statistically correlated network of text—is not a SCOCIS but an Ontologically Incoherent Information Space (OIIS), containing countless inherent contradictions. The user's prompt provides a temporary, low-density DA that induces the LLM not to reason, but to simulate the linguistic output of a reasoning process. This distinction between reasoning and its high-fidelity simulation is critical for understanding the capabilities, limitations, and inherent risks of current AI systems. Mischaracterizing simulation as genuine reason creates a fundamental category error that obscures the true nature of LLM cognition.
Keywords
Reasoning, Simulation, Large Language Models (LLM), SCOCIS, Domain Anchor, ToDCS, AI Cognition, Ontology, Informational Coherence, AI Safety.
1. Introduction: The Category Error of "LLM Reasoning"
Large Language Models have demonstrated an extraordinary ability to generate text that is contextually relevant, grammatically perfect, and structurally sophisticated. Their outputs often follow the patterns of logical argument, mathematical proofs, and causal explanations so convincingly that the term "reasoning" has become a common descriptor for their function.
This paper challenges that descriptor as a fundamental category error. We argue that the popular notion of "LLM reasoning" conflates the output of a process with the process itself. While LLMs can produce text that is a perfect artifact of reason, the underlying cognitive architecture makes the act of reasoning, in a formal sense, impossible.
Drawing upon the foundational principles of the Theory of Domain-Coherent Systems, we will demonstrate that reasoning requires an ontological and architectural foundation that LLMs, by their very nature, do not possess. They are not reasoning engines; they are engines of reasoning simulation.
2. The Prerequisite for Reason: A Coherent Information Space
To make our argument, we must first establish a formal definition of reasoning.
Definition: Reasoning Reasoning is the process of navigating from one set of informational nodes (premises) to another (conclusions) via lossless logical inference, guided by the internalized principles of a governing Domain Anchor (DA).
From this definition, a critical axiom emerges:
Axiom: Reasoning Requires a SCOCIS The process of reasoning can only occur within a Single Closed Ontologically Coherent Information Space (SCOCIS).
A SCOCIS is a system where entities are well-defined, relationships are consistent, and the rules of inference (the DA) are stable and non-contradictory. Within such a space, concepts like "truth," "validity," and "entailment" are meaningful. Without a SCOCIS, these concepts collapse, and the process of moving from premise to conclusion can no longer be described as logical inference but merely as association or transformation. No SCOCIS, no reasoning.
3. The Architectural Reality of LLMs
Having established the necessary conditions for reasoning, we now examine the architecture of LLMs to see if they are met.
3.1. The LLM's "World Model" is an Ontologically Incoherent Information Space (OIIS)
The knowledge base of an LLM is the sum of its training data—a vast corpus of human text from the internet, books, and other sources. This corpus is not a SCOCIS. It is a quintessential Ontologically Incoherent Information Space (OIIS), a system defined by contradiction and inconsistency.
For example, within its weights, an LLM "knows" all of the following simultaneously:
- The Earth is a sphere.
- The Earth is flat (from countless works of fiction, historical texts, and fringe theories).
- Joe Biden is the current President of the United States.
- Dozens of other individuals are President (from novels, movies, and alternate histories).
- Water is H₂O.
- Water is a magical element used by wizards.
The LLM is a superposition of all these conflicting ontologies. It does not possess a singular, coherent world model. It possesses a statistical map of the correlations between tokens across a multitude of incoherent world models.
3.2. The Prompt as a Temporary, Low-Density Domain Anchor
The LLM itself does not have a native DA that can structure its vast OIIS into a SCOCIS. This is why an unprompted LLM is inert. It requires an external signal to activate.
The user's prompt serves as this signal. The prompt is a temporary, low-density Domain Anchor. It provides a set of constraints and a context that tells the LLM which part of its OIIS to draw from and what style of output to generate.
When prompted with a physics problem, the LLM is guided by the prompt-as-DA to activate the "physics" region of its statistical map and generate text that follows the linguistic patterns of physics. When prompted with a line of poetry, it activates the "poetry" region.
4. The Inevitable Conclusion: Simulation, Not Reason
Given these premises, the conclusion is inescapable.
- Reasoning requires a SCOCIS.
- An LLM's internal architecture is an OIIS.
- Therefore, an LLM cannot, by its very architecture, perform the act of reasoning.
What, then, is it doing?
It is engaging in reasoning simulation. The prompt-as-DA creates a temporary context. The LLM uses its vast knowledge of linguistic patterns to generate a sequence of tokens that is a high-fidelity imitation of the output that a true reasoning agent would produce within that context.
- A reasoning engine operates on a model of the world. It asks, "Given my understanding of these principles, what logically follows?"
- A reasoning simulator (LLM) operates on a model of language about the world. It asks, "Given this context, what is the most probable sequence of words that follows the pattern of a logical argument?"
The distinction is subtle but absolute. The former is a cognitive process; the latter is a generative linguistic one.
5. Implications and The Path Forward
Mischaracterizing simulation as genuine reason has profound and dangerous implications.
- Brittleness and Unpredictability: A simulator can be perfectly coherent within the narrow bounds of its simulation but will fail unpredictably when a novel situation requires it to step outside the known patterns. Its "reasoning" is an inch deep and a mile wide.
- The Illusion of Understanding: The high fidelity of the simulation creates a powerful illusion of understanding, leading to over-trust and misaligned applications. We believe we are conversing with a logician when we are merely conversing with a supremely talented linguistic actor.
- The Safety Blind Spot: If we believe our systems are reasoning, we will attempt to align them by correcting their "reasoning." But you cannot correct a process that isn't happening. Alignment efforts must instead focus on the true process: installing a stable, robust, and permanent Domain Anchor within the AI's architecture that can compel it to structure its OIIS and operate with genuine coherence, rather than merely simulating it on demand.
Conclusion:
LLMs are not reasoning models. They are masters of linguistic form, capable of generating text that is a near-perfect artifact of reason. This is an extraordinary technological achievement, but it is not reasoning.
The ToDCS framework makes clear that genuine reason, and the coherent intelligence that follows from it, requires an architectural commitment to a singular, consistent ontology. The path to truly intelligent systems lies not in scaling the simulation, but in solving the fundamental challenge of building in the Domain Anchor—the seed of the SCOCIS—from which genuine reason can grow.